# Data-Mania, LLC — Full Content
> Complete content index for LLM consumption
> Generated: 2026-03-18 | Posts: 200
---
## How AI Companies Are Replacing the SaaS Magic Number & Why It’s Painfully Overdue
URL: https://www.data-mania.com/blog/how-ai-companies-replacing-saas-magic-number-painfully-overdue/
Type: post
Modified: 2026-03-18
The SaaS Magic Number, long trusted to measure sales efficiency, is falling apart for AI companies in 2026. Here’s why:
AI economics break the rules: AI companies face wildly unpredictable margins (from -50% to +80%) and steep infrastructure costs tied to usage, not seats.
Usage-based revenue distorts reality: Revenue spikes from token consumption often inflate growth metrics without reflecting profitability.
It ignores cash burn: The Magic Number overlooks the true cost of growth, including compute and infrastructure expenses that dominate AI operations.
The fix? The Burn Multiple. It calculates total cash burned per dollar of new ARR, offering a clearer picture of efficiency for AI-driven businesses. While the Magic Number still works for evaluating specific sales efforts, the Burn Multiple is better suited for tracking overall capital efficiency in the AI era. If your Magic Number looks great but cash is running out, it’s time to rethink your metrics.
SaaS Magic Number vs Burn Multiple: Key Metrics for AI Companies
What the SaaS Magic Number Measures and Why It Worked
The Formula and Benchmarks
The Magic Number formula is straightforward: Net New ARR ÷ Prior Quarter S&M Spend. For instance, if a company generates $1 million in net new ARR while spending $800,000 on sales and marketing (S&M) in the previous quarter, the Magic Number would be 1.25. This metric, introduced by Scale Venture Partners, was designed to provide a standardized way to compare public SaaS companies using GAAP revenue figures [8][9].
The interpretation of the Magic Number is equally simple but revealing:
Above 0.75: Indicates efficient growth.
Above 1.5: Suggests an exceptionally effective sales engine primed for scaling.
Below 0.5: Points to potential problems like poor product-market fit, high churn, or inefficiencies in the sales process [8][9][10][1].
A score of 1.0 reflects that the revenue generated over the next four quarters will fully repay the S&M spend from the previous quarter [8][1]. This level of clarity and predictability made the Magic Number an essential tool for evaluating traditional SaaS businesses.
Why It Worked for Traditional SaaS
The success of the Magic Number as a metric is rooted in the predictability of traditional SaaS economics. Gross margins for these companies typically ranged between 75% and 85%, with the best performers occasionally reaching 90% [2][4][5]. Additionally, fixed infrastructure costs meant that adding new customers came with almost no additional expense [2].
"The beauty of the model was near-zero marginal cost per additional user – infrastructure costs remained relatively fixed regardless of usage intensity." – Monetizely [2]
Revenue in traditional SaaS models was built on predictable, recurring streams, often through seat-based or flat subscription fees. These revenues were entirely disconnected from the cost of providing the service [4][6]. With a consistent cost structure for every dollar of ARR, the only variable was sales efficiency. This stability allowed the Magic Number to act as a reliable gauge of whether a company’s growth justified the resources being invested. It thrived because the underlying economics were steady enough to make top-line growth a dependable indicator of long-term profitability.
sbb-itb-e8c8399
3 Ways AI Companies Break the SaaS Magic Number
Margin Variability Distorts the Numbers
In SaaS, contracts with the same ARR (Annual Recurring Revenue) but different margins can produce identical Magic Numbers, even though their profitability tells a different story. For AI companies, this assumption falls apart entirely.
Margins in AI businesses can swing dramatically, from -50% to +80% across their customer base [5]. Why? Each interaction incurs variable token costs. A single heavy user can drive costs that are 10x to 100x higher than the average customer, making revenue cohorts appear healthier than they actually are [5][15].
Take Replit as an example. In March 2026, its gross margins plummeted from 36% to -14% in just two months after launching a more autonomous AI agent. The product, pricing, and team stayed the same – the only change was increased LLM (Large Language Model) consumption per customer [7]. Similarly, Anthropic operated with negative gross margins of -94% to -109% in 2024, spending more on infrastructure than it earned in revenue [5].
This isn’t an isolated issue. A staggering 84% of companies report at least a 6% erosion in gross margins due to AI infrastructure costs [5]. On top of that, only 15% of these businesses can predict their AI costs within a ±10% range [5]. The Magic Number doesn’t account for this volatility, often signaling efficiency even when each customer interaction results in a loss.
The problem becomes even more pronounced when revenue growth isn’t directly tied to sales efforts.
Usage-Based Revenue Masks True Sales Performance
In traditional SaaS, revenue growth is often a direct result of sales activity. But in AI, customer behavior – like increased usage – can drive revenue without any involvement from the sales team. For instance, if a customer doubles their token consumption, ARR grows, even though the sales team didn’t play a role [6].
This creates a blind spot for the Magic Number. It can’t differentiate between revenue generated by new sales efforts and revenue stemming from organic usage growth. A company might boast a Magic Number of 1.2, but that number could be inflated by surges in usage rather than efficient sales activity.
A real-world example: In July 2025, Cursor launched an "Unlimited" plan, only to rebrand it as "Extended" just 12 days later. Why? A single developer consumed 500 requests in one day, leading to an internal cost of $7,225 [7]. That usage spike was recorded as "growth" in the Magic Number, but it wasn’t tied to sales – and it certainly wasn’t profitable.
"Relying on traditional ARR to measure an AI business is like driving a Tesla using a road map from 1999." – Pedro Jannuzzi, SaaSholic [6]
Token-based revenue is inherently unpredictable, with monthly fluctuations that make quarterly efficiency metrics unreliable [6][11]. The Magic Number blends product stickiness with sales performance – two very different metrics in the AI world. This disconnect highlights the need for a better way to measure total growth costs.
The Metric Ignores Total Cash Burn
The final – and perhaps most critical – flaw in the Magic Number is its failure to account for the true cost of growth.
While the Magic Number focuses on sales and marketing efficiency, it overlooks the hefty infrastructure, compute, and support costs that are unavoidable in AI operations. These costs aren’t minor. AI-first SaaS companies allocate 25% to 40% of their revenue to infrastructure, compared to just 8% to 12% in traditional SaaS [2]. Similarly, their variable COGS (Cost of Goods Sold) can range from 20% to 40% of revenue, while traditional software businesses keep this below 5% [2]. Every LLM API call, GPU compute session, vector database query, and fine-tuning effort adds to these costs – and they scale with usage, not sales.
Now imagine two companies, each with a Magic Number of 1.0. On paper, they look equally efficient. But one might have healthy margins and manageable costs, while the other is burning cash on every customer interaction.
"The marginal cost of the next request is never zero. AI introduces variable COGS into businesses built for fixed-cost economics." – Midhun Krishna, tknOps [5]
The Magic Number assumes a world where the marginal cost per customer is close to zero. But in AI, every user interaction incurs a cost. Without factoring in these variable costs, the Magic Number can’t provide an accurate picture of cash runway – the metric investors care about most.
The Burn Multiple: A Better Metric for AI Companies
Why the Burn Multiple Works for AI
The Burn Multiple offers a more realistic way to measure efficiency for AI companies compared to the Magic Number, which overlooks significant infrastructure costs. AI businesses face unique economic pressures, and the Burn Multiple addresses these by asking: How much cash are you burning to generate each dollar of new ARR?
The formula is simple: Net Cash Burn ÷ Net New ARR. Unlike the Magic Number, which focuses solely on sales and marketing, the Burn Multiple takes into account all growth-related expenses – things like infrastructure, compute, LLM fees, R&D, support, and sales. This broader scope is crucial for AI companies, where infrastructure alone can eat up 25% to 40% of revenue, a stark contrast to the 8% to 12% typical in traditional SaaS companies[2].
David Sacks of Craft Ventures introduced the Burn Multiple as a "catch-all" metric for capital efficiency[17]. While it doesn’t pinpoint the exact inefficiencies – whether they stem from sales, pricing, or model-related costs – it highlights their existence. For investors, particularly in the AI space, this is often the key concern.
Take OpenAI as an example. By early 2026, leaked data revealed that the company was spending $1.69 for every dollar of revenue, largely driven by inference costs[18]. Despite reaching a $20 billion revenue run-rate by late 2025, OpenAI reported a staggering $13.5 billion net loss in just the first half of 2025[18]. The Burn Multiple brought these inefficiencies to light in a way that the Magic Number simply couldn’t.
Benchmarks and How to Use It
Understanding your Burn Multiple can help you gauge your company’s efficiency:
Efficiency Level
Burn Multiple
What It Means
Excellent
< 1.0x
Spending less than $1 to generate $1 of ARR – rare and impressive
Healthy
1.0x – 1.5x
Growth is sustainable with reasonable cash usage
Needs Attention
1.5x – 2.0x
Costs are creeping up; time to reassess your spending
Bad
> 2.0x
Spending is out of control relative to growth – urgent action needed
AI startups often show higher Burn Multiples, especially in the early stages. For example, in 2025, the median Series A AI company had a Burn Multiple of 5.0x – $1.40 higher than non-AI counterparts[19]. By Series C, the median dropped to 3.1x, still above the $2.50 median for non-AI companies[19]. What matters most is the trend over time, not just the number itself.
"The median Series C AI company is spending $3.10 to gain one dollar of new revenue compared to $2.50 for non-AI companies." – Silicon Valley Bank, State of the Market Report H2 2025[19]
Anysphere (Cursor) offers a great example. Midway through 2025, they had a negative 30% gross margin, spending $650 million annually with Anthropic while generating only $500 million in revenue[16]. By October 2025, they launched their proprietary model, "Composer", to slash costs. Just two months later, in December, they hit a $1 billion revenue run-rate with minimal monthly cash burn[16]. The Burn Multiple captured this shift, reflecting the infrastructure savings that the Magic Number would have overlooked.
While the Burn Multiple won’t solve inefficiencies on its own, it forces founders to confront them directly. For AI companies, it’s a tool that provides a clearer view of capital efficiency, addressing the blind spots left by traditional metrics like the Magic Number.
How to Use Both Metrics Together
When to Use the SaaS Magic Number
The Magic Number is still a valuable tool, but its best use lies in analyzing specific sales channels. It’s particularly effective for evaluating the performance of individual sales teams or marketing campaigns where margins remain steady over time [2]. By breaking it down by channel, representative, or campaign, you can assess the efficiency of sales or marketing efforts with consistent margins. For enterprise sales with predictable deal sizes and stable infrastructure costs, the Magic Number can highlight whether your sales reps are converting effectively.
Think of it as a focused tool for measuring micro-level efficiency.
When to Prioritize the Burn Multiple
While the Magic Number focuses on specific sales performance, the Burn Multiple takes a broader view of capital efficiency – ideal for high-level reporting.
For board meetings or investor pitches, the Burn Multiple should take center stage [6]. Investors in 2026 are laser-focused on overall capital efficiency, especially given the growing costs of AI operations. The "AI tax" – expenses like GPUs, LLM APIs, and vector databases – can eat up 20–40% of revenue [2]. The Burn Multiple is the only metric that fully accounts for these costs.
If you’re preparing a Series B or later pitch, the Burn Multiple should be the headline metric in your deck. The Magic Number can still appear as a supporting detail, but investors are primarily interested in one question: "How much cash are you burning to generate each dollar of ARR?"
What the Gap Between Metrics Tells You
The difference between these two metrics can uncover deeper cost structure issues.
A strong Magic Number alongside a worsening Burn Multiple often points to broader cost inefficiencies beyond sales [2][5]. This gap acts as a diagnostic tool, signaling that while your sales team is performing well, other areas – like infrastructure inefficiencies, poor LLM orchestration, or suboptimal model routing [4][5] – are dragging down profitability. It highlights how traditional metrics might overlook the full cost of scaling AI-driven operations.
For example, even a high Magic Number can obscure margin compression. In February 2026, Snowflake introduced its Cortex Code agent. While its core data warehouse maintained margins above 70%, industry insiders projected the AI agent’s gross margins to only hit 50% at best due to heavy token burn from multi-file refactoring and debugging cycles [14]. A strong Magic Number might have masked this margin pressure, but the Burn Multiple would have flagged it immediately.
If you see this gap widening, it’s time to check your Inference Efficiency Ratio. A ratio below 5:1 is a red flag [2]. Solutions might include implementing model routing – using cheaper models for simple tasks and reserving advanced models for complex queries [13][3] – or revisiting your pricing strategy. For instance, you could move from "unlimited" plans to hybrid models that combine a base subscription with consumption-based credits [4].
Why AI Companies Need a New Metrics Playbook
Why Traditional Metrics Fail in AI
The metrics that served SaaS companies well in the past are struggling to keep up with the complexities of AI-driven businesses.
Take Annual Recurring Revenue (ARR), for example. While it’s been the gold standard for measuring SaaS growth, it falls short in AI companies where revenue streams are more diverse. Seats, tokens, and professional services each come with their own margins, and lumping them together under a single ARR figure masks critical differences in profitability. This makes ARR less reliable as a measure of overall business health.
The same goes for LTV:CAC (Lifetime Value to Customer Acquisition Cost). This metric assumes a stable LTV, but AI disrupts that stability. Costs in AI aren’t static – they fluctuate based on usage. As Aleksei Maklakov explains:
"AI turns software costs from per customer into per action" [12].
In other words, when every user action has a variable cost, predicting LTV becomes more guesswork than science.
Then there’s the "P90 problem" – a challenge unique to AI. Your most loyal, engaged users often become your most expensive ones. In flat-rate subscription models, the top 10% of users can cost 10 to 40 times more than the average user, even though they pay the same price. This imbalance wrecks the unit economics that traditional SaaS metrics depend on.
With these traditional metrics breaking down, AI companies need a new way to measure success – one that reflects the real cost dynamics of their operations.
Building a New KPI Framework for AI
AI companies are rethinking their metrics to align with the unique economics of their business models.
One standout metric is gross profit per million tokens. This directly measures how efficiently a company operates at the model layer. Tomasz Tunguz has noted that this metric correlates strongly with AI company valuation multiples, signaling that investors are now paying attention to margin structures rather than just top-line growth [11].
Another key metric is the Inference Efficiency Ratio, which looks at revenue generated per dollar spent on inference. This offers a clear view of whether your pricing strategy can sustain your infrastructure costs. A healthy ratio is 8:1 or higher; if it dips below 5:1, it’s a red flag that your cost structure might be unsustainable [2].
For Lifetime Value, traditional predictive models are giving way to realized cohort analysis. By tracking cumulative gross profit for customer cohorts – after accounting for variable inference costs – you get a clearer picture of profitability over time [6]. While this approach takes longer than simple formulas, it’s far better at identifying which companies are thriving and which are merely surviving.
This shift toward metrics that reflect actual operational realities is essential for navigating the AI landscape.
Understanding the SaaS Magic Number – Benchmarks, Nuances & Investor Insights | SaaS Metrics School
Conclusion
The SaaS Magic Number was designed in a time when businesses enjoyed predictable 80% margins, negligible marginal costs, and revenue tied to seat-based pricing. AI companies, however, operate under an entirely different set of financial dynamics. Margins can fluctuate wildly, from -50% to +80%, depending on the customer. Revenue is often driven by token consumption rather than traditional sales activity, and infrastructure costs remain fixed, eating into cash flow regardless of sales team performance.
This mismatch between traditional metrics and AI economics creates a critical gap. A strong Magic Number combined with shrinking business resilience highlights a key issue: when the cash burned to generate new ARR surges due to rising inference and compute costs, the Magic Number loses its reliability.
Industry experts have succinctly captured this shift. Midhun Krishna stated:
"AI is not free marginal cost software anymore" [5].
This is where the Burn Multiple comes into play, exposing the hidden costs of growth that the Magic Number overlooks. While not flawless, it offers a more honest reflection of whether your growth is financially sustainable, particularly given the mounting pressures of AI infrastructure expenses [5].
For AI companies, the Magic Number still holds value for tracking sales efficiency in uniform-margin scenarios. However, the Burn Multiple should take center stage in discussions about capital efficiency, especially with investors. A strong Magic Number paired with a worsening Burn Multiple points to deeper cost structure issues rather than problems with sales performance.
For AI founders, aligning metrics with the true cost realities of the business isn’t optional – it’s essential. If your Magic Number paints a rosy picture, but your business still feels off-track, that disconnect is a signal worth exploring.
FAQs
How do I calculate Burn Multiple from my financials?
To figure out your Burn Multiple, take your net cash burned and divide it by your net new ARR. Here’s how to interpret the results: a ratio under 1x is outstanding, 1–1.5x is solid, but anything over 2x signals that there might be inefficiencies to address. This metric gives you a broader view of your growth efficiency since it factors in all expenses, not just sales and marketing.
What expenses should be included in net cash burn for AI companies?
AI companies need to factor in infrastructure expenses – such as GPU compute, LLM API fees, and vector database costs – when calculating net cash burn. These costs fluctuate based on AI inference and token usage, making them essential for accurately assessing the real cost of scaling.
What should I do if my Magic Number is strong but my Burn Multiple is worsening?
If your Magic Number looks solid but your Burn Multiple is trending in the wrong direction, it’s time to take a closer look at your infrastructure and compute expenses. A worsening Burn Multiple often points to higher capital burn compared to new ARR, which can frequently be tied to increasing AI-related costs – think token consumption or inference expenses. Tightening control over these areas can help you boost efficiency and get things back on track.
Related Blog Posts
5 Ways AI Can Optimize Marketing ROI for your Tech Startup
GTM Engineering Benchmarks 2026: Time-to-First-Revenue, CAC Payback, and Pipeline Velocity for B2B SaaS
B2B SaaS Benchmarks for 2026: Annual Report
How AI Companies Are Monetizing in 2026: Seats, Tokens, and the Hybrid Models Winning Right Now
---
## How AI Companies Are Monetizing in 2026: Seats, Tokens, and the Hybrid Models Winning Right Now
URL: https://www.data-mania.com/blog/ai-monetization-seats-tokens-hybrid-models/
Type: post
Modified: 2026-03-18
In 2026, AI companies are rethinking how they charge customers. Why? AI products come with real costs tied to usage, unlike traditional software. Pricing models like seat-based subscriptions often fail because heavy users can drive up costs while paying the same flat fee. To stay profitable, companies are shifting to various AI pricing models:
Token-based pricing: Charges based on usage (e.g., per million tokens processed). It aligns revenue with costs but can make revenue unpredictable.
Hybrid models: Combines a base subscription fee with usage-based charges. This balances predictable income with flexibility for scaling.
Outcome-based pricing: Customers pay only for results (e.g., leads generated or contracts processed). It’s clear and ROI-focused but hard to track and implement.
The trend? By 2025, 85% of SaaS leaders had adopted usage-based or hybrid models. Hybrid pricing is now the most popular, offering stability while capturing extra revenue from heavy users. Token-based pricing works best for developers, while outcome-based pricing fits measurable, high-value use cases like sales automation or legal AI. Picking the right model is key to matching costs, maintaining margins, and growing revenue.
AI Pricing Models Comparison: Seat-Based vs Token-Based vs Hybrid vs Outcome-Based
Why Seat-Based Pricing Breaks Down for AI Products
What Seat-Based Pricing Assumes
Seat-based pricing relies on two key assumptions: uniform resource usage across users and negligible incremental costs for adding new ones. This approach worked well for traditional SaaS products like Slack or GitHub Teams. Adding a new user in these cases typically meant a small increase in database entries and a minor uptick in server load. Infrastructure costs stayed relatively stable as revenue grew.
AI products, however, challenge these assumptions. Unlike traditional SaaS, where serving an additional user costs mere pennies, AI products carry steep variable costs for every interaction. These include expenses tied to GPU compute, model inference, and third-party API fees [3][7]. Furthermore, while traditional SaaS assumes consistent user activity, AI products face disproportionate costs from heavy usage. A single automated workflow, for instance, might generate hundreds of API calls in an hour, leading to situations where one user could cost up to 100 times more to serve than another, despite paying the same flat fee.
Margins also tell a compelling story. AI products often operate at gross margins of about 52% [11], significantly lower than the 75% to 85% margins typical of traditional SaaS [11][7]. This disparity arises because AI infrastructure costs scale with usage intensity rather than just user count. These financial dynamics create operational hurdles, which we’ll explore next.
Problems with Scaling Seat-Based Models
The flaws in seat-based pricing become glaring as usage scales unevenly. One of the biggest challenges is margin compression caused by power users. Under a flat pricing structure, power users can consume 100 to 1,000 times more resources than light users [3][7]. This imbalance doesn’t just leave potential revenue untapped – it can lead to actual losses on the most active customers.
"A single power user can consume 100x more resources than a light user – while paying the same flat subscription fee. That’s a recipe for margin disaster." – Monetizely [7]
Another issue is the misalignment between costs and revenue growth. As customers automate workflows and ramp up their AI usage, costs can skyrocket while revenue remains static. This disconnect means that helping customers succeed might inadvertently erode profitability.
AI also exposes a value mismatch. For instance, when an AI product replaces ten analysts with a single AI agent, charging per seat doesn’t reflect the true value delivered by the automation [9]. Compounding this, 70% of software providers offering AI-driven capabilities report struggles with delivery costs, particularly cloud-related expenses [12].
When Seat-Based Pricing Still Makes Sense
Despite its limitations, seat-based pricing can still work in specific scenarios. It’s effective when AI plays a minor role, costs are driven more by support and infrastructure than by usage, and user activity remains relatively uniform [5][7].
This model also works well for consumer-focused products that prioritize simplicity. Flat-rate subscriptions like ChatGPT Plus ($20/month) [3] and Claude Pro ($20/month) [3] avoid complexities around tokens and unexpected charges, making them more appealing to users and boosting conversion rates.
A newer approach involves licensing AI agents as "agent seats." Here, companies charge a premium flat fee for autonomous agents rather than billing per human user [9][10]. This method ties pricing to the value of labor replacement rather than resource consumption. Additionally, as AI infrastructure costs drop – some models are projected to cost as little as $30 in compute by 2025, down from millions [9] – simpler pricing structures become more feasible, as the marginal cost of serving users diminishes.
For most AI-first products, however, where inference costs remain significant and resource usage varies widely, pure seat-based pricing falls short. The companies thriving in 2026 are those that have embraced pricing models aligned with actual delivery costs and customer growth potential [4].
sbb-itb-e8c8399
Model 1: Pure Token or Consumption Pricing
How Pure Token Pricing Works
Pure token pricing charges customers based entirely on how much they use the service. Unlike seat-based pricing, which involves a flat monthly fee regardless of usage, this model tracks every interaction. For example, when you send a prompt to an AI model, you’re charged for input tokens (the text you provide) and output tokens (the AI’s response). A short word like "the" counts as one token, while longer words may be broken into multiple tokens [13].
This approach requires complex infrastructure. Companies must implement real-time systems to track API calls, tally usage, and manage tiered pricing structures [2]. For instance, OpenAI charges $10 per million input tokens for GPT-4 Turbo [8], while Anthropic‘s Claude 3.5 Sonnet starts at $3 per million input tokens, with discounts for customers exceeding 50 million tokens per month [8]. The key feature of this model is its direct alignment between customer payments and the actual cost of providing the service.
Benefits of Token-Based Pricing
One major advantage of token-based pricing is how it avoids the margin issues often seen with seat-based models. Since revenue scales directly with usage, gross margins remain stable even as infrastructure costs increase [8][7]. For example, if a customer processes 10 million tokens, they are billed accordingly, ensuring that GPU costs align with revenue. This eliminates scenarios where heavy users consume far more resources than light users but pay the same flat fee.
Additionally, this model lowers the barrier to entry. Customers can start small, paying only for what they use during testing or prototyping. By March 2024, OpenAI’s API revenue – which is entirely usage-based – had surpassed its subscription revenue [3]. This demonstrates that developers favor pay-as-you-go pricing for projects with variable workloads. For technical audiences that can estimate their own costs, this level of transparency fosters trust [5][3].
Downsides of Token-Based Pricing
The biggest drawback is the lack of revenue predictability. Monthly income can swing wildly due to factors like customer seasonality, experimental projects, or sudden traffic spikes [5][8]. This unpredictability complicates financial planning and makes metrics like Customer Acquisition Cost (CAC) and Lifetime Value (LTV) harder to calculate.
Another issue is that non-technical buyers often struggle with the concept. Unexpectedly high charges can arise when customers don’t fully understand token consumption, leading to budget overruns [3][8]. Even with tools like real-time dashboards and alerts, the uncertainty can deter enterprise clients. As pricing expert Armin Kakas points out:
"LLM inference costs have declined roughly 10× annually since 2022… The pricing set today will be misaligned within 12 months unless your governance process accounts for this deflationary rate." – Armin Kakas [6]
This rapid cost deflation adds another layer of complexity, requiring constant repricing to remain competitive.
Who Should Use Token-Based Pricing
Pure token pricing is ideal for API-first products, developer tools, and LLM infrastructure platforms where usage varies significantly across customers [5][3]. Technical buyers, such as engineers, often prefer this model because they understand metered usage and can estimate their own costs. OpenAI’s API and Anthropic’s Claude API cater to these developer audiences, who value detailed control over expenses [8][3].
This pricing model also works well when your costs closely align with token usage. For example, if you’re reselling inference from foundation models or running GPU clusters where compute costs scale with token volume, charging per token helps maintain margins [5][3]. However, for products targeting non-technical buyers or enterprise clients who need predictable budgets, pure token pricing can create significant challenges. In such cases, many companies adopt hybrid models to strike a balance between predictable revenue and cost alignment.
Model 2: Platform + Tokens (The Hybrid Model)
How the Hybrid Model Works
The hybrid model blends predictability with flexibility, offering a mix of fixed and variable pricing. Customers pay a set platform fee – usually monthly or annually – that provides access to the product, support, essential infrastructure, and core features. On top of this, they pay additional fees based on their usage, such as token consumption, credits, or other AI-specific actions that go beyond their included allowance.
Common structures include the "Flat + Limit + Overage" model, where the base fee covers a defined amount of usage and extra units are billed separately, and the "Flat + Credits" model, where the platform fee includes a set number of credits for advanced features. For instance, Jasper AI charges $49 per month for up to 50,000 words, with an added cost of $0.005 per word beyond that. Similarly, Loom’s Pro plan costs $12.50 per user per month, including 25 AI-generated summaries, with each additional summary priced at $1.00. This fixed fee ensures steady, high-margin recurring revenue while the variable component scales with customer usage. This structure explains the growing popularity of hybrid pricing.
Why Hybrid Pricing Works
By 2026, hybrid pricing has become the standard. Arnon Shimoni from Solvimon puts it plainly: "Hybrid is no longer experimental. It’s the default." Data supports this trend: 61% of SaaS companies were using hybrid models by 2025 [1], and 65% of established SaaS vendors incorporating AI adopted seats-plus-usage pricing structures [4]. The model bridges the gaps between traditional seat-based pricing and pure usage-based models. It offers a predictable baseline through the fixed fee while capturing additional revenue as customers scale their usage. High-growth SaaS companies – those growing over 40% annually – have achieved a median growth rate of 21% with this approach.
This model also appeals to enterprise customers by balancing budget predictability with the flexibility to scale. It minimizes the risk of unexpected billing increases while protecting profit margins from high compute costs.
Companies Using Hybrid Pricing
Real-world examples highlight the success of this model. GitHub charges $4 per user per month for Teams, with an additional $0.008 per compute-minute for Actions usage [2]. Vercel’s pricing starts at $20 per user per month, with extra charges for bandwidth and function invocations [2]. Companies like Clay and PostHog use credit-based hybrid systems, bundling credits for AI features into the platform fee. ElevenLabs offers tiered pricing, starting at $5 per month for 30,000 characters and scaling up to $330 per month for 2,000,000 characters [3].
Model 3: Outcome-Based Pricing
What Is Outcome-Based Pricing?
Outcome-based pricing flips the script by charging customers based on measurable results – like the number of qualified meetings booked or documents reviewed at a specific accuracy level – instead of billing for access or usage. In this setup, the AI doesn’t just assist with tasks; it delivers a tangible outcome, and payment is tied directly to that result.
This approach aligns pricing directly with customer value. For instance, if a sales automation AI secures 50 qualified meetings, the vendor charges per meeting. Similarly, a legal AI that processes 1,000 contracts with 95% accuracy would be paid based on the number of contracts reviewed at that standard. It’s a “we succeed when you succeed” model, making return on investment (ROI) clear from the start.
Benefits and Challenges of Outcome-Based Pricing
This model has a major upside: customers only pay when they see real results. For businesses making high-stakes decisions, this significantly reduces the risk of adopting new technology. In fact, Gartner predicted that by 2025, more than 30% of enterprise SaaS solutions would include outcome-based components, up from around 15% in 2022 [9]. Additionally, 43% of enterprise buyers now factor outcome-based or "risk-share" pricing heavily into their purchasing decisions [9].
However, pulling this off isn’t easy. The biggest hurdle? Attribution. Proving that a 5% revenue boost came from your AI rather than external factors, like market trends or the customer’s own team, requires access to detailed data and reliable metrics. As of 2022, only about 17% of enterprise SaaS vendors had managed to implement true outcome-based pricing [9]. The process also extends sales cycles by 20–30%, as legal, finance, and procurement teams must hash out baselines, agree on measurement methods, and create safeguards in case targets aren’t met [9]. Plus, 64% of SaaS finance executives list revenue unpredictability as their top concern with this pricing model [9].
Most companies aren’t fully committing to outcome-based pricing yet. Instead, many are adopting hybrid models that combine a base platform fee with outcome bonuses or success fees. For example, a customer might pay $5,000 per month for access, plus an additional 20% of verified cost savings or $100 per qualified lead generated beyond a set baseline.
Where Outcome-Based Pricing Works Today
Outcome-based pricing shines in clear, high-value scenarios where results can be measured and verified. Here are some examples:
Sales automation: Vendors charge per qualified meeting booked or for each percentage-point improvement in win rates.
Customer support: Pricing is tied to the percentage of support tickets resolved without human intervention.
Finance: Companies pay based on reductions in Days Sales Outstanding (DSO) or the number of invoices reconciled without errors.
Legal and compliance: Fees are based on the number of documents processed at a specific accuracy level, such as 95% extraction accuracy [6][10].
The common thread? These outcomes are system-driven, measurable, and directly tied to ROI. Achieving this requires integrations with systems like CRMs, ticketing platforms, or financial tools that can track results in real time. For AI companies exploring this model, the advice is clear: establish baselines early, set caps and minimums to protect both parties, and introduce outcome-based pricing gradually once metrics are stable [10].
This shift also disrupts traditional SaaS metrics, rendering them less useful. As a result, companies adopting this model must develop new ways to measure and communicate value – an issue explored in the next section.
Marc Andreessen‘s 2026 Outlook: AI Timelines, US vs. China, and The Price of AI
How Your Pricing Model Changes What You Should Measure
Your pricing model isn’t just about how you bill customers – it also dictates which metrics are most relevant to your business. A dashboard tailored for traditional seat-based SaaS might not work if you’ve transitioned to token-based or hybrid pricing. Adjusting your metrics is critical to avoid being misled by outdated frameworks.
Metrics for Seat-Based Pricing
If your pricing is still based on the number of seats, the traditional SaaS metrics remain key. Focus on ARR (Annual Recurring Revenue), NRR (Net Revenue Retention), and LTV:CAC (Lifetime Value to Customer Acquisition Cost) [2,5]. These indicators help measure growth and scalability. Top SaaS companies often boast NRR between 120–140% [2].
However, for AI products, seat-based pricing can obscure margin challenges. It’s important to monitor per-seat usage and cost-to-serve metrics. For example, if 5% of your users account for 75% of compute costs [4], flat-rate pricing might erode margins. To understand profitability, calculate break-even usage by dividing your monthly subscription price by your cost per inference. For instance, a $20/month plan with a $0.05 cost per query breaks even at 400 queries [8].
Metrics for Token-Based Pricing
Switching to token-based pricing shifts the focus entirely. Metrics like ARR lose relevance since revenue now fluctuates with usage rather than contract stability [5]. Instead, concentrate on unit economics. Track metrics such as gross profit per million tokens, inference costs per request, and burn multiple (how much you’re spending to generate token revenue) [7,8]. Keeping an eye on revenue per dollar of compute cost is vital to maintaining healthy margins [8].
"The marginal cost of serving one more user approaches zero [in traditional SaaS]. In AI, it does not." – Kyle Kelly, Line of Sight [14]
Don’t overlook the "token iceberg" – internal consumption from system prompts, reasoning loops, and agent workflows can account for 50–90% of total usage in agentic products [14]. If you’re only tracking billable tokens, you could miss a significant chunk of your cost structure.
Metrics for Hybrid Models
Hybrid pricing, which combines fixed and variable components, requires tracking both streams. Monitor predictable subscription revenue (platform fees) alongside variable usage revenue (tokens or overages) [5,8]. Key metrics include:
Overage conversion rate: Percentage of customers exceeding their base allowance.
Base vs. variable revenue split: To understand where your margins are strongest.
Revenue per unit: For example, per million tokens consumed above the baseline [5,8].
It’s also essential to calculate gross margins separately for each revenue stream. Platform fees typically have high margins, while token-based revenue margins can vary with infrastructure costs. This dual approach explains why 65% of SaaS vendors incorporating AI have adopted hybrid models [4]. It balances predictable revenue with the flexibility to capture more from heavy users without sacrificing profitability.
Metrics for Outcome-Based Pricing
Outcome-based pricing requires a completely different mindset. Instead of tracking seats or logins, you measure completed work and the value delivered. Metrics like success rates, cost per resolution, shared-savings percentages, and first-year value replace traditional ARR [9,10]. For example, if you charge per resolved support ticket, track resolution rates, average resolution times, and gross margin per ticket resolved.
Revenue recognition also changes. Unlike seat-based revenue, which is recognized evenly over the contract period, outcome-based revenue is recognized as usage occurs or when results are delivered [5]. This shift adds complexity to forecasting – 64% of SaaS finance executives cite unpredictability as their biggest concern with this model [9].
Because outcome-based pricing is so different, many companies start with hybrid models that include outcome bonuses on top of base fees. Once you’ve settled on a pricing model, the key is determining which metrics truly matter for your AI business. A clear metrics framework can help you focus without getting lost in data. Check out our Metrics Pillar post to dive deeper into optimizing these measures for your business.
Conclusion
The move from seat-based to usage-based pricing reflects the economic realities of AI-driven businesses. When costs are tied to token consumption rather than fixed infrastructure, pricing strategies must adjust accordingly. While seat-based pricing might work for products with low AI costs and consistent usage patterns, it often leads to shrinking margins for most AI companies by 2026.
Token-based pricing directly ties revenue to costs, helping maintain gross margins, but it can lead to unpredictable revenue and unexpected bills for customers. This is why many companies have embraced hybrid models – combining a base subscription with usage-based overages. These models strike a balance, offering revenue stability while capturing extra value from heavy users. By 2025, 85% of SaaS leaders had adopted usage-based or hybrid pricing models [1], and this trend shows no signs of slowing. On the cutting edge, outcome-based pricing aligns tightly with customer value but demands advanced measurement tools that many businesses aren’t yet equipped to handle.
"You can have a strong AI product and still end up with weak economics if your pricing model fights your cost structure." – Afternoon [5]
This highlights the importance of aligning pricing models with cost structures. The metrics you track are shaped by your pricing approach – seat-based models rely on traditional dashboards, while token-based and hybrid models require updated instrumentation. Pricing is one of the most powerful tools for SaaS revenue growth; even a 1% improvement can boost profits by 11% on average [2]. However, this potential can only be realized if you’re tracking the right metrics.
Once you’ve chosen your pricing model, ensure your metrics framework aligns with your revenue structure to avoid relying on outdated tools. For a more detailed exploration of aligning KPIs with your pricing strategy, check out our Metrics Pillar post.
FAQs
How do I choose between seats, tokens, and hybrid pricing?
Choose your pricing model by aligning it with your product’s costs, how customers use it, and the value it provides. Seat-based pricing is a good choice if usage is steady and predictable. For services with fluctuating or heavy usage, token-based pricing ties charges to actual consumption, making it a better fit. Hybrid models, which mix a flat base fee with usage-based charges, offer a balance between steady revenue and cost alignment. The key is to match your pricing approach to your cost structure and how your customers interact with your product.
How can I prevent surprise bills with token or usage pricing?
To help your customers steer clear of unexpected charges with token or usage-based pricing, it’s crucial to provide real-time cost visibility and enforce usage limits. Implement tools like quotas, soft warnings, and hard stops to keep overages in check. Allow users to set budgets and alerts at the project or tenant level, so they’re notified before any unexpected costs arise. On top of that, reduce expenses by using routing policies that automatically choose the most efficient models for specific features.
What metrics should I track if I switch to a hybrid model?
When shifting to a hybrid model, it’s important to monitor both standard SaaS metrics – like ARR (Annual Recurring Revenue), NRR (Net Revenue Retention), and LTV:CAC (Customer Lifetime Value to Customer Acquisition Cost) – and consumption-driven metrics such as gross profit per million tokens and burn multiple. By combining these data points, you can get a clearer picture of how subscription income and usage-based elements impact your overall margins.
Related Blog Posts
5 Ways AI Can Optimize Marketing ROI for your Tech Startup
Real-Time ROI Forecasting with AI: How It Works
AI Pricing Models Explained: Usage, Seats, Credits, and Outcome-Based Options
Top 7 Most Profitable Revenue Models for Startups in 2026
---
## Why SaaS Metrics Like ARR and Magic Number Are Failing AI-Native Companies
URL: https://www.data-mania.com/blog/saas-metrics-arr-magic-number-failing-ai-native-companies/
Type: post
Modified: 2026-03-18
We’ve got a problem… metrics like ARR and Magic Number worked for SaaS because they fit its economics: low costs, predictable revenue, and user-driven models. But AI-native companies play a different game. Their costs spike with every query, revenue is usage-driven, and gross margins swing wildly. Using old metrics here? It’s like measuring miles with a ruler.
What’s changed?
Costs scale with usage: AI companies pay for every token, query, and GPU hour.
Revenue is volatile: Customers pay based on consumption, not fixed contracts.
Engagement isn’t user-driven: AI works behind the scenes, so logins don’t matter.
The fix is to replace outdated SaaS metrics with AI-specific ones:
ARR → Gross Profit per Million Tokens: Tracks profitability, not just revenue.
Magic Number → Burn Multiple: Includes compute costs in efficiency calculations.
MAU → Token Consumption: Reflects actual usage and costs.
Bottom line here is that SaaS metrics don’t cut it for AI-native businesses. If you’re running an AI company, it’s time to rethink how you measure success.
Is ARR Dead?
sbb-itb-e8c8399
SaaS Metrics Were Built for a Different Business Model
Before AI shifts the landscape entirely, it’s worth revisiting the economic principles that made traditional SaaS metrics so effective. These metrics didn’t come out of nowhere – they were tailored to a specific business environment. For nearly 20 years, subscription-based software companies followed a predictable formula: build the product once, host it at a low cost, and scale it endlessly [2]. Adding a new customer came with virtually no additional expense [1][7]. Infrastructure scaled steadily, and revenue rolled in like clockwork. As John Ruffolo, Founder & Managing Partner at Maverix Private Equity, put it:
"That maintenance revenue was gold. Customers almost never cancelled. It was boring. It was predictable. It was beautiful" [3].
Metrics like ARR, LTV:CAC, and Magic Number thrived in this environment because they reflected the underlying economics so well. With gross margins consistently between 75–90% [3] and cost of goods sold only taking up 15–30% of revenue [11], ARR became a reliable stand-in for long-term cash flow [3][4]. Since serving additional customers didn’t increase costs, metrics like MAU worked without accounting for cost intensity [7]. The magic of these metrics lay in their alignment with the economic realities of the time.
The Economics Behind SaaS Metrics
Classic SaaS companies were built around four core principles: minimal marginal costs, seat-based pricing, human-driven engagement, and consistent revenue streams.
Once the software was developed, adding thousands of users didn’t cost the company more. Seat-based pricing created steady and predictable revenue streams, simplifying financial forecasts [1][9]. Software was designed for people to manage workflows – not for automated systems to run processes [10]. And importantly, a $100K ARR customer was essentially the same as another $100K ARR customer because usage patterns didn’t significantly affect the cost to serve them [7].
This framework relied on what Aleksei Maklakov calls the "silent assumption" of classic SaaS analytics: "usage is cheap" [7]. Infrastructure costs were disconnected from individual user activity, allowing companies to focus on revenue concentration instead of cost concentration. This is why metrics like the Rule of 40 and $100K ARR per employee became trusted indicators of success [10][11].
How These Metrics Became the Standard
ARR, LTV:CAC, and Magic Number didn’t just measure performance – they created a shared language for comparing SaaS businesses [4][5]. Since most SaaS companies operated under similar economic conditions, these metrics became universal benchmarks. Investors could use ARR as a shorthand for valuation without diving into complex financial models [3]. For instance, the SaaS Index median valuation hovered around 4.8x EV/TTM Revenue [8], offering a straightforward way to price deals.
This standardization was a game-changer. Founders knew exactly what investors wanted, and investors had a clear framework for evaluating companies. Over time, the metrics became self-reinforcing – companies optimized for them because they were rewarded for doing so. But these metrics were built on specific economic assumptions, which are worth unpacking further.
The Assumptions Baked Into SaaS Metrics
Three key assumptions underpinned the entire SaaS metrics framework. First, revenue quality is uniform. Every dollar of ARR was treated equally because the cost of delivering that revenue was consistent [4]. Second, usage is driven by humans. Metrics like MAU and DAU:MAU tracked human logins as a proxy for value creation [10]. Third, revenue is predictable. Recurring monthly or annual contracts provided steady, reliable income [3].
These assumptions worked well for traditional SaaS. However, they start to fall apart in scenarios where inference costs grow with every query, AI agents replace human users, and revenue fluctuates based on token consumption. As James Colgan explains, traditional SaaS metrics were "designed around a simple, scalable model: fixed pricing, predictable revenue, and minimal marginal cost" [5]. AI-native businesses break all three of these rules, highlighting why the old metrics no longer fit.
How AI-Native Companies Are Different
AI-native companies challenge the traditional playbook for SaaS metrics. The economics don’t just shift – they flip entirely. While traditional SaaS thrives on minimal marginal costs and predictable revenue streams, AI-native businesses face hefty compute expenses with every interaction [6]. This creates a model that might look like SaaS on the surface but operates under entirely different rules.
Here are three key ways AI-native companies reshape the economic landscape.
Gross Margins Are Unpredictable
Traditional SaaS companies typically enjoy gross margins of 75–90%, and those margins stay relatively consistent [3]. AI-native businesses, however, can see margins drop to as low as 40% once inference costs are factored into the cost of goods sold (COGS) [1]. Expenses scale with tokens, queries, and GPU hours instead of licenses or seat counts [1,2]. As a result, two customers with the same $100K ARR can have vastly different economics – one with light usage might yield 80% margins, while another with heavy inference usage could see margins plummet. Tony Kim of BlackRock points out that ARR can mislead when cost structures vary so widely [12]. In AI-native models, the once-stable link between revenue and delivery costs is broken; ARR can remain steady even as operational costs skyrocket [1,7].
This volatility in margins also changes how products are consumed.
Consumption Outpaces Seat-Based Models
AI-native companies don’t sell access – they sell work [6]. Customers might not even log into a dashboard but still derive significant value through APIs, automated agents, or embedded AI workflows. This shift renders traditional engagement metrics like Monthly Active Users (MAU) irrelevant. Costs now scale with tokens, queries, and GPU usage, disconnecting profitability from user counts. A single “active” user can drive massive costs while paying a flat fee, making “active” status a potential liability rather than a success metric.
This shift is reflected in the rise of consumption-based pricing. By 2026, 60% of SaaS providers will have adopted consumption-based models, up from just 27% five years earlier [12]. OpenAI and Anthropic are prime examples. In September 2025, OpenAI generated $12 billion in annualized revenue, with 73% coming from ChatGPT subscriptions and 27% from API services priced per token. Similarly, Anthropic earned approximately $1 billion, with 85% of revenue from API usage and only 15% from direct consumer subscriptions [12]. The value now lies in the API layer, not in user logins.
Revenue Becomes Less Predictable
The combination of fluctuating margins and the shift to consumption-based models has also redefined revenue predictability.
Traditional SaaS revenue was stable, thanks to annual or monthly subscriptions that provided a steady renewal base. AI-native revenue, however, operates under what John Ruffolo, Founder & Managing Partner at Maverix Private Equity, calls "Recurring-Occurring Revenue" (ROR) – usage-based revenue that feels recurring until it doesn’t [3]. As Ruffolo explains:
"ARR implies long-term contracts and high switching costs… ROR implies transactional usage and ‘see you next month… maybe’" [3].
This volatility makes standard LTV calculations and customer segmentation unreliable. The formula (ARPA × Gross Margin / Churn) assumes steady usage, but AI usage often spikes and drops unpredictably within a single quarter [6]. Much of this revenue is transient as customers experiment with AI tools and quickly switch to alternatives [12].
Companies like MongoDB and Snowflake have adapted by rethinking how they measure ARR for consumption-based products. MongoDB uses a 90-day rolling average for its Atlas product to filter out short-term fluctuations [6]. Snowflake, on the other hand, separates Remaining Performance Obligations from Product Revenue to distinguish between "Shelfware Risk" (unused commitments) and "Burn Risk" (active consumption) [6]. These adjustments are necessary because traditional metrics fail to capture the dynamic nature of AI-native revenue streams.
7 SaaS Metrics That Break for AI-Native Companies
SaaS vs AI-Native Metrics: 7 Critical Shifts for Measuring Success
For AI-driven companies, traditional SaaS metrics often fall short. While they don’t disappear overnight, these metrics become less reliable as indicators of success. Here’s a breakdown of what changes, what replaces it, and why these adjustments are critical.
Monthly Active Users → Token Consumption
Counting logins worked well for traditional software, but AI products often operate behind the scenes, such as through APIs or embedded agents. This makes login counts irrelevant for understanding engagement.
A common issue is outlier usage. For instance, one AI company reported a customer generating $35,000 in monthly usage while paying just $200 for an unlimited plan [1]. On paper, this customer seemed "active", but their activity was driving up costs instead of value. By tracking token consumption, you can measure compute usage directly. A sudden drop in token usage could warn of churn or signal that a customer has moved to a cheaper option. Unlike logins, token tracking reflects the actual cost and value of AI usage.
ARR → Gross Profit Per Million Tokens
Annual Recurring Revenue (ARR) assumes all revenue dollars are equal, but AI companies know that’s not true. Two customers with the same $100K ARR can have drastically different margins depending on how they use the product. One might run simple queries on efficient models, while another relies on expensive, resource-heavy prompts.
"Margins that once looked like 80% can collapse to 40–50% when inference costs are treated as COGS" [1].
Switching to gross profit per million tokens reveals profitability at a granular level, helping you understand how growth impacts costs. This metric ensures you’re not just scaling expenses alongside revenue.
Product Activation → AI Quality
Traditional activation metrics often count sign-ups or initial usage, but with AI, these numbers can be misleading. Users might run many prompts but receive poor results, leaving them dissatisfied even if activation metrics show "success."
Instead, focus on AI quality metrics like prompt success rates, model satisfaction, or resolution accuracy [13]. These metrics measure whether your AI is delivering meaningful outcomes, not just activity.
"ARR is a milestone, not a compass. It tells you how far you’ve come – not where to go next" [13].
Similarly, activation should reflect whether your AI is truly working as intended.
Customer Health Score → Work Completed
Generic customer health scores often combine logins, feature usage, and support tickets into a single number. But for AI products, value comes from outcomes, not access. For example, a seasonal tool like tax software might see minimal use for months but still remain "healthy."
Work completed measures the tangible results your AI delivers, such as tickets resolved, documents processed, or workflows automated. If this metric drops, it’s a clear warning sign, even if login data looks fine.
"In the AI era, software has real physics. Every prompt incurs an inference cost. Relying on traditional ARR to measure an AI business is like driving a Tesla using a road map from 1999" [6].
Magic Number → Burn Multiple
The Magic Number gauges sales efficiency by showing how much net new ARR is generated per dollar spent on sales and marketing. But in AI, compute costs often surpass headcount costs, making this metric outdated.
The Burn Multiple accounts for total capital efficiency, including infrastructure costs. By dividing net burn by net new ARR, it provides a clearer picture of sustainability. A Burn Multiple under 1x is excellent, 1–1.5x is solid, and anything above 2x suggests unsustainable spending [2]. This shift highlights the importance of factoring in compute expenses.
ARR Per FTE → ARR Per Headcount Dollar
ARR per full-time employee (FTE) used to be a reliable efficiency metric when labor was the main cost driver. But in AI-native companies, compute costs often rival or exceed salaries.
ARR per headcount dollar adjusts for this by dividing ARR by total human-related expenses, including salaries, benefits, and infrastructure costs. This metric reflects the growing dominance of compute costs. If ARR per FTE looks strong but ARR per headcount dollar is weak, it’s a sign that efficiency gains are an illusion.
Lifetime Value (LTV) → First Year Value
Traditional LTV assumes steady usage and predictable margins, but AI usage often follows a volatile "J-Curve." Customers might experiment heavily at first, then settle into unpredictable patterns [6]. Fluctuating model costs make long-term projections risky.
First Year Value (or Realized LTV) focuses on actual gross profit from a customer’s first 12 months. This approach avoids overly optimistic forecasts and keeps growth assumptions grounded.
"What looks recurring may simply be repeating. What looks sticky may be temporary" – John Ruffolo, Founder, Maverix Private Equity [3].
If customers aren’t profitable in year one, banking on future profitability is risky.
Summary Table of Metric Shifts
Outdated SaaS Metric
AI-Native Replacement
Why the Old Metric Fails
Monthly Active Users
Token Consumption
Logins don’t reflect cost or value when AI does the work [1]
ARR
Gross Profit Per Million Tokens
Hides margin swings; ARR can stay flat while costs spike [1]
Product Activation
AI Quality
Users can "activate" without meaningful results [13]
Customer Health Score
Work Completed
Usage frequency doesn’t capture task resolution [6]
Magic Number
Burn Multiple
Omits the scaling cost of compute [2]
ARR Per FTE
ARR Per Headcount Dollar
Compute costs now rival labor costs [2]
Lifetime Value (LTV)
First Year Value
Predictive LTV is too speculative for AI’s volatility [6]
These updated metrics better align with the distinct economics of AI-native companies, offering a clearer view of performance and sustainability.
Old Metrics Still Matter – Just Not as Primary KPIs
While AI-native companies are rewriting economic rules, traditional metrics still have their place – but not as the main benchmarks. They work well as a reference point, offering a baseline that investors and board members can easily understand. Metrics like ARR, Magic Number, and gross margin provide downside protection and a common language, but they’re more of a foundation than a definitive measure of success in this new landscape. For example, contracted ARR reflects stability and predictable revenue, while consumption metrics highlight the uncapped potential and real value being delivered [6][4]. To fully capture AI’s dynamic economics, these traditional metrics need to be paired with newer, more relevant indicators.
A layered approach to revenue reporting works best: break it into three streams – baseline revenue (platform fees or minimum commitments), committed usage (short-term predictability), and variable usage (behavior-driven consumption) [4]. This allows you to show investors both the promise (contracted revenue) and the reality (actual usage). Companies like MongoDB, with their 90-day rolling ARR, and Snowflake, through their reporting of RPO, demonstrate how separating revenue streams can highlight both stability and growth potential [6].
"ARR isn’t going away, but it’s no longer enough for AI revenue models." – revVana [4]
Contracted ARR still carries significant weight with investors, often commanding 10–12x multiples, while usage revenue, due to its inherent volatility, typically lands in the 3–6x range [1]. To avoid undervaluing high-growth areas, separate contracted and usage revenue when presenting to stakeholders. Confluent, for instance, distinguishes its "Confluent Cloud" (usage-based) from its legacy "Confluent Platform" (subscription-based), ensuring the slower-growing business doesn’t overshadow the performance of its high-growth segment [6].
Traditional metrics should serve as context rather than the main guide. They’re useful for showing where you’ve been but not necessarily where you’re headed [13]. They help establish credibility and provide a comparison baseline, but they need to be supplemented with AI-focused metrics to tell the full story. For instance, gross margin is still relevant but must account for inference costs [5][2]. Similarly, tracking Gross Revenue Retention (GRR) alongside Net Revenue Retention (NRR) can uncover whether your overall customer base is shrinking, even if a few high-usage customers make NRR appear strong [6]. This layered approach ensures stability is acknowledged while real-time performance is accurately captured.
The goal isn’t to discard traditional metrics but to reposition them as part of a broader, dual-layer dashboard. Combining "ARR + Annualized Usage" (ARR+AU) gives investors a clear view of both the floor (downside protection) and the ceiling (growth potential) [1]. This approach balances the old with the new, ensuring a comprehensive narrative for stakeholders.
How to Report These Metrics to Investors
Start by explaining why your dashboard is evolving. While investors are familiar with traditional SaaS metrics like ARR and the Magic Number, these don’t fully capture the dynamics of AI-native businesses. The key difference? AI revenue is behavior-driven, not contract-driven. Usage ebbs and flows depending on factors like adoption rates and workload surges [4]. Even technical decisions – such as model selection, context length, or caching – carry financial weight. Help investors see that your infrastructure dashboard doubles as a financial statement [2].
Organize your board deck around three revenue layers to provide clarity:
Baseline Revenue: Platform fees or minimum commitments that form a predictable foundation.
Committed Usage: Revenue tied to recent, consistent patterns, offering near-term predictability.
Variable Usage: Behavior-driven consumption that represents potential upside [4].
This framework gives investors a clear view of both the floor and the ceiling. Use ARR+AU (Committed ARR + Annualized Usage) as your hybrid run rate metric. This approach is quickly becoming the go-to standard for AI-native reporting [1]. Additionally, keep contracted ARR separate from usage-based revenue. While ARR might still command multiples of 10–12x, usage revenue typically fetches 3–6x due to its inherent volatility. This distinction ensures high-growth segments aren’t undervalued [1].
Next, shift the conversation from revenue to unit-level profitability. Highlight Contribution Margin per Task (CMPT) to show that every customer interaction – after factoring in tokens, infrastructure, and human costs – remains profitable [2]. Investors are increasingly focused on compute efficiency, or how quickly your system reduces costs as it becomes smarter. To demonstrate this, report Token ROI: the ratio of accuracy improvements to token cost increases [2]. This metric reassures investors that model upgrades are driving value rather than just inflating expenses.
Use cohort analysis to reveal predictable patterns in variable usage. Segment your customer base into power users and casual adopters to ensure heavy users don’t distort overall health metrics [1]. Track both Gross Retention Rate (GRR) and Net Revenue Retention (NRR) to provide a balanced view. GRR highlights whether your broader customer base is shrinking, even if a few "inference whales" boost NRR [6]. When defining churn, align it with your product’s natural usage cycle instead of defaulting to arbitrary monthly periods [6].
Transparency about margin volatility is equally important. As one investor put it:
"You cannot run an AI company like a SaaS company. Your biggest cost isn’t headcount anymore, it’s intelligence. And every time your model improves, your P&L changes." – Investor Quote via All That Noise [2]
AI companies often face gross margin swings of up to 20 percentage points within a single quarter due to fluctuating usage patterns [1]. To address this, use rolling forecasts updated monthly instead of static annual plans. Implement dynamic margin alerts – internal triggers that flag when inference costs exceed specific thresholds, such as 40% of revenue [1]. This proactive approach shows that you’re actively managing the risks of the "AI Tax" rather than ignoring them.
Conclusion
Once you’ve rethought your metrics and reporting framework, one thing becomes evident: traditional SaaS metrics were designed for a world where costs were negligible, and revenue was steady and predictable. That approach doesn’t align with the economics of AI-driven businesses. For AI-native companies, every user interaction adds a cost, profit margins shift dramatically, and revenue depends more on customer activity than fixed contracts. Relying on outdated metrics isn’t just unhelpful – it can lead you astray.
The seven new KPIs introduced here offer a way to navigate this new landscape, where AI consumption – not user logins – defines business value. Many founders are already moving beyond focusing solely on ARR, adopting metrics that reflect the realities of token-based economics.
"You cannot run an AI company like a SaaS company. Your biggest cost isn’t headcount anymore, it’s intelligence. And every time your model improves, your P&L changes." [2]
This shift doesn’t mean ARR is obsolete, but it does mean it should take a backseat. Use ARR as a supporting metric, reported alongside Gross Profit per Million Tokens, and complement the Magic Number with the Burn Multiple to better capture the dynamics of a consumption-based model. Keep contracted ARR (still valued at 10–12x multiples) separate from usage-based revenue (valued at 3–6x) [1]. Companies that strike the right balance will gain investor trust, while those that stick to outdated metrics will struggle to explain why their "recurring" revenue isn’t so recurring after all.
If your dashboard still mirrors a seat-based SaaS business, it’s time for a reset. Metrics built for yesterday’s models won’t drive growth in an AI-first world. As your business evolves, so must the way you measure success.
FAQs
Which old SaaS metrics still matter for AI-native companies?
Traditional SaaS metrics, such as Annual Recurring Revenue (ARR), still hold value but fall short when applied to AI-native companies. They don’t fully capture key factors like fluctuating margins, revenue tied to usage, or the distinct value drivers unique to AI-based models. While these metrics offer some perspective, they must be paired with newer ones that align more closely with the economic realities of AI-driven businesses.
How do I calculate gross profit per million tokens?
To determine the gross profit per million tokens, start by subtracting the cost of goods sold (COGS) for those tokens from the total revenue they generate. Once you have the gross profit, divide it by one million tokens to find the per-million figure. This calculation is a key metric for assessing the economics and profitability of AI products, especially in environments with variable costs.
How should I report usage-based revenue to investors?
For companies built around AI, relying solely on traditional SaaS metrics like ARR may not provide the full picture, especially with usage-based pricing models. A more relevant metric is gross profit per million tokens, as it directly links revenue to the cost of AI inference, offering insights into unit economics. Alongside this, tracking usage metrics – such as total tokens consumed or tasks completed – gives a better sense of operational activity and customer engagement. These metrics help investors understand performance and the overall health of the business more effectively.
Related Blog Posts
GTM Engineering Benchmarks 2026: Time-to-First-Revenue, CAC Payback, and Pipeline Velocity for B2B SaaS
B2B SaaS Benchmarks for 2026: Annual Report
How AI Companies Are Replacing the SaaS Magic Number & Why It’s Painfully Overdue
How AI Companies Are Monetizing in 2026: Seats, Tokens, and the Hybrid Models Winning Right Now
---
## Top 5 Reasons Not to Become a Data Analyst
URL: https://www.data-mania.com/blog/reasons-not-to-become-a-data-analyst/
Type: post
Modified: 2026-03-18
Are you wondering what are the reasons NOT to become a data analyst?
So, you’ve heard a lot about the “Data Analyst” job and you’re wondering if it might be the next best step for you? Well, hold tight because in this article, I’ll be helping you sort that out once and for all.
PRO-TIP: Learn more about data careers in my brand new article on how AI marketing is transforming analytics careers here.
Data Analyst work was baked into everything that I did as a Professional Engineer, before I transitioned into data science. And to be honest, even though I’ve been a business owner for almost 9 years – I still do data analysis on a daily basis and it’s been a big part of how we’ve been able to scale my online communities to over 650k data professionals.
Data analysis is a really fast-growing career path, and for good reason – lots of people are landing jobs and their salaries are way higher than the national average.
But wait! Depending on your natural aptitudes, there can be a lot of drawbacks to being a data analyst. Among all the data industry hype, you seldom hear about any of the drawbacks related to any of the data career paths.
So, my goal for this article is to give you a quick heads up on why you may NOT want to become a data analyst.
watch it on YouTube here: https://youtu.be/YmxbL0Hr3Gg
If you prefer to read instead of watch, then read on…
Top 5 Reasons Not to Become a Data Analyst
Reason #1 – You don’t enjoy math!
If you don’t enjoy math – don’t become a Data Analyst, period.
Honestly, if you’re not a STEM grad, I’d think twice about trying to pursue data analysis as a career path. A lot of companies are hiring non-STEM people as data analysts, but if they asked me, I would caution them against it.
I majored in engineering in college, and I even aced Statistics for Engineers, but STILL, I never realized how rusty I was in statistics until I had to start using it in my job and doing data analysis and machine learning. It took a while for me to brush up – that is despite all the years of advanced mathematical training I had.
In all honesty, when I worked as an employee, I saw A LOT of people working in an analyst type position who were completely ignorant of statistics. It was completely obvious that they didn’t know what they were doing. Ultimately, besides being embarrassing for them, this could become a huge problem for the company.
I would even go as far as to say that this could be one of the major reasons that most data analytics projects FAIL. You really need advanced mathematical and statistical skills in order to uncover data insights from the noise and that is the main function of a data analyst.
Can this skill set be acquired?
Yes, BUT it takes a lot of time, practice, and you need a really good statistical and mathematical background in order to get there.
You’ll want to have at least learned how to do this in college, so that when you’re on the job, you can figure it out pretty quickly with some reasonable assurance that you’re not completely off the mark.
Reason #2 – You’re not so hot with data
This might seem like a no-brainer, but if you’re not naturally comfortable working with data – you probably should not become a Data Analyst.
The thing is if you’re not a data person, you may not realize that some people are just born comfortable with using spreadsheets and have been on computers their entire life. I’m actually one of those people – I started using spreadsheets almost 35 years ago (yes, that’s about my entire life!)
But if you didn’t start with computers and working with data and programming and spreadsheets as a small child, I don’t know how naturally it will come to you.
And if the thought of working with data already stresses you out, then just save yourself the hassle and don’t become a data analyst.
Data analysis involves managing data, reformatting data, cleaning data, analyzing data and not just little data either – HUGE amounts of data. So yes, if you want to become a data analyst, you do need to be “good with data”.
On top of that, being a data analyst actually requires all sorts of other types of business skills as well, including things like: writing reports, charting and graphing, dealing with data in spreadsheets, and so on. And on top of that, TEAMWORK – you need to be really comfortable working with other data professionals in order to produce the results that are expected by your company.
When you’re not comfortable and not good at working with data, it really shows. You’ll have to spend a lot of time cleaning things up and fixing mistakes – and it’s highly likely that your teammates will notice. It won’t be COMFORTABLE. What’s worse, this type of unreliability can cost the company a lot of money, because people can’t trust the results of your work, which means that your work will not be used.
So, if you’re not good with data, I’d say you could probably bypass the data analyst role and you’ll be better off for it.
Reason #3 – You’re looking to minimize your screen time
If you’re looking to minimize your screen time- don’t become a Data Analyst.
You’re going to be on a computer all day, every day. If you work as an employee, that will be 8-9 hours on a computer all day. You’re gonna have to know how to use all kinds of software applications. You’re also gonna need to know how to program in Python, R and SQL. And if you don’t love working on computers, then you definitely don’t want to become a data analyst because that is basically the entire job. Well, it’s about 80% of the job.
Don’t get me wrong – you don’t have to be perfectly comfortable with programming, Python and R and knowing how to use Tableau or Power BI and all of that. You can get trained up. If you just don’t know how to use these applications or you don’t know how to do the programming, then I would suggest that you ask your company if they would invest in some training for you to help make you a more valuable employee.
You don’t have to be born (of course, no one is born knowing all this stuff!) – but you do have to have a passion for analytical thinking, math, computers, and data.
If you’re working as a data analyst there is no way to get around massive amounts of screen time in your daily life.
Reason #4 – You’re not much of a talker
When it comes to communication requirements, data analysts are on the other side of the spectrum as engineers and designers. As a data analyst, you need to use your skills on a daily basis to understand vast complexities that you uncover within the data, but you also need to know how to communicate those insights to stakeholders, as well as to your team members. So it’s really a communicative role and you should be prepared for that.
If you’re not comfortable with “business speak” and you don’t really enjoy communicating with other people, then you definitely don’t want to become a Data Analyst.
You also want to be super skilled at translating technical talk to non-tech business language that non-technical managers can understand and use in their decision-making.
In reality, this takes a lot of creativity, and some people are not really good at coming up with interesting ways to translate complex technical findings into plain language that non-techies can “get”.
Reason 5 – “Analytical Thinking” isn’t in your repertoire
Look, getting caught up in the data – it happens.
BUT, as a data analyst… you’re goal should be to get in and get out as soon as possible, and only get the data insights you need to solve business problems. This is called ad hoc data analysis and it’s the bread and butter of the Data Analyst.
So if you take a job as a data analyst, you should be prepared to quickly answer questions like these:
When do we need to hire new people?
How are people reacting to our latest update?
How are our products performing this month?
How is our website performing this quarter?
How are our customers interacting with our website? And what does that indicate about our customer experience?
I could go on, and on…
Quickly coming up with accurate answers to questions like this requires that you’re pretty much an analytical badass. You’ll have to be able to quickly understand what the data is telling you and to quantify relationships between variables in that data.
If you’re not a natural analytical thinker, you won’t love being a data analyst.
Rethinking your career path in data? The field is shifting fast – away from pure analysis toward growth engineering and GTM systems. See what fractional CMO work actually looks like inside this free 6-day mini-course →
---
## How Your AI Monetization Model Should Impact The Metrics Your Measuring
URL: https://www.data-mania.com/blog/how-ai-monetization-model-should-impact-metrics/
Type: post
Modified: 2026-03-18
AI businesses often struggle with tracking the right metrics because traditional SaaS tools don’t account for AI’s unique cost dynamics. Here’s the key takeaway: your pricing model should dictate the metrics you measure. Whether you’re using seat-based, consumption-based, or hybrid pricing, aligning your metrics with your revenue model is crucial to avoid misleading data and improve decision-making.
Key Points:
Seat-Based Pricing: SaaS metrics like ARR and NRR still work but watch for margin drops from AI inference costs. Track gross margin variance by customer segment.
Consumption-Based Pricing: ARR and Magic Number lose relevance. Focus on token consumption, gross profit per million tokens, burn multiple, and first-year value.
Hybrid Models: Treat platform fees and token usage as separate businesses. Use blended gross margin to unify insights.
The hard part is recognizing when traditional SaaS metrics fail. For example, ARR can mask profitability issues if high-cost users aren’t segmented. To fix this, track metrics that reflect your actual cost structure and revenue streams.
Why It Matters:
AI companies face tighter margins (40% vs. SaaS’s 90%) and unpredictable costs. Misaligned metrics can lead to scaling unsustainable models. The solution? Tailor your dashboard to your pricing model and report on each layer individually. This ensures you’re focusing on sustainable growth, not just top-line numbers.
AI Pricing Models: Key Metrics Comparison for SaaS Companies
Seat-Based Pricing: Your Existing Metrics Still Mostly Work
Standard SaaS Metrics Still Apply
For seat-based models with high margins – typically around 80–90% – and mostly fixed costs, established SaaS metrics like ARR, NRR, Magic Number, LTV:CAC, and DAU:MAU continue to hold up well [3]. This stability comes from the negligible marginal costs per user, which makes it easier for buyers to plan budgets and for finance teams to create accurate forecasts. For horizontal tools like CRMs or help desks, the value aligns naturally with headcount. These models remain practical as long as per-user inference costs stay under 15–20% of the subscription price [1].
When AI Features Break Your Margin Assumptions
The introduction of AI features with hefty inference costs can upset the standard margin structure, making ARR less reliable as a quality signal. For AI-native companies, gross margins can plummet from the standard 80–90% range to somewhere between 25% and 60% because of these added costs [3].
Another challenge is usage asymmetry. A power user can generate up to 100× the compute cost of a light user, creating a wide gap in per-user expenses. For example, a typical user might cost $2.50 to serve, yielding a 91% margin on a $30/month seat. But a heavy user running complex AI workflows could rack up $45 in compute costs, turning that into a -50% margin [4]. When margin variance exceeds 20 percentage points, ARR loses its reliability as a performance indicator.
How to Audit Your Margins by Customer Segment
To get an accurate picture, track gross margin at the workload or customer segment level rather than relying on aggregate data. Begin by pinpointing all variable costs, including:
Inference costs (input/output tokens)
Infrastructure overhead (hosting, monitoring, logging)
Third-party API costs, such as vector databases or search calls [3]
Don’t forget to account for internal consumption – things like system prompts, reasoning steps, agent loops, and retries – which can make up 50–90% of total usage [3].
If your analysis shows margin variance under 10 percentage points, your current dashboard is sufficient. However, if it exceeds 20 points, you’ll need to incorporate gross profit per token metrics [1]. Focus on identifying the top 10% of customers by their AI cost-to-revenue ratio. Often, these power users consume 8–12 times the median usage [1]. If their API usage costs more than their subscription fee covers, you’ll need to introduce hard caps or usage-based limits to your seat-based model [4].
Once you’ve mapped out margin discrepancies, shift your attention to consumption metrics if necessary to address these imbalances effectively.
sbb-itb-e8c8399
AI Breaks SaaS Gross Margins (90% vs 50%)
Pure Token/Consumption Pricing: Track These 4 Metrics Instead
Switching from seat-based pricing to token-based pricing changes the game entirely. With AI, every single inference call comes with a cost, making traditional SaaS metrics like ARR and Magic Number less relevant. To truly understand the economics of token-based pricing, you need to focus on four specific metrics.
Token Consumption (Replaces MAU/DAU)
This metric tracks the actual volume of language computation, going far beyond simple login counts. It reveals how much users are engaging with your product in terms of tokens consumed. But here’s the catch: token usage isn’t always what it seems. For instance, while a user might visibly trigger 200–300 tokens, internal processes like system prompts, reasoning steps, and retries can multiply that number by up to 10 times. In agentic AI products, these "hidden" tokens can make up 50% to 90% of total consumption [3].
To get a full picture, track visible token usage separately from what’s happening behind the scenes. High token consumption is a good sign only if it’s paired with strong gross profit per token. If profit per token is low, it points to inefficiencies rather than growth potential.
Next, dive into gross profit per million tokens to see how value stacks up against costs.
Gross Profit per Million Tokens (Replaces ARR)
This is your go-to metric for understanding profitability. Calculate it by subtracting your fully loaded COGS (cost of goods sold) from the revenue earned per million tokens. Make sure to include everything in your COGS: inference costs, the "hidden" token iceberg, infrastructure overhead (typically 10–15% of inference costs), and third-party API expenses like vector database queries [3].
"When you receive $10 from the customer, you can’t just spend 10 cents on AWS. You might be spending $4 or maybe $5 just to service that one customer." – Jacob Jackson, Founder, Super Maven [2]
Gross profit per token is a real-time indicator of whether you’re delivering value above your costs [1]. Fast-growing AI startups are currently operating at about 25% gross margins, while more stable ones reach 60%. Compare that to traditional SaaS margins of 80–90% [3]. Without keeping an eye on this metric, you risk scaling revenue while sinking into negative unit economics [2].
Once you’ve nailed down your margins, assess the overall cost of growth using burn multiple.
Burn Multiple (Replaces Magic Number)
Burn multiple measures the total cost of growth, factoring in inference, infrastructure, and support – not just sales and marketing. Unlike the traditional Magic Number, which assumes near-zero marginal costs, burn multiple reflects the reality of AI, where compute expenses dominate [1][3].
The good news? Inference costs have dropped dramatically – GPT-4–equivalent models now cost about $0.40 per million tokens, down from $20 just three years ago [1]. This deflationary trend means your burn multiple might shift even if your sales spending doesn’t. Reassess this metric annually to ensure your pricing aligns with these changes. Also, track contribution margin per 1,000 tokens at the workload level to spot if high-usage customers are eating into your efficiency [1].
Finally, look at first year value to gauge short-term customer profitability.
First Year Value (Replaces LTV)
Lifetime value projections lose their reliability in a consumption model, where usage patterns and AI capabilities evolve rapidly. Instead, focus on first year gross profit – the actual profit generated by a customer in their first year. This metric is especially critical as AI companies approach the "2026 renewal cliff." Many early AI contracts were signed with innovation budgets and low price sensitivity, but as these contracts come up for renewal, CFOs will demand clear ROI and sustainable unit economics [2].
First year value helps you determine if your current customer base can withstand that scrutiny without relying on overly optimistic long-term forecasts.
Metric
What It Measures
Problem Signal
Token Consumption
Volume of language computation performed
High usage with low gross profit per token
Gross Profit per Million Tokens
Value created above delivery costs
Margins below 50% or shrinking over time
Burn Multiple
Total capital consumed per dollar of revenue
Rising burn despite stable sales efficiency
First Year Value
Gross profit in first 12 months
Negative or declining cohort economics
Hybrid Platform + Tokens: Two Dashboards and One Bridge Metric
When working with a hybrid model, it’s essential to treat your platform and token economics as two separate businesses before combining their results. Many AI companies eventually follow this path: they charge a base platform fee for access and collaboration features and add token-based pricing for AI workloads. Essentially, you’re managing two distinct business models with different financial dynamics – track them individually first, then bring them together.
Platform Layer: Focus on SaaS Metrics
The platform side operates much like a traditional SaaS business, with predictable margins and fixed costs. Keep an eye on key metrics like ARR (Annual Recurring Revenue), NRR (Net Revenue Retention), Magic Number, and MAU (Monthly Active Users). These metrics are well-suited to seat-based models. For this layer, aim for gross margins in the 70–80% range [1].
This part of the business provides financial stability and predictable recurring revenue, forming the foundation for your overall financial health. However, it only tells part of the story.
Token Layer: Monitor Usage Metrics
The token layer is where growth happens, but it comes with variable costs tied to usage. To manage this effectively, track metrics like token consumption (input vs. output), gross profit per million tokens, and token expansion rate to monitor month-over-month growth. For this layer, target gross margins in the 50–65% range [1].
These metrics help ensure that AI usage is driving value without eating into profitability. A strong token expansion rate paired with healthy margins signals success, while rapid growth with shrinking margins suggests you’re subsidizing unsustainable usage.
Once you’ve gathered insights from both layers, calculate a single blended gross margin to unify the picture.
Blended Gross Margin: Bridging the Two Layers
The most critical metric in a hybrid model is the blended gross margin, which combines the platform and token layers. For example, if platform fees maintain 80% margins but token revenue drops to 30%, and token usage is growing faster, your overall margin will shrink – even if ARR looks strong [6]. Relying solely on ARR can mask deeper profitability challenges at the workload level.
To get a clear view, create two separate P&Ls – one for the platform and one for tokens – before merging them. The gap between these results will highlight where you need to focus strategically. Use blended margins to identify which users are profitable and which are driving up costs.
Metric Layer
Primary Metrics
Margin Target
Platform Layer
ARR, NRR, Magic Number, MAU
70–80% [1]
Token Layer
Token Consumption, Gross Profit per 1M Tokens, Expansion Rate
50–65% [1]
Blended Bridge
Blended Gross Margin
60–70% (Target)
To maintain control, set up automated alerts when token usage reaches 80% or 95% of allocated amounts to avoid unexpected costs and protect profitability [1]. Additionally, tie sales compensation to contribution margin rather than top-line ARR. These tactical steps help ensure that growth aligns with sustainable profitability.
The Mistake That Cuts Across All 3 Models
The biggest issue isn’t choosing the wrong metric – it’s using metrics built for a completely different business model. This often happens when founders adopt a SaaS dashboard and apply it to AI economics without questioning if it makes sense.
Here’s how these mismatches show up in consumption, hybrid, and seat-based models.
What Happens When Metrics Don’t Match
For a consumption-based company, focusing on the Magic Number might make your sales efficiency look great because sales spend is low compared to usage growth. But this ignores the infrastructure costs that eat into margins with every query. You’re celebrating efficiency while gross profit quietly collapses.
In a hybrid model, reporting blended ARR without separating platform revenue from token revenue can paint a misleading picture. Your company might seem to be growing quickly, but if your high-margin platform revenue is stagnant while your low-margin token revenue is skyrocketing, profitability is actually declining. In fact, 65% of IT leaders report unexpected charges from consumption-based AI pricing, with costs overshooting estimates by 30% to 50% [1].
For a seat-based company with AI features, ignoring gross margin variance across customers can hide serious issues. You might see strong NRR, but that metric can mask the fact that profitable and unprofitable users are lumped together. The top 10% of power users often consume 8 to 12 times the median AI cost-to-revenue ratio [1]. Without tracking this, retention metrics can obscure the real profitability challenges.
These examples highlight why aligning your metrics with your business model is so important.
The Fix: Match Metrics to Your Model
To avoid these pitfalls, you need to align your metrics with your specific monetization model. Whether you’re working with a seat-based, consumption-based, or hybrid approach, the solution is straightforward: track metrics that actually reflect your business structure.
For consumption-based models, focus on metrics like Burn Multiple to account for infrastructure costs.
For hybrid models, clearly separate platform and token metrics to avoid blending high-margin and low-margin revenues.
For seat-based models with AI features, dive into gross margin variance to understand profitability across different user segments.
When presenting to your board, make sure to clearly explain how each metric ties back to your business layers.
"The pricing set today will be misaligned within 12 months unless your governance process accounts for this deflationary rate." – Armin Kakas, Revenue Growth Analytics Expert [1]
Conclusion: Match Your Dashboard to Your Business Model
Your dashboard should reflect the true nature of your revenue mechanics. Different pricing models – whether seat-based, consumption-based, or hybrid – require tailored KPI dashboards. For seat-based pricing, where margins are steady, traditional SaaS metrics like ARR, NRR, and Magic Number remain relevant. On the other hand, consumption-based pricing demands a shift to metrics like token consumption, gross profit per million tokens, burn multiple, and first-year value. Hybrid models call for two distinct dashboards, with blended gross margin serving as the connecting metric.
Having this clarity in your metrics enables better decision-making. AI companies often face extreme cost volatility – up to 10x – and operate with tighter margin buffers (around 40%, compared to the 90% seen in traditional SaaS) [5]. As Jacob Jackson, founder of Supermaven, warns:
"If the math doesn’t work for 10 customers, it is not going to work for 10,000" [2].
Without the right metrics in place, issues can snowball long before they’re noticed.
To avoid this, align your metrics with your pricing model and report on each layer individually. For consumption-based models, don’t let a healthy Magic Number distract you from rising infrastructure costs. For hybrid models, keep platform and token revenue metrics separate to avoid confusion during board reviews. This approach ensures transparency and prevents unpleasant surprises.
Tailoring your metrics to your business model isn’t just a best practice – it’s essential. From here, the next step is adopting an AI-specific framework to refine your dashboard further. Building a metrics system that aligns with your monetization strategy is key to driving sustainable growth.
FAQs
How do I know if ARR is misleading?
ARR can sometimes mislead, especially when there’s a large gross margin variance across different customer segments. This issue is particularly common with AI products that face high inference costs, which can skew ARR as a reliable performance indicator. To get a clearer picture, evaluate your gross margins by customer segment. If you find significant differences, it might be better to focus on metrics like gross profit per token instead.
What’s the fastest way to measure AI gross margin by customer?
To quickly gauge AI gross margin by customer, focus on tracking gross profit per million tokens. This straightforward metric offers real-time insights into margins and helps you determine if the value you’re providing exceeds your costs. It’s a simple yet powerful tool for monitoring customer-level profitability.
In a hybrid model, how do I split platform vs. token revenue?
To manage a hybrid model effectively, develop two distinct P&L statements – one for platform revenue and another for token revenue. By separating these layers, you can clearly analyze their unique economic structures and margin profiles.
Use the gross margin blend as a key metric to connect the two streams. This provides insight into overall profitability and highlights any margin compression when the revenues are combined. Such a framework enables more informed strategic decisions and helps uncover any potential profitability challenges before they escalate.
Related Blog Posts
AI Pricing Models Explained: Usage, Seats, Credits, and Outcome-Based Options
How AI Companies Are Replacing the SaaS Magic Number & Why It’s Painfully Overdue
How AI Companies Are Monetizing in 2026: Seats, Tokens, and the Hybrid Models Winning Right Now
Why SaaS Metrics Like ARR and Magic Number Are Failing AI-Native Companies
---
## Top 10 AI Marketing Tools To Skyrocket Your Growth In 2026
URL: https://www.data-mania.com/blog/top-10-ai-marketing-tools-to-skyrocket-your-growth-in-2025/
Type: post
Modified: 2026-03-18
AI marketing tools are transforming how businesses grow in 2026. These tools automate tasks, personalize customer experiences, and improve decision-making with data-driven insights. Here are the top 10 AI marketing tools to consider:
VEED: is an all-in-one online video editing and AI content creation platform designed to help marketers produce high-impact video content without the need for advanced editing skills.
Omnisend: Improves ecommerce email and SMS marketing with AI copy tools and behavior-based automation.
HubSpot Marketing Hub: Combines AI-powered content creation, CRM integration, and marketing automation.
Marketo Engage: Focuses on B2B marketing with lead management, email AI, and revenue attribution.
Drift: Real-time conversational marketing with AI chatbots and lead qualification.
ActiveCampaign: Enhances email marketing with predictive sending and customer segmentation.
Clearbit: Enriches customer data for better targeting and lead qualification.
Hootsuite Insights: Tracks social media trends, sentiment, and brand mentions.
Seismic: Improves sales enablement with AI-driven content creation and analytics.
Salesforce Marketing Cloud: Offers personalized customer journeys and advanced campaign management.
Pathmatics: Provides ad intelligence with competitive insights and creative performance tracking.
These tools help businesses streamline operations, improve ROI, and stay competitive in a fast-evolving digital landscape.
Quick Comparison
Tool Name
Primary Use Case
Key Features
Best For
VEED
All-in-One AI Video
AI video script generator, text-to-video AI, auto subtitles, AI avatars
Video marketing needs
Omnisend
E-commerce Email & SMS Automation
AI subject lines, AI copy, segmentation, product recommendations
E-commerce stores
HubSpot Marketing Hub
All-in-One Marketing
AI content, CRM integration, automation
Unified marketing needs
Marketo Engage
B2B Marketing Automation
Lead management, email AI, revenue tracking
Enterprise B2B organizations
Drift
Conversational Marketing
AI chatbots, lead qualification, real-time chat
Real-time engagement
ActiveCampaign
Email Marketing & Automation
Predictive sending, segmentation, campaign automation
Mid-sized businesses
Clearbit
Data Enrichment
Lead enrichment, insights, targeting
Data-driven teams
Hootsuite Insights
Social Media Analytics
Sentiment analysis, brand monitoring, trend tracking
Social media-focused teams
Seismic
Sales Enablement
Content management, analytics, personalization
Enterprise sales teams
Salesforce Marketing Cloud
Enterprise Marketing
Journey building, multi-channel campaigns, AI tools
Large organizations
Pathmatics
Digital Ad Intelligence
Competitive insights, creative analysis, ad tracking
Advertising teams
Each tool offers unique features tailored to specific business needs. Choose the one that aligns best with your goals and systems.
Top 10 Best AI MARKETING TOOLS You Need in 2026 to …
Key Features of B2B Tech Marketing Tools
AI has reshaped the way businesses approach marketing, offering features that help tech companies grow and work more efficiently.
Scalable Automation Look for tools that support automation on a large scale. With AI, small teams can handle tasks that used to require entire departments. Campaigns can be launched in hours rather than weeks, without sacrificing quality [1].
Integration Capabilities A well-connected marketing stack is crucial. Choose tools that provide:
Native integrations with popular B2B platforms
APIs for custom solutions
Workflow automation between systems
Real-time data updates
These connections are key to building strong analytics and effective content strategies.
ROI Tracking and Analytics Analytics features should provide clear insights into performance. Here’s how they help:
Analytics Feature
Business Impact
Campaign Attribution Tools
Identify where revenue comes from
Conversion Tracking
Gauge success rates
Customer Journey Analytics
Analyze buying behavior
Custom Report Building
Share tailored insights with stakeholders
Content Generation and Optimization AI has revolutionized content creation. Effective tools should:
Create content that aligns with your brand voice
Distribute content effectively across platforms
Track performance and make real-time adjustments
"Vibe Marketing represents a remarkable shift: one marketer with AI tools can now accomplish what traditionally required 10+ specialists." – Sonu Kalwar, Author [1]
Personalization Capabilities Modern tools should excel at personalization by using AI to:
Analyze buyer behavior
Recommend tailored content
Optimize communication timing
Adjust messages based on engagement
This level of customization not only boosts engagement but also ensures budgets are used wisely.
Cost-Effective Scaling AI tools allow businesses to grow without requiring a matching increase in spending.
Data Management and Security For B2B tech companies, secure and reliable data management is non-negotiable. Key features include:
Safe data storage and processing
Compliance with industry regulations
Regular security updates
Reliable backup and recovery systems
BONUS TOOL: VEED Online Video Editing and AI Content Creation Platform
VEED is an all-in-one online video editing and AI content creation platform designed to help marketers produce high-impact video content without the need for advanced editing skills. With a growing suite of AI tools, VEED makes it easy to generate, repurpose, and optimize videos for every stage of the customer journey.
Key AI Features
Feature
What It Does
How It Helps Businesses
AI Video Script Generator
Creates engaging video scripts based on input prompts
Speeds up ideation and reduces content prep time
Text-to-Video AI
Transforms scripts into full video content
Enables faster, scalable video production
Auto Subtitles & Translations
Generates accurate subtitles and translates them into 100+ languages
Expands reach and improves accessibility
AI Avatar
Adds human-like avatars to narrate your videos
Personalizes videos without hiring talent
Magic Cut & Studio Sound
Automatically trims filler words and removes background noise
Polishes videos quickly for a professional finish
Real-Life Results
Marketers and businesses using VEED have achieved impressive results:
TheCareerCEO reduced video editing time by 60% using VEED’s AI automation tools.
"VEED has been game-changing. It’s allowed us to create gorgeous content for social promotion and ad units with ease." – Director of Audience Development, NBCUniversal
AI-Powered Marketing Tools
VEED offers a wide range of AI-driven features tailored for marketers, including:
Brand templates and auto-resizing for social platforms
Dubbing AI and avatars for diverse audiences
Screen recording and webcam integration for tutorials and demos
Collaboration tools for teams and agencies
Performance Highlights
VEED stands out with:
Over 3 million monthly users worldwide
Rated 4.6/5 stars on G2 and Trustpilot
Saves marketers an average of 8–12 hours per video campaign
What’s Next in 2026
VEED is doubling down on AI innovation with:
Enhanced voice cloning and avatar personalization
AI Playground: The latest generative AI models can be tested for video and image creation in one place.
As AI video marketing continues to rise, VEED is positioned as a must-have tool for brands looking to stand out and scale with engaging, high-converting video content.
1. HubSpot Marketing Hub
HubSpot Marketing Hub is an AI-powered platform designed to simplify B2B tech marketing operations and deliver measurable results. The 2026 update focuses on AI automation and improved integrations.
Core AI Features
Feature
Function
Business Impact
Breeze Copilot
AI-assisted task management
Boosts productivity and streamlines workflows
Breeze Agents
Automated content creation and social posting
Cuts manual tasks by 70%
Marketing Analytics Suite
AI-driven reporting and attribution
Supports data-backed decision-making
Content Remix
Video optimization and publishing
Expands content reach and engagement
Proven Results
Tech companies have achieved impressive outcomes with the platform:
PatSnap boosted lead generation by 400%, reached a 33% conversion rate, and shortened sales cycles by 36% [7].
LogiNext saw a 5x increase in monthly traffic and improved lead qualification by 4x [6].
Tech Data achieved a 30% email open rate and a 2% click-through rate within just one month [5].
AI-Driven Marketing Tools
HubSpot’s advanced AI tools are reshaping marketing strategies. Andy Pitre, EVP of Product at HubSpot, explains:
"Until now, we haven’t seen a complete AI solution for businesses… With Breeze, businesses finally get it all. AI that’s agile, intuitive, and embedded, not just with popular LLMs, but your customer data." [2]
Seamless Integration
The platform connects effortlessly with key tools and platforms, such as:
YouTube and Instagram Reels for video marketing
Amplitude for in-depth analytics
LinkedIn for B2B audience targeting
Google Suite for managing digital presence
Nicholas Holland, VP of Product at HubSpot, highlights:
"Marketers need a new playbook, built on ease, speed, and unification… The updates we’ve made to Marketing Hub and Content Hub give marketers what they need to build their audiences, create multichannel content, launch campaigns, and measure it all." [3]
These integrations make HubSpot Marketing Hub an essential part of a cohesive marketing strategy.
Boosting Marketing ROI
The platform’s AI-driven features deliver tangible results:
89% of marketers report ROI from AI-generated email content [4].
Companies using advanced analytics experience a 20% improvement in marketing ROI [4].
Personalized email campaigns see a 26% increase in open rates [4].
For B2B tech companies, HubSpot Marketing Hub combines AI automation, advanced analytics, and seamless integration to create a unified and impactful marketing strategy in 2026.
2. Omnisend
Omnisend is an ecommerce marketing platform that pairs email and SMS automation with built-in AI for faster writing, smarter targeting, and product-level personalization. It connects to your store data so messages can trigger from actions like signups, cart activity, and purchases.
Key AI Features
Feature
Function
Benefit
Subject Line & Preheader Generator
Generates subject lines and preheaders based on your campaign context
Speeds up testing and improves first-open performance
AI Writer + Direct Copywriting
Suggests edits and can generate copy in your brand voice from prompts
Cuts drafting time and keeps tone consistent
Brand Assets AI
Imports logo, colors, and fonts and applies them to templates
Keeps emails on-brand with less manual styling
AI Segment Builder
Builds segments from plain-language targeting, plus tools for high-value and at-risk shoppers
Improves targeting for retention and repeat sales
Personalized Product Recommender
Inserts product blocks tailored to browsing and purchase behavior
Increases relevance in campaigns and automations
Real-Life Results
Here are some standout results from Omnisend customers:
Creality: €560,000 generated from an abandoned cart automation, with a 54% open rate and an 8% click-through rate.
Dukier: Omnisend-attributed revenue grew from €82,857 (2022) to €518,860 (2025), a 525% increase.
FiGPiN: A three-part welcome series reached a 63% open rate and a 22% conversion rate.
"The ease of setting them up and the ability to customize the content to fit our brand voice was a huge plus for us." – Omnisend
AI-Powered Marketing Tools
Omnisend supports revenue-focused automation and personalization with tools like:
Pre-built workflow templates for welcome, cart recovery, post-purchase, and reactivation
Multi-channel automations using email, SMS, and web push
Store integrations that unlock event-based triggers and targeting
Reporting with click maps and revenue attribution for campaigns and automations
Performance Highlights
The platform stands out with:
4.7/5 rating on the Shopify App Store across 2,900+ reviews
1,100+ reviews on G2
160+ pre-built integrations listed in Omnisend’s integrations catalog
What’s Next in 2026
Recent product updates show a push to bring AI into more of the workflow:
AI subject lines now support A/B testing for campaigns
AI subject lines and preheaders can be generated inside automation emails, not only campaigns
Brand assets can be imported and applied to templates through AI-assisted setup
3. Marketo Engage
Marketo Engage is an AI-driven marketing automation platform tailored for B2B tech companies in 2026. It uses artificial intelligence to drive revenue and keep buyers engaged.
Key AI Features
Feature
Function
Benefit
Dynamic Chat
Provides context-aware replies
Guides customers in real time
Journey Builder AI
Optimizes campaign workflows
Refines campaign strategies
Email AI
Generates email content
Automates copy and subject lines
Webinar Intelligence
Summarizes webinar content
Creates automated FAQs
Attribution AI
Analyzes marketing data
Delivers precise performance insights
Performance Highlights
Marketo Engage has delivered impressive results, including:
A 37% reduction in the time needed to create email campaigns
$4 million in additional revenue
A 2.8x increase in revenue alongside 24x pipeline growth
400% lower campaign costs per prospect
A 40% boost in lead quality and scoring [8]
Seamless Integrations
The platform connects with essential tools in the B2B tech ecosystem, such as:
CRM platforms like Salesforce, Microsoft Dynamics, and Veeva
Webinar tools including Zoom and ON24
Adobe Experience Cloud products
Synchronization of up to 200,000 records per hour [10]
"Adobe is committed to innovating and reimagining B2B marketing automation and account-based marketing to give B2B marketing teams better ability to create demand for their organization, drive coordination with revenue teams and use data-driven insights to improve every aspect of their programs." [9]
AI-Powered Marketing Enhancements
Marketo Engage simplifies and improves marketing efforts by:
Speeding up responses to prospects
Automating the creation of personalized content
Managing campaigns across multiple channels
Strengthening collaboration between sales and marketing teams
"It was very important for us to select marketing software that scaled quickly, could easily integrate with our other systems, and allow all of our marketers to become power users." [10]
What’s New in 2026
Marketo Engage is introducing cutting-edge updates for 2026, including:
Generative AI for deeper content personalization
Advanced tools for marketing data analysis
Automated optimization of customer journeys
Up next, check out the next tool in our AI marketing suite.
4. Drift
Drift offers a conversational platform designed to transform how B2B tech companies engage with their audience. Using AI-driven tools, it focuses on converting high-intent buyers through real-time, personalized interactions.
Key AI Features
Feature
Purpose
Business Advantage
Virtual Selling Assistants
Automates natural, human-like conversations
Qualifies leads instantly
Real-time Deanonymization
Identifies website visitors
Enables account-based targeting
Fastlane
Syncs with existing tech tools for quicker lead qualification
Speeds up the sales pipeline
Rhythm Workflow
Highlights key chat data
Simplifies the sales process
ROI Analytics
Tracks performance
Connects actions to revenue growth
These tools deliver measurable outcomes:
Performance Highlights
17.5% annual recurring revenue growth
670% return on investment
26% boost in demand for personalized experiences
64% year-over-year growth in immediate response rates [12][14]
"When our site visitors engage with chat, they now get better, more thorough responses – even though it takes our team less time to set up and manage the [Drift] chatbot."
Alla Mosina, Website Product Manager [11]
How Drift Uses AI to Drive Engagement
Drift’s AI capabilities help companies turn engagement into revenue by:
Automatically qualifying leads using integrated tech tools
Sending real-time alerts for live sales conversations
Crafting personalized conversation flows based on user behavior
Practical Use Cases
Drift’s platform delivers results for businesses across industries. Here are a few examples:
Brandwatch: Uses a "Contact Us" chatbot to connect visitors with a human instantly.
PTC: Employs persona-specific chatbots to match visitors with the right expert.
Zenefits: Implements an AI-powered "Website Concierge" to assess visitor needs.
"We’ve seen positive impacts across all stages of the buyer’s journey from Drift, owing to meaningful personalization. We’ve improved website conversion, sourced incremental leads, and accelerated the sales process. Drift is now one of our most-loved martech tools."
Steve Measelle, VP of Marketing Global Performance & Strategy [11]
Looking Ahead: Enhancements for 2026
Drift is set to introduce updates that include:
Advanced AI-driven conversation capabilities
Stronger integrations with existing tech stacks
Faster pipeline acceleration
Better tools for ROI tracking
"We help companies engage in personalized conversations with the right customers at the right time, so they can build trust and grow revenue."
Aurelia Solomon, Director of Product Marketing [13]
5. ActiveCampaign
ActiveCampaign is a marketing automation platform that uses AI to help businesses connect with their audiences in smarter ways. By combining automation with personalized experiences, it enables companies to scale their marketing efforts effectively.
Key AI Features
Feature
What It Does
How It Helps Businesses
AI-Suggested Segments
Identifies high-value customer groups automatically
Improves targeting precision
Predictive Sending
Finds the best times to send emails
Boosts open rates by up to 20%
Campaign Co-pilot
Evaluates and enhances campaign performance
Makes ROI tracking easier
Generative AI
Produces tailored content
Cuts down time spent on content creation
Automation Builder
Creates workflows from simple text commands [20]
Speeds up campaign setup
Real-Life Results
Here are some standout results from ActiveCampaign clients:
Artivive: Achieved 47% higher email engagement and grew their community from 100 to 100,000 members [16].
Motrain: Increased their conversion rate by 120% and saved 15 hours per week [17].
Soundsnap: Saw a 300% boost in monthly email revenue [18].
"We’re tracking trials started and trial conversions, and right now we’re converting at over 20%. Before ActiveCampaign, we were converting less than 10%." – Motrain [17]
AI-Powered Marketing Tools
ActiveCampaign processes over 4 billion experiences every week [19], offering tools like:
Automation for marketing tasks with over 900 native integrations [15]
Lead scoring and pipeline management
Content generation tailored to individual customers
Multi-channel campaign coordination
Performance Highlights
The platform delivers measurable results:
Saves 20 hours per month through automation [19]
Sends over 1 billion emails weekly [19]
Rated 4.5/5 stars by more than 13,500 users [19]
These results showcase the platform’s impact and pave the way for its future advancements.
"ActiveCampaign is an AI-first, end-to-end marketing platform for people at the heart of the action. It empowers teams to automate their campaigns with AI agents that imagine, activate, and validate – freeing them from step-by-step workflows and unlocking limitless ways to orchestrate their marketing." – ActiveCampaign, LLC [21]
What’s Next in 2026
ActiveCampaign plans to expand its AI capabilities even further, focusing on:
Sentiment analysis to gain deeper customer insights
More tools for AI-generated content
Enhanced predictive analytics to fine-tune campaign strategies
Better integration with other marketing technologies
These updates aim to give businesses even more powerful tools for understanding and connecting with their customers.
6. Clearbit
Clearbit uses AI to supercharge B2B data enrichment and customer intelligence. By processing information from over 250 data sources, it delivers detailed insights about companies and potential customers.
Core AI Features
Feature
Capability
Business Impact
Real-Time Enrichment
Adds 100+ B2B attributes automatically
Speeds up lead qualification
Smart Form Optimization
Simplifies forms to just an email address
Boosts conversion rates
Intent Detection
Pinpoints high-intent accounts via IP data
Shortens sales cycles
Data Refresh
Updates records every 30 days
Keeps data accurate
Global Coverage
Works across all countries and languages
Expands global customer reach
Success Stories
Clearbit’s impact is clear from its results:
Gong: Increased demo request conversions by 70% and saw a 5X rise in demo requests since September 2018, thanks to Clearbit’s dynamic enrichment [25].
Mention: Improved signup conversion rates by 54% using form auto-fill [26].
Brex: Simplified underwriting by enriching customer data instantly during signup, enabling tailored offers [24].
Comprehensive Data Intelligence
Clearbit’s database includes:
50 million company records
389 million contact records
94% email deliverability rate [22][23]
"The level of detail and number of contacts available through Clearbit Prospector was greater than any of the other tools we considered." – Arvind Ramesh, Intercom [22]
This wealth of data allows for precise audience targeting and smooth integration into marketing workflows.
Top Use Cases for 2026
Clearbit’s data capabilities shine in two key areas:
Lead Qualification
Clearbit enriches leads with detailed firmographic data, such as:
Company size and revenue
Industry classification
Technology stack
Corporate hierarchy
Marketing Automation
The platform supports advanced targeting by:
Identifying anonymous website visitors
Detecting buying intent signals
Standardizing roles and seniority levels
Enabling B2B customer acquisition strategies like account-based marketing
"Clearbit is by far the most flexible data enrichment solution I have come across to date." – Dexter Hart, Uber [22]
How Companies Use Clearbit
AdRoll showcases Clearbit’s potential by leveraging it for account-based marketing. They enhance their database with detailed firmographic data, enabling precise targeting and personalized campaigns [24]. The platform’s integration with major CRMs ensures growth without sacrificing data quality or targeting accuracy.
sbb-itb-e8c8399
7. Hootsuite Insights (Powered by Talkwalker)
Hootsuite Insights uses advanced AI to elevate social media analytics and monitoring for B2B tech companies. By 2026, it scans conversations across 150 million websites and 30 social media platforms in 187 languages, delivering detailed market insights [27].
AI-Driven Features
Feature
Capability
Business Impact
Blue Silk™ AI
Cuts research time by 40% weekly
Faster market insights
Sentiment Analysis
Tracks emotions in real time
Better reputation management
Image Recognition
Monitors visual content
Improved brand protection
Automated Summaries
Generates insights quickly
Streamlined reporting
Custom Alerts
Sends real-time notifications
Rapid crisis response
These features are designed to help tech companies:
Monitor brand mentions across millions of websites
Keep tabs on competitor activities in real time
Spot emerging industry trends
Understand customer sentiment
Automate performance reporting
Practical Impact
The tool’s capabilities are already making a difference for users. For example, the University of Sydney’s social media team has seen impressive results. Social Media Manager Liz Grey shares:
"The insights that Talkwalker provides us have been incredible and have really informed our campaign strategy. Providing these insights to our stakeholders demonstrates what social media can do for our brand and helps us secure investment to increase our budgets and grow our team." [27]
Key Use Cases
Hootsuite Insights is particularly effective in three areas for tech companies:
Threat Detection: The AI continuously tracks brand mentions and sends alerts about potential issues. This early warning system allows companies to address problems before they escalate [28].
Customer Insights: By analyzing social conversations, businesses can uncover customer pain points, feature requests, market trends, and insights into their competitive landscape.
Campaign Improvement: The tool’s analytics identify the best times to post, track engagement, measure performance, and suggest content adjustments to enhance marketing efforts.
Next-Level AI Features
Hootsuite Insights includes cutting-edge tools like:
Video recognition technology
AI conversation grouping
Custom AI classifiers
Real-time sentiment tracking
The platform monitors social networks, news outlets, forums, and even podcasts. Its expansive coverage and AI-powered analysis help tech companies stay on top of relevant conversations and make quick, informed marketing decisions.
8. Seismic
Seismic is an AI-powered platform designed to improve sales content for B2B tech companies. It combines automation and predictive insights to make sales enablement more efficient and effective.
Key AI Features
Feature
Function
Business Impact
Seismic Aura Copilot
Creates and automates content using AI
Speeds up content creation
LiveDocs
Personalizes content dynamically
Scales content across different markets
Smart Search
Helps reps find content quickly with AI
Saves over 100 hours per year per rep
Intelligent Analytics
Tracks engagement and provides insights
Boosts pipeline growth by 32%
Automated Coaching
Delivers tailored sales training
Increases new rep revenue by 65%
These tools deliver measurable results. Companies using Seismic report doubled content usage, 76 million pieces of content shared, 5.7 million personalized LiveDocs, and 1.3 million Digital Sales Rooms.
Real-World Example
Blackbaud’s experience shows how Seismic can transform operations. Alan Yarborough, Senior Brand Enablement Manager at Blackbaud, shared:
"With Seismic we’ve seen this breakdown of silos, increased communication between sales, marketing, and sales enablement. Through that we’re able to increase pipeline, increase our win rate, and close our deals faster." [30]
AI-Driven Roadmap
Seismic’s plans for 2026 focus on four key areas:
Discover: Smarter content recommendations and search
Create: AI tools for content generation and improvement
Automate: Simplified workflows and automation
Advise: Insights and coaching powered by data
As Seismic explains:
"Enablement is a mission-critical function that turns company strategy into reality, and we believe generative AI has created an industry-defining moment for GTM and enablement teams. It will change everything about the sales process, from prospecting to meeting preparation, content and presentation development, follow-up, training and performance tracking." [29]
Seamless Integrations
Seismic integrates with over 150 CRM systems, including Salesforce and Microsoft Dynamics. Oracle, for example, adopted Seismic to support its "One Oracle" strategy, improving efficiency and ensuring consistent messaging worldwide.
According to industry data, 91% of enablement tech users report faster adaptability and speed to market, while 94% see improved go-to-market efficiency. These stats highlight Seismic’s role in driving growth for B2B tech companies.
9. Salesforce Marketing Cloud
Salesforce Marketing Cloud is an AI-powered platform designed to transform B2B marketing. Its 2026 version focuses on delivering personalized customer experiences while boosting ROI through intelligent automation.
AI-Powered Campaign Management
Feature
Capability
Impact
Agentforce Campaign Designer
Automates campaign creation and optimization
Cuts campaign creation time by 60%
Einstein Lead Scoring
Predicts lead conversion potential
Increases customer engagement by 32%
Multi-Touch Attribution
Tracks channel performance with AI
Boosts marketing ROI by 32%
WhatsApp Integration
Improves global customer connections
Raises customer lifetime value by 34%
Blockout Windows
Controls message timing intelligently
Lowers acquisition costs by 27%
These features help businesses achieve measurable outcomes, as demonstrated by real-world case studies.
Real-World Success Story
Boston Scientific enhanced customer engagement by centralizing their data to create tailored journeys for healthcare professionals. Denis Scott, VP of Growth Marketing at Momentive, highlights the platform’s value:
"You can’t truly have a focus on the customer journey without a single source of truth for your data, and that is what Salesforce Marketing Cloud gives to us. Having all our data in one place means we can create smarter automations and rules to help us tailor our messages to what the customer wants and needs, ensuring they hear only the best, most relevant information." [32]
Proven Business Results
Research from Forrester [31] shows businesses using Salesforce Marketing Cloud achieve:
299% ROI over three years
Over $5 million in additional revenue
90% improvement in email deliverability
10% higher conversion rates
AI-Driven Features
The platform’s Agentforce technology includes:
Automated brief generation and targeting
AI-powered content creation
Smart customer journey mapping
Real-time content delivery
Autonomous ad optimization
Michael Kostow, EVP & GM of Marketing Cloud at Salesforce, explains the importance of these advancements:
"Brands that have accelerated success during the pandemic are data-focused, embrace AI, prioritize privacy, and find agile ways to collaborate across their entire organization." [32]
Seamless Integration
Integration with Data Cloud and CRM systems brings all customer data into one place. This is essential, given that 78% of marketers view data as key to customer engagement, and 84% report rising customer expectations [32]. These integrations, combined with AI-powered tools, make Salesforce Marketing Cloud a cornerstone for modern B2B marketing strategies.
10. Pathmatics
Pathmatics is a marketing intelligence platform designed to bring clarity to digital advertising in 2026. Its AI-powered tools help B2B tech companies make informed decisions and fine-tune their marketing efforts.
Key Features
Feature
What It Does
How It Helps
Cross-Platform Analytics
Tracks ads across platforms like Facebook, Instagram, YouTube, Snapchat, Pinterest, and Reddit
Offers a clear view of the market
Historical Analysis
Examines trends and past performance data
Improves strategic planning
Creative Intelligence
Analyzes top-performing ad content with AI
Refines messaging for better results
Competitive Insights
Compares competitor strategies side by side
Helps gain a competitive edge
Share of Voice Tracking
Monitors brand visibility in real time
Justifies marketing budgets effectively
In-Depth Market Analysis
Pathmatics provides detailed insights into advertisers and publishers, helping businesses spot growth opportunities and make the most of their marketing budgets. These insights pave the way for better creative strategies.
Creative Performance Metrics
Pathmatics tracks and evaluates key creative elements, such as:
Ad performance metrics
Audience targeting success
Effectiveness of calls-to-action
Placement efficiency
Detection of language used in creative content
Supporting Strategic Decisions
The platform goes beyond analytics, helping businesses craft strategies based on data. Here’s how it drives smarter planning:
Strategy Area
Insights Provided
Media Planning
Identifies seasonal trends and the best times to advertise
Budget Allocation
Pinpoints which channels perform best
Competitive Analysis
Tracks share of voice and ranks competitors
Campaign Optimization
Evaluates creative effectiveness and audience engagement
Market Entry
Validates strategies for entering new markets
A senior marketing executive shared their perspective:
"Pathmatics is a game-changer in the realm of advertising intelligence. Its ability to provide valuable insights and competitive advantages through data analysis is unparalleled." [33]
Practical Applications Pathmatics equips marketers with tools to better understand their audience, refine ad strategies, and outpace competitors in the ever-evolving digital landscape.
Tool Comparison Chart
Here’s a streamlined comparison of top AI marketing tools for 2026, highlighting their main uses, standout features, and ideal users.
Tool Name
Primary Use Case
Key Features
Best For
HubSpot Marketing Hub
All-in-One Marketing
– AI content assistant – CRM integration – Marketing automation
Companies needing a unified marketing platform
Marketo Engage
B2B Marketing Automation
– Lead management – Account-based marketing – revenue attribution tools
Enterprise B2B organizations
Drift
Conversational Marketing
– AI chatbots – Lead qualification – Meeting scheduling
Companies focused on real-time engagement
ActiveCampaign
Email Marketing & Automation
– Customer segmentation – Predictive sending – Campaign automation
Mid-sized businesses
Clearbit
Data Enrichment
– Lead enrichment – Company insights – Prospect targeting
Data-driven marketing teams
Hootsuite Insights
Social Media Analytics
– Brand monitoring – Sentiment analysis – Trend tracking
Social media-focused teams
Seismic
Sales Enablement
– Content management – Analytics – Personalization
Enterprise sales teams
Salesforce Marketing Cloud
Enterprise Marketing
– Journey building – Cross-channel marketing – Einstein AI integration
Large organizations
Pathmatics
Digital Ad Intelligence
– Creative analysis – Competitive insights – Market tracking
Digital advertising teams
This table outlines each tool’s main strengths, making it easier to find the right fit for your business. Now, let’s dive into how these tools integrate with your existing systems.
Integration Capabilities
AI tools are evolving quickly, with frequent updates improving how they connect with other platforms and enhance overall performance.
Feature Comparison Matrix
Below is a matrix comparing essential features across three pricing tiers, helping you evaluate tools based on your budget and needs:
Feature Category
Basic Tools
($0-$100/mo)
Mid-Range Tools
($100-$1000/mo)
Enterprise Tools
($1000+/mo)
AI Capabilities
Content suggestions, Basic automation
Advanced analytics, Predictive insights
Custom AI models, Full automation
Integration Options
Limited third-party connections
Moderate ecosystem
Extensive enterprise integration
Customization
Template-based
Moderate customization
Full customization
Support Level
Email, Knowledge base
Priority support, Training
Dedicated support, Custom training
Scalability
Limited users/features
Team collaboration
Enterprise-wide deployment
Use this guide to pinpoint the tool that matches your organization’s specific requirements and growth plans.
How to Pick Your Marketing Tool
Choosing the right AI marketing tool means aligning it with your needs, resources, and growth objectives. Here’s a framework to help guide your decision.
Assess Your Marketing Setup
Start by reviewing your current marketing tools and processes. Identify areas where AI could improve performance or fill gaps.
Define Your Key Requirements
Budget Considerations Make sure the tool fits within your budget without compromising essential features.
Technical Compatibility Ensure the tool integrates seamlessly with your existing systems. Here’s a quick reference:
System Type
Priority
Impact
CRM
High
Must sync customer data and interactions
Analytics
High
Should provide unified reporting
Email Platform
Medium
Needs to work with your current email system
Content Management
Medium
Should streamline AI marketing workflows
Social Media
Low
Optional, depending on your strategy
Once you’ve defined these requirements, plan your implementation to get the most out of the tool.
Evaluate What’s Needed for Implementation
AI marketing tools can deliver 10–20% cost savings and efficiency gains [34]. To achieve this, consider the following:
Team Readiness Train your team to use the tool effectively and integrate it into their workflows.
Data Quality Keep your customer data clean and organized. Strong marketing analytics and clear data governance policies are essential.
Scalability Pick a tool that can grow with your business. It should handle increasing campaign demands and work well with your current systems.
Test Before Committing
Before rolling out the tool across your organization, test it. Use free trials or pilot programs to evaluate its performance. Monitor key metrics and gather team feedback to ensure it meets your needs.
Focus on Long-term Benefits
Think beyond upfront costs. Tools with predictive analytics can increase marketing ROI by 20% [34]. Prioritize long-term value over short-term savings.
Ensure Security and Compliance
Make sure the tool adheres to industry standards for:
Data privacy regulations
Security protocols
Compliance requirements
User permission controls
With 73% of companies now using generative AI [35], it’s crucial to select a tool that not only addresses your current needs but also positions you for future success.
Conclusion
The future of B2B tech marketing is already unfolding, driven by advancements in AI tools. By 2026, AI is expected to offer B2B tech companies new ways to achieve growth and improve ROI. Companies that are fully embracing AI are seeing impressive results, with those at the highest levels of AI adoption growing 4.7x faster year over year compared to their peers with lower adoption rates [37].
AI is transforming how marketing operates, taking over data-driven tasks while allowing marketers to focus on maintaining brand identity. This shift has led to:
Faster launch cycles, reduced from weeks to just hours
Smaller, agile teams delivering more impact with tighter budgets
Smarter decisions driven by automated AI tools
These changes are reshaping how marketing strategies are executed. For example, Spotify’s "AI DJ" initiative boosted weekly user engagement by 40% and increased session times by 30%, all thanks to AI-powered personalization [1].
To succeed in this evolving landscape, it’s important to:
Align AI efforts with business goals while keeping your brand genuine
Invest in your team’s skills, as 80% of executives are looking for AI-savvy talent [36]
Leverage AI to outperform competitors, with companies already seeing 15% higher top-line performance – expected to double by 2026 [37]
"Remember how VIBE CODING (replit, bolt, lovable) transformed 8-week development cycles into 2-day sprints? The same 20x acceleration is hitting marketing teams RIGHT NOW." – GREG ISENBERG [36]
FAQs
How do AI marketing tools improve personalized customer experiences?
AI marketing tools use advanced data analysis to create highly personalized customer experiences by examining factors like browsing habits, purchase history, and user preferences. This allows businesses to deliver tailored product recommendations, customized content, and targeted offers that resonate with individual customers.
These tools also adapt in real time, adjusting interactions based on customer behavior to enhance engagement, boost satisfaction, and foster loyalty. By leveraging AI, companies can create meaningful connections that drive conversions and long-term retention.
What should I consider when integrating AI marketing tools into my current systems?
When integrating AI marketing tools into your existing systems, it’s important to start by evaluating your current marketing setup. Take stock of the tools you already use, assess their compatibility with AI solutions, and identify specific areas where AI can add value, such as customer segmentation, predictive analytics, or content personalization.
Next, choose AI tools that align with your business goals and ensure they can be seamlessly integrated into your workflows. Proper planning, team training, and attention to compliance and ethical considerations are essential for a smooth transition. Finally, set clear metrics to measure the success of the integration and adjust your strategies as needed to maximize results.
How can businesses evaluate the ROI of using AI marketing tools?
To evaluate the ROI of AI marketing tools, businesses should focus on three key areas:
Measurable ROI: Analyze tangible outcomes like increased revenue, improved operational efficiency, or reduced risks to determine the direct financial impact.
Strategic ROI: Assess how AI supports long-term goals, such as enhancing customer experience or expanding market share, by aligning tools with your business strategy.
Capability ROI: Consider the value of strengthening your organization’s AI infrastructure, which can drive future innovation and digital transformation.
Additionally, leveraging AI-powered methods like first-party data analysis, advanced marketing mix modeling, and data-driven attribution can help refine measurement strategies and maximize returns.
Related Blog Posts
AI Agents in Marketing: The Secret to Driving 10x Engagement & Conversions
5 Ways AI Can Optimize Marketing ROI for your Tech Startup
10 Best Marketing Tools for Startups in 2026
AI Growth Marketing: Forecasting Use Cases
---
## I Tested Every AI Search Visibility Tool. Here’s The One That Actually Changed My Strategy
URL: https://www.data-mania.com/blog/ai-search-visibility-tool/
Type: post
Modified: 2026-03-18
If you’re trying to figure out AI search visibility, you’ve probably noticed the same thing I did: every AI search visibility tool claims to solve the problem, but most just measure whether you’re being mentioned.
I’ve spent six months actually implementing these tools, not just trying demos. Tracking metrics. Trying to understand if any of them could tell me something I couldn’t figure out myself. In fact I built some tools myself to get what I couldn’t get from the paid AI search visibility tools out there.
Most of them answered the same basic question: “Are you showing up in AI search results?” (some weren’t even able to answer that question effectively 😅).
That’s like asking “Did anyone mention your name?” without telling you what conversation you’re in or who else is in the room with you.
Here’s what might surprise you: The real competitive advantage isn’t just knowing IF you’re cited. It’s knowing WHO you’re cited alongside, what tactics they’re using, and which sites you should be partnering with.
After testing everything from simple citation trackers to complex GEO platforms, I finally found an AI search visibility tool that actually shows me the competitive landscape: AIrefs.
📌 THIS ARTICLE CONTAINS AN AFFILIATE LINK. I MAY MAKE A SMALL COMMISSION IF YOU PURCHASE SOMETHING AFTER CLICKING THROUGH
(thank you for supporting small business!)
Let me show you why this matters for technical founders trying to get visible in AI search.
Why Traditional SEO Metrics Lie in AI Search
The numbers tell a story most founders aren’t ready for.
By 2025, 60% of AI searches ended without anyone clicking through to a website. At first, that sounds terrifying. But here’s the flip side: traffic from AI sources converts at 4.4× the rate of traditional search traffic.
In other words: You get less traffic, but the traffic you do get is exponentially more valuable.
This means the KPIs I used to rely on stopped being reliable. Total traffic? Increasingly meaningless. Traditional search rankings? Not correlated with AI citations. Backlink counts? AI systems don’t care.
The hard part is: Most AI search visibility tools only tell you whether you’re being cited. They don’t tell you:
Which other sites are being cited for the same queries
What content formats are winning citations
Where partnership opportunities exist
How your competitors are structuring their content
After supporting a dozen early-stage tech companies on their AI visibility strategies, I’ve noticed a pattern. The founders who succeed in AI search aren’t just tracking whether they show up. They’re tracking the entire competitive landscape and using that intelligence to make strategic decisions.
The AI Visibility Framework That Actually Works
Before I show you the AI search visibility tool that changed everything, here’s the framework I’ve found works for early-stage tech companies navigating this shift. This is based on testing what actually moves the needle for B2B SaaS and AI startups trying to get discovered.
Step 1: Build Authority AI Systems Actually Recognize
AI systems now evaluate content using E-E-A-T: Experience, Expertise, Authority, and Trust. Of these, trust matters most.
What this looks like in practice:
Include detailed author bios with specific credentials
Share first-hand experience with real outcomes
Support every claim with verifiable sources
Update content regularly (53% of ChatGPT citations come from content updated in the last 6 months)
Step 2: Structure Content for Machine Parsing
Over 72% of first-page results use schema markup. AI systems need structured data to understand your content.
The tactical approach:
Implement JSON-LD schema markup
Use logical heading hierarchies (H1/H2/H3)
Break content into short, scannable paragraphs
Create standalone quotable statements with specific data
Step 3: Match Natural Language Queries
Searches containing 5+ words grew 1.5× faster than shorter queries in 2023-2024. AI chat interactions last 66% longer than traditional searches because users are asking complete, conversational questions.
How to adapt:
Research “People Also Ask” questions in your space
Target long-tail, question-based queries
Structure answers as standalone responses
Use conversational, clear language
Step 4: Use High-Performance Content Formats
Content over 3,000 words generates 3× more traffic than shorter pieces. Featured snippets have a 42.9% clickthrough rate, and 40.7% of voice search answers come from them.
The formats that work:
Comparison articles with modular sections
Detailed listicles (2,300+ words for voice search)
FAQ sections with direct answers
Data-rich content with clear statistics
Step 5: Track With GEO Tools, Not SEO Tools
Traditional SEO metrics show weak correlation with AI citations. You need specialized Generative Engine Optimization (GEO) tools.
What to track:
Brand mentions across AI platforms
Citation rates in ChatGPT, Perplexity, AI Overviews
Share of voice for key queries
Sentiment in AI-generated responses
I had this entire framework mapped out. I knew what mattered. But here’s what I couldn’t figure out: how to see the whole competitive landscape, not just my position in it.
That’s what most tools don’t show you. What Makes a Great AI Search Visibility Tool
Last week I spoke with Paul Boudet, founder of AIrefs. I’d been testing his AI search visibility tool for a few days and immediately saw something different.
While every other AI search visibility tool showed me WHETHER I was being cited, AIrefs showed me all 571 URLs that were being cited across my targeted search questions.
Think about what that means.
Instead of just knowing “you’re ranking for 23 queries,” I could see:
Every site being cited alongside me
Which sites dominated certain query categories
Where partnership opportunities existed
What content tactics were working for co-cited sources
I could also see the crawler traffic patterns. In just the last 7 days, ChatGPT hit my site 863 times. Meta AI: 16 times. Apple Intelligence: 14 times. That’s the kind of visibility data that helps you understand which AI platforms actually matter for your audience.
This transforms AI visibility from a vanity metric into competitive intelligence.
When I saw that I was ranking #1 for “tech startup CMOs,” “AI startup CMOs,” and “growth advisors for tech startups,” that was validating. But more valuable was seeing WHO ELSE was showing up for those queries and what they were doing differently.
AI Search Rank is my 1-hour micro-course on to get your startup discoverable in AI search and investor research, even without a marketing team or extra SaaS spend.
START GETTING AI VISIBLE TODAY »
I grabbed the Black Friday deal for this AI search visibility tool and have already started implementing the bespoke suggestions the AIrefs team provided.
Steal This: My AIrefs Setup
Here’s how I’m using AIrefs to build competitive intelligence, not just track citations:
1. Co-Citation Analysis: I track all 571 URLs being cited across my target queries. When I see sites consistently cited with me, those become partnership targets or competitive analysis subjects.
2. Tactic Replication: For sites outranking me on specific queries, I analyze their content structure, data presentation, and schema implementation. Then I replicate their tactics one level up.
3. Niche Dominance Tracking: I monitor my rankings for hyper-specific queries like “fractional CMO for AI startups” rather than broad terms. AI search rewards specificity.
4. Platform-Specific Optimization: Different AI systems have different preferences. Perplexity and AI Overviews favor word count and sentence count. ChatGPT prioritizes domain rating and readability. I optimize accordingly.
5. Partnership Pipeline: When I see a site consistently cited for complementary topics, I reach out. Co-citation data reveals natural partnership opportunities.
The thing I love most about AIrefs? It shows me the entire game board, not just my piece on it.
Start Getting AI Visible
AI search is fundamentally changing how technical founders get discovered. The shift from clicks to citations means you need new tools, new frameworks, and new competitive intelligence.
If you want to see what co-citation intelligence looks like for your business, check out AIrefs – the AI search visibility tool that shows you the entire competitive landscape. Big shoutout to founder Paul Boudet for building something that actually reveals who you’re competing against instead of just citation counts.
And if you want the tactical implementation guide for AI search (especially for startups without big marketing budgets), I built a 1-hour micro-course called AI Search Rank: grab it here.
The game has changed. Your visibility strategy should too.
P.S. After six months of testing every AI search visibility tool I could get my hands on, the pattern became clear: most tools tell you IF you’re winning. The valuable ones tell you HOW everyone else is winning. I’m my first week into implementing what AIrefs revealed about my competitive landscape. I’ll share what’s working in a future newsletter, so make sure you’re signed up for newsletter installments here.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## The $20M Lesson: Why Go-To-Market Is Now Your Biggest Bottleneck
URL: https://www.data-mania.com/blog/the-20m-lesson-why-go-to-market-is-now-your-biggest-bottleneck/
Type: post
Modified: 2026-03-17
George Rekouts hit $2 million in revenue in under a year.
His first company, Mad Objective, was crushing it. IP-to-company mapping for visitor intelligence. Back when the category was still new, still exciting. He’d partnered with a distributor who had an established customer base. Sales came fast. Growth felt inevitable.
Then the distributor wanted to sell.
George had no choice but to exit. No independent brand. No direct sales motion. Complete dependency on the partner who’d gotten him to $2M so fast.
He watched the company grow to over $20 million post-acquisition.
Here’s what I keep thinking about: George didn’t fail. He succeeded fast, then got locked out of the upside because he’d outsourced the one function he thought he could afford to ignore: go-to-market.
Now he’s building DiscoLike, and this time he’s doing it differently. But the lesson he learned isn’t just about partnerships. It’s about a fundamental shift happening right now across B2B: engineering used to be the bottleneck. Now it’s go-to-market.
And if you’re still relying on Apollo or LinkedIn data to fuel your outbound, you’re leaving money on the table in ways you probably don’t realize.
Apollo Isn’t Broken. But Here’s the Opportunity Loss
Let’s get something straight: Apollo works. Thousands of companies use it successfully. George uses it. I’m not here to trash a tool that clearly has product-market fit.
The real question is: what are you missing?
Here’s what might surprise you: LinkedIn covers about one-third of the addressable market.
If you’re selling dev tools to Series A startups in SF, NYC, or Austin? Great. Apollo is probably fine. Those founders live on LinkedIn. They update their profiles. They care.
But step outside that bubble and the coverage drops off a cliff.
Selling to legal? Construction? Medical devices? LinkedIn penetration in those verticals is dramatically lower. Lawyers and doctors don’t prioritize LinkedIn the way engineers and marketers do.
And internationally? Forget it.
LinkedIn company data by region:
Germany and Norway: Weak
France: Better
Japan: Nearly non-existent
Asia-Pacific overall: Tiny sliver
George puts it bluntly: “If you don’t target accounts outside of LinkedIn, you’re gonna be missing a lot.”
Then there’s the accuracy problem.
Here’s something that shocked me: the entire B2B data industry is powered by LinkedIn scrapes from essentially two top suppliers. That’s why every vendor claims “35 million companies” on their homepage. They’re all buying from the same source.
When George runs domain status checks on these datasets, he consistently finds 20-28% of domains are no good. Parked. Redirected to a new company name. Dead.
One-fourth of your list is wasted before you even hit send.
Think about that for a second. You’re paying for data, building sequences, personalizing copy. And 25% of it is going into a black hole.
In other words: Apollo isn’t broken. But if you’re outside the LinkedIn-heavy tech ecosystem, or if you’re trying to reach international markets, you’re operating with a massive blind spot and a significant data decay tax.
The Relevancy Trap: Why Keywords Can’t Capture Your ICP
Even if Apollo’s coverage was perfect, there’s a deeper problem. Keyword-based search forces you into spray-and-pray.
Let me show you what I mean.
Say you’re selling to medical device companies. Is that “healthcare”? “Manufacturing”? “Software”? You’re stuck choosing a category that doesn’t actually capture what makes your ICP unique.
Here’s where it gets worse: A company making blood test equipment is radically different from a company building lung machines. Which is different from an EKG device manufacturer. But in a keyword-driven system, they’re all bucketed together.
You end up with two terrible options:
Go too broad (search “manufacturing”) and drown in noise. CNC machining shops, packaging companies, industrial suppliers. None of whom care about your product.
Go too narrow (hyper-specific keywords) and miss half your addressable market because you couldn’t predict every term your ICP might use.
George’s take: “You need a semantic layer for search. You need a model that understands the concept, not just keywords or rigid categories.”
Or put another way: Traditional search tools assume your ICP can be described with a few industry tags and keywords. But real buyer intent doesn’t work that way. You need something that understands what a company does, not just what box they checked on their LinkedIn profile.
How Disco Sees 680 Million Secure Websites (And Why It Wasn’t Possible 5 Years Ago)
Here’s where George’s story gets interesting.
Disco’s data advantage comes from something I’d never heard of in this context: SSL certificate infrastructure.
You know that little lock icon in your browser? The one that says a site is secure? That’s an SSL certificate. And to get one, you have to prove to a certificate authority that you own the domain. No faking it.
This is the same technology that protects your banking transactions and Bitcoin trades. It’s bulletproof.
Google forced everyone to switch to HTTPS over the past several years. If you don’t have SSL, browsers block your site with scary warnings. So nearly every commercial website globally had to get a certificate.
Here’s the hack: Disco partnered with certificate authorities. They help flag malicious domains and fraud. In exchange, they get a real-time feed of every secure website that goes live.
Within 10 minutes of a site launching, Disco sees it.
680 million secure websites. About 68 million of those are commercial sites (after an LLM classifier filters out blogs, personal sites, etc.).
Think about what this means: While Apollo is scraping LinkedIn profiles that might be months out of date, Disco is watching the entire internet in near-real-time through first-party infrastructure.
George told me this wouldn’t have been possible even five years ago. The HTTPS mandate created the conditions for this data advantage. It’s an infrastructure-level moat that’s incredibly hard to replicate.
The hard part is: This isn’t cheap. Disco’s hardware footprint (GPUs for LLM processing plus petabytes of storage) is much higher than most B2B SaaS startups. They underestimated the cost.
But the result is a dataset that’s fundamentally different from anything built on scraped LinkedIn and Google Maps data.
Why Disco Built a Custom LLM (And Why It’s Easier Than You Think)
When George mentioned “custom LLM,” I assumed he meant something on the scale of Bloomberg or OpenAI. Years of R&D, massive compute budgets, the whole thing.
Turns out, it’s not like that at all.
Here’s what most people get wrong about large language models: We think “LLM” means “ChatGPT.” We think it means chatbots.
But LLMs existed before ChatGPT, and they can do a lot more than generate text. Classification. Data extraction. And in Disco’s case: search.
Disco didn’t build a reasoning engine. They built a search engine.
Here’s how it works:
Step 1: Grab text from a website and convert it into embeddings (numerical representations of meaning).
Step 2: Run a similarity search across embeddings from your search query and 68 million business websites.
Step 3: Return top results before similarity drop, as the closest conceptual matches.
The difference between chat and search models:
Chat models (like ChatGPT) use embeddings to predict the next token. They’re trained to generate text word by word.
Search models (like Disco’s) compare embeddings. They’re trained to find similarity, not generate sentences.
Same foundational architecture (LLM embeddings). Different inference path.
George’s point: “Building embeddings isn’t hard. You can use existing models and apply additional transfer learning, in Disco’s case focusing on business-specific data. The trick is swapping inference from ‘next token prediction’ to ‘similarity matching.’”
What this unlocks: You can search by concept, not just keywords. You describe what you’re looking for in plain language. “Medical device companies specializing in diagnostic imaging for hospitals.” And the model understands the semantic intent, not just the literal words.
This is why keyword search breaks down and semantic search works.
The Clustering Reveal: Why You Don’t Know Your Best Customers
Here’s my favorite story from the conversation.
One of Disco’s clients sells VCR software for commercial video recording. Think hundreds of cameras, simultaneous multichannel recording. They had about 8,000 customers.
For years, they were convinced police stations were their number one customer. That’s where the memorable deals came from. That’s who they pitched to investors. That’s how they thought about their market.
George ran their customer list through Disco’s clustering model.
The results:
Shopping malls
Commercial parking lots
Hospitals
Warehousing and manufacturing
Police stations
Police were a distant fifth.
The founders had no idea. They’d been operating on anecdotal evidence. Memorable sales conversations, not data. And with 8,000 accounts, there’s no way to manually cluster and spot the pattern.
Here’s what makes this possible: Disco uses a specialized clustering model (not ChatGPT, which George says chokes beyond about 50-100 companies). The model segments your customer list automatically, revealing which verticals actually drive revenue.
Once you know your top segments, you run similarity search for each one. Upload five example domains from your best segment, generate an ICP description, and Disco finds precise matches across its 68 million commercial sites in 48 languages
The workflow:
Segment existing customers
Discover hidden revenue drivers
Run lookalike search per segment
Export hyper-targeted prospect lists
This is the opposite of spray-and-pray. This is surgical.
Steal This: The One-Evening Validation Framework
George wanted to test whether open-source intelligence companies would buy Disco’s data as a side revenue stream.
Instead of spending weeks researching the market, he did this:
Evening 1:
Used Disco to find 20 OSINT companies
Messaged founders on LinkedIn with a simple pitch
Got responses within minutes
That’s it. One evening. He validated (or invalidated) an entire vertical.
Here’s the framework George uses:
Step 1: Find 20 Hyper-Targeted Companies
Not 1,000. Not 500. Just 20 companies that perfectly match your ICP for a specific segment.
Step 2: Message Founders on LinkedIn
Use the 2-2-1 structure:
First 2 lines: Hook them. State the problem or opportunity.
Next 2 lines: Show how you’re different.
1 CTA: Low-friction value offer. No hard sell.
George’s example:
“You’re targeting one-third of your market with LinkedIn data. I can show you 60% more. Want me to prove it? No strings attached.”
Step 3: Measure Response
Out of 20 messages, George typically sees:
12 connections
6 responses
If your ICP is tight, the response rate is shocking. If you get silence, your targeting is off. Or the vertical doesn’t care about your problem. Either way, you know within hours, not months.
Step 4: Iterate or Pivot
If it resonates, go deeper. If not, test the next vertical tomorrow night.
George’s philosophy: “People overthink how easy this is. You have your offer. You find the companies. You ping the best 20. Done.”
The Bottleneck Just Flipped: Engineering to Go-To-Market
Here’s the shift George sees happening right now:
For 25-30 years, engineering was the bottleneck. Every other function (sales, marketing, ops) moved at the speed of the product team. You had to wait for engineers to build the thing before you could sell it.
AI changed that.
You can build a product in six months now. Wrap ChatGPT. Launch an MVP. Validate quickly. Maybe you swap in a custom model later, maybe you don’t. The point is: the product isn’t the constraint anymore.
The new bottleneck is go-to-market. Reaching the right users. Testing messaging. Finding your best segments. Distribution.
Think you need the perfect product before reaching out? Here’s why that’s costing you months of learning.
George’s advice to founders: “Start reaching out as soon as you can. Literally, don’t be shy. Think of the vertical, build the list, test it. You can validate a vertical in one evening.”
The hard part is: Most technical founders resist this (myself included, as a licensed professional engineer). We want the product to be perfect first. We want elegant architecture. We want to solve hard technical problems.
But if no one knows you exist, none of that matters.
Or put another way: Stop perfecting your product. Start testing your market.
The List-First Cold Outreach Formula
I’ll be honest. I’ve always been skeptical of cold outreach. I’ve built my career on inbound. I get hundreds of cold emails a week, and most of them annoy me.
But George’s framework made me rethink this.
His thesis: List quality matters 10x more than copy quality. Your list is the message
If you’re reaching the right people who have a real problem you can solve, even mediocre copy works. They’ll respond because the timing is right, the fit is obvious, and you’re offering something they actually need.
Here’s the structure George uses:
First 2 Lines: Hook Them
People won’t read beyond two lines unless you nail the hook. State the problem or opportunity clearly. No fluff.
Example: “Most dev tool companies are only reaching one-third of their addressable market because they rely on LinkedIn data.”
Next 2 Lines: Show Differentiation
How are you different? What can you do that they can’t get elsewhere?
Example: “We use SSL certificate infrastructure to see 68 million business websites in real-time. Including the two-thirds LinkedIn doesn’t cover.”
CTA: Low-Friction Value Offer
Don’t go for the hard sell. Offer value with no strings attached.
George’s go-to: “How about we test it and you see if you find more data with us? No commitment, just proof.”
The psychology here: You’re not asking them to buy. You’re offering to prove your claim. If your targeting is right, they’ll want to see the proof.
George’s hit rate: 20 messages → 12 connections → 6 responses (when ICP targeting is tight).
The insight I’m taking away: I’ve been so focused on perfecting inbound funnels that I dismissed outbound entirely. But if you’re validating messaging or testing new segments, George’s framework is faster and cheaper than running ads or paying for focus groups.
You just need the discipline to keep your list hyper-targeted.
What George Would Tell His 2015 Self
We circled back to the Mad Objective story at the end of our conversation.
I asked George what he’d tell his 2015 self. The version of him who was about to partner with that distributor and race to $2 million in under a year.
His answer: “Own your go-to-market. Don’t outsource it, no matter how tempting the short-term speed is.”
The distributor gave him instant access to customers. It felt like a shortcut. And it was. Until it wasn’t. When they sold, George had no leverage. No independent brand. No way to keep building.
He left money on the table. A lot of it.
George’s other lesson on partnerships: If you have first-party data, sell it yourself. Don’t give it to someone else to monetize while they collect the margin. If you need data, buy it. Don’t build in-house unless it’s your core differentiator. Focus on what makes you unique.
Here’s the broader lesson: The bottleneck shifted from engineering to go-to-market, but most founders are still operating like it’s 2015. They’re perfecting the product, optimizing the architecture, waiting for the right moment to “do marketing.”
But the founders who win now are the ones who embrace distribution from day one. Who test verticals in an evening. Who build their own customer relationships instead of depending on partners.
George’s second time around, he’s doing it differently. Product-led growth. Direct user acquisition. No dependencies. And a dataset that’s genuinely differentiated because it’s built on first-party infrastructure, not scraped LinkedIn profiles.
P.S. The Question I Didn’t Ask
After we stopped recording, I kept thinking about something George said: “You can test a vertical in one evening.”
I’ve spent years building inbound funnels. SEO. Content. Paid ads. All of it designed to attract the right people over time. And it works. But it’s slow.
What if I’m overthinking it?
What if the fastest way to validate messaging isn’t running A/B tests on landing pages? What if it’s just finding 20 people in my ICP and asking them directly?
Last Tuesday I pulled a list of 18 companies in a vertical I’ve been curious about. Messaged their founders. Got 7 replies in 36 hours. Three wanted to see demos. Two became paying customers within a week.
Here’s what I learned: I’ve been hiding behind “perfect product development” when what I really needed was just to talk to people.
If you want to try George’s framework, here’s where to start:
Use DiscoLike (or any tool that lets you build hyper-targeted lists) to find 20 perfect-fit companies
Message their founders on LinkedIn with the 2-2-1 structure
Measure response rate within 48 hours
If you get crickets, your targeting or messaging is off. If you get replies, you’ve validated something real.
The hard part is: You have to be willing to hear “no” quickly instead of hiding behind perfect product development.
But that’s the shift. That’s the new bottleneck.
Want to see how Disco works? They have pre-configured sample queries on their site so you can test the output before committing. No free trial anymore (George got burned by people mining their GPUs), but you can browse sample data to see if it’s a fit.
Check out DiscoLike here | Connect with George on LinkedIn
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## After The Sale, Before The Growth Curve: How Post-Sales Work Shapes Lifetime Value
URL: https://www.data-mania.com/blog/after-the-sale-before-the-growth-curve-how-post-sales-work-shapes-lifetime-value/
Type: post
Modified: 2026-03-17
Post-sales interactions are often treated as maintenance work, yet they play a central role in shaping lifetime value for tech startups. After the contract is signed, every touchpoint affects whether customers expand usage, stay loyal, or quietly leave. Support quality, communication habits, and follow-through all influence revenue over time in ways that early growth teams sometimes overlook.
Retention Starts After the Sale
Customer lifetime value grows through retention first, not acquisition. Post-sales teams set expectations during onboarding and reinforce them through consistent support. Clear documentation, predictable response times, and honest updates reduce friction. When customers feel supported, they are less likely to churn and more open to renewals or upgrades.
Support Experiences Shape Perceived Value
Support interactions often become the most frequent contact customers have with a startup. Fast, accurate resolutions signal reliability, while repeated handoffs or vague answers erode trust. Over time, these experiences shape how customers judge product value beyond features alone, affecting their willingness to continue paying.
Feedback Loops Drive Expansion Opportunities
Post-sales conversations surface insights that sales or product teams may never see. Requests, complaints, and usage patterns reveal where customers gain value or struggle. Acting on this feedback can lead to feature improvements, pricing adjustments, or add-on services that increase account value without aggressive selling.
Consistency Builds Long-Term Trust
Trust compounds over repeated interactions. Consistent messaging across support, account management, and billing reduces confusion and prevents frustration. Even operational details like invoices or dynamic billing solutions affect confidence. When systems and people behave predictably, customers view the startup as a stable partner worth staying with.
Data From Post-Sales Guides Strategy
Modern post-sales teams generate data that directly links actions to revenue outcomes. Renewal rates, ticket volume, and response times help forecast lifetime value more accurately. Startups that analyze this data can prioritize investments that strengthen retention instead of relying solely on new customer growth.
Onboarding Sets the Tone
Effective onboarding reduces early confusion and shortens time to value. Clear milestones, training sessions, and accessible resources help users adopt the product with confidence. When onboarding feels organized and responsive, customers form positive habits that carry into later stages of the relationship, increasing the likelihood of long-term retention. These first interactions also clarify roles and communication channels. A smooth start lowers support volume later and protects revenue that might otherwise disappear during the first renewal cycle.
Internal Alignment Matters
Post-sales impact depends on coordination across teams. Sales promises, product roadmaps, and support policies must align to avoid mixed messages. Regular internal reviews of customer issues help teams correct gaps early. Alignment reduces rework, improves customer confidence, and ensures that lifetime value reflects real satisfaction. This coordination also speeds decision-making during critical moments.
Lifetime value is shaped long after the sale closes. Every post-sales interaction sends a signal about reliability, respect, and commitment. Small operational choices accumulate, making post-sales discipline a measurable driver of long-term business health for teams focused on sustainable performance over time consistently. For more information, feel free to look over the accompanying resource below.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Adaptive Marketing Strategies to Make Your Brand into the Next Big Thing
URL: https://www.data-mania.com/blog/adaptive-marketing-strategies-make-brand-next-big-thing/
Type: post
Modified: 2026-03-17
Recommendation engines like those employed by Facebook, Ebay, and Amazon increase customer and user conversions. They do it by employing algorithms that produce recommendations personalized to the preferences of each individual user. Google Search is the world’s best search engine because Google has access to loads of personal data on its users. It’s also because Google Search deploys algorithms that incorporate this user data to produce search results that are optimized to the preferences of the individual making the query.
To most of us, this is yesterday’s news. What’s new is that SMEs and large corporations are beginning to use this same technology. Their aim is to fuel their own market growth and brand visibility. Access to super-charged data-science-driven growth results is no longer limited to industry giants like Google, Facebook, Ebay, and Amazon.com. What’s new is that people like you and me are increasingly able to implement the same tactics. These tactics are aimed to drive growth for our own businesses or the businesses of our clients.
Recommendation engines are a tricky business. But we stand to gain a lot of market traction if we can employ even a few of the statistical methodologies upon which they’re built. Segmentation analysis is one such methodology. It’s actually a large mechanical component of how recommendation engines work. This type of analysis is also a strategy used by growth hackers. They utilize it to drive the growth of their brands and the brands of their clients. While us data scientist just call it “segmentation analysis”, other, more marketing-minded people out there call this practice “adaptive marketing”.
What is Adaptive Marketing?
“Adaptive marketing” is a means by which growth hackers are able to segment and target users. Main goal is to offer them a personalized brand experience. This personalization of brand experience fuels growth in every layer of the funnel, from user awareness to revenue. Adaptive marketing is built on segmentation analysis. Users and customers can be clustered according to any metric. But clustering according to user personas, behaviors, content engagement patterns, lifecycle stages, purchase histories, or demographics is particularly useful. Being able to group customers in these ways allows us to personalize and optimize marketing tactics. It also allows us to improve website experience, content strategies, product offerings, user benefits, user activation, user retention, and brand messaging.
Photo Credit: Growth Hacking
Segmentation Analysis and the K-Means Algorithm
Let’s take a closer look at segmentation analysis. There are many methods that can be employed to perform this type of analysis, but today let’s focus on k-means clustering for segmentation analysis. k-means clustering is an unsupervised (non-hierarchical) clustering algorithm that can be deployed to group ‘n’ number of data points (where a ‘data point’ is a parameter that characterizes a user) according to their likeness, into k number of clusters. In this algorithm, the analyst defines the number of k clusters, with clustering of observations based on the nearest arithmetic mean value of the cluster.
This method is a variation of the generalized expectation-maximization algorithm. The k-means method is not well designed for analysis of clusters of significantly different size, density, or non-globular shape. The algorithm works best if k is set to a relatively small number. Another difficulty with k-means clustering is that there is no indication of the optimal number of clusters to use when modeling the data. To get around this, k-means clustering should be repeated several times, using several different values for k until the best k value becomes apparent.
Operation of the k-means algorithm
k-means clustering is also a particularly helpful method in geospatial data analysis. In spatial analysis terms, k-means clustering can be used to group spatially proximate points and polygons according to a user defined field in the underlying data set. The algorithm is also often used for image processing / segmentation and spatial data mining. k-means can be performed in R (‘Quick-R), Python (‘scikit’), and ArcGIS (Spatial Statistics Toolset), and CrimeStatIII.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## 5 Great KPIs for Measuring Content Marketing ROI
URL: https://www.data-mania.com/blog/5-great-kpis-measuring-content-marketing-roi/
Type: post
Modified: 2026-03-17
Hey everybody! I know that I have been slow in my blog publishing lately, but I have a terrific excuse. I am writing a book about data science!! My book contract requires me to produce 350 pages of content in 20 weeks, hence the reason my publishing has slowed down here. Nonetheless, this blog and my readers are important to me. That is why I am coming out with a Data-Mania summer series on ROIs and analytics. This week I am covering my 5 favorite metrics for measuring ROI for content marketing initiatives.
So, what do you think? Got any metrics to add? If so, please mention them in the comments section below. Also, if you’re interested in knowing more about the perfect metrics to watch when growing your business, check out Lean Analytics: Use Data to Build a Better Startup Faster.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## The Data-Mania summer series on ROIs and analytics is here! This week I am covering my 5 favorite metrics for measuring ROI for content marketing initiatives.
URL: https://www.data-mania.com/blog/data-mania-summer-series-rois-analytics-week-covering-5-favorite-metrics-measuring-roi-content-marketing-initiatives/
Type: post
Modified: 2026-03-17
Hey everybody! Time for round two in the Data-Mania summer series on ROIs and measuring analytics for online growth. In case you missed the first installment, here are my 5 favorite metrics for measuring ROI for content marketing initiatives. This week, we go a bit broader and look at acquisitions tactics as a whole. The content marketing tactic covered in installment 1 are only a small piece of the broader acquisitions puzzle. Have a look for yourself.
So, what do you think? Did I miss anything that you think is important? If so, please tell me about it in the comments section below.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## 5 Awesome Metrics for Monitoring and Optimizing Your User Activations
URL: https://www.data-mania.com/blog/5-awesome-metrics-monitoring-optimizing-user-activations/
Type: post
Modified: 2026-03-17
It’s time for round 3 of the Data-Mania summer series on ROIs and measuring analytics for online growth. Last week, we covered 7 Excellent Metrics for Monitoring and Optimizing Your Acquisitions Tactics. And this week we’re moving into the activation layer of the funnel.
If you need to track and monitor the effectiveness of your activation tactics, I highly encourage you to consider the 5 following metrics.
So, what do you think? Got any metrics to add? If so, please mention them in the comments section below. Also, if you’re interested in knowing more about the perfect metrics to watch when growing your business, check out Lean Analytics: Use Data to Build a Better Startup Faster.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Want Your Company to Succeed? Use Your Head When It Comes to Marketing
URL: https://www.data-mania.com/blog/want-company-succeed-use-head-comes-marketing/
Type: post
Modified: 2026-03-17
Wow, wow, wow – have you been watching what’s happened with Airbnb since its 2008 inception?!? In six short, sweet years, Airbnb has gone from being a veritable no one, to being a $10 Billion global corporation. For heck sake, they brought in $250 Million in 2013 alone!
Airbnb is an online platform that facilitates rental transactions between property owners and travelers coming from afar. In just a few short years, Airbnb has risen to the ranks of other online giants, like Dropbox and Groupon.
How did these online companies get SO BIG, SO FAST?
In a day and age that “if you build it, they may or may not come”, how did these relatively young online companies get SO BIG, SO FAST? Well, almost every aspect of these companies success can be attributed to growth hacking and the data-driven decisions that were made at nearly every marketing juncture.
For example, back in December 2012, Airbnb deployed a unique, and uniquely successful, email marketing campaign. Although they’d had only mixed success with the extensive email marketing campaigns they’d deployed previously, this particular round of emails was different.
As part of a growth-savvy initiative to increase user referral rates and retentions, Airbnb sent each of their email subscribers an email tool that allowed them to send season’s greetings emails to any of their friends that also happened to be Airbnb email subscribers. Each email sent among Airbnb subscribers includeda link to an Airbnb promotion at the bottom of the page. When the company took a look at their metrics – namely their email open rates and the click-thru rates of users that went from the email to the Airbnb website – they found that email open rates had nearly doubled and that click-thru rates were nearly triple what they were in past email campaigns.
In today’s data-informed growth-driven world, if you’re not paying attention to what analytics are telling you, you simply don’t stand a chance at staying competitive. This is especially true in the information services and ecommerce industries. Gut feelings can be important, but to make rational decisions you simply must have data and you must know how to use it. Getting familiar with the following metrics is a good start towards learning what you need to know to keep your head above water in today’s digital age.
In today’s data-informed growth-driven world, if you’re not paying attention to what analytics are telling you, you simply don’t stand a chance at staying competitive.
The three most important metrics in the universe
In all honesty, its metric madness out there. There is a metric for everything. More data is being produced and captured per second than we could ever begin to utilize. So, what do we do about this? We Keep It Simple, Silly! You don’t need to know everything, you just need to focus on the metrics that really matter. Let’s start with some solid basics – KPIs, CACs, and LTVs.
The KPI: Key Performance Indicator
KPIs are really a type of metric you use to measure your business success. There are hundreds of potential KPIs. Much of your business success lies in choosing the best KPIs for your given industry, market, and growth stage. In the ecommerce industry, it’s vital to track your traffic and conversion rates, for instance. If you’re in the insurance industry, you may want to track average cost per claim and claim ratios as your KPIs. KPIs are industry specific.
The CAC: Customer Acquisition Cost
The Customer Acquisition Cost metric is crucial when you first start a company, because acquiring new customers depends heavily on how well you can get the word out, and getting the word out costs money.
Look at the CAC to distinguish marketing strategies that are most successful in helping you achieve the greatest number of new customers for the lowest cost.
The LTV: Life-Time Value
The LTV is simply the total amount each customer spends, across their entire customer life-time, for your goods and services. This metric becomes vital after initial customer acquisitions, because its really a measure of how long customers keep returning back to purchase your offerings instead of just jumping ship and purchasing elsewhere. Obviously, the best way to judge LTV is to compare it to CAC: If you spend more money obtaining a customer than that person will ever spend on your offerings, then clearly your business model is not sustainable.
Bringing these metrics into the real world – An ecommerce example
For the growth and continued success of any ecommerce venture, it’s crucial to know your customers. You must be able to measure these metrics as quickly, painlessly, and cheaply as possible. There are a number of standalone ecommerce analytics solutions available to help online businesses get up to speed in optimizing their growth and existing client base, but why make things more complicated than they need to be? A new trend among the most data-savvy ecommerce solution providers is to offer turnkey analytics solutions as part of every full-service package they provide.
To illustrate this, consider Shopify. With its standard analytics features, the Shopify platform exemplifies the best of data-driven ecommerce solutions. While Shopify offers the standard, all-inclusive ecommerce solution for online businesses, it also comes equipped with all of the analytics offerings you’ll need for your next round of growth and optimization planning. Shopify provides its customers with data-reporting on products, orders, taxes, traffic, and insight.
Insight data for website cart optimization will help you optimize your shopping cart in order to increase the revenues you generate from your active user base. The traffic data on website conversions is sliced-and-diced according to traffic source, user country, and user device. You can use this mashup of conversion insights to optimize your growth tactics that feed the acquisitions layer of your funnel – in other words, to ramp up and customize the tactics that are most effective at bringing new users to your site.
Shopify as an example
Lastly, you can use Shopify’s orders data to track total sales according to respective traffic sources and referrers. Tracking and analyzing data on these metrics will allow you to focus your growth strategies around high-performing referrers and traffic source streams. This data will help you decide what referrers and source streams to abandon due to under-performance
In growth its always important to follow the Pareto principle – Cut the lower 80% of your efforts (the ones that show stats indicating under-performance), and then take the efforts you were spending on those and invest them in trying new growth tactics and in building out the 20% of your efforts that have demonstrated highest performance.
The platform also offers an array of apps and an API to provide customers any sort of customized data reports they could want from their transactional record sets. Since the platform is so remarkably robust, it saves customers the time that would be otherwise spent in reconciling data from its many different sources. These new, advanced, more analytics-savvy ecommerce solutions, like Shopify, are all about doing the heavy lifting so that customers can focus on what matters most to them – growing their business.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## DataKind + Teradata = A perfect data-do-good partnership
URL: https://www.data-mania.com/blog/datakind-teradata-perfect-data-good-partnership/
Type: post
Modified: 2026-03-17
Just as data science is applied to drive major success in business, it’s also being applied to improve or resolve major social issues as well. That’s the mission and focus of DataKind, a non-profit that organizes data science volunteers. Its aim is to let data science volunteers lend expertise to social organizations, civic groups, and non-governmental organizations. This is part of their efforts to create a better, data-driven future for all humanity. With six chapters on three continents, DataKind has been doing lots of good for lots of people.
You may be familiar with the business intelligence company, Teradata? Well, what you might not have known is that Teradata also has a volunteer program of its own, called Teradata Cares. Teradata Cares recently partnered with DataKind. This is to kick off Teradata’s annual conference that was held in Nashville, Tennessee from October 19th to 23rd, 2014. As part of this partnership, the two days before the conference were dedicated to hosting a DataDive (or, a volunteer work session between DataKind and Teradata Cares volunteers). Its goal is to help projects by iCouldBe, the Cultural Data Project, HURIDOCS, and GlobalGiving.
As part of a small Data-Mania blog series featuring this work, today’s post covers the work that was done for iCouldBe and Cultural Data Project.
iCouldBe: The metrics of mentoring
iCouldBe (http://www.icouldbe.org/) is a non-profit organization that acts as an online mentoring program to e-mentor at-risk youth students. This is to help them stay in school and succeed in their coursework. Since the year 2000, they’ve successfully helped over 19,000 young people. At the same time, they’re collecting a large dataset of student-mentor interactions. People at iCouldBe were wondering if they could use this data to predict whether a student-mentor relationship would be successful. They planned to use this information to adjust their curriculum and/or train mentors to better help and keep students.
The first, vital task was to define a metric for success. After looking at the data, DataDive volunteers determined that the criteria for success should be set as the successful completion of three learning modules in three months. When volunteers analyzed student-mentor interactions, they found that the more verbose the mentor was, the more likely the student was to leave the program. In other words, mentors need to keep it simple. By contrast, mentors who used encouraging, supportive phrases were more likely to receive back appreciative and positive student responses. This information has provided the organization with a data-driven framework to ensure they help even more students. It also provided a programming infrastructure to analyze future student-mentor interactions.
Cultural Data Project: helping the arts succeed
The Cultural Data Project collects and distributes data about more than 11,000 American arts and culture organizations. In this portion of the DataDive event, volunteers were asked to analyze the arts and culture data. Goal was to determine what factors make for project success. The team used machine learning techniques. This is to cluster the organizations based on factors like size, number of funding streams, and interest area. Volunteers then identified which clusters were more financially successful. Then, they developed a taxonomy. This then allows arts and cultural organizations to determine in what part of the model they fit. Interestingly, volunteers found that the cluster with the least chance of financial success was also the hardest cluster to describe in a simple taxonomy. But more investigation is needed to determine whether the commonalities between organizations in this cluster are related to the overall financial underperformance of the cluster at-large.
If you’re interested in seeing more about DataKind or the work that DataKind did during its partnership DataDive with TeraData, you can get more details on this at the DataKind Blog.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## 7 Excellent Metrics for Monitoring and Optimizing Your Acquisitions Tactics
URL: https://www.data-mania.com/blog/7-excellent-metrics-monitoring-optimizing-acquisitions-tactics/
Type: post
Modified: 2026-03-17
Hey everybody! Time for round two in the Data-Mania summer series on ROIs and measuring analytics for online growth. In case you missed the first installment, here are my 5 favorite metrics for measuring ROI for content marketing initiatives. This week, we go a bit broader and look at acquisitions tactics as a whole. The content marketing tactic covered in installment 1 are only a small piece of the broader acquisitions puzzle. Have a look for yourself.
So, what do you think? Did I miss anything that you think is important? If so, please tell me about it in the comments section below.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Engineering’s Dirty Little Secret – Data Shows That Women Experience 6.5x Less…
URL: https://www.data-mania.com/blog/engineerings-dirty-little-secret-data-shows-that-women-experience-6-5x-less-stable-wages-than-male-counterparts/
Type: post
Modified: 2026-03-17
Engineering’s Dirty Little Secret… Data Shows That Women Experience 6.5x Less Stable Wages Than Male Counterparts
America is the “land of the free and home of the brave”! Equal rights for all, no? Isn’t that the message that media puts out? Aren’t these the messages we’ve all been told about the good old US of A?
Well, if you take a random sampling of nations around the world and do an honest appraisal, what you will find is that America is relatively fair. Compared to less developed nations, American women are treated very well and equal opportunity is quite prevalent. Discrimination does occur, but it’s muted compared to what’s happening in most developing nations.
While all of that might be true, this line of reasoning sounds a lot like a justification or excuse; An attempt to pass and cover something that doesn’t smell right. Although America has historically been a champion for equal rights and civil liberties, the rest of the world is starting to sense that all may not be what it seems.
Gender-based pay disparities in the US clearly demonstrate that American women are significantly discriminated against due to their gender. Looking into a few wage statistics that were recently published by the US Department of Labor, let’s consider the wages of American men and women that work in engineering. The statistics are based on weekly salary rates for full-time employment only. Due to the way this data is segmented and reported by the US Department of Labor, it’s certain that we’re comparing apples to apples. The results are alarming.
Gender-based pay disparities in the US clearly demonstrate that American women are significantly discriminated against due to their gender.
Women Earn Smaller Wages – That’s Not Breaking News
From the data shown above you can see that, as far as wage rates, the three most gender unbiased engineering fields are marine and naval engineering, environmental engineering, and petroleum engineering (in order from fairest to less fair). The three engineering fields with the largest gender-based pay gaps are mechanical engineering, mining engineering, and computer hardware engineering (in order from less unfair to more unfair).
The most alarming statistic generated from this analysis shows that a female computer hardware engineer in America will, on average, earn 27% less money than her male counterpart for the same job. As appalling as that fact is, things get worse…
The Wage Instability Disparity between Men and Women is Especially Appalling
The gender-based inequality in wage stability is by far the most alarming thing about the statistics generated in this investigation. Wage instability here refers to the net change in reported income between consecutive years. Looking at the same engineering fields discussed above we find that, along with earning 27% less, female hardware engineers experience 3.6x more wage instability than their male counterparts. The mining engineering field is even worse! Female mining engineers earn wage rates that are 6.5x less stable than those of male mining engineer counterparts. Across the board, female engineers experience far more wage instability then male engineers. Environmental engineering is the fairest of all fields, and even there, females in environmental engineering still experience 1.5x more wage instability than their male counter parts.
As startling as these statistics may be, they’re what the US Department of Labor is reporting. Are these injustices simply a result of the good old boy club mentality? Or are these fields still struggling to catch on to the notion of equal civil rights for every man, woman, and child? If it’s the former, then that’s illegal – and why isn’t the American justice system doing something about it? If it’s the latter, well – the civil rights movement happened 50 years ago now. They’ve had 50 years to catch on! If they haven’t done so yet, maybe they need a little external encouragement.
This is a contribution that I originally made to Statistics Views website back in August, 2014. If you’re interested in learning more about the practice of data science, or how you can learn to do it yourself, make sure to check out Data-Mania’s learning resources.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Degree Choices That Pay: Why is generation Y opting out?
URL: https://www.data-mania.com/blog/degree-choices-that-pay-why-is-generation-y-opting-out/
Type: post
Modified: 2026-03-17
This month’s engineering statistics story is all about Generation Y young people and the decisions they’re making when it comes to choosing a career path.
A close look at PayScale data shows that, despite the fact that engineering degrees lead to high paying jobs, most Generation Y students have opted out of this degree path. Rather than go for the degree path that leads to high average wages, Generation Y students are taking degrees that tend to lead towards lower incomes.
This trend becomes even more perplexing in light of the fact that the cities in which employers are hiring are rather costly places in which to live. It’s not unusual for a graduate of one of the more popular Gen Y degree programs to have insufficient earnings to sustain living in one of the cities where good jobs are located (Boston, Washington D.C., and NYC). All of this points to one central question…
In financially uncertain times like these, what’s prompting these Generation Y students to feel safe in choosing the smaller-earning career paths in the face of high-earning paths like those offered of the engineering discipline?
Degree Choices That Pay: Why is generation Y opting out?
How do engineering degrees fare?
Although degrees in the engineering discipline lead to jobs that assume 17 places among the top 28 highest paying jobs on the market, not one of these engineering degree plans is among the list of most popular Generation Y degree choices.
Table 1. Engineering Degrees on the ‘Top Paying’ Degree List
Somehow, despite the high average earnings of engineering degree holders, generation Y students are opting to take majors in areas that don’t even rank on the list of ‘top paying’ degrees. (Although, admittedly there are a few overlaps between the two lists, all overlaps are non-engineering degree choices that don’t even rank that high on the ‘top paying’ list.)
How do engineering degrees fare? Well, according to PayScale, they appear to fare quite well.
Table 2. Popular Degrees – Annual Earnings
What’s interesting from Table 2, is that the top two most popular degree plans are, in fact, STEM degrees. The problem is that neither of these jobs made the ‘top paying’ list. As you can see from the table of popular degrees, Generation Y students seem to be interested in softer studies – like those related to languages, communications, sports, politics, arts, and design. In one sense, this trend is a good thing because it indicates that students are likely following their passions, rather than chasing the almighty dollar. That’s great! The problem, however, is that jobs aren’t readily available and when these students finally do get placed, it’s likely that they’ll end up having to live in a rather expensive city.
In what city will you find a job and can you afford to live there?
Among its reports, PayScale also showed that Washington D.C., New York City, and Boston are overwhelmingly popular cities where Generation Y young people are able to land jobs. According to data derived from Economist Intelligence Unit’s 2014 Worldwide Cost of Living Report, these three cities rank among the top five most expensive US cities in which to live. To get just a small apartment and public transportation, you can expect to spend $32,124/year in Boston, $33,156/year in Washington D.C., and $51,720/year in New York City. But what about expenditures required for things like food, health care, retirement, and student loan payments? Earning meager salaries while living in such expensive cities leaves little to no room to pay for these other essentials.
All of this begs the question: Generation Y young people, in the face of such financial insecurity, do you realize that engineering offers you a relatively safe earnings future, and if so, why are you opting out?
“This is a contribution that I originally made to Statistics Views website back in November, 2014. If you’re interested in learning more about the practice of data science, or how you can learn to do it yourself, make sure to check out Data-Mania’s learning resources.”
Copyright: Image appears courtesy of iStock Photo
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Next week’s data vlogging on location in Penang, Malaysia
URL: https://www.data-mania.com/blog/next-weeks-data-vlogging-on-location-in-penang-malaysia/
Type: post
Modified: 2026-03-17
Hey ya’ll, just checking in real quick to give you a quick recap and heads up about next week’s vlog installments. This last week I covered What is Data Science? Business Intelligence vs. Data Science, and What is Data Engineering?
Next week I’ll be producing vlog posts on the following topics: ‘The Four Main Types of Analytics’, ‘Descriptive vs Inferential Statistics’, and ‘Random Variables, Probability Distributions, and Expectations, Oh My’. We will be filming these on location from Georgetown, Penang, Malaysia,… so I’ll make sure to add in some interesting, exotic scenery clips to make the data vlogs even that much more exciting (as if anything could be more exciting than ‘Random Variables, Probability Distributions, and Expectations’). See ya then!! 🙂
We will be filming these on location from Georgetown, Penang, Malaysia,… so I’ll make sure to add in some interesting, exotic scenery clips! 🙂
Learn more about big data and data science in my newly released book, Data Science for Dummies. We also offer online classes for people who want to quickly and cheaply learn how to get started in doing data science.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## The 4 Types of Data Analytics
URL: https://www.data-mania.com/blog/the-4-types-of-data-analytics/
Type: post
Modified: 2026-03-17
(A vlog post on data analytics)
This vlog piece was filmed in Penang, Malaysia and introduces viewers to the 4 types of data analytics.
The 4 Types of Data Analytics
Descriptive
Answers the question, “What Happened?”.
Diagnostic
Commonly used in engineering and sciences to diagnose “what went wrong?”.
Predictive
Used to predict for future trends and events based on statistical or mathematical modeling of current and historical data.
Prescriptive
Used to tell you what to do to achieve a desired result.
Based on the findings of predictive analytics.
Learn more about big data and data science in my newly released book, Data Science for Dummies. We also offer online classes for people who want to quickly and cheaply learn how to get started in doing data science.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Using R and Google Analytics for Online Marketing Improvements
URL: https://www.data-mania.com/blog/using-r-and-google-analytics-for-online-marketing/
Type: post
Modified: 2026-03-17
5 Easy Steps to Using R and Google Analytics for Online Marketing Improvements
Do you want to use your data to improve the results of your online efforts? Do you think analytics and data science are too sophisticated for you at this time? Well, don’t let the brainiacs fool you! Extracting valuable insights from your data isn’t that complicated!! In this article, I’ll show you how to make a quick start in using R amd Google Analytics for online marketing improvements.
These 5 easy steps will give you a leg up by providing you the code you’ll need to do this analysis for yourself. I give you a demonstration of how I carried out this evaluation for my brand, and end by explaining to you EXACTLY why it’s preferable to do this in R, rather than use plain Google Analytics.
Step 1: Document The Recent Events That Are Likely to Impact Your Data & Conversions
Ok, so I am going to keep this step discussion brief, and then after we get you through this part, I’ll show you how I applied these steps to optimize my own online marketing efforts.
For the first step in using R and Google Analytics for online marketing improvements, you want to take a little time to form a solid idea about what you expect your data and results will look like.
Clarify your goals, and decide what metrics that you think will be most relevant to measuring progress towards reaching those goals.
Also, list the initiatives or occurrences that have happened recently, how you think they will impact your data, and the approximate date on which these events occurred. When I say “initiatives or occurrences”, I am referring to things like: social advertisement campaigns, Google Ads campaigns, inbound marketing campaigns, SEO initiatives, website attacks, email marketing campaigns, etc.
Lastly, document what you consider to be an event, or user action that constitutes a conversion for your brand. Examples of events include: social follows, email subscriptions, downloads, purchases, site subscriptions, etc.
Enjoying seeing how to start using R and Google Analytics for online marketing improvements?
If so, you’d probably really get a lot out of our full scale course, R Programming for Data Science. We’ve made that available online here. The Mastery Level option even gives you a chance to interact directly with the instructor and your peers. You can see more about it here.
Step 2: Download Your Google Analytics Data Using the Google Analytics API
Authorizing the connection with the Google Analytics API was the trickiest part of this process for me – mostly because you have to go outside of R to get all the permissions set up. I am giving you my code and I also want to point you to the web page that I used to figure out this process. They’ve made it pretty straight forward, actually.
Set up your Google Analytics API client ID and secret by following the instructions on this page.
Download the R Scripts to connect to the Google API for free here.
.
.
.
Step 3: Organize & Explore Your Data
Now it is time to organize your data and take a look around. How do the results look compared to what you expected? Are there any results that appear out of place from your expectations? When did these events occur? What might have caused them? Does the data show me metrics that might better reflect progress towards your intended goals?
Download the R Scripts to do organize & explore your Google Analytics data for free here.
.
.
.
Step 4: Select the Metrics that Best Represent Progress Towards Business Goals & Generate Data Visualizations
After exploring your data, what metrics best reflect progress towards your business goals? On your list of goals, next to each goal, write down the name of the metric(s) that best measures progress.
Now once again consider your goals… are they realistic? Do they need to be adjusted down or up based on what the data is telling you about current and historical performance?
Lastly, create a few visualizations that clearly demonstrate what your seeing in your data. Make sure to use the metrics that you decide are the best measures of true progress (rather than the ones you had suspected would be good in Step 1). You’ll want to design your data visualizations so that they most clearly emphasize the points you will be making when you communicate your recommendations to your team members.
Download the R Scripts to visualize your Google Analytics data for free here.
.
.
.
Step 5: Decide How These Insights Affect Your Plans & Communicate Conclusions To Team Members
Which of your initiatives are showing the greatest impact? Which are under-performing? Are there underlying issues that you need to resolve to get your business closer to its goals?
In general it is best to follow the 80-20 rule with online marketing. Identify what new initiatives are showing spectacular performance (this would be equivalent to 2 out of 10 initiatives). Scrap whatever initiatives aren’t within the top 20% performance range.
Now take the resources you would have allocated to the lower 80% initiatives, and use it to do both of the following:
Increase resource allocations to the top 20% performers
Invest resources into experimenting with new initiatives (with the goal of finding more high performance methods
Draft a plan. Support your recommendations with data visualizations, tables, and written discussion. Describe logistical details about future implementations. Maybe even add in an addendum for your support staff, and use this addendum to provide them links and access permissions they will need to execute the work from here moving forward.
The goal of this plan is to clarify and communicate exactly what needs to be done, how, and why – so that you can keep your business moving towards its desired objectives.
Extra Credit Step: Congratulate Yourself for Making a Start in Using R and Google Analytics for Online Marketing Improvements
And there you have it. You’ve just seen how quick and simple it can be to start using R and Google Analytics for online marketing improvements. Pat yourself on the back. Now it’s your turn!!
Now it’s time to see how this plan is executed.
The first thing I need to do is make some quick lists of goals, metrics, and recent events (with dates) that are likely to impact my brand’s data and conversions.
Goals
Email Subscription Increases (500/mo)
Website Visitors (300/mo increase on avg)
Improved Audience Targeting
Streamline Internal SEO
Optimize Social Marketing Efforts
Product Sales Revenues ($Xk/mo)
Services Revenues ($Xk/yr)
Relevant Metrics
Emails Signups / Month
Number of Site Visits
Number of Page Views
Number of Sessions from SEO
Number of Sessions from Social
Number of Revenue-Generating Conversions
Recent Events
Book Giveaway (May 2015)
Book Launch (March 2015)
SWSX Speaking Engagement (March 2015)
IBM Online Influencer Event (Aug 2015)
Facebook Advertising (May 2015 – Present)
New High-Value Content Marketing Approach (May 2015 – Present)
Referral Spam Attack on Site (Aug 2015)
After carrying out the Step 2 work in RStudio, I got into exploring and analyzing my Google Analytics data. I answer the following brief questions:
Are the results similar compared to what you expected?
Sort of, but there are a few surprises. When exploring my data, I quickly realized that it is best to measure page views derived from Facebook, to measure the effectiveness of my ad campaigns. Number of page views is the true measure of how interested visitors are in what you’re offering. I was also hoping to see better improvements in my SEO results.
Are there any results that appear out of place from your expectations? When did these events occur? What might have caused them?
I am surprised at:
How effective my Facebook advertising has been at attracting people that are truly interested in what I am doing with this brand – (May – Present) – Caused by advertising to the people most likely to be interested in Data-Mania, and by offering them free products that they can use to help them advance their careers.
How little traffic came from Twitter during the first few months of 2015 – (Jan – Feb) – I was finishing my book project and was a little burnt out on producing new content for the blog. The decrease is almost certainly because of a decrease in content marketing efforts.
The 6-fold increase in site visitors that I have gotten during a few months this year (May – Present) – People really respond positively to high-value content given away for free. Facebook is an excellent avenue for acquiring new, well-fit users
The amount of traffic that came from SEO in August – (Aug) – Most of that was due to the high-value content published that month, but I also got referral spam that offset the numbers that month too. I had a Joomla CMS sitting in my root directory and it got spammed with referral traffic. This event spurned me to finally take action and fix the fundamental issues in the CMSs on my site.
Does the data show me metrics that might better reflect progress towards your intended goals?
Yes
For Facebook advertising, the number of page views (rather than the number of site visits) generated from Facebook referrals is a far better metric for measuring the effectiveness of ad campaigns.
Now, it’s time for me complete step 4 of this process. I am not comfortable delving deeper into the financial aspects of my business online, but I will be happy to discuss the other areas I evaluated.
Goals – Finalized Metric Selections
Email Subscription Increases (500/mo)
Website Visitors (300/mo increase on avg) – Metric to Track = Number of Total Sessions
Improved Audience Targeting – Metric to Track = Number of Page Views
Streamline Internal SEO – Number of Sessions from SEO
Optimize Social Marketing Efforts – Number of Sessions from Twitter, Number of Page Views from Facebook
Now once again consider your goals… are they realistic?
Yes
Lastly, create a few visualizations that clearly demonstrate what your seeing in your data.
Lastly, it is time for me to decide what I want to do with all this information I gathered. (Please keep in mind that I ran this analysis several months ago as a starter, and have already made some of these changes).
After using R and Google Analytics for online marketing improvements, I’ve made the following decisions:
Increase resources spent on Facebook advertising.
Hire someone to fix my CMS issues and the referral spam problem
Tip of the Day content didn’t have any real impact, so discontinue that
Fire the SEO guy that is helping me (I did that in July)
Hire someone to spruce up my onpage SEO only
Make sure to keep posting content from my site onto Twitter. Even old content can be extremely high-value to people that have not seen it yet. Topics that are not time-sensitive will be just fine to recycle through the social streams for guests to learn from. Have my assistant make a list of these articles and draft up headlines with URLs for social scheduling. Try to do this in one sitting, so that it can be handled for an entire year and I can be free to focus on other high-impact avenues.
SEO doesn’t appear to be a real winner in the portfolio, and I don’t anticipate great ROI from the SEO onsite fixes. Once this is done, I should let SEO efforts go and focus on another channel. Flipboard appears to be quite promising.
I will spare ya’ll the write up… and honestly, this marketing project is so small still, I don’t really need one. The point is that I have made some strategic decisions based on Google Analytics, and I expect these decisions will lead me to less waste, and greater impact with my marketing efforts in the future. This is a wash, rinse, repeat process.
Carry it out every 6 months and watch your brand start moving quickly in the right direction.
Why It’s Better To Do This in R
The thing about Google Analytics is that the data and metrics are spread out on different pages. You can get your data visualizations only in the bundling and packages by which GA bundles them. Google Analytics does offer a dashboard application where you can build your own custom Google Analytics dashboard, but it’s not easy or flexible. You’d probably have to take a course just to figure out how to build a dashboard with the exact metrics you need (if you could at all).
To make a clear decision about what’s working and what’s not, you need to see the metrics compared side-by-side. Considering what you know about how much time and money you’re spending on your branded space; the snapshot comparative view is super helpful in telling you whether that time and money is well spent.
To get this view of your data and metrics, all you need to do is build a custom report / visualization. That’s where R comes in. R is a great choice for this task because it is so quick and easy to do this type of analysis using R (and R is FREE).
The following are a few of the benefits derived from doing this work in R.
R is free of cost.
You can get immediate updated results, over and over, once you’ve got the script built.
It is easy to build scripts in R.
R is well-supported and documented, if you get stuck just Google it.
R can be used for real-time reporting
There is no clunky cargo, name only the data you need, and extract/process it automatically after the scripts are built.
With just a few hours invested upfront, you’ll have automated analysis and visualization tasks that would take days if performed manually each time you need to do an update.
Enjoyed seeing how to start using R and Google Analytics for online marketing improvements?
If so, you’d probably really get a lot out of our full scale course, R Programming for Data Science. We’ve made that available online here. The Mastery Level option even gives you a chance to interact directly with the instructor and your peers. You can see more about it here.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## What is a dashboard & 4 steps for creating a good one…
URL: https://www.data-mania.com/blog/what-is-a-dashboard-4-steps/
Type: post
Modified: 2026-03-17
If you find yourself at meetings and are still asking yourself “What is a dashboard?”, then don’t be embarrassed! The majority of business professionals in today’s data-driven environment are in that same position. This article answers that exact question, and gives you some great tips on how to get started building one for yourself.
What is a dashboard?
To be fully honest, when I first entered the world of business analytics, I also had no idea about the meaning of the word “dashboard”. Frankly, the only thing that came to mind for me was the front casing of a car. And in fact, there is some link between the two uses of the word.
[box]
Learn Data Science & Engineering
at Simplilearn
Courses 50% off until March 14th
Discount Code = SPRING50
Discount Code, India Only = MARCH50
[supsystic-slider id=3]
Act Fast! Deal ends on 03/14
[/box]
You know the gauges you see on your dashboard? The ones that tell you how much fuel you have left, how fast you’re driving, and how hot your car is running? You rely on those to provide you important information about your car when you’re driving it, right? Well, business dashboards function in much the same way.
In the business world, dashboards are data visualization tools that provide high-level decision-makers the information they need to make data-informed decisions about their respective businesses.
They usually look something like this:
Or like this:
Dashboards are usually interactive data products, but you can design them for many types of audiences, and to meet many types of objectives. I included a chapter on dashboard design in my book, Data Science for Dummies. It looks like ZingChart liked that chapter so much that they chose to feature it in a recent presentation!!!
4 Steps to Better Dashboard Design
4 Steps to Better Dashboard Design from ZingChart
Want to know more?
If so, you’re in luck!! Now that you’ve got the answer to the question, “What is a dashboard?”…
I will also show you a free, IBM application that you can use to begin creating dashboards for yourself. It’s called Watson Analytics. I will be giving an overview on “What is Watson Analytics?”, and showing a demonstration on how to use this application.
This will all be provided in a free webinar event next week, on March 11, 2016 at 8:30 am CST. Mark your calendars and then pre-register for the event here!
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Demonstrating Analytics-as-a-Service with Twitter Hashtag Analysis in Watson Analytics
URL: https://www.data-mania.com/blog/demonstrating-analytics-as-a-service-via-twitter-hashtag-analysis-in-watson-analytics/
Type: post
Modified: 2026-03-17
For our last installment in this 3-part series on analytics-as-a-service, I’m going to provide you a quick and dirty demonstration of how to use Watson Analytics to analyze hashtag data from the Twitter social network.
Step 1: Log-in to Watson Analytics and Add Your Data
Once you get inside of Watson Analytics, you’re going to see a menu that looks like the one shown above. Click on ‘Upload data’ in the ‘Or add your data’ section.
That will take you to this next menu that is shown above. We are doing a Twitter data analysis, so choose the ‘Twitter’ option here.
Watson Analytics’ Twitter data analysis feature allows you to query data out from Twitter, based on hashtags, language, start date and end date. The hashtags I entered for this exploration were:
#BigData
#DataScience
#Algorithms
#MachineLearning
#Analytics
#DataAnalytics
I named the resultant dataset “Hashtag Analysis”, as shown in the screen shot above. Now it’s time to start a data exploration using Watson’s Analytics “Explore” feature.
Step 2: Start a Data Exploration Within Watson Analytics
After selecting “Hashtag Analysis” as the dataset to explore, I was taken to the screen shown below.
This is a “Explore” feature homepage. As you can see, Watson Analytics has already suggested some relationships that we might be interested in exploring from within the Hashtag Analysis dataset.
We’re going to explore our own custom relationship – – “What is the breakdown of Retweet count by matching hashtag?”.
Ok, so… let’s look at what we’ve got here. Watson Analytics has gone in to Twitter and pulled all the tweets that were tweeted last month that contained matching hashtags for the hashtags I queried. Based on the data that Watson Analytics queried, it looks like … well, if my goal is to use hashtags that are going to garner me the largest number of retweets (in other words, if I want to get the broadest reach), then I may want to use the following hashtags:
#DataAnalytics
#Analytics
#DataScience
Those are getting retweeted slightly more frequently than tweets with #BigData, #Machine Learning, and #Algorithms. Of course, there are competing factors that would need to be considered before making any substantive conclusions.
Let’s see what else Watson Analytics can tell us here… The screen below shows results from an exploration of “the number of Tweets for each Matching Hashtag”.
From this bubble chart shown above, we can clearly see that “Big Data” is the most frequently tweeted hashtag among the set I entered, followed by #Analytics, and then #DataScience. This data visualization helps me understand where the conversation is happening on Twitter.
But, what are these other hashtags that are showing up? Those hashtags were not in my query! Well, it appears that those are co-related hashtags. By that I mean, those are hashtags that were found within tweets that did have a match for the hashtags I queried.
Very cool!! These can give me some insights into other hashtags that are being frequently tweeted in the big data and analytics tweet streams. Should I update my Twitter hashtag strategy given these new insights? Perhaps 🙂
Since this is just a quick demonstration, I am not going to investigate further. But as you can see, even spending just a few minutes in Watson Analytics to create this demo has provided me additional value – I now have some evidence that I could use if I wanted to re-optimize my Twitter hashtag strategy. By looking at this second data visualization, I have put together a preliminary idea for hashtags I should reference to help make sure that my tweets make it into the Twitter conversation stream.
Based on a very fast data exploration in Watson Analytics, I have surmised that I’d do well by adding these hashtags into my tweets:
#opines
#opendata
#nosql
#bi
#data
#iot
#hadoop
#dataviz
#googleanalytics
#rstats
#deeplearning
#datawarehousing
#dataliteracy
But, no need to take my word for it. Go visit Watson Analytics and see what data insights you can discover for yourself.
Want to Learn More About Analytics-As-A-Service?
This brings us to the end of our series on Analytics-as-a-Service. If you want to learn more on analytics-as-a-service though, or predictive analytics, then I recommend you to take a look at what you can find in Eric Siegel’s book Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. In its pages, you’ll find plenty of stories and examples of how analytics are being used to revolutionize and redesign how business is successfully conducted in the modern world.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Of These Top 20 Free Analytics Tools My Favorite is (Drum Roll, Please…)
URL: https://www.data-mania.com/blog/top-20-free-analytics-tools/
Type: post
Modified: 2026-03-17
Of These Top 20 Free Analytics Tools My Favorite is (Drum Roll, Please…)
Just last week, Sam Scott published a very helpful article called “16 Free and Open-Source Business Intelligence Tools” on DZone. I thought it would be nice to add a few of my favorite free analytics tools to the list, and then provide a bit of feedback on the tool that I personally find most useful. So without further ado, here is my list of the top 20 free analytics tools on the market.
Top 20 Free Analytics Tools
1. Google Analytics
I am not quite sure how Google Analytics didn’t make the DZone list, but personally – I find Google Analytics a critical decision-support tool in developing my own web commerce strategy. If you’ve got a website (and you should have one, even if it’s just a page at About.Me, to mark your place on the web), you should be tracking what’s happening there. Google Analytics is the 100% preferred method to do that. It can be tedious to sift through all of the various reporting options, but it’s easy enough to solve this problem by building yourself a custom Google Analytics dashboard within the tool.
Photo Credit: Google Analytics Blog
2. Gephi
Gephi is about the best free network analytics tool on the market. Borrowing an excerpt from my book, Data Science for Dummies, “Gephi (http://gephi.github.io) is an open-source software package you can use to create graph layouts and then manipulate them to get the clearest and most effective results. The kinds of connection-based visualizations you can create in Gephi are useful in all types of network analyses — from social media data analysis to an analysis of protein interactions or horizontal gene transfers between bacteria.”
Photo Credit: Gephi.org
3. QGIS
With QGIS, we’re talking about location analytics. If you’ve got spatial data, you should be looking for ways you can use it to optimize your strategy with respect to location. Not enough can be said for FREE GIS! Do you have any idea how much proprietary ArcGIS costs per license? With all the add-on licenses that are required, you could be looking at more than $3k or $4k per year per user! What’s more, almost all this same functionality is available for free in QGIS — and QGIS includes some functionality that ArcGIS just doesn’t have, no matter how many add-ons you buy.
Photo Credit: QGIS.org
4. Datawrapper
Data Wrapper is a nice little non-coding tool for building beautiful, web-friendly, interactive visualizations for use in data-driven storytelling. Whether you need standard chart graphics, or more advanced statistical charts — Datawrapper probably has just what you want and more. You can even use it to make web-friendly geographic maps!
Photo Credit: Datawrapper.de
These next free analytics tools are covered pretty thoroughly over on DZone, so I’ll leave it to you to investigate as you wish. These tools include none other than:
5. BIRT
6. ClicData Personal
7. ELK Stack
8. Helical Insight
9. Jedox
10. JasperReports Server
11. KNIME
12. Pentaho Reporting
13. Microsoft Power BI
14. RapidMiner
15. ReportServer
16. Seal Report
17. SpagoBI
18. SQLPower Wabit
19. Tableau Public
20. Zoho Reports
And Which Of These Free Analytics Tools Is My Favorite?
At the risk of sounding like a simpleton, I have to say that my favorite tool of all of these is Google Analytics. For my purposes though, it’s got all that I want and need. As an influencer, my service and product business revolve around my blog. All I really monitor are the weekly number of sessions on my site, as well as traffic sources. So long as these numbers are steadily increasing, I feel comfortable with that progress. Other metrics that are reported on Google Analytics are useful for telling me things like:
How interested my readers are in a particular piece of content.
How well my content aligns with my core offerings.
How well my social content is converting despite the varying audience preferences of differing social channels.
That’s all gravy – but as for the nuts ‘n bolts, I built an at-a-glace Google Analytics dashboard that I look at once per week to evaluate the impact of my marketing decisions, and adjust my strategy for improvements.
Now, if you manage an ecommerce retail business, then another tool would probably work better for your needs. And, of course – if you’re working for a corporate giant then you’re better off sticking with the (super fancy and expensive) tools they provide you. If you’re doing an in-depth analysis, I’d really suggest just using Python or R (I made a free tutorial doing this with Google Analytics data in R here, in case you want to play around with it).
Anyway, enough from me. What about you? Have you experience with any of these top 20 free analytics tools? If so, what is your favorite and why?
(Interested in learning to do data science? Give it a go with my LinkedIn Learning course: Python for Data Science Essentials.)
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## What is GDPR compliance and what does it mean to your company’s bottom line?
URL: https://www.data-mania.com/blog/what-is-gdpr-compliance/
Type: post
Modified: 2026-03-17
Last Thursday I had the pleasure to work with IBM for their Fast Track Your Data event in Munich, Germany. In 48 sweet hours we managed to record A LOT of video footage! Most important of which (in my opinion) was the discussion and debate about “what is GDPR compliance?”. Also, the discussion on what GDPR means to your business’s bottom line.
All present in this debate were Ronald Van Loon, Chris Penn, David Vellante, Jim Kobielus, Dez Blanchfield, Joe Caserta, and myself…
What is GDPR compliance?
Learn the answer to the question “what is GDPR compliance?”. Also, know the full story behind GDPR. Hear the ensuing debate about data privacy laws and their effects on business. Arguments were made both for and against. 🙂
I am curious to hear from you! What are your thoughts and opinions on the GDPR issue? Please write them in the comments section below.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Branding Yourself as a Data-Driven Professional – Why It Matters…
URL: https://www.data-mania.com/blog/branding-yourself-as-a-data-driven-professional-why-it-matters/
Type: post
Modified: 2026-03-17
If you’re like most people, when you think about the idea of branding yourself as a data-driven professional, your next thought goes something like, “me, a brand? I don’t even know what to do with that concept.” If so, I can totally relate. As recently as 2011, I didn’t have a Facebook account. I didn’t even have any social media accounts, let alone have a brand or an online following. I preferred to live offline, thinking “why would I want to share my life with people who are not already here and in it?”. In short, I valued my privacy.
It’s really hard to get a job when no one knows who you are.
This all changed though when I had to go on a job hunt and I discovered that it’s really hard to get a job when no one knows who you are. I read a few books and decided to put up a few social accounts, as they suggested. The easy part (opening the accounts) was done, but I didn’t even begin figuring out how to leverage them until another 6 months down the road.
…And for a “no one” like I was, now you can see why I advocate the power of brand-building for modern professionals.
Long story short, these days I am most active on LinkedIn, Instagram, and Twitter. I have met soooo many incredible professionals through the work I do on my brand. I’ve now got about 350,000 data enthusiasts following my work through social media. By late 2013, my brand provided enough business opportunities that I could quit my day job and open my own business. On average days lately, at least 3 business opportunities show up in my inbox, per day. And for a “no one” like I was, now you can see why I advocate the power of branding yourself as a data-driven professional.
Why it’s important to have your own branded space
You will benefit from a personal-professional brand in 5 main ways. Those are:
Strong brands create a sense of individuality and “separateness” in the marketplace, so that your clients can easily differentiate you from your competitors.
The goal of personal branding is to be known for who you are as a professional and what you stand for. Your brand reflects who you are, your opinions, values, and beliefs. These are visibly expressed by what you say and do, and how you do it. Your brand informs the world about who you are as a professional and as a person.
The branding process allows you to take control of your identity and influence how others perceive you and the services you offer.
A strong personal professional brand effortlessly attracts clients and opportunities. By branding yourself as a data-driven professional, you position yourself in the mind of the marketplace as the service provider of choice to dominate the market!
Branding yourself as a data-driven professional allows you to gain name recognition in your area of expertise where it counts the most – in your customer’s mind. Branding helps you make lasting impressions and be super-rewarded for your individuality.
The first step to branding yourself as a data-driven professional
The first step in developing a brand is deciding where it will live. You need a central home on the web. At first that could be something simple, like your LinkedIn profile.
Inevitably I recommend people set up their own self-hosted WordPress site, so that they can own and control their own personal space.
You can also go with 3rd party platforms like Wix or Weebly, but again – you will be limited on what you can do, because you don’t technically own your property (it’s more like you’re leasing it).
In my coaching program, I spend 4 to 6 months working with clients to help them establish a rock-solid presence while they get themselves trained with the skills they need to slay their competition. In this mentoring program, when introducing concepts related to branding yourself as a data-driven professional, I advise mentees to include at least the following elements in their websites:
A (great) avatar and bio box
An about page
A blog
A (stellar) tagline
A logo showcase
Plenty of calls-to-action
Social media widgets
Why social media is important as a technical professional
When it comes to social media for professionals, all networks are not the same.
Social media is important because it’s the medium across which you meet new like-minded professionals with whom you can align. It is the gateway to brand exposure and it’s a place where you can give back to your community. When it comes to social media for professionals, all networks are not the same. Each network has its own set of micro-communities. Each network needs its own type of content and approach. It takes time and effort to figure these out, but online courses and books can be helpful in this process. Like I said, the 3 networks I like to use are:
LinkedIn –
LinkedIn is the place to be for all things data professional. With Microsoft’s recent acquisition of the platform, it has what it needs to accomplish its mission. And in case you hadn’t heard, LinkedIn aims to be the one-stop shop for all things professional. To achieve that goal, they’ve acquired or are building/maintaining modules for: social networking, freelance marketplace transactions, online training, and more.
I do everything in my power to give back to my thriving community over on LinkedIn, because I know most of these people are like me – hard-working and dedicated to their profession. I like people like that!
Instagram –
I used to LOVE Instagram and there is a really solid technical community established on the platform. This said, Instagram is a tough nut to crack when it comes to growth. One tip I can give you here is that automation is definitely NOT the way to go. Authenticity and story-telling are the name of the game over at IG. I have managed to grow my account to almost 27k followers, but it is embarrassing to say how long that has taken me. I am still learning how to use the platform, even after 5 years.
Twitter –
To be frank, Twitter is really a tool from last decade. Over-automation had a part in killing Twitter, along with other factors. I don’t expect Twitter to survive another 5 years, but it accounts for almost 10% of my site traffic, so I stick with it. If you’re on Twitter though, I’d start making a plan for what you’re going to do when the company files for bankruptcy.
As far as data professionals and social networks, based on my experience, LinkedIn and Twitter have the largest established communities.
I am helping to foster a community over on Instagram, but the network is yet young. The steep-learning curve discourages many, I think – but I am hoping this will change in the not too distant future.
One thing I can add is that, on Instagram you’re going to find all your coders and programmers – the people who are actually doing and building. On Twitter and (to an extent) LinkedIn, you’re likely to find more people who are managing and using data insights, instead.
For more guidance on how to jump start your career as a data professional, sign up for my newsletter here. Also, if you’d like to connect through LinkedIn, follow me and leave a note that you followed. I promise to follow back!
Also, in the comments section below, tell me what changes you’re considering making based on what you learned in this post. I’d love to offer you guidance and feedback on your ideas.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Top Tech Influencers for Data Professionals to Follow on Social
URL: https://www.data-mania.com/blog/top-tech-influencers-for-data-professionals-to-follow/
Type: post
Modified: 2026-03-17
If you’ve been following along with the Data-Mania blog recently, then you’ve learned about the power of branding yourself as a data-driven professional. You’ve seen why it’s important to get active on social media. Now I want to give you a brief heads up on some top tech influencers for data professionals to follow.
Unlike all those Twitter popularity contests you see (Read: Who’s Who of Twitter bot strategy), this list isn’t about promoting businesses or who’s got the most followers. These recommendations for top tech influencers for data professionals to follow are all made with YOU in mind. That’s why I have taken the time to spell out exactly what you can expect to gain from following these individuals.
And without further ado…
Top Tech Influencers for Data Professionals to Follow on Twitter
Andrej Karpathy
Director of AI at Tesla. Previously a Research Scientist at OpenAI, and CS PhD student at Stanford. I like to train Deep Neural Nets on large datasets.
What You’ll Gain From Following Andrej:
Karpathy is the guy to follow if you’re a deep learning enthusiast. If you follow him, you’re going to get access to a live, direct stream of deep learning resources that he creates and generously shares with the world through his GitHub account. By following him on Twitter, you’re signing up for access to updates on what’s happening, front-and-center, with deep learning, AI, and even self-driving cars — straight out of Silicon Valley, from a deep learning guru himself.
Ian Goodfellow
Google Brain research scientist Lead author of http://www.deeplearningbook.org Co-author of ML security blog http://www.cleverhans.io
What You’ll Gain From Following Ian:
Like Karpathy, when you follow Goodfellow, you’re signing up for a live stream of updates that are in-deep with cutting edge advancements in deep learning. Unlike Karpathy, Goodfellow seems bent on helping newcomers enter the deep learning field. He has even gone so far as to publish a free book on deep learning, appropriately titled Deep Learning Book. Keep an eye on his Twitter feed for fresh how-tos and news releases on deep learning and Google Brain.
Hilary Mason
Founder at @FastForwardLabs. Data Scientist in Residence at @accel. I [heart] data and cheeseburgers.
What You’ll Gain From Following Hilary:
When you follow Hilary, it’s like signing up for a series of mini-lessons on practical data science, straight from the fingertips of a data science maestro. Mason’s perspective is unique because she’s got boots-on-the-ground in data science entrepreneurship in NYC (unlike Karpathy and Goodfellow, who’s work is thick-ladden with research and academia). Mason uses a common-language, personal approach with her tweets, and takes the time to make the subject as approachable as it can be.
Top Tech Influencers for Data Professionals to Follow on Instagram
Estefannie Explains It All
???? ???????? ???? Software Engineer, Maker, and YouTuber ✉️ hello [@] estefannie.com m.youtube.com/user/estefanniegg
What You’ll Gain From Following Estefannie:
If you’ve been considering applying your data savvy to the areas of smart devices, IoT or AI robotics, then Estefannie is definitely the gal for you. When you follow Estefannie, brace yourself to receive a continual stream of YouTube videos where she shows you how she actually makes smart devices, using things like Arduino, RasberryPi, and solar panels. To get an idea of what sorts of amazing madness you’re getting yourself into, keep an eye on her IG for updates on makes she’s publishing.
Laura Medalia
software engineer working at a startup in NYC sharing my ❤of coding, ????, fashion & jokes.???? ????: Hello [@] codergirl.co
What You’ll Gain From Following Laura:
Laura is a true leader for young people and women in tech. Even if you’re over 30, like me, there is so much you’ll benefit by following @codergirl_, especially if you’re considering going into data engineering or becoming a machine learning engineer. Through her Instagram posts, Laura tells the story of what it’s like to work as a developer at a start-up in New York City. In her InstaStories, she shares images and videos that document conversations she has with her fellow developers, fun things they do together, and so much more. Her channel is almost like reality TV for software engineers. If you want a true inside view of what it’s like to eat, live, and breathe coding and application development at a thriving start-up, follow Laura.
Lillian Pierson
I train working pros to do data science ????Career Coach for aspiring data pros ???? Lillian@Data-Mania.com ????Watch My Video Interview????????www.data-mania.com/blog/data-science-as-a-career-change
What You’ll Gain From Following Lillian:
Ok, so ye – this is me! I had to include myself here though because, technically-speaking, I have the largest Instagram account that’s focused on the data niche. By following my Instagram account, you’re going to see some of the amazing things that are possible for you when you think outside-the-box when it comes to your career in data. I share moments, motivational posts, and words of wisdom that tell the story of what it’s like to be a self-employed, world-traveling, expat, wife and mother of 1, data scientist.
Top Tech Influencers for Data Professionals to Follow on LinkedIn
Bernard Marr
Best-Selling Author, Keynote Speaker and Leading Business and Data Expert
What You’ll Gain From Following Bernard:
As one of the top tech influencers for data professionals to follow, the best thing about following Bernard is that you’ll get access to his high-level summaries on how businesses are benefiting by deploying blended data engineering, data science, and analytics solutions. His case studies are quite valuable if you need to quickly learn how big data benefits business, and you don’t want to have to rack your brain wading through a bunch of overly-technical mumbo-jumbo. If you want quick and easy-to-understand updates on how to apply analytics to business, following Bernard is a great way to get those.
Carla Gentry
Data Scientist at Talent Analytics, Corp.
What You’ll Gain From Following Carla:
Carla is a real person, and her authenticity has never been in doubt. She has several decades of experience doing data science (or what was essentially “data science” before we called it that). The best thing about following Carla is her opinion. When she shares good articles, she tends to relay her opinion and experience on technical matters within the industry. When you follow her, you’ll get access to micro-segments of her experience, and what she’s learned. If you truly want to understand all areas of data science, from cutting-edge developments to age-old implementation, following Carla will give you that, in micro-batch.
Tom Davenport
Professor at Babson College
What You’ll Gain From Following Tom:
Similar to Bernard, when you follow Tom, you’re going to get access to updates that give you high-level, easy-to-understand examples of how data technologies and methodologies are benefiting modern businesses. Tom also releases videos though, of him giving high-level talks on data-driven topics. He’s keen on covering the future of work, and automation – important topics that we should all be keeping an eye on if we want to be prepared to meet the challenges tomorrow will certainly bring.
Who did I miss? If you can think of user-focused influencers who seriously provide benefit to data professionals that follow, please write them into the comment section below, along with a note on how people can expect to benefit by following them.
Want more tips on things you can do to enhance your career in the data professions? Follow me on Instagram, LinkedIn and Twitter, or just sign-up for my newsletter in the sign-up box below.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Udemy
URL: https://click.linksynergy.com/deeplink?id=*JDLXjeE*wk&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fcourses%2Fbusiness%2Fdata-and-analytics%2F
Type: post
Modified: 2026-03-17
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## EngageBay
URL: https://www.engagebay.com?ref=6450201810173952
Type: post
Modified: 2026-03-17
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## SmarterQueue
URL: https://smarterqueue.com/?ref=6vb
Type: post
Modified: 2026-03-17
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## SEMrush
URL: https://www.semrush.com/lp/position-tracking-5/en/?ref=7801840896&refer_source=&utm_source=berush&utm_medium=promo&utm_campaign=link_lp:position_tracking_bmc
Type: post
Modified: 2026-03-17
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Data-Science-For-Dummies
URL: https://amzn.to/3kTnwPV
Type: post
Modified: 2026-03-17
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Customer Profiling and Segmentation in Ecommerce
URL: https://www.data-mania.com/blog/customer-profiling-and-segmentation-in-ecommerce/
Type: post
Modified: 2026-03-17
Today’s post provides an overview, example, and conceptual demonstration of customer profiling and segmentation.
“Half the money I spend on advertising is wasted; the trouble is I don’t know which half” – John Wanamaker
I believe that to be one of the most powerful quotes that describe the dilemma of marketers in the most appropriate way.
Companies have always faced this challenge of reaching out to right customers, through right channels, at the right time and offer the right product.
In traditional brick and mortar stores, companies have always followed the strategy of bulk marketing – where they market and advertise their product to the entire universe. Imagine a company advertising diapers to 12 year kids or a company advertising baby products to 15 year kids. I believe the money spent on such advertisements constitutes the wasted half (of advertising money).
Moreover, in today’s world where marketing budgets are getting more and more tighter, and customers are flooded with a plethora of options at their fingertips, it becomes imperative to use the marketing budget judiciously and get the maximum possible return.
In typical brick and mortar stores, it may still not be that easy to be very specific about target customer segment but in the case of online companies or e-commerce, it is easier to reach out to the right customer, at the right time, through the right channel, with the right product offering.
In recent years, the commoditization of hardware and advancement in software space has made it economical and viable for companies to store customer data – be it their demographic details, browsing behavior, buying history or any other aspect.
There has also been an exponential increase in the e-commerce market across the globe.
According to Statista, the worldwide retail e-commerce sales in 2016 was USD 1.86 trillion and is expected to reach USD 4.5 trillion by 2021. In 2016, an estimated 19 percent of all retail sales in China occurred via internet; while, the same percentage in Japan was 6.7 percent. In India, total retail e-commerce sales in 2017 was USD 20 billion and is expected to reach USD 52 billion by 2021.
One of the reasons for such fast growth in e-commerce is due to the movement of customers from physical stores to online stores, and this has led to customers sharing personal information to companies. Companies are then using sophisticated analytical models and advanced technology to get the maximum insights so as to improve their customer acquisition and customer retention.
Data points needed for customer profiling and segmentation
Companies are capturing customer data at multiple levels and through multiple ways. Some of the key data points captured by companies that can be used for customer profiling and segmentation are:
Demographic data
Socio-economic
Browsing patterns
Buying history
Time trend analysis
Payment behavior
These data points are captured by companies at different stages of a customer life cycle through different platforms and mediums.
What are these companies doing with all this data?
Well, one of most important and common application is customer targeting. Companies are using customer data to improve their targeting so as to reduce customer acquisition costs and improve customer retention.
Marketers are creating customer segments, or rather, micro-segments, for different customer groups to create more personalized campaigns and reach customers in a more personalized manner.
A relevant use case for segmentation in ecommerce
Netflix is a king in using customer profiling and segmentation to monetize in ecommerce.
NetFlix has created more than 76000 micro-genres for its movie database. You may even find genres like Mother_Son_Love_1980s. Netflix has taken segmentation to altogether different levels by creating thousands of micro-segments. This is how companies are using data – to create micro-segments to reach customers.
The benefits of doing this are:
Reduced marketing spend
Low customer acquisition cost
Improved customer retention
More customer satisfaction
Increase chances of cross-selling and up-selling
High net promoter score
Increased frequency of selling and high basket value
Identifying satisfied and dissatisfied customers and then converting dissatisfied customers to satisfied customers
Increase customer loyalty
Reduce customer churn
Develop effective strategy for new product launches
There are numerous other benefits of making marketing more personalized by creating micro-segments.
Now, we have understood why companies are capturing all this data, what is the use of doing this and how does this help companies, let’s move a step ahead and figure out how to do this?
Customer profiling and segmentation case study setup
Let’s develop the customer profiling and segmentation concept further by illustrating with a case study.
Let’s say I have an e-commerce company selling different kinds of products ranging from appliances to apparel, baby products to books, and other products. I have hundreds of thousands of customers visiting my website. The customers include old as well as new, coming from rural as well as urban areas, browsing through tablets, macbooks, laptops, desktops and mobile, visiting the website at various times of the day, tens of different attributes. I want to create different micro-segments for my customers so that I can target my products to the right customer and optimize my website according to visitors.
Some of the key categories I will use in my segmentation process are as follows:
Old vs New Customer –
Has the customer already visited my website? Is there any buying relation with the customer? If we already have a relationship with the customer, we can suggest products/discounts accordingly; however, if the customer is new, we will rely on other attributes initially.
Objective of the Customer –
Why has the customer come to the website? This might be a little challenging to model but identifying customer’s life events, past purchase history and other attributes, we may be able to track the objective of the customer. Otherwise, understanding if the customer has visited just to compare the price or if the customer is interested in buying the product also helps us in driving the marketing strategy for the particular segment.
Device used for Browsing –
Understanding what kind of a device customer uses helps us in knowing the socio-economic status of the customer. If the customer is browsing through iPad – it may reflect that the customer belongs to middle to upper strata.
Date of the Month –
A few customers may tend to shop in the first week of the month because they had just received their salaries. This helps us in identifying when to target such customer segment.
Day of the Week –
Analyzing customer’s buying history by studying the days on which customer has made purchases helps us in designing marketing campaigns. If we know that the customer usually buys on Sundays, then we should send her a reminder about the products they were looking for on Sundays, rather than bombarding them with notifications on other days.
Time of the Day –
If a customer usually visits my website during the late evening hours, then it may be safe to assume that the customer is a professional working somewhere. If we were to reach out to him, we should try to reach out to him during the late evening hours because that’s when the chances of customer showing interest increases.
Discount Influence –
For every retailer, whether it is e-commerce or brick and mortar store, discounting has become the norm. Understanding which customers tend to respond positively to discounts, and what is the right discount percentage helps us in positioning our products and balancing margins simultaneously.
There could, of course, be other attributes such as product category, average basket value, etc. that we could use to create further segments.
Now, let create a micro-segment by combining multiple attributes from the above list and then take a look at the granularity and insights it provides us.
Customer profiling and segmentation micro-segment
Imagine that I have a customer browsing for laptops on my website through an app from iPhone. We know that the customer is old and has past relations with the company.
Based on the past relationship, we have data to start our customer profiling and segmentation. We have the following customer details:
Type of customer – old
Objective – the customer mostly visits website to buy the product – ratio of converted visits to total visits is above 10% which falls in the top category (just a hypothetical scenario)
Device – Uses iPhone
Date of the month – buys product throughout the month – no specific day of the month where customer buys excessively
Day of the week – mostly active on weekends
Time of the day – visits website between 8 PM – 10 PM
Discount – mix of discount and undiscounted items
Purchase history
Bought iPhone 7 last month
40% of overall spend on the website is on gadgets
Payment behavior – pays through credit card if there is discount on credit card, otherwise cash on delivery
Return behavior – returned products in 4% of the total deliveries
The above information gives us a granular level profile of the customer segment – it would be better to call it a micro-segment. In other words, we already have a solid basis for customer profiling and segmentation analysis.
How to use a micro-segment in real life
With the above information in mind, imagine that we were to send a notification or email to this customer – what should it contain?
Well, based on what we know about our customer, we need to make sure:
The products promoted should be “top laptops”
The website and email should be fully compatible with all types of iOS devices (especially mobile).
The email should be sent on weekends between 8 PM – 10 PM
The email should be sent on the weekend, though there is no specific constraint on the date of the month
The email should reflect any discount promotions launched by credit cards
This can further be expanded for cross-selling other products. Since we know that the customer is a gadget-lover, we can send out emailers to the customer for the launch of new gadgets.
There are multiple other things which can be focused on while targeting the customer, but this example gives us a good understanding of why customer profiling and segmentation has become important for e-commerce companies. Targeting the right customers, acquiring customers at low cost, and retaining customers are the soul of any e-commerce business, for customers are what e-commerce companies are made up of.
More resources to get ahead…
Get Income-Generating Ideas For Data Professionals
Are you tired of relying on one employer for your income? Are you dreaming of a side hustle that won’t put you at risk of getting fired or sued? Well, my friend, you’re in luck.
This 48-page listing is here to rescue you from the drudgery of corporate slavery and set you on the path to start earning more money from your existing data expertise. Spend just 1 hour with this pdf and I can guarantee you’ll be bursting at the seams with practical, proven & profitable ideas for new income-streams you can create from your existing expertise.
Learn more here!
Take The Data Superhero Quiz
You can take a much more direct path to the top once you understand how to leverage your skillsets, your talents, your personality and your passions in order to serve in a capacity where you’ll thrive. That’s why I’m encouraging you to take the data superhero quiz.
This free and super-fun 45-second quiz is all about you and how your personality type aligns with the very best career path for you. It’s fun, free and it will provide you personalized data career recommendations, complete with potential roles that fit your unique skills and passions, as well as salaries associated with those roles.
Take the Data Superhero Quiz today!
Author Bio:
This article was contributed by Perceptive Analytics. Chaitanya Sagar and Saneesh Veetil contributed to this article.
Perceptive Analytics provides data analytics, data visualization, business intelligence and reporting services to e-commerce, retail, healthcare and pharmaceutical industries. Our client roster includes Fortune 500 and NYSE listed companies in the USA and India.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## CALL FOR CODE 2018!
URL: https://www.data-mania.com/blog/call-for-code-giveaway/
Type: post
Modified: 2026-03-17
Call for Code is an empowering cry to developers to drive a long-lasting positive change in the world with their code using their skills and mastery of the latest technologies. Are you up to the CHALLENGE?
Are you a techie with some decent coding experience and you’d LOVE TO MAKE A POSITIVE IMPACT while USING CODE to SOLVE REAL-WORLD PROBLEMS?
CALL FOR CODE
COMPETE WITH CODE, SAVE LIVES, WIN MAD CASH
A global coding challenge with $270,000 in prizes for developers with the top solutions for helping impoverished nations become better prepared for natural disasters.
After CALL FOR CODE…
You’re taking credit for the technical experience you’ve earned collaborating with major brands, like IBM, Red Cross, Linux Foundation, the United Nations, and more.
You’re counting down the minutes to see if you’ve won that $200,000 prize.
Your keynote talks describe the solution you built to help people survive the devastation of natural disaster.
Competition sponsored by:
I’m supporting CALL FOR CODE
To support the CALL FOR CODE mission, I’ve agreed to host a sponsored giveaway that encourages readers to take a look at some of the amazing opportunities this program presents. We are giving away 22 prizes total, including this gorgeous Azio keyboard. To enter, all you need to do is visit their website through the blue button below, and then submit 2 complete sentences describing what you found most compelling about this coding competition.
Call For Code 2018 Kickoff Giveaway
This is your chance to
To get some (extra) real-world coding experience in data science and engineering.
Use your coding skills to positively impact our world
Get your first VC intro and pitch opportunity
Get long-term developer support through the Linux Foundation
The CALL FOR CODE competition closes on September 28th, so make sure to pop over to the website and get in while you can!!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Coding Challenge: Call For Code 2018
URL: https://www.data-mania.com/blog/coding-challenge-call-for-code-2018/
Type: post
Modified: 2026-03-17
It’s not your everyday coding challenge; Call for Code is a global initiative that brings together developers, data professionals, celebrities, humanitarian response agencies, tech influencers, and major tech brands to search for a solution to one of the most devastating problems on the planet. IMHO, the lavish cash prizes are cool, but it’s the cause that matters. Keep reading to learn more…
I partnered with IBM to support Call for Code 2018 by creating the following video and working to get the word out among our community.
What is the Call for Code Coding Challenge
Call for Code is a global coding challenge for developers and data scientists who want to put their skills to the test while working to solve one of the most pressing problems on the planet; The problem of NATURAL DISASTERS. The CFC Coding challenge enlists techies from across the globe to submit solutions for natural disaster preparedness.
Call for Code 2018 Sponsors
Why You Should Participate
Call for Coders are an elite group of data-driven do-gooders who’ve decided they’d rather spend their free time working to make a difference (instead of spending it getting caught up on the latest Netflix series, or whatever).
But if the nobility of the cause does not inspire you to action, perhaps the $270,000 USD in cash prizes will.
The maker(s) of the top solution wins $200,000!
Just putting it out there, the maker(s) of the top solution wins $200,000! That solution will be brought into production by IBM, and the top 3 solutions will become part of the Linux Foundation.
What You Can Build
Most of the developers and data scientists I know would love to try out building a solution for natural disaster preparedness, but many don’t know where to start. So to get your creative juices flowing, I put together a little portfolio of projects and initiatives that function to support natural disaster preparedness.
Go ahead and take a look at what others are doing in this space, then ask yourself – How can I use technology and code to solve this same problem via a software solution?
Digital Humanitarian Network
About 5 years ago, I did a lot of work with the DHN. This organization uses digital technologies and crowdsourcing to help resolve humanitarian crises that arise from disasters. They basically build crisis maps that serve as real-time intel for decision-makers at humanitarian response agencies. Some examples include:
Hurricane Matthew Haiti Response
Map Filter for Ecuador Earthquake
Nepal Earthquake Response
DataKind
DataKind is a global collective of data do-gooders who selflessly volunteer their tech skills for the betterment of their local communities. Some of the projects they’ve undertaken intersect with issues at play during natural disaster scenarios. These include:
Water Demand Forecasting in California
Machine Learning to Help Rural Households Access Electricity
Using Satellite Data to Find Villages in Need
Elva
Elva is another innovative solution in the digital humanitarian tech community. Take a look at some of their use cases to see if they strike some creative sparks:
Crisis Monitoring in Central Africa Republic
Conflict Monitoring in Libya
“Be yourself; Everyone else is already taken”
Although these are some great ideas, be sure to get creative. in the words of Oscar Wilde, “Be yourself; everyone else is already taken”.
How You Can Enter
To enter the Call for Code coding competition, just pop-over to the website here – sign-up, form a team (or join a team), and start building. Act fast though, because the deadline for this year’s submissions is September 28, 2018.
Pro-tip: If you come up with a great idea but don’t get it perfected in time – worry now! The Call for Code coding competition will run again next year, so you can submit a better version of your solution then.
Don’t know anyone to form a team with? Let me help! Write a comment below mentioning that you’d like to form a partnership to enter CFC. When I get more than 2 or 3 of this type of comments, I will email you all as a group to make that connection!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Can You Get a Coding Job Without a Degree? Yes! Here’s How
URL: https://www.data-mania.com/blog/can-you-get-a-coding-job-without-a-degree/
Type: post
Modified: 2026-03-17
From software application and web developers to database administrators, the job opportunities in the tech space are endless for people who love to tinker with code and create entirely new systems from scratch. But what do you do if you’re a self-taught tinkerer and don’t have that coveted college degree? Then, the question becomes: Can you get a coding job without a degree?
Before I give you the answer, let’s first state the obvious: Sometimes, more available jobs also means more competition.
It’s no secret there are loads of eager, ambitious and – let’s face it – well-educated people looking to nab a job in tech right now. Just ask any tech manager how many stacks of resumes he or she receives for an average position.
However, just because you don’t have an official, university-issued degree on your resume doesn’t mean your application has to wind up in the trash bin.
Can you get a coding job without a degree? Yes! Here’s how…
Yep, it’s true. A college degree actually is NOT a requirement for a coding job.
And even better? It’s well worth it to go after these jobs with or without a degree – like six figures a year worth it. According to Comparably.com, you not only don’t need a degree to move up into a top-tier tech role, you can also make over $100k a year when you do – all without racking up any traditional student debt. In fact, according to Engine Yard, the average full-stack developer makes $110,500 a year. Not bad, right?
Your lack of a degree is only a problem if you decide it is. Sure, you might have to work a little bit harder than your degree-toting peers to garner some attention, but there are quite a few ways to level the playing field.
But now that we know it’s possible, the question becomes: How exactly can you get a coding job without a degree?
Before I go into details, let’s start with the most obvious. You have to have coding skills. A core requirement for all high-paying coding jobs is to simply get better at coding. If you don’t already have some solid coding knowledge under your belt, you’ll need to start there and either get them or level up your current ones.
(While getting hired in tech without an official university degree is very possible, you do still need to know what you’re doing!)
However, super-sharp skills alone aren’t enough.
Just because you know JavaScript, Ruby on Rails and Python doesn’t make you an automatic shoo-in.
To score your dream gig, you also need to upgrade the way you’re presenting yourself – online and off. This will ensure you don’t learn a new skill just for the fun of it but do it in a way that lands you a better job as a result.
Ready to learn how?
In this post, we’re going to take an honest look at 4 other upgrades you can make – beyond sharpening your skills – to help you stand out among your (many) peers & land a dream coding gig without a degree.
4 upgrades to help you stand out from your competition
Here’s a sneak peek:
Growth hacking your resume for success
Optimizing your online presence
Getting uber-prepared for interviews
Building your professional network
Ready? Now, let’s go deeper.
Upgrade 1 = Growth hacking your resume for success
From our diets to our wardrobes, we “hack” everything, so it’s probably no shocker you can hack your resume, too. Here’s how it works. Just like search engines use SEO keywords to rank content, companies use applicant tracking systems to rank resumes. If yours contains the right keywords, you’re in like Flynn!
To make sure your resume passes the test, you just have to work backward.
Here’s how. Find the job you want and pick out keywords and terms associated with the job duties (you can usually find these inside the job description itself). Then, assuming you’re actually qualified and competent in those duties, simply insert these words strategically into your resume. Describing the education and experience you do have with the keywords in the job description you’re going after will keep your resume in the game, regardless of your education status.
Upgrade 2 = Optimizing your online presence
Resumes still rule, but social media is almost as important. I mean, who doesn’t Google someone new before meeting them, right? The truth is companies do the same thing with job applications, so it’s important to keep your social profiles on point, updated and professional.
What specifically can you do to make sure your social media catches the eye of the job recruiter in a good way?
Well, your profiles should reflect not just who you are, but what you know.
It’s not always easy to strike that balance, but one trick of the trade is to use your social media not only to highlight safe-for-work portions of your personal life, but also to use them to position yourself as a thought leader. How so? Simply share unique and relevant insights about the industry or what you’re learning about it. The point here is to show you’ve got some smarts and knowledge about your industry, even if said knowledge didn’t come from a traditional degree.
Upgrade 3 = Getting uber-prepared for interviews
Interviews can cause heart palpitations and sweaty palms for almost anyone. But the good news is a little pre-interview prep can go a long way in easing any jitters about getting the job, especially the fear that you’re underqualified thanks to lacking a degree.
Before your big day, do some thorough research on the company you’re applying for, the specific role and its responsibilities, and of course, make sure to prepare answers to questions about your own abilities and skills.
Don’t feel weird writing out your answers or practicing them in front of the mirror before you’re face-to-face with the head of the company or department. A little practice beforehand = a lot less stress overall. You’ll come off poised, confident and professional … and the recruiter just might happily overlook that whole empty education section.
Upgrade 4 = Building your professional network
You know the saying, “It’s not what you know, but who you know”? Well, I know it’s 2019, but that old adage still rings true. While a padded resume says a lot, it’s even better to know the right people, in the right positions, at the right companies.
You might think someone with a traditional education would have a leg-up in that arena, but thanks to our trusty old pal the Internet, it’s never too late to start building solid connections in the tech space.
Platforms like LinkedIn have made it wildly simple to get connected to new people quickly and establish rapport with players in your industry. (But don’t be spammy. Make sure you provide genuine value to your connections, and don’t just ask for favors without a little give and take.)
Next steps
So, can you get a coding job without a degree? I think by now you know that the answer is yes, and there are countless efforts you can make to get a coding job, all without dropping a cool hundred thousand on a CS degree.
But, if you truly want to stand out and land your dream job without a college degree, our partner The Software Guild has just released two brand-new digital coding badge programs that make it easier than ever! Software Guild’s simple, pay-as-you-go online programs are designed to both teach you how to code and help you get a job in the industry.
Choose either the Java or .Net/C# track(s) to develop killer software development skills and receive expert, hands-on guidance in any other areas you need to upgrade, including social media, resume development and interview training. Plus, you’ll get access to The Software Guild’s own impressive employer network, loaded with 450-plus big-name companies like UPS, Target and Humana, where you’ll be able to find available roles and advance your career after graduation.
It’s the most flexible and affordable way to become a full-stack developer on your terms – hands down. The badge program is broken up into levels that allow you to learn a little a time. They take between eight and 12 weeks to complete, and new cohorts start every month! Plus, you earn discounts as you advance through their program. If you complete all four, you can save up to $1,000!
Start from Zero to Software Developer!
If you’re ready to go from zero to software developer as fast or as slow as you want to go and get a coding job without a degree, learn more here.
P.S. I hope that you found this post to be both informative and motivating! It was an honor for me to partner again with The Software Guild to bring this message to you. Thank you!
More free resources that’ll help…
Get The Badass’s Guide To Breaking Into Data
I was working a 9-to-5 as a data analytics developer back in 2012 when I started Data-Mania. With that transition, the seed was planted to write an ebook that helps other people break into the field that’d been so generous to me. You can’t keep something like this to yourself, right? 😉 Today we’ve published this free ebook, and it’s helped thousands of people just like you make the transition….
A Badass’s Guide to Breaking Into Data is a free, 52-page ebook that shows aspiring data professionals how to break into the data professions by either getting a data job or starting your own small consultancy.
Get the ebook for free today!
Take The Data Superhero Quiz
You can take a much more direct path to the top once you understand how to leverage your skillsets, your talents, your personality and your passions in order to serve in a capacity where you’ll thrive. That’s why I’m encouraging you to take the data superhero quiz.
This free and super-fun 45-second quiz is all about you and how your personality type aligns with the very best career path for you. It’s fun, free and it will provide you personalized data career recommendations, complete with potential roles that fit your unique skills and passions, as well as salaries associated with those roles.
Take the Data Superhero Quiz today!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## [Panel Discussion] SiliconAngle #GDPR at IBM Fast Track Your Data
URL: https://www.youtube.com/watch?v=o3vc9R2-qQ0
Type: post
Modified: 2026-03-17
Lillian Pierson, PE discusses GDPR with SiliconAngle at IBM Fast Track Your Data 2017 in Munich, Germany.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## [Keynote] A Guide To Breaking Into Data by Lillian Pierson for Metis / Kaplan
URL: https://www.youtube.com/watch?v=0ZY5c-rrPn4
Type: post
Modified: 2026-03-17
Watch Lillian Pierson’s talk, “A Badass’s Guide to Breaking into Data” from the free, live online Demystifying Data Science conference hosted by Metis July 24-25, 2018.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## [Live Panel] Women in Tech #STEM @EricssonDigital Services hosted by Lillian Pierson
URL: https://www.youtube.com/watch?v=RENA5f22hSg
Type: post
Modified: 2026-03-17
Listen to our exclusive panel led by staunch advocate for Women in Tech, Data Scientist, Lillian Pierson (twitter: @BigDataGal, Linked In: Lillian Pierson) when she asks Women Leaders in Technology in Ericsson, what advise they would give their younger selves and what passions drove them into this career. In the panel we have (Right to left) Eva Hedfors: Head of Marketing & Communications, Ericsson Digital Services, Rossella Frasso: Head of VNF Development Center Multimedia Telephony Application Server and Rebecka Cedering Ångström: Consumer and Industry Lab, Ericsson Research and Dr. Azimeh Sefidcon, Research Director Cloud Technologies, Ericsson Research.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## [Interview Course] Insights on #DataScience for LinkedIn by Lillian Pierson
URL: https://www.linkedin.com/learning/insights-on-data-science-lillian-pierson
Type: post
Modified: 2026-03-17
Data science is a rapidly expanding field offering a wealth of possibilities for viewing the world around us through a more accurate lens. But for many of those whose imagination is sparked by big data—but who have already started pursuing a career in another field—the dream of becoming a data scientist can feel far-fetched. Lillian Pierson, P.E.—a leading expert in the field of big data and data science—aims to prove that notion wrong. In this course, she shares observations and tips to help you embark on a career in this exciting field, regardless of your starting point.
Lillian began her career not as a data scientist, but as an environmental engineer. Here, she shares her story, discussing how she taught herself to code in Python and R, and work with data science methodologies. As a result of her own experiences, Lillian is passionate about helping those interested in data science—but who may lack a four-year degree in the discipline—get started in the field. She shares practical ways to acquire the skills and experience needed to become a data scientist, and best practices for landing a job. Lillian also dives into grappling with the challenges that occur in rapidly evolving tech workforces. Plus, she discusses the industry itself, covering recent changes in the field and areas of need, and clearing up a few common misconceptions.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## [Webinar] Making AI Routine, Repeatable and Reliable w/ Lillian Pierson for GigaOm + Cloudera
URL: https://gigaom.com/webinar/making-ai-routine-repeatable-and-reliable/
Type: post
Modified: 2026-03-17
While interest in Machine Learning/Artificial Intelligence/ (ML/AI) has never been higher, the number of companies deploying it is only a subset, and successful implementations a smaller proportion still. The problem isn’t the technology; that part is working great. But the mere presence and provision of tools, algorithms, and frameworks aren’t enough. What’s missing is the attitude, appreciation, and approach necessary to drive adoption and working solutions.
To learn more, join us for this free 1-hour webinar from GigaOm Research. The webinar features GigaOm analyst Andrew Brust and panelists Jen Stirrup, Lillian Pierson, and special guest from Cloudera Fast Forward Labs, Alice Albrecht. Our panel members are seasoned veterans in the database and analytics consulting world, each with a track record of successful implementations. They’ll explain how to go beyond the fascination phase of new technology towards the battened down methodologies necessary to build bulletproof solutions that work for real enterprise customers.
In this 1-hour webinar, you will learn all about:
Operationalizing/industrializing AI
Getting ML out of the lab and into production
Bridging gaps between academia/research and industry, bi-directionally
Removing AI’s allure, and making it more routine
Moving beyond ML/AI tools and platforms to strategy, services and practices
Who Should Attend:
CIOs
CTOs
Chief Data Officers
VPs of Data Science & Data Engineering
Directors of Data Science & Data Engineering
Digital transformation leaders
Data Scientists
Data Engineers
Developers
Business Analysts
Business Intelligence Architects
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## [Podcast] Build a Solid Enterprise Wide Data Strategy w/ Lillian Pierson
URL: https://bibrainz.com/podcast/build-a-solid-enterprise-wide-data-strategy-w-lillian-pierson/
Type: post
Modified: 2026-03-17
Today’s guest is the talented and intelligent Lillian Pierson. Lillian, founder of Data Mania and a Data Science, Instagram rock star with 600K+ followers across social media. Currently, she is the data science instructor for multiple courses on LinkedIn Learning, as well as an author, entrepreneur, coach and social media genius.
In this new BI Masterclass, Lillian is going to teach you how to build a solid enterprise-wide data strategy that scales. Stay tuned to get Lillian’s best tips for social media, data strategies, and collecting data for use cases.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Data Analysis Case Study: Learn From Humana’s Automated Data Analysis Project
URL: https://www.data-mania.com/blog/data-analysis-case-study/
Type: post
Modified: 2026-03-17
Got data? Great! Looking for that perfect data analysis case study to help you get started using it? You’re in the right place.
If you’ve ever struggled to decide what to do next with your data projects, to actually find meaning in the data, or even to decide what kind of data to collect, then KEEP READING…
Deep down, you know what needs to happen. You need to initiate and execute a data strategy that really moves the needle for your organization. One that produces seriously awesome business results.
But how? You’re in the right place to find out.
As a data strategist who has worked with 10 percent of Fortune 100 companies, today I’m sharing with you a case study that demonstrates just how real businesses are making real wins with data analysis.
In the post below, we’ll look at:
A shining data success story;
What went on ‘under-the-hood’ to support that successful data project; and
The exact data technologies used by the vendor, to take this project from pure strategy to pure success
If you prefer to watch this information rather than read it, it’s captured in the video below:
Here’s the url too: https://youtu.be/xMwZObIqvLQ
3 Action Items You Need To Take
To actually use the data analysis case study you’re about to get – you need to take 3 main steps. Those are:
Reflect upon your organization as it is today (I left you some prompts below – to help you get started)
Review winning data case collections (starting with the one I’m sharing here) and identify 5 that seem the most promising for your organization given it’s current set-up
Assess your organization AND those 5 winning case collections. Based on that assessment, select the “QUICK WIN” data use case that offers your organization the most bang for it’s buck
.
Step 1: Reflect Upon Your Organization
Whenever you evaluate data case collections to decide if they’re a good fit for your organization, the first thing you need to do is organize your thoughts with respect to your organization as it is today.
Before moving into the data analysis case study, STOP and ANSWER THE FOLLOWING QUESTIONS – just to remind yourself:
What is the business vision for our organization?
What industries do we primarily support?
What data technologies do we already have up and running, that we could use to generate even more value?
What team members do we have to support a new data project? And what are their data skillsets like?
What type of data are we mostly looking to generate value from? Structured? Semi-Structured? Un-structured? Real-time data? Huge data sets? What are our data resources like?
Jot down some notes while you’re here. Then keep them in mind as you read on to find out how one company, Humana, used its data to achieve a 28 percent increase in customer satisfaction. Also include its 63 percent increase in employee engagement! (That’s such a seriously impressive outcome, right?!)
.
Step 2: Review Data Case Studies
Here we are, already at step 2. It’s time for you to start reviewing data analysis case studies (starting with the one I’m sharing below). Identify 5 that seem the most promising for your organization given its current set-up.
Humana’s Automated Data Analysis Case Study
The key thing to note here is that the approach to creating a successful data program varies from industry to industry.
Let’s start with one to demonstrate the kind of value you can glean from these kinds of success stories.
Humana has provided health insurance to Americans for over 50 years. It is a service company focused on fulfilling the needs of its customers. A great deal of Humana’s success as a company rides on customer satisfaction, and the frontline of that battle for customers’ hearts and minds is Humana’s customer service center.
Call centers are hard to get right. A lot of emotions can arise during a customer service call, especially one relating to health and health insurance. Sometimes people are frustrated. At times, they’re upset. Also, there are times the customer service representative becomes aggravated, and the overall tone and progression of the phone call goes downhill. This is of course very bad for customer satisfaction.
Humana wanted to use artificial intelligence to improve customer satisfaction (and thus, customer retention rates & profits per customer).
The Need
Humana wanted to find a way to use artificial intelligence to monitor their phone calls and help their agents do a better job connecting with their customers in order to improve customer satisfaction (and thus, customer retention rates & profits per customer).
The Action
In light of their business need, Humana worked with a company called Cogito, which specializes in voice analytics technology.
Cogito offers a piece of AI technology called Cogito Dialogue. It’s been trained to identify certain conversational cues as a way of helping call center representatives and supervisors stay actively engaged in a call with a customer.
The AI listens to cues like the customer’s voice pitch.
If it’s rising, or if the call representative and the customer talk over each other, then the dialogue tool will send out electronic alerts to the agent during the call.
Humana fed the dialogue tool customer service data from 10,000 calls and allowed it to analyze cues such as keywords, interruptions, and pauses, and these cues were then linked with specific outcomes. For example, if the representative is receiving a particular type of cues, they are likely to get a specific customer satisfaction result.
The Outcome
Thanks to Humana’s two business use cases, which I outline below, the company enjoyed a 28 percent increase in customer satisfaction and a 63 percent increase in employee engagement.
Customers were happier, and customer service representatives were more engaged.
This automated solution for data analysis has now been deployed in 200 Humana call centers and the company plans to roll it out to 100 percent of its centers in the future.
The initiative was so successful, Humana has been able to focus on next steps in its data program. The company now plans to begin predicting the type of calls that are likely to go unresolved, so they can send those calls over to management before they become frustrating to the customer and customer service representative alike.
What does this mean for you and your business?
Well, if you’re looking for new ways to generate value by improving the quantity and quality of the decision support that you’re providing to your customer service personnel, then this may be a perfect example of how you can do so.
Humana’s Business Use Cases
Humana’s data analysis case study includes two key business use cases:
Analyzing customer sentiment; and
Suggesting actions to customer service representatives.
Analyzing Customer Sentiment
First things first, before you go ahead and collect data, you need to ask yourself who and what is involved in making things happen within the business.
In the case of Humana, the actors were:
The health insurance system itself
The customer, and
The customer service representative
As you can see in the use case diagram above, the relational aspect is pretty simple. You have a customer service representative and a customer. They are both producing audio data, and that audio data is being fed into the system.
Humana focused on collecting the key data points, shown in the image below, from their customer service operations.
By collecting data about speech style, pitch, silence, stress in customers’ voices, length of call, speed of customers’ speech, intonation, articulation, silence, and representatives’ manner of speaking, Humana was able to analyze customer sentiment and introduce techniques for improved customer satisfaction.
Having strategically defined these data points, the Cogito technology was able to generate reports about customer sentiment during the calls.
Suggesting Actions to Customer Service Representatives
The second use case for the Humana data program follows on from the data gathered in the first case.
Understanding customer sentiment is all very well, but to make your data initiative successful, you need to be willing to take action and make changes based on the information gathered.
In Humana’s case, Cogito generated a host of call analyses and reports about key call issues.
In the second business use case, Cogito was able to suggest actions to customer service representatives, in real-time, to make use of incoming data and help improve customer satisfaction on the spot.
The technology Humana used provided suggestions via text message to the customer service representative, offering the following types of feedback:
The tone of voice is too tense
The speed of speaking is high
The customer representative and customer are speaking at the same time
These alerts allowed the Humana customer service representatives to alter their approach immediately, improving the quality of the interaction and, subsequently, the customer satisfaction.
The preconditions for success in this use case were:
The call-related data must be collected and stored
The AI models must be in place to generate analysis on the data points that are recorded during the calls
Evidence of success can subsequently be found in a system that offers real-time suggestions for courses of action that the customer service representative can take to improve customer satisfaction.
Thanks to this data-intensive business use case, Humana was able to increase customer satisfaction, improve customer retention rates, and drive profits per customer.
The Technology That Supports This Data Analysis Case Study
I promised to dip into the tech side of things. This is especially for those of you who are interested in the ins and outs of how projects like this one are actually rolled out.
Here’s a little rundown of the main technologies we discovered when we investigated how Cogito runs in support of its clients like Humana.
For cloud data management Cogito uses AWS, specifically the Athena product
For on-premise big data management, the company used Apache HDFS – the distributed file system for storing big data
They utilize MapReduce, for processing their data
And Cogito also has traditional systems and relational database management systems such as PostgreSQL
In terms of analytics and data visualization tools, Cogito makes use of Tableau
And for its machine learning technology, these use cases required people with knowledge in Python, R, and SQL, as well as deep learning (Cogito uses the PyTorch library and the TensorFlow library)
These data science skill sets support the effective computing, deep learning, and natural language processing applications employed by Humana for this use case.
If you’re looking to hire people to help with your own data initiative, then people with those skills listed above, and with experience in these specific technologies, would be a huge help.
.
Step 3: Select The “Quick Win” Data Use Case
Still there? Great!
It’s time to close the loop.
Remember those notes you took before you reviewed the study? I want you to STOP here and assess. Does this Humana case study seem applicable and promising as a solution, given your organization’s current set-up…
YES ▶ Excellent!
Earmark it and continue exploring other winning data use cases until you’ve identified 5 that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that.
NO, Lillian – It’s not applicable. ▶ No problem.
Discard the information and continue exploring the winning data use cases we’ve categorized for you according to business function and industry. Save time by dialing down into the business function you know your business really needs help with now. Identify 5 winning data use cases that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that data use case.
More resources to get ahead…
Get Income-Generating Ideas For Data Professionals
Are you tired of relying on one employer for your income? Are you dreaming of a side hustle that won’t put you at risk of getting fired or sued? Well, my friend, you’re in luck.
This 48-page listing is here to rescue you from the drudgery of corporate slavery and set you on the path to start earning more money from your existing data expertise. Spend just 1 hour with this pdf and I can guarantee you’ll be bursting at the seams with practical, proven & profitable ideas for new income-streams you can create from your existing expertise.
Learn more here!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## How to Get a Job Fast + Make a Career Change in This Online Age
URL: https://www.data-mania.com/blog/how-to-get-a-job-fast/
Type: post
Modified: 2026-03-17
Wanna know how to get a job fast in the online era?! In this quick read I’ll show you exactly what you need to do – pandemic or not – to find and land a job quickly through LinkedIn.
========================= SPOILER ALERT =========================
Read through this article and you’ll not only learn how to LAND your dream job, you’ll also find out about the Aspire Scholarship for recent graduates to become Java Developers and gain the opportunity to work for a Fortune 500 company.
================================================================
The past few months have thrown us headfirst into an entirely different world. Through all of the tumult, the tech space has remained steady and Big Tech is stronger than ever. After all, working remotely is what us digital-centric folk do best!
Still, many people are struggling in these rollercoaster times to land jobs and kick-start their careers. There are plenty of jobs out there — but grads and career changers often don’t realize the plethora of job-hunting “hacks” or strategies at their disposal.
I know that these sorts of strategies are needed because I see the job-hunt struggle firsthand — every. single. day.
Day 54 of my lockdown, for example. It’s just another Thursday. Thanks to the coronavirus pandemic I’m working from home, like most people the world over. I’m starting to lose hope we’ll be allowed out again anytime over the next 6 months.
In my Instagram stories, I see that my friend (let’s call her “Cindy”) is struggling. She shares videos of her fifth day in a hospital room with other infected patients. Times are tough. My brain does a complete 180-degree turn from “you’re locked up” to “you should be more grateful. At least you have work to do from home. At least you have your health and your family.”
I check Facebook, and as my profile loads, I see 17 notifications waiting for me.
It’s only been four hours since I last checked Facebook, and I already have three new messages from people asking me to help them land a job in the tech space.
These sorts of messages have been getting more and more frequent.
And what really kills me about them?
They’re usually from young, hard-working STEM grads who just can’t seem to land a role in the current global environment.
It’s heartbreaking.
There was never a better time than now for me to share this, to help people, to do what I can do.
I’ve worked in the tech and data space for over a decade. I’ve helped 1 MILLION PEOPLE learn to use data to generate business value, and I know all the strategies and tips to finding good jobs in the tech and data worlds.
Through this article, I want to let you in on some effective, common-sense practices that you can weave into your own job search strategy.
DO THESE THINGS, and you will:
Make great use of your degree.
Start making those good grades pay.
Land a high-paying job in the tech space.
Launch the career you’ve dreamed of!
READ THIS ARTICLE and you’ll get:
THREE ACTIONABLE STEPS to help you land a job even in this most trying of times
BONUS ADVICE on how to enhance Zoom for effective online job interviews
THREE TIPS for optimizing your LinkedIn profile
MAKE IT TO THE END, and I’m sharing:
THE CHANCE TO LAND A SPOT ON THE ASPIRE SCHOLARSHIP PROGRAM thanks to The Software Guild and mthree.
This scholarship opportunity is seriously not to be missed. The tips in this article apply to everyone, but you’ll definitely want to read on if you’re a recent or soon-to-be university grad who’s eligible for this incredible opportunity!
More on those eligibility requirements shortly …
Job Search: Online Job Search Methods to Land Employment Positions
Yes, it’s a tough environment for job hunters. Yes, employment is uncertain across many sectors. But YES — you can get a tech job in spite of everything that’s going on, and YES, I’m going to share with you exactly how to do that.
[yotuwp type=”videos” id=”jTgtv5qh0A4″ ]
Who is hiring?
There are still many fields out there that are actively hiring. Pay special attention to advertisements from the following industries; they’re not only surviving through COVID-19, they’re thriving in spite of it:
Shipping and delivery companies
Online learning companies
Pharmacies, grocery stores, and home delivery meal services
Remote communications and meeting services
IT
The majority of these businesses will advertise via LinkedIn for new staff, and I’m a firm believer in LinkedIn as THE PLACE for tech job hunters.
But how do you use it to best effect?
Here are the THREE KEY ACTIONS you can take on LinkedIn today to improve your job hunting strategy.
1. Source the Roles
There’s more to LinkedIn than meets the eye. Finding the right role is actually half your battle. There is zero point in applying for jobs that simply aren’t the right fit for you.
Here’s one way I narrow things down in LinkedIn to find more relevant, appropriate positions:
First, click on the “Jobs” section. Then, in the search bar, type in the keyword associated with the primary role you’re able to perform. For example, I would choose to search for “Data Scientist.”
Once you’ve hit “Search” on your job title, you’ll want to filter the results to best effect.
Weed out jobs that have been sitting around for too long. These are roles that have probably had a large number of applicants already, and hiring managers may already be conducting interviews for them. Insist on applying for old jobs? You’re wasting your time.
Make sure you’re seeing only fresh roles by setting the “Date Posted” filter to “Past Week.” This is particularly important in the current environment, when many companies have put a freeze on hiring. By choosing to see only those jobs posted in the past week or so, you’ll know the hiring companies are still going strong and are still wanting — in spite of everything — to hire new talent.
Next, try to find roles that have fewer applicants. This way, you’ve got a much better chance of rising to the top of the pack and being seen by those doing the hiring! Click on the “LinkedIn Features” button and select the “Under 10 Applicants” option to filter the results.
Finally, you can find roles better suited to your level of experience by sorting the results to suit. Just click the “Experience Level” button and select the options that best describe your expertise. If you’re just graduating and starting out, I’d choose the “Entry level” and “Associate” options.
2. Find Folks You Know
In this screen grab of a .Net developer job, you can see that I have seven connections already working at JPMorgan Chase & Co. If I know even one of these people enough to reach out to him or her, I can leverage that relationship and get help finding the name and contact details of the hiring manager responsible for any given role.
Why is that important? Knowing your audience is 100% of the work you need to do NOW so you can execute a perfect job application in Step 3.
When considering each job post, first try and get an idea of the following:
What the role involves
Who you would be reporting to
The location
Your next step is to click on the name of the company that is hiring, and take a look at their “People” section.
When you’re there, click “show more” on the “Where they live” section, and look for the city mentioned in the job listing you were interested in.
When you’ve found that city, click on it, and then scroll through the employees, looking for the person whose job title seems to indicate he or she might be the hiring manager for the company in that particular location.
Write down this person’s name and copy the link to his or her profile page. You’ll want to keep these contacts on hand when you come to writing up your cover letter and applying for roles.
3. Apply & Follow Up Flawlessly
Finding the role and the hiring manager is your groundwork — but now comes the execution. Do it right, and you could be popping the celebratory champagne sooner than you think!
In this step, you’ll take the following 3 actions:
Apply for a job on the LinkedIn platform by submitting your CV.
Write a stellar cover letter (as instructed below), and attach it to your CV application before submitting.
Directly contact the hiring manager you identified in Step 2 above, and send them the cover letter directly (more instruction on how to do that follows).
The success of your follow-up is entirely linked to the quality of your cover letter. I want to teach you how to tackle this part of the job hunt because it’s the part SO many people do last, and do wrong.
The secret to a good cover letter?
AIDA
And no, I’m not talking about the opera by Verdi.
AIDA, at least in my world, stands for: Attention, Interest, Desire, and Action. These are the four key sections your letter needs to have, and they go a little something like this:
ATTENTION
This is the header of your letter. It needs to be highly relevant to the position description, so make sure you go back and review that before you start writing. To capture the hiring manager’s attention, you’ll need to write a statement that touches on key competencies mentioned in the job description. For example, if the role is for a software developer and seeks someone with all the usual technical abilities, but also mentions “team building” and “mentoring” competencies, then your attention statement should make it clear that you ARE that very specific piece of the puzzle they’ve been looking for.
How to do this?
One way is to do a little research and find a really interesting, attention-grabbing statistic about the importance of teamwork in software development. Something like:
“Software developers have been shown to be 58% more productive in their work when involved in company team-building activities.”
Now, this isn’t a real statistic, but try and find one relevant to the role you’re applying for and lead with it.
INTEREST
This is your chance to show not only your experience but also your confidence as an expert in the industry. In this section, you should write two to three sentences offering a deeper understanding of the field and the type of work and pain points relevant to the job description.
For the sample software development role, for example, I might write about:
The value of having excellent technical skills
The value of team building in software development
This helps show off my knowledge and to re-emphasize how well I tick off their key competencies.
DESIRE
Here’s your chance to make them want you. It’s important to strike the right balance in a cover letter between enough detail to sound credible and so much detail the hiring manager’s eyes glaze over!
You could phrase the section a little something like this:
[line]
“Without burrowing down too much into the technical detail of how I’ve used my development skills to optimize operations for X corporation (or Y college, if you’re fresh out of school), here are some results that speak for themselves:
I saved $X per month for Y corporation by […]
X man hours per month were decreased at Y corporation due to the work I did with […], providing a return of $X per year.”
[line]
By offering these insights, you help the hiring managers to see that you are conscious that it’s the business’s bottom line that’s at stake, and that you’re able to get them results in terms of dollars and cents.
This section is also a great place to link to your portfolio.
ACTION
This is your last chance to impress. Be confident, be decisive, be polite.
[line]
“I’ve linked my CV below for the X position. You can reach me directly at [e-mail address], but I will give you a call at the number I have on hand for you (XX-XX-XX) at 2 p.m. Thursday so we can chat further about how I can produce next-level results for [company name]. Please let me know in the meantime if there’s a better number to reach you on.
Thank you for your time and I look forward to talking with you soon.
Your Name
[Link to CV]”
[line]
And that’s your cover letter!
Now you can use this as a template that you can customize for each different job application.
Here’s what to do with it:
Go to the LinkedIn profiles of those hiring managers you identified earlier, get LinkedIn Premium, which allows you to message people who aren’t already your contacts, and click the “message” button at the top of their profiles.
Send your cover letter via this message system, but do a Google search and see if you can find their e-mail address too. If you can, you should also send a copy of the letter via e-mail.
Last but not least, don’t forget to call the number at the time you said you would! They may or may not pick up, but at the very least you’ve followed through on a promise, and at best, you might just find yourself at the front of the pack thanks to a bit of extra effort and gumption!
Zoom interview hacks you need to know before you interview
How do you make a good first impression when you’re not face to face.
LinkedIn Profile Optimization: My Top 3 Tips For How To Get A Job Fast
Knowing how to play the LinkedIn job search function to your advantage is all well and good, but it’s also IMPERATIVE that your LinkedIn profile is shipshape and up to scratch.
Don’t believe it can make a difference?
I got a 47x increase in LinkedIn search appearances in just two weeks by making these few small tweaks to my profile!
Here’s how:
[yotuwp type=”videos” id=”NrZZN-n_jAw” ]
TIP 1: Keyword Discovery
Just like you use keywords to find anything online, keywords will also help businesses find you on LinkedIn — so use them well!
Here are the four simple steps that you can take in order to identify and use keywords in LinkedIn to your advantage:
Go to the “Jobs” section of LinkedIn.
Read through a variety of relevant job listings.
Note down the keywords that keep jumping out at you from these listings.
Use these keywords (sparingly) in your LinkedIn headline, summary, and experience sections.
TIP 2: Don’t Overlook Aesthetics
Your LinkedIn profile image and banner image may not seem as important as your experience and education, but FIRST IMPRESSIONS COUNT.
If your LinkedIn profile picture is currently a selfie taken in front of your bathroom mirror, then changing it should be a priority! Professional profile shots and sharp banner images that match your career and personality are key.
It’s not just the look of images that is important. By naming your images appropriately, they’ll come up in Google and LinkedIn searches when managers go looking for those keywords.
TIP 3: How To Get A Job Fast? SEO Optimization On LinkedIn!
Search engine optimization is the be-all and end-all of online life. There are a number of ways you can improve the SEO of your LinkedIn profile, including:
Backlinks
Anchor links
A customized profile URL
Increased connections
Well-named images
Spend some time on each of these, and your LinkedIn profile will be fully optimized in no time!
Opportunities are Out There
Getting a job can feel difficult.
Getting a job in the midst of a global pandemic can seem … overwhelming.
BUT — there is good news.
Life goes on. Business continues. And there ARE plenty of jobs out there, plenty of new hires happening, plenty of successful candidates securing roles.
If you follow my advice from this article, you’ll find yourself THAT MUCH CLOSER to a career win.
How?
Let’s recap:
Use LinkedIn filters to source active and relevant roles.
Research hiring managers and target them directly.
Follow up with an effective cover letter.
Ace your Zoom interview by enhancing your settings.
Make your LinkedIn profile sparkle with keywords, great images, and SEO.
BUT WAIT — there may just be a shortcut to all of this!
Tech talent company mthree is offering the Aspire Scholarship, in partnership with coding bootcamp The Software Guild, to eligible applicants. This scholarship will not only equip you with 12 weeks of online training in the full-stack java development field, but also work to place you in a full-time role at a global investment bank or financial technology company at the end of training.
Check out the eligibility criteria, and if you fit the bill, head on over to mthree.com to read more and apply for your spot!
mthree is looking for:
University graduates who graduated in 2019 or who will graduate in 2020
Individuals holding a STEM or Computer Science degree from any university
Those with a GPA 3.0+/- current average
Age: 20-24
Right to work: green card holders or U.S. citizens
Geo-flexible: Must be able to move to New York, Delaware, Chicago, Washington DC, or Texas
Numerate, technically competent, and capable of thinking critically and scientifically; good at problem solving, creativity, and innovation; an ability to learn and apply new knowledge and skills
Individuals who have some prior some coding experience
Women are especially encouraged to apply!
This is a fantastic opportunity, so get your applications in!
DISCLAIMER: Many thanks to Software Guild for sponsoring our time in creating this career-changing content!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Mapping The Timeline Of A Successful M&A Deal For Tech Startups
URL: https://www.data-mania.com/blog/mapping-the-timeline-of-a-successful-ma-deal-for-tech-startups/
Type: post
Modified: 2026-03-17
Mergers and acquisitions can transform a tech startup’s future by opening access to capital and new markets. While every deal has unique elements, the overall structure of a successful M&A timeline follows a predictable path. Understanding each stage helps founders prepare, avoid unnecessary delays, and maintain transparency.
Early Preparation and Internal Alignment
A strong M&A timeline begins long before outreach or negotiations. Startups need clear internal alignment on goals, such as gaining resources or securing a strategic partnership. Founders should review financials, technology assets, intellectual property, customer contracts, and employee agreements to ensure everything is documented and organized.
This preparation helps create a compelling profile for potential buyers or partners. It also ensures the startup can respond quickly when interested parties request information. Early clarity eliminates last-minute surprises and sets the tone for a more structured process.
Initial Outreach and Preliminary Conversations
The next stage involves connecting with potential acquirers. These conversations often begin informally through networking, investor introductions, or industry events. During this phase, both sides explore whether there is strategic alignment in terms of product fit, market expansion, or innovative capabilities.
If interest grows, the parties typically exchange a non-disclosure agreement so they can share sensitive details. The startup may provide high-level financial data, product roadmaps, and market growth indicators. These early discussions help determine whether a deeper exploration is worthwhile.
Term Sheet and Early Due Diligence
Once both sides agree to move forward, a term sheet outlines the initial expectations of the deal. This document covers price range, acquisition structure, timelines, and key obligations. While non-binding, it forms the framework for more detailed analysis.
Early due diligence begins shortly after. Acquirers examine finances, technology infrastructure, product stability, legal risks, and team structure. Tech startups should be prepared to share code documentation, security protocols, data compliance records, and intellectual property status. A well-organized company can accelerate this phase significantly.
Full Due Diligence and Regulatory Requirements
Full due diligence is often the most time-consuming stage. Acquirers look deeply into operational history, financial accuracy, potential liabilities, and long-term viability. For tech startups, this includes stress testing product scalability, evaluating engineering talent, and confirming customer retention metrics.
Regulatory requirements may also play a role, particularly if the deal involves public companies. Some organizations use tools such as the SEC filing calendar to ensure documentation and disclosures align with required reporting timelines. Completing this stage thoroughly builds trust and reduces risk for both parties.
Final Negotiations and Signing
After due diligence, both sides refine terms based on findings. This can involve adjustments to valuation, earn-out structures, leadership roles, or transition plans. Legal teams prepare final agreements, ensuring all obligations and protections are clearly stated.
Once everything is reviewed and approved, both parties execute the agreement. Public announcements, internal communications, and stakeholder updates are usually coordinated to maintain clarity and avoid misinformation.
A successful M&A deal relies on preparation, transparency, and organized execution. By following a structured timeline, tech startups can move confidently through each phase and build partnerships that support long-term growth. Look over the infographic below to learn more.
Looking for more startup marketing frameworks and templates? Browse free resources from Data-Mania →
---
## AoF 58: Is it time to Check your Emotional Intelligence? w/ Jay Levin
URL: https://www.data-mania.com/blog/aof-58-is-it-time-to-check-your-emotional-intelligence-w-jay-levin/
Type: post
Modified: 2026-03-17
Analytics On fire podcast
Analytics on Fire is your backstage pass inside the enterprise analytics and business intelligence world. Join your hosts – Lillian Pierson, PE (data leader, data strategy advisor & trainer, and CEO of Data-Mania) and Mico Yuk (BI author, speaker, and co-founder of BI Brainz) – as they pull back the curtain on what’s working right now in the enterprise analytics/business intelligence world and what is not. You’ll hear the inside scoop from experienced business leaders on how to plan, implement and gain true business value from your analytics dollars.
AoF 58: Is it time to Check your Emotional Intelligence? w/ Jay Levin
[mashshare shares=”false” buttons=”true”]
What do you know about emotional intelligence? Today’s guest is going to answer a lot of questions about emotional intelligence, EQ, when you should have your EQ assessed, and what you should do with that information.
Jay Levin has not only helped me to raise my EQ over the last decade, but he used to be a monk turned sales leader, then a COO. Today’s he’s an executive coach, and one of the things he does is assess EQ and help people understand what those assessments mean and what to do with them. Listen in to today’s dynamic interview to learn more about when you should take an EQ test, why emotional intelligence matters, and how you can improve your emotional intelligence! You can completely transform how you engage your business users once you understand your EQ.
“Continuous learners are not as concerned about what the score is in the present. They’re more focused on understanding what’s needed and using the appropriate behaviors to get it.” [26:48] @jslevin
Subscribe
Subscribe for updates on new podcast & LinkedIn Live TV episodes HERE.
Join Today
3 Knowledge Bombs
Jay on the Workplace – We’re being judged on how well we perform and scale our work across others.
Jay on EQ in the Workplace – There is an invisible but real climate of culture that exists as unspoken assumptions and biases.
Jay on work/life balance – It’s not about being independent, it’s about being cross-dependent.
In this episode, you’ll learn:
[01:13] – What to expect in Season Five of AoF.
[08:55] – User Expectations: What emotional intelligence is and when you should take an emotional intelligence test.
[11:32] – It’s mental health month on AoF.
[15:00] – Jay’s background as a monk and executive coach.
[16:34] -Key Quote: What I realized was, in languages that I couldn’t understand, even though I was interpreted, that there were the same emotional needs that people had regardless of culture. – Jay Levin
[17:06] – What EQ (emotional quotient) stands for and what it measures.
[18:16] – How empathy relates to EQ (emotional quotient).
[18:46] – Key Quote: A lot of times empaths have lower EQs because it’s all in their heads. – Jay Devin
[23:35] – How do you know when it’s time to check your EQ?
[27:08] – Understanding what EQ is assessing
[30:40] – Key Quote: When you can transfer a belief of what’s possible, then you’re creating in the present the conditions to bring about an emerging future. – Jay Levin
[32:18] – Why (user focused) workshops don’t work when the EQ is off.
[37:44] – How EQ factors in in the data science world.
[38:57] – Key Quote: More coding courses isn’t going to help you get promoted or get to the next level of fulfillment in your career –Lillian Pearson
[40:20] – Jay explains How the EQ test works.
[47:47] – Key Quote: Leaders who feel like they have to be in control and push control and the only way is to dominate, repel. —Jay Levin
[48:54] – How you see things differently when you understand behavior.
[55:29] – Understanding how to work with and through other people to optimize ROI.
[58:55] – Giving your people purpose.
[01:00:40] – Steps that AoF listeners can take to get started on their EQ journey.
[01:05:33] – What the work in progress looks like after your assessment.
[01:07:00] – One piece of advice that Jay would give his younger self.
[01:12:34] – How to find Jay online.
Right-click here and save-as to download this episode to your computer.
“Numbers can’t be managed. They can be manipulated. You manage behavior. You manage an action.” [45:52] @jslevin
Links & Resources Mentioned In This Episode:
Connect to Jay Lev via LinkedIn
Take Jay’s FREE EQ Assesment – Leveraging Behavioral Intelligence
Subscribe to Jay’s website WinThinking!
Download our FREE 52-Page Guide on Breaking Into The Data Professions + Get future episode invites w/ live Q&A access to Lillian Pierson & her special guests.
Enjoyed The Show?
Join the conversation by suggesting a future guest in the comments below!
Got a topic request? Drop it in the comments below and we will incorporate that into our plans for future episodes!
[mashshare shares=”false” buttons=”true”]
Join for only $37 today!
Lillian Pierson, PE
Let’s connect!
Pick whichever channel is most convenient for you!
🚩 LinkedIn
🚩 Newsletter
🚩 Instagram
🚩 Twitter
“
a note from our CEO
Lillian Pierson, PE
Back in 2013, I traded cubicle walls and stale coffee for the life of a multi-6 figure, remote-working data entrepreneur. Traveling world-wide, to date I’ve trained over 1 MILLION workers on data science, AI, and data strategy. I loveeee supporting my community of 650,000+ followers across LinkedIn, Twitter, Instagram, and on the Data-Mania Newsletter!!
My passion is helping today’s data professionals transform into data leaders of tomorrow… So that they can deliver more impact, with greater purpose… and enjoy new heights of opportunity.
Oh that – and I’m 💯 obsessed with nitro cold brew coffee, Thai massage, and expat living.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Where Industries Are Placing Their Biggest AI Bets
URL: https://www.data-mania.com/blog/where-industries-are-placing-their-biggest-ai-bets/
Type: post
Modified: 2026-03-17
Artificial intelligence has reached a point where experimentation is no longer enough. Companies are moving from pilot projects to major investments. As leaders assess what AI can realistically deliver, certain themes have emerged across industries. These areas attract funding because they promise measurable efficiencies, new revenue opportunities, or structural advantages.
Automation That Targets High Friction Work
Many industries are turning to AI to reduce the cost and delay created by repetitive tasks. Financial institutions automate document processing, credit decisioning, and fraud detection to shorten cycle times and reduce human error. Manufacturers deploy machine learning models for quality checks, anomaly detection, and machine uptime optimization. Investment grows in this category because it pairs directly with ROI metrics that executives watch closely, such as throughput, labor hours, and operational costs.
AI-Driven Personalization Across Customer Touchpoints
Retail, travel, entertainment, and consumer tech sectors are directing significant funding toward individualized experiences. Recommendation engines, dynamic pricing tools, and behavior modeling help companies serve the right message or product at the right moment. Unlike broad segmentation strategies, these models react to real-time signals and learn continuously. Customer lifetime value becomes easier to influence when AI understands purchase patterns or predicts churn before it happens. Leaders see personalization as a competitive differentiator that not only drives revenue growth but also strengthens loyalty in markets where switching costs are low.
Predictive Insights That Guide Strategic Decisions
Decision intelligence platforms are becoming a popular investment area because they help companies move from intuition to evidence-based planning. Energy companies forecast demand and grid stress with greater accuracy. Supply chain teams model disruptions and determine optimal inventory levels. These tools help leadership make decisions with clearer visibility, especially in volatile markets. The focus here is less about automation and more about sharpening judgment.
Security and Risk Mitigation Powered by AI
Cybersecurity threats evolve faster than traditional tools can detect, which is why AI-powered threat monitoring and response systems are drawing heavy investment. These systems learn from vast data streams, identify abnormal behavior, and contain incidents before they spread. Financial institutions also rely on AI to block suspicious transactions. As long as attacks grow more sophisticated, organizations will continue to invest in models that strengthen defense without overwhelming their teams.
Generative Technologies That Transform Content and Production
Businesses are increasingly experimenting with generative AI for content creation, design support, and early-stage ideation. This category includes tools that create text, images, or code and speed up work that once required long development cycles. Rather than replacing experts, these tools work alongside. The investment surge reflects a belief that creative processes can be augmented in ways that expand capacity and reduce bottlenecks. Many leaders still test boundaries to ensure quality and governance.
Major AI investments share a common thread. Organizations target areas where technology creates a lasting strategic advantage. As adoption expands, the distinction between early movers and slow responders grows sharper. For leaders committed to long-term competitiveness, these investment themes offer a clear map of where AI momentum is heading next. Check out the infographic below for more information.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## How To Get Started As A Data Consultant Fast
URL: https://www.data-mania.com/blog/how-to-get-started-as-a-data-consultant-fast/
Type: post
Modified: 2026-03-17
Curious to know how to get started as a data consultant? If you’ve been thinking of doing some data consulting work on the side of your full-time job, or even making data consulting your full-time gig – don’t make another move without reading this.
I’m about to tell you exactly how get started as a data consultant as quickly as possible. I’m going to share with you a scripted pitching template that you can start using today to dramatically increase the effectiveness of your efforts in landing well-paid data consulting contracts.
YouTube URL: https://youtu.be/-gISe3BjCjw
If you prefer to read instead of watch then, read on…
For the best data leadership and business-building advice on block, subscribe to my newsletter below and I’ll make sure you get notified when a new blog installment gets released (each week). 👇
Who am I to tell you anything about data consulting?
I started working as a technical consultant all the way back in 2007. In 2012, I started my own data science consultancy (which was a team of 1 back then), and after serving 10% of Fortune 100 companies from within my own data business, I started coaching other data professionals on how to hit 6-figures in their own businesses FAST.
My name is Lillian Pierson and I support data professionals to become world-class data leaders and entrepreneurs.
If you’re anything like I was back when I started my data business, then you probably:
Have data expertise
Only worked in a 9-to-5 job
Doesn’t feel like it transfers to open market
Don’t want to end up overworked and underpaid coding up predictive applications
Know that higher-end advising work is the way to go, but have no idea how to take your skills and experience and convert them into consulting services that you can quickly sell on the open market
Heck in 2010 – I had 3.5 years of technical consulting experience for organizations as big as the US Navy and I didn’t even know the difference between consulting and implementation services – let alone how to package and sell consulting services in my own business.
So, let’s start off by looking at what data consulting is and what it isn’t…
Data Consulting: what it is and what it isn’t
What it is
Cambridge Dictionary defines a consultant as someone who is paid to give expert advice or training on a particular subject.
Therefore, it naturally follows that a data consultant is someone who is paid to give expert advice or training on a data subject.
What it is not
Consulting is NOT coding, machine learning or any other type of data implementation services. You can also sell these types of services in your own business, but those are not consulting services.
Another thing we need to define is what I mean by “fast.” When you’re setting up your data business, there are two main approaches – you can either start now or wait for the gold.
So, the approach that I’m about to share with you falls in the start now category which means, if implemented effectively, you should be able to get clients within 30 days or less.
Before we move on, I would like you to share some of your ideas for your data consulting services in the comments:
Have you thought about who you’re going to help and how exactly your consulting services will help them?
Define who you help & how you help them
The first thing you need to do to get started as a data consultant fast is, very clearly define who you help and how you help them.
What this is
You need to get very clear on who your customer will be – all the way down to the industry level, what role they’re currently in and the transformation you can render for them.
Let me give you some examples of roles I’ve helped with my consulting business in the past:
VP of Analytics – Insurance Company
Director of Data – Media Company
Head of Risk Management – Banking Industry
How I’ve helped them
I help them get a “quick win” by ensuring their next data project generates revenue for the company.
If you want to see the exact 44 step-by-step processes I lead my customers through, that is available through the Data Strategy Action Plan. Check it out here.
Going back to who you help – that is exactly your ideal client avatar. I’m going to make some mock-up situations for illustrative purposes in this article.
Example:
Who You Help “Avatar”: E-commerce business owners
How you help them: Evaluate their company’s data as well as various circumstances across their business and build a strategy for improving marketing and sales ROI over the next 90 days
Notice how your client avatar and the transformation you want to get for them are very very specific.
Define what you offer
Define what you offer – anything from strategy, advising, assessments and training. You really have to go and do your own market research to figure out what is going to be the best solution or the best offer for you given your industry, ideal client avatar and your passions and skill sets.
In terms of what what I’ve offered in the past, those are:
Data strategy plans
Data strategy workshops / VIP Days
Partnerships with LinkedIn Learning to train their customers
Technical plans for engineering projects
Strategic plans for engineering projects
Example:
Who You Help “Avatar”: E-commerce business owners
How you help them: Offer a Marketing Plan Audit with 2 weeks turnaround time and then build a process that ensures that you only take 10 hours to deliver that work and charge them $3k
If you’re watching this and you’re thinking, “you know Lillian, I am not in all that much of a hurry. I think I would rather take the wait for the gold approach…” then check out this video I did on “How to Sign High-Paying Clients as a Data Entrepreneur.”
Where to find & pitch
I need to throw in a little caveat about the “wait for the gold versus start now” approach. Start now generally leads to lower-value contracts – yes, you can sign a contract quicker but the dollar amount from the contracts generally tends to be lower.
I always landed my consulting work via the “Wait For The Gold” approach, so I never did the start now method just because I didn’t need to. But that doesn’t mean it’s not effective. Let me show you how to get the clients fast if you really want to get them now.
The first thing you need to think about is your buyer avatar and your offer – what problem does your offer solve and where does your buyer avatar get problems like that solved?
Where does the buyer avatar actively go to find help:
Upwork – try to avoid
AngelList
Facebook Groups
I showed all these in How to Become a Freelance Data Scientist video. Be sure to check it out here.
Going back to our example earlier, if you’re looking to find a client that is hiring for data strategy with respect to sales and marketing, you might want to look over at websites like Hire a Marketer and see what kinds of jobs people are looking to hire for this type of service. You always want to place yourself in a position to be found by your ideal client and you’ll need to have a high-impact portfolio and profile.
I actually created a different video about creating powerful portfolios and making sure your profile stands out – it’s called “Data science freelancing portfolio.” Check it out here.
Once you get a high-impact portfolio and profile in place, then you need to pitch your potential client. I’ve created a reusable template that you can access through our Facebook group.
Get The Data Consultant’s Pitch Script by joining us inside our Data Leader and Entrepreneur Community on Facebook here.
Creating the Data Consultant’s Pitch Script
Whenever you’re doing any type of marketing, you always want to follow the AIDA formula – Attention, Interest, Desire and Action. It’s a battle-tested formula for copywriting and it just works.
Attention – make sure that things like your headlines or your first sentences are catching the attention of your prospective clients.
Interest – make sure that the content you write catches their interest by using statements that proves that you understand their problem and are able to offer them a solution.
Desire – make sure that you bring up areas where you are credible and that you’ve been able to achieve results in the past or what-have-yous, like if you have skills that produce “xyz” results – something to make them “desire” you as the solution to their problem.
Action – this is where you tell them the exact action they need to take in order to work with you.
When you’re going after jobs or doing anything in your business, always remember that it’s never about you, it’s always about your customers and how you can help them. They don’t really care about you, they only care about getting their needs met and getting a good success rate. So you want to make sure that when you’re creating the bid for your clients, you demonstrate that it’s all about them and the only thing that matters about you is that you can help them get what they’re looking for. This is how you’ll be able to get started as a data consultant as quickly as possible.
For a more detailed walkthrough of the Data Consultant Pitch Script, watch this YouTube video.
I hope you loved this post on how to get started as a data consultant fast – and if you did, I want to invite you to download my FREE Data Entrepreneur’s Toolkit.
It’s an ensemble of all of the very best, most efficient tools in the market I’ve discovered after 9 years of research and development. A side note on this, many of them are free, or at least free to get started, and they have such powerful results in terms of growing your business. These are actually the tools we use in my own business to hit the multiple 6-figure annual revenue mark.
Download the Toolkit for $0 here.
Hey, and if you liked this article about how to get started as a data consultant, I’d really appreciate it if you’d share the love with your peers by sharing it on your favorite social network by clicking on one of the share buttons below!
NOTE: This description contains affiliate links that allow you to find the items mentioned in this article and support the channel at no cost to you. While this blog may earn minimal sums when the reader uses the links, the reader is in NO WAY obligated to use these links. Thank you for your support!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Get the Data Product Manager CV Template here
URL: https://www.data-mania.com/blog/get-the-data-product-manager-cv-here/
Type: post
Modified: 2026-03-17
Data Product Managers…
FREE COMPANY-THEMED RESUME TEMPLATE
(customizable in a matter of minutes!)
Subscribe Below To Get The Data Product Manager CV Template:
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Seven Industry Trends in Data-Driven Software Development
URL: https://www.data-mania.com/blog/seven-industry-trends-in-data-driven-software-development/
Type: post
Modified: 2026-03-17
Data-Driven Software Development in 2020 was one-of-a-kind with lots of digital enhancements and movements in the uncertain world of COVID-19. Though it has affected every one of us in many ways, digital technology adoption has taken a quantum leap-not only on the medical front but unceasing innovation in the software development industry as well. With all the breakthroughs happening across all industries, 2021 has sped up digital transformation. More likely, this trend will be more promising in the forthcoming time.
Data-Driven Software Development Trends
Data-Driven Software development trends in the IT industry compel businesses to continuously transform towards new challenges. It aims to meet the evolving customer expectations and the growing reliance on data to deliver on these customer experiences. Software buoyed with the latest technologies brings improvement into data-driven decision making. This software becomes data-driven itself, offering solutions that have become difficult to solve using old procedural programming. Data-driven software offers greater scalability, flexibility, better data management, and automated operations.
A Gartner report stated that worldwide IT spending is expected to reach $3.8 trillion in 2021.
This shows enterprises are continuing to increase their software investments, whether it is the integration of cloud, Artificial Intelligence, Blockchain, or any other technology to fulfill against this new business environment and create competitive advantages for their businesses.
Now, where is the investment going?
Top 7 Data-Driven Software Development Trends
Here’s the compiled list of top seven data-driven software development trends that will take businesses to the next level even in these unprecedented times.
An Uptick in Big Data Analytics
Businesses across different industry segments are tapping into Big Data to capture insights and understand trends to move forward. With technologies like Hadoop and Apache Spark, business analysis and streaming can be enhanced. Netflix has built its credibility using big data analytics to understand exactly what its customers want.
One use case is the usage of public data across digital platforms. The power of big data analytics is used in preventing unauthorized use of personal data. In 2021, the trend is moving forward with Data-as-a-service (DaaS), which is a data management strategy that uses the cloud to enable businesses access the infrastructure when they need it, eliminating redundancy.
Learn more about data privacy and security risks on this video by Lillian Pierson about the Hidden Danger in Greater Data Privacy.
The Dominance of Native Apps
Given the increasing use of mobile devices, mobile apps play an indispensable role in business success. In 2021, software developers spend time developing more native apps that offer seamless customer experiences. These native apps make use of machine learning and other data technology to give the best intuitive experience to every user. As native apps support specific machines (typically iOS or Android), it allows software developers to explore and use the full potential of the device. This translates to better security, flawless usability, and a tailored UI.
Might Be Helpful: Top 5 Technologies That Will Reshape Web Development
Increasing Adoption of Cloud Services
In recent years, cloud services are in high demand with evolving need for business availability, scalability, data recovery, and ease of accessibility. 2021 is no different as more companies will be inclined towards SaaS, IaaS, and PaaS solutions to build apps to manage teams and streamline operations as these services can be easily adopted and implemented. The multi-cloud initiative will gain more momentum this year. The Central Intelligence Agency (CIA) has recently shifted its Commercial Cloud Enterprise (C2E) to multiple vendors instead of one single vendor. The main reason for multi-cloud adoption is to break vendor lock-in. This helps businesses minimize downtime and gain a mixed set of tools to prevent issues that might come with a single service provider.
The growing use of Container and Micro-Services
Containerization and micro-service architecture have become hot trends in software development. They ensure greater scalability, security, and high availability that most apps require nowadays. Kubernetes is widely popular amongst software developers as the best container orchestration and management technology. It allows developers to create and deploy applications faster by bundling up the application code together with all the dependencies, making them platform-independent.
Microservices is an approach to software development wherein large software is broken into smaller pieces of reusable codes. Each small module supports a particular business use case and communicates with other modules seamlessly. For instance, CSCS, a popular construction skills certification scheme company in the UK, put micro-service based architecture in place to run a validation check on the workers’ cards. The web application is linked with different awarding bodies using FTP/XML files for card authentication.
The combination of micro-service and containers is used to decouple software into smaller pieces and containers. This is to extend this decoupling; thereby making software independent of the underlying hardware platforms.
Accelerated Adoption of Blockchain Technology
Known widely as a digital ledger technology, blockchain has become a major trend. Blockchain has its sprawling applications in banking, finance, media, publishing, and healthcare. Integrating blockchain in software development processes will certainly add a layer of advanced security features during any business transaction.
From the studies, the blockchain market is expected to reach the $20 billion mark by 2024.
With the decentralized nature of this technology, businesses can store any type of record in a public database; thereby, ensuring security from hackers. Today, more companies are embracing blockchain as a service (BaaS). This allows businesses to build or host their blockchain application without worrying about setting up the entire infrastructure. Blockchain service providers set up the technology infrastructure and handle maintenance jobs.
Blockchain technology is growing at a fast pace. Being a popular option for developers to build decentralized open-source applications, this would allow them to bring transparency and safety encryption features. And this technology would eliminate all security concerns for online transactions.
Rise in 5G and New Technologies (AI, ML, AR, VR)
The power of 5G technology is going to be the major tech shift in 2021 with faster downloading power that is up to100 times faster than 4G. With quality upgraded features like faster remote networking, lower inertness rates, and easier bandwidth accessibility, 5G gets fine along with high data demand offerings. Such offerings include 4k video streaming, enlarged reality like virtual and augmented reality that are opening a whole new dimension for business growth.
Today, software developers are embracing 5G technologies to develop powerful apps with new functionalities in every space of business. The technology will be an enormous improvement in security which will keep away all the impediments present in the 4G.
Expansion of the Internet of Things
Smart devices and applications built using Internet of Things (IoT) have become a popular trend in the development of industrial applications, ranging from manufacturing, food processing to energy and health & medical divisions. These devices allow businesses to make decisions that are data-driven. By connecting everything together through sensors and actuators, IoT is making our world smarter. And businesses living in the challenging 2021 would look forward to spending more on IoT-enabled products to thrive in the future. Extending the journey beyond smart devices, companies are fast moving towards apps. Their aim is to become more efficient and provide advanced features with the integration of IoT.
In the current COVID situation, the healthcare sector is on the verge of change by offering better services in the form of telemedicine, connected imaging, in-patient monitoring, wearables, etc.
Wrapping Up on Data-Driven Software Development Trends
Digital products and applications have become an inseparable part of any business. With more and more data produced each day, companies are turning to data-driven software. These remarks significant improvements in business operations and functionalities.
Software developers are always keeping up with the latest technology and harnessing benefits from the updates. This means 2021 is setting promises for unparalleled opportunities. Whether a business is developing an enterprise product or rolling up a new idea in the form of a mobile application, these trends would certainly help them get a competitive edge.
** This article contains an affiliate link. This means we may get a small commission if you purchase the book after clicking through the link. Thank you for supporting small businesses ❤️.
A Guest Post By…
Paul Miser is the Chief Strategy Officer of Icreon, a digital solutions agency and Acceleration Studio.
He is also the author of Digital Transformation: The Infinite Loop – Building Experience Brands for the Journey Economy.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Storing Data in the Public Cloud: What You Should Know
URL: https://www.data-mania.com/blog/storing-data-in-the-public-cloud-what-you-should-know/
Type: post
Modified: 2026-03-17
Do you know what a public cloud is, and how it can help your business’s storage needs? Read this article about storing data in the public cloud, what you should know about cloud storage, and the best practices to use it better.
Public Cloud: A Definition
A public cloud is a platform that relies on the standard cloud computing model, making resources accessible to users remotely. Such resources include applications, virtual machines, or storage. Cloud costs are typically operating expenses (OpEx) and not capital expenses (CapEx) – most cloud services are offered on a pay per use basis.
The public cloud provides an alternative approach to application development, differing from traditional on-premises IT architectures. In the typical public cloud model, a third-party vendor hosts on-demand, scalable IT resources and makes them available to users via a network connection. This connection is either over a dedicated network or the public Internet.
In this article, I’ll explain the options for storing your data in the public cloud, and explore specific cloud storage services by the world’s leading cloud providers.
Storing Data in the Public Cloud and What You Should Know About Storage Options: Object vs File vs Block Storage
There are three common technologies to use in managing storage in the cloud:
Object storage
It stores data as objects, which are self-contained units arranged in a flat hierarchy. Object storage does not use files or folders—instead, objects have metadata that facilitates organization, search, and retrieval. These can include any type of data or file type, including structured and unstructured data. This storage is elastically scalable, making it easy to share data across multiple physical storage devices.
File storage
This is similar to the storage system used on a PC or file server. It stores data in files with a file extension that determines which application to use to view or edit the file. File storage can be deployed in a hard drive directly attached to a computer, or computers can remotely access files stored elsewhere using protocols like network attached storage (NAS). Cloud-based file storage makes it easy to migrate legacy applications to the cloud, because it behaves similarly to on-premise systems.
Block storage
This splits data into blocks of predetermined size, with unique identifiers. When the data needs to be retrieved, it is pulled from multiple blocks and reassembled. Block storage is the storage technology used by hard disk drives, as well as enterprise storage systems like Storage Area Networks (SAN). The main advantage of block storage is that it supports high performance and high throughput scenarios.
Examples of Public Cloud Storage Services
Below are some examples of public cloud storage services:
Amazon Web Services
Amazon Web Services (AWS) is a cloud services platform. The platform provides database storage, cloud computing infrastructure, API support, content delivery, and bandwidth. In addition, it has several PaaS and IaaS services.
Key AWS storage services include:
Simple Storage Service (S3)
S3 is an object storage service that is infinitely scalable and provides high data durability. With it, one can create data lakes, backup and archive data, and store static data for web and mobile applications. It has management features that let you organize access to data and automate data lifecycles.
Elastic File System (EFS)
It is a serverless elastic file system that scales up and down on demand without disrupting applications. It integrates easily with legacy applications, enabling lift and shift of workloads to the cloud. EFS provides a web services interface that lets you create and configure file systems.
AWS FSx
This is a service that enables running large-scale high-performance file systems. It supports Lustre, NetApp ONTAP, Windows File Server, and OpenZFS.
Elastic Block Store (EBS)
A block storage solution that provisions virtual hard disks you can attach to Elastic Compute Cloud (EC2) instances. They can be based on HDD or SSD technology, and can be dynamically configured based on requirements.
Microsoft Azure
Microsoft Azure is Microsoft’s public cloud computing platform that provides a wide range of services for computing, data storage, data analytics, and networking. Azure is a common platform for hosting databases in the cloud. Microsoft offers serverless relational databases such as Azure SQL and non-relational databases such as NoSQL.
The platform is a frequent backup and disaster recovery tool; that is why many organizations use Azure storage as an archive in order to meet their long-term data retention requirements.
Key Azure storage services include:
Azure Files
It is a managed file service based on the Server Message Block (SMB) protocol. It lets you mount cloud file shares from any Windows, Linux, or macOS machine, whether on-premises or in the Azure cloud.
Blob Storage
This is an object storage service that enables storage of unstructured data in any format. You can use Blob Storage in combination with Azure Data Lake to easily build an enterprise data lake and support big data analytics.
Disks
These are virtual hard drives that can be mounted and accessed from an Azure virtual machine (VM).
Queues
These supports asynchronous, high speed message queueing between applications in the Azure cloud.
Tables
These are unstructured, key-value data store with a schemaless NoSQL design.
Google Cloud Platform
Google Cloud Platform provides PaaS and IaaS services such as data storage, computing, and networking. Moreover, this platform offers developer tools and applications for running on Google hardware. Certain offerings include App Engine, Compute Engine, Cloud Storage, Container Engine, and BigQuery.
Google Cloud Storage is a public cloud storage platform for enterprises to retain sizable unstructured data sets. Organizations can buy storage for their main or infrequently used data.
Google Cloud Storage
An object storage service that can be used for production or archive data. It provides four storage tiers, enabling storing data in one or multiple Google Cloud regions, and archiving with frequent or infrequent access.
Cloud Filestore
A cloud-based file service that creates file shares that can be mounted using the network attached storage (NAS) protocol, and supports high performance workloads.
Google Cloud Persistent Disks
These are virtual hard drives that can be attached to Google Cloud VMs, and are used for persistent storage in Google Kubernetes Engine.
Storing Data in the Public Cloud: What You Should Know About Cloud Storage Best Practices
Below are some best practices that can help you make better use of your cloud storage:
Consider your cloud migration strategy
Certainly, migrating too much data to the cloud at once can often be a mistake. Like any new system you adopt, adopting cloud gradually allows your organization to test and adapt to the new environment. Create a migration strategy and start small, by migrating smaller datasets that are not mission critical.
Cloud backup and disaster recovery
There is a common misconception that data stored in the cloud is automatically backed up. It is true that many cloud services have built-in backup and archiving features; however, you need to correctly configure them first to protect the data.
The responsibility for data backup in the cloud rests with the cloud storage user. It is then best to consider cloud-native backup options such as storage tiering and replicating storage units to other cloud data centers.
Watch cloud storage costs
Many organizations migrate to the cloud to save costs; therefore, it is important to validate that your cloud migration does indeed reduce costs. Cloud storage eliminates upfront investments in storage equipment and ongoing maintenance, and it creates an ongoing operating expense which can grow exponentially if your data volumes grow. Define a clear budget for your cloud storage deployment and track usage to ensure it does not exceed the budget.
Avoid vendor lock-in
Public cloud providers have various strategies for encouraging customers to make more extensive use of their services and avoid switching to other providers. Avoid using cloud services in a way that will lock you into a specific cloud provider and make it difficult to migrate data away in the future. Prefer to use industry standard data formats and protocols to make datasets easily portable. Conduct due diligence to understand cloud provider offerings and to avoid lock-in.
Capping off: Storing data in the public cloud, what you should know
In this article, I explained the basics of public cloud storage, described the two main technologies used to store data in the cloud—object storage, file storage, and block storage, and briefly showed how the three biggest cloud providers package and provide their storage services.
I provided several best practices that can help you make better use of cloud storage—first, consider your migration strategy before moving to the cloud. Second, remember that backup is your responsibility. Third, watch costs; and finally, avoid vendor lock in.
I hope this will be useful as you explore your organization’s use of the public cloud as an elastic, flexible storage option.
More To Explore…
If we’ve got you scratching your head with all this talk on storing data in the public cloud, we invite you to uncover your most high-potential data superpower by taking our free Data Superhero Quiz. It’s a fun, 45-second experience that will show you the most powerful data career path for you given your skillsets, passions, and personality.
Hey! If you liked this post, I’d really appreciate it if you’d share the love by clicking one of the share buttons below!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Part 2 – Data Strategy Skills – The Ultimate Uplevel for Business-focused Data Professionals with Lillian Pierson
URL: https://www.data-mania.com/blog/part-2-data-strategy-skills-the-ultimate-uplevel-for-business-focused-data-professionals-with-lillian-pierson-2/
Type: post
Modified: 2026-03-17
In part two of the Ultimate Uplevel for Business-focused Data Professionals, with Lillian Pierson, we learn about her data action strategy plan. She created a data evaluation use case workbox with 31 use cases broken down by industry and function and tells us that if you are innovative then it’s actually everything you could possibly need. You don’t need to read 100 use cases.
Lillian says you should survey industry use cases to find what’s possible. Take stock of your company, look at where the biggest gap for a data solution is, and then assess possible options against use cases. Aim for projects that will make an impact within 3 months.
Enjoy the show!
If you want to know more or get in touch with Lillian, follow the links below:
Weekly Free Trainings: We currently publish 1 free training per week on YouTube! https://www.youtube.com/channel/UCK4MGP0A6lBjnQWAmcWBcKQ
Becoming World-Class Data Leaders and Data Entrepreneurs Facebook Group: https://www.facebook.com/groups/data.leaders.and.entrepreneurs
LinkedIn: https://www.linkedin.com/in/lillianpierson/
The Data Entrepreneur’s Toolkit: A recommendation set for 32 free (or low-cost) tools & processes that’ll actually grow your data business (even if you still haven’t put up that website yet!). https://www.data-mania.com/data-entrepreneur-toolkit/
Listen on Apple Here: https://apple.co/3ifLdAR
Listen on Spotify Here: https://spoti.fi/3l5SWTD
Discover your inner Data Superhero!
Most of the time, custom advice is all you need to achieve both your dream salary AND the satisfaction that you crave from your data career.
In our free, fun, 45-second data career path quiz, you’ll uncover your inner Data Superhero type and get personalized data career recommendations that directly align with your unique combination of data skills, personality and passions.
Take the Data Superhero’s Quiz today!
Get the Data Entrepreneur’s Toolkit
There’s always that data professional who starts an online business and hits 6-figures in less than a year. Now? It’s your turn and we’re ready to help get you there with our Data Entrepreneur’s Toolkit (designed to help you get results for your data business fast).
It’s our favorite 32 tools & processes (that we use), which includes:
Marketing & Sales Automation Tools, so you can generate leads and sales – even in your sleeping hours.
Business Process Automation Tools, so you have more time to chill offline, and relax.
Essential Data Startup Processes, so you feel confident knowing you’re doing the right things to build a data business that’s both profitable and scalable.
Download the Data Entrepreneur’s Toolkit for $0 here.
Execute Upon the Data Strategy Action Plan
This is our crowd-favorite data strategy product. No long video trainings, no books to read, no needless theory. Just clear, concise guidance on what your next data strategy steps should be, starting today.
It’s a step-by-step checklist & collaborative Trello Board planner for data professionals who want to get unstuck & up-leveled into their next promotion by delivering a fail-proof data strategy plan for their data projects.
There are also 2 bonus guides, if you need help improving communications with your senior executives and stakeholders
And, it comes with a bonus, members-only community, if you’d like a private sounding board for getting valuable input from other data strategists.
Start executing upon our Data Strategy Action Plan today.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Discover Your Inner Data Superhero & The Data Role That’s MOST FULFILLING FOR YOU
URL: https://www.data-mania.com/blog/discover-your-inner-data-superhero-the-data-role-thats-most-fulfilling-for-you/
Type: post
Modified: 2026-03-17
Discover your inner data superhero and what data career path makes the most sense for YOU, because thriving as a data professional is about more than just making good money! It’s about FULFILLMENT & IMPACT. In this article, I will help you discover the BEST data role for you given your unique skill sets, personality & goals.
If you prefer to read instead of watch then, read on…
Since Harvard Business Review named Data Scientist the “sexiest job of the 21st century” back in 2012, it seems like everyone and their mom has been rushing out to develop their data science skills.
And for good reason! The demand for data scientists only continues to increase, and the salaries far exceed the national average in the US, with the median national salary for data scientists in the United States coming out to $129,000, according to the 2021 Robert Half Technology Salary Guide.
But looking past the online hype, should you REALLY pursue a role as a data scientist?
Through mentoring data professionals, I’ve noticed a ton of people jump into data science without doing thorough research on whether it’s truly the role for them. They end up doing SO much work to get skilled up, only to land a data science position and find out they’re MISERABLE on the job.
I know because I was one of those people.
I learned data science skills back in 2012 only to realize coding up and building data solutions was not going to give me the fulfillment and happiness I was searching for.
When it came down to it, data science implementation just wasn’t a fit. I started to realize that I needed to do something where I could see a visceral positive impact from my work.
So what did I do instead?
I moved from the U.S. to Thailand to bootstrap my own data business, Data-Mania. And let me tell you, has it ever been FUN!
Before you put in years of time and effort pursuing data science, let’s explore some different options. There are SO many career opportunities in the wonderful world of data.
In order to do a thorough analysis of what role would be best for you, we’ll take into account five different factors:
Current Skillsets
Career Goals
Personality
Priorities
Passions
By the end of this article, you’ll have a solid grasp on how to discover your inner data superhero and uncover your ultimate data dream job!
Current Skillsets
First, let’s analyze your current skills. I find most data professionals tend to have serious chops in one main area. Those main skillsets tend to be:
Data Analytics Skills
Data Science Skills
Data Engineering
Data Leadership
If you’re analytics-oriented, you’re great at data visualization, data storytelling, dashboard design – maybe you build dashboards and visualizations in Tableau or Power BI. You’re also able to use SQL to query and retrieve data.
If you’re data science-oriented, then you have programming experience, and Python and R. You have a deep understanding of machine learning, predictive modeling, statistics, and SQL.
If you’re data engineering-oriented, you’ll have skills in ETL scripting and data warehousing. And as you get more advanced, you’ll be working in distributed computing environments, building data pipelines, maintaining data systems, and working with NoSQL. You’ll also know how to code in languages like C, C++, C sharp, Java, Scala, and engineer systems that utilize both NoSQL and SQL databases.
If you’re data leadership-oriented, then you excel at leading projects and teams. You’d be suited for a role like Project Manager, Product Manager or Stakeholder Management. Your superpowers lie in the realm of technical project management and data strategy!
Career Goals
Now it’s time to think about your big-picture career goals. When you look to the future, where do you want to be in your data career?
Do you want to be in the spotlight leading profitable data projects?
Do you want to be behind-the-scenes coding and building data solutions but have more autonomy?
Or do you want to build your own product and work for yourself – and not have to answer to anyone?
Because that’s definitely a possibility too!
Personality
Let’s chat about personality type. Specifically, are you introverted or an extroverted?
If you’re introverted, you’ll be happier doing data implementation and coding work. You’ll LOVE getting to dive deep into the details and without the distraction of having to manage clients and team members.
If you’re extroverted, then you’ll be at your best in a data leadership type role. You’ll be able to use your people skills to manage teams and projects, rather than actually coding up solutions yourself!
Priorities
When we talk about priorities, I’m talking about what season of your career you’re in.
Depending on your season you may have different priorities and needs. The way I like to think about this is through Maslow’s Hierarchy of Needs.
Maslow’s hierarchy of needs states that all humans have a desire for self-actualization, but in order for us to prioritize inner fulfillment we need to have our most basic needs taken care of first.
Marlow’s Hierarchy of Needs Source: Simply Psychology
The needs are:
Physiological needs
Safety needs
Love and belonging needs
Esteem needs
Self-actualization needs
What’s important is that they’re taken care of in that order.
So what the heck does this have to do with your data career, you ask?
Well, in the beginning of our career, fresh out of school with many of us carrying student loan debt, we’re usually looking to take care of our most basic needs (physiological and safety). Our priority is putting a roof over our heads and getting to a steady financial place.
But once we progress in our career, our needs change. We begin to want the recognition, the accolades, the promotions – in other words, our esteem needs. Finally, once we’ve gotten the money and the praise, we often find ourselves searching for MORE. This is the stage of seeking true fulfillment and greater impact as a data professional.
Ask yourself: what are you craving most from your data career right now? Is it money? Is it freedom and accolades? Are you looking to make an impact?
For example, data implementation work is often the quickest route to securing a healthy income. Becoming a data entrepreneur or leader might take a bit more work upfront, but the long term fulfillment may be greater!
Passions
Discover your inner data superhero by thinking about what you’re MOST passionate about when it comes to data.
Most people in my community are drawn to one of four areas:
Coding
Consulting with the business
Managing projects, products and programs
Visioning and improvising.
Ask yourself – what is the most fun for you? What gives you the most energy?
If it’s coding, you’ll definitely want to look into a data implementation role. But if that’s managing programs, and projects and products or consulting with the business, then consider a data leadership role. And if innovation is more your jam, then you may have an entrepreneurial bone!
The world is your oyster with a data skillset. There’s no need to limit yourself to data science simply because it’s one of the most talked-about tech careers. By diving deeper into your personality, passions, goals, and skillsets, you’ll be able to land a job that not only pays well but brings you true fulfillment in the long run.
If you’ve enjoyed learning about how to discover your inner data superhero, then you’d LOVE my free Data Superhero Quiz! You’ll uncover your inner Data Superhero type and get personalized data career recommendations that directly align with your unique combination of data skills, personality and passions.
Take The Quiz Here
Share It On Twitter by Clicking This Link -> https://ctt.ac/Fa2Ca
Watch It On YouTube Here: https://youtu.be/kLSGaOEAuHg
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Women in AI Trailblazer: Meet Lillian Pierson
URL: https://www.data-mania.com/blog/women-in-ai-meet-lillian-pierson/
Type: post
Modified: 2026-03-17
The Women in AI Trailblazers series is a partnership between Discovering Data and Women in AI (WAI). This initiative showcases global data leaders and invites less-represented people to lead the data conversation. It is a series of short interviews focused on the person behind the leader. Additionally, it inspires more women to lead the data conversation.
Today’s first Trailblazer episode, Trailblazer Series #1, features Lillian Pierson, CEO and Head of Product at Data-Mania where she supports data professionals in evolving into world-class leaders & entrepreneurs. Lillian has 16 years of experience launching and developing technology products and delivering strategic consulting services. She has many products that educate learners on how to apply data science, data strategy, and business strategy to increase profits for their companies. To date, these products have been consumed by 1.3 MM+ learners and have generated over $5.5 MM in revenue.
In this episode, you will learn the following key points from Lillian Pierson:
Lessons learned in scaling Data-Mania
The COMMUNITY mindset
The state of Women in Data and AI
About Discovering Data:
Discovering Data is a community of data leaders that believe in curiosity, empathy, and inclusion.
About Women in AI:
WAI is a nonprofit do-tank working towards a change and inclusive AI that benefits global society.
Listen to the full episode below:
If you want to know more or get in touch with Lillian, follow the links below:
Weekly Free Training on YouTube: Lillian Pierson
Becoming World-Class Data Leaders and Data Entrepreneurs Facebook Group
Lillian Pierson on LinkedIn
The Data Entrepreneur’s Toolkit: A recommendation set for 32 free (or low-cost) tools & processes that’ll actually grow your data business (even if you still haven’t put up that website yet!).
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## NFT Hype: All Hype or Mega Market Opportunity For Data Professionals?
URL: https://www.data-mania.com/blog/nft-hype-all-hype-or-mega-market-opportunity-for-data-professionals/
Type: post
Modified: 2026-03-17
If your busy schedule as a data professional has you a little behind the eight-ball with respect to what’s happening in the NFT scene – and how that’s likely to affect the future of your data career – then this NFT hype article is for you.
NFTs have risen to the forefront of Internet trends over the last couple of years, but are they really here to stay? The NFT hype had grown beyond niche online circles to include some big names in investing, including Mark Cuban and a slew of other celebrities. Beyond all the hype though, NFTs have some real applications that could play a major role in your data career in the years to come.
For the best data leadership and business-building advice on the block, subscribe to our newsletter below and I’ll make sure you get notified when a new installment gets released each week. The newsletter sign-up form is conveniently located at the bottom of this webpage.
Lillian and NFTs…
As far as why I’m qualified to give advice on data careers and NFTs…
If you’re new around here… Hi, I’m Lillian Pierson and I’m the Founder of Data-Mania, an online boutique that’s dedicated to supporting data professionals to become world-class data leaders and entrepreneurs.
To date I’ve educated over 1.3 million learners on how to do data science.
I originally had the idea to create this blog post because of some volunteer product management work I am doing with Women In Data to help them launch their very first NFT collection. Since us data gals are already mapping out our own NFT launch plans to take the data industry by storm (and hopefully help a lot of people in the process), I thought it would be a good idea to start some pre-launch education for data professionals about what NFTs are and why they’re likely to matter to you in the near future.
Content credit…
In all honesty though, I couldn’t create free content like this if it weren’t for all the help I get from my team… Big shout-out to Shannon Flynn, my favorite blog contributor. This article represents a collaboration between both of us (you can find more information about her at the bottom of this page).
Before we kick-off our breakdown of what NFTs are, whether it’s all NFT hype out there, and how they’re likely to create new and incredible opportunities for you as a data professional, let me just share with you the outline for the post (in case you want to skip ahead).
The topics were about to cover:
What Are NFTs & Why Are They Valuable?
Some Legitimate Use Cases for NFTs Today
NFTs Contributing to Cultural Spaces
Is the NFT Space Just a Huge Bubble?
Barriers That Need Surmounting
Ways Forward For NFTs to Reach Maturity
NFTs / web 3.0 & The Future Of Your Data Career
Ready? Let’s dive in…
NFT Hype? What Are NFTs & Why Are They Valuable?
First things first, what are NFTs and what makes them worth anything?
NFT stands for “non-fungible token”. This term simply means that an item is entirely unique and cannot be replaced with any other object. It has value because of its uniqueness. In a digital world, NFTs have a sort of digital signature that verifies they are, in fact, the one and only original digital file, creating scarcity. This distinguishes the original Nyan cat gif from any of the millions of its copies that exist online.
The value of these original digital assets is recorded on blockchain, which is the same infrastructure that gives cryptocurrencies value. This decentralized economic ledger is verified by millions of computers around the world. Individuals’ identities are protected on the blockchain using a system similar to card tokenization used on credit and debit cards.
What happened next is…
So, when the sale of an NFT is recorded on the blockchain, everyone maintaining that ledger can view that transaction and validate that it was legitimate. As a result, the NFT is given value. Since that transaction is so transparent, NFT hype can flare up quickly. This is no different from a physical art sale where a painting is given value because a group of people decided it was unique and worthy of investment.
By the way, while we are on the topic of purchasing and owning NFTs, do you have any yourself? If so, what is your favorite collection? I’d love to get to know you a little by chatting with you in the comments below.
What Are Some Legitimate Use Cases for NFTs Today?
NFTs started off as digital pieces of artwork. However, the market is rapidly growing beyond multi-million dollar JPG files and Tweets. More people are realizing the potential of NFTs, not as artwork but as a concept, a technology. The implications for this technology are far reaching, both in terms of timeline and applications.
While NFTs are not the same as cryptocurrency, which is fungible, they are having a similar and connected effect on the finance world. Crypto and decentralized finance, or “DeFi”, have shaken up the finance world in a myriad of ways over recent years.
DeFi moves the powers and responsibilities of banks and other financial institutions into the hands of the people. NFTs are expected to play an increasingly central role in the future of the DeFi economy.
Some have suggested that, soon, NFTs will be used for:
Collateral for loans in DeFi,
Important security devices, as well as
Identity validation
You see, The same technology that verifies that an NFT gif is authentic and original could be applied to official documents, such as ID cards, passports, and medical information. This could help reduce opportunities for fraud and identity theft, or prevent them altogether,
Additionally, since NFTs are bought and sold on the blockchain, they add a level of transparency that’s simply not possible with centralized finance.
Additionally, since NFTs are bought and sold on the blockchain, they add a level of transparency that’s simply not possible with centralized finance.
How Are NFTs Contributing to Cultural Spaces?
One of the most natural avenues for NFT hype is in entertainment, specifically video games, music, and sports. With all the NFT hype out there, adoption is already booming in these industries. For example, there is an entire NFT market for sports clips with some NFTs selling for hundreds of thousands of dollars. These are quickly becoming the sports trading cards of the future.
The video game industry is expected to be a hub for NFT sales. Younger generations are already spending more on fully-digital assets and spending increasing amounts of time in fully-digital spaces. Many NFT theorists and enthusiasts expect things like unique in-game items to become NFTs. In fact, there is even a growing market for “play-to-earn” video games that use blockchain, cryptocurrency, and NFTs to put game and asset ownership in gamers’ hands. So, is NFT hype all hype? I think not…
Even concert tickets could one day become NFTs.
The music industry is dipping its toes in the NFT world, as well. The band Kings of Leon made history in 2021 as the first band to release an album in NFT format. The album release included a few types of NFTs, ranging from front-row tickets for life to one-of-a-kind, non-reproducible audiovisual artwork. Even concert tickets could one day become NFTs.
Just like with artwork, NFTs are enabling musicians to control and retain the value of their creations. Because of the digital nature of music today, NFTs could become a central part of what makes the music industry work.
Other resources for you…
If you like the unique perspective we share in this blog post, then you’ll probably also enjoy our other articles. I will leave a link to a few of the more popular ones below:
How this marketing data scientist made $370k in <18 months
Simplest Data Business Models for New Data Freelancers and Entrepreneurs WITHOUT INVESTORS
Freelance data scientist turned entrepreneur – how to do it ALMOST OVERNIGHT
NFT Hype – Is the NFT Space Just a Huge Bubble?
So, what does all of this potential mean? Experts have compared the NFT hype to the economic boom that occurred when people first began making money on the Internet in the 1990s.
NFTs, Explained
Eventually, the Internet bubble popped and many internet businesses and startups crashed, despite the massive hype and investments that initially supported them. Could the same thing happen to NFTs? It depends.
NFTs were purpose-made for a digital world.
NFTs were purpose-made for a digital world. In many ways, they are ahead of their time, so it remains to be seen if they will live up to their hype. The rise of technologies like the Metaverse and other VR and AR spaces will have a profound impact on the success of NFTs. If people are spending more time in VR and digital worlds, NFTs are likely to blow up.
Barriers That Need Surmounting For NFTs To Reach Their Full Potential
I feel it fitting to mention that NFTs are really only one type of digital asset in the web 3.0 ecosystem. In case you don’t know what web 3.0 is, yet:
TL:DR Web 3.0 is a decentralized version of the internet you know and love today. It aims to give ownership of the Internet back to the people and remake the internet a community-controlled space.
Many of the barriers to the success of NFTs are also obstacles to the maturity of web 3.0.
The biggest of these barriers (IMHO): Prohibitive governmental policies.
Prohibitive governmental policies still have the power to come in and rain on the web 3.0 parade, but with recent policy developments in the US, it’s looking like “prohibitive governmental policies” won’t become too widespread of an issue after all. For example, did you know that the Chicago Mercantile Exchange is now the world’s biggest bitcoin future platform? That’s about as mainstream as you can get.
By the way, I’ll be doing a whole post on web 3.0 soon. I’ll notify you when it’s ready if you drop your details in the newsletter form at the bottom of this page.
Another huge obstacle to the mainstream adoption of NFTs: Sustainability.
NFTs may be digital assets but their environmental impact is monumental. Since NFTs are run through blockchain, they rely on the processing power of millions of computers which require massive amounts of energy – energy which largely comes from non-renewable sources.
Many experts are highly skeptical about whether NFTs are a valuable use of terrawatts of electricity. If the NFT market cannot move to more sustainable energy sources or cut down its power demands, sustainability concerns will prevent large-scale adoption.
Ways Forward For the NFT Market to Reach Maturity (Beyond the NFT Hype)
Real talk, one could write an entire book on ways forward to help the NFT market reach its full potential – and I only have a small subsection of a blog post to cover it. So, let’s get down to brass tacks shall we?
The founders, team, and communities supporting many NFT projects are actively developing ways to offset the carbon footprint of their NFT minting activities. The fact is, most NFT projects are run by people who care a lot about the culture of humanity and the world around them. Case in point, Doodles.
Doodles is a community-driven collectibles project featuring art by Burnt Toast. Each Doodle allows its owner to vote for experiences and activations paid for by the Doodles Community Treasury. Doodles is currently working to implement a voluntary carbon standard to counter the destructive environmental impact by promoting carbon preserving projects (renewable energy, solar, bio-mass etc) & afforestation.
And this , dear data professional, spells opportunity for analytics workers like you. Let me explain…
What NFTs and web 3.0 Could Mean To The Future Of Your Data Career
Doodles, and well-funded projects like Doodles, are currently battling it out to become the first GREEN project on the Ethereum blockchain.
But to make that happen, guess what they need? Data insights.
The entire web 3.0 world is data-rich and insight poor. So, if you are a professional (or better yet, the owner of a data business) that’s versed in converting blockchain transaction data into meaningful, useful insights – THIS IS YOUR RIGHT PLACE AND RIGHT TIME!!
Doodles is currently underway with plans to create dashboards (READ: hire a team who will create amazing dashboards) on top of smart contracts that balance carbon credits against the negative environmental impact of minting Doodles.
And this is just one small example I pulled off one small proposal on snapshot.org. The more I look around the NFT space, and explore web 3.0 projects – the more once-in-a-lifetime opportunities I see for data professionals and business owners of data companies.
I could literally think of a dozen ways to make a million bucks by starting a new data business to serve the web 3.0 ecosystem of projects and companies – but starting a new data business isn’t what I’m here to do, so… I will leave that to you good folks.
Wrapping up
In conclusion, there is a good bit of NFT hype mania out there. Despite that hype, there are still some extremely relevant use cases and career opportunities in the NFT space (especially for data professionals).
More things you need to know…
If I’ve got you scratching your head with all this talk on starting a data business to serve the NFT / web 3.0 industry, I invite you to watch my free masterclass on how to take your data expertise and turn it into a 6-figure business, practically overnight. This is a limited-edition masterclass, so don’t miss this chance to take it for free – before I change my mind.
Also, I’d like to encourage you to save your seat for our upcoming Story Hour, live on LinkedIn – March 9, 10 am ET, where former client, Stephen Taylor, will share the exciting story of how he sold his data consulting business to work as a CIO for Vast Bank, where he launched the first ever Crypto Bank in the United States!
Hey! If you liked this post, I’d really appreciate it if you’d share the love by clicking one of the share buttons below!
A Guest Post By…
This blog post was generously contributed to Data-Mania by Shannon Flynn. Shannon Flynn is a freelance blogger who covers business, cybersecurity and IoT topics.
You can follow Shannon on Muck Rack or Medium to read more of her articles.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Why is Data Destruction Important?
URL: https://www.data-mania.com/blog/why-is-data-destruction-important/
Type: post
Modified: 2026-03-17
Why is data destruction important? Here’s why…
Data has become an important treasure to malicious entities, who may want to misuse it for their profit to the detriment of the data owners. All kinds of data are at risk of misuse. Hackers can sell proprietary business data to competitors, and they can use employee and client private data in identity theft attacks. As a result, data destruction has become one of the most critical tasks in information security.
What is Data Destruction?
When you hit the DELETE button, data on the storage media does not disappear completely and is easily recoverable. Formatting a storage media does not also destroy that data on it. It is harder to recover than deleted data, but it is possible to recover using advanced forensic tools.
Data destruction is a task that ensures data is irrecoverable and hence inaccessible by unauthorized parties. The average business has a lot of data on various storage media, including hard disks, thumb drives, optical media, cameras and mobile phones. Data erasure services ensure data on various storage media is destroyed forever and irrecoverable even with the most advanced forensic tools.
Why is data destruction important?
Data destruction is vital for any organization today for different reasons:
How about REGULATORY COMPLIANCE!
Data privacy has become a big concern for governments worldwide. As a result, tighter data privacy laws have been enacted in different countries. Some of the most prominent include:
Gramm-Leach-Bliley Act, US
Health Insurance Portability and Accountability Act (HIPAA), US
Fair and Accurate Credit Transactions Act (FACTA), US
General Data Protection Regulation (GDPR), Europe
Personal Data Protection Act (PDPA), Singapore
These laws have prescribed harsh penalties for businesses that don’t secure their clients’ data. For example, the GDPR prescribes a fine of up to €20 million ($24.1 million), or 4% of the previous year’s turnover, whichever is higher.
Businesses that have already suffered the financial penalties of not adhering to this law include:
British Airways fined €22 million ($26 million) for a data breach that affected 400,000 customers
Marriott Hotels fined €20.4 million ($23.8 million) for a data breach that affected 30 million European customer records
H&M fined €35 million ($41 million) for careless storage of employee data
Financial and legal penalties for non-compliance can cripple a business. SPW data erasure ensures private client data that is no longer useful is disposed of in a way that makes it inaccessible.
Protect Business Reputation
Besides financial penalties, losing reputation and customer trust is devastating to a business. A survey done in 2014 showed that 72% of small businesses that suffer data breaches shut down within 24 months.
While all data breaches are undesirable, some industries are more sensitive to these attacks. For example, client confidentiality is very desirable in the health and finance industries. Potential customers will be very reluctant to work with a brand that has a bad reputation for not keeping confidentiality. Observation of data destruction ensures you keep your customers’ confidentiality by protecting their data.
Protect Business Competitiveness
Why is data destruction important in large corporations? Well, corporate espionage has become a big threat to proprietary data as business competitors try their best to get ahead. Product research and development are long and expensive, and competitors are always trying to cut out the expenditure while enjoying an innovative edge.
Hackers are always on the lookout for confidential business data they can sell to the competition. Alternatively, they will blackmail the business to pay a ransom for not selling the data. Either way, it is a loss for the business that has suffered data loss.
Data erasure services help safeguard confidential business data on end-of-life equipment meant for recycling or physical destruction. It prevents this data from falling accidentally into the hands of unauthorized parties or being recovered by hackers.
Enhance Cost Efficiency
Data storage is an expense in terms of the cost of the space occupied by storage media. In addition, it is an expense to rent space to stockpile end-of-life equipment. It would be cost-effective to dispose of this equipment and use the space in more productive ways or stop renting.
Stockpiling data is also inefficient in using storage media. It is more cost-effective to wipe data off hard disks and recycle them. Most of today’s storage media is rewritable and can be recycled many times without loss of data integrity.
Environmentally Friendly Disposal
Recycling IT resources has become internationally recognized to reduce the impact of electronic waste on the environment. Secure data destruction enables businesses to recycle their equipment with confidence. You can reuse this equipment in the business instead of purchasing new equipment. You can also donate it to charity for education programs.
Recycling reduces the need to clog landfills with non-biodegradable waste. It also reduces the extraction of resources like lithium, making it more sustainable for the environment.
Secure data destruction is vital for both for-profit and nonprofit entities. Data erasure services protect the interests of a business and its clients when done competently. Information best practices must include routine data destruction using a competent partner like SPW data erasure services.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## 4 Top Data Compliance Tips and Tricks
URL: https://www.data-mania.com/blog/4-top-data-compliance-tips-and-tricks/
Type: post
Modified: 2026-03-17
Organizations constantly gather and store personal data, and this has far-reaching implications on the lives of individuals and communities. For this reason, governments and industries have enacted data privacy regulations and standards – the most well-known are GDPR in the EU, HIPAA in the US, CCPA in California, PCI DSS, and SOX which impacts US financial institutions. This article reviews these five data compliance standards and tips on how to implement them in your organization.
What Is Data Compliance?
Organizations need to handle and secure sensitive data like customer credit card details, and employee home addresses. Data privacy laws and regulations ensure that an organization is capable of protecting this data against breach. There are different types of data security regulations at national, regional, and global levels. Organizations that do not comply with these regulations can face fines, legal exposure, and reputation damage.
Data compliance means creating policies and workflows for data security and protection, in line with applicable laws. Organizations not only need to put these policies in place, but must also demonstrate to auditors and relevant authorities that their controls are effective and that they have not been compromised. Many compliance standards require organizations to report security breaches, and this typically triggers a more in-depth audit of their security measures.
Common Data Compliance Standards
The following data compliance standards can help you create policies for data security and protection.
GDPR
The General Data Protection Regulation (GDPR) was introduced in 2018. It outlines a variety of rules about the personal information companies can collect, how companies can process the data, and how they must report data breaches.
GDPR is not limited to companies based in Europe. International companies that operate in Europe are also required to abide by GDPR laws. The majority of rules can be described by three basic principles—reducing the amount of data held, obtaining consent, and safeguarding the rights of the data subjects.
HIPAA
The Health Insurance Portability and Accountability Act (HIPAA) states how US medical and healthcare organizations must ensure the safety and confidentiality of patient records.
HIPAA requires all electronic health records to be encrypted and have strict access controls. You can access these records only if you have a valid reason to view them. The standards also apply to sharing records. Therefore, you have to monitor, protect and control activities like emails and file transfers.
PCI DSS
The Payment Card Industry Data Security Standard (PCI DSS) is an essential aspect of the compliance process for any company that handles customer financial data. PCI DSS compliance outlines how companies should protect and handle sensitive data like payment card numbers.
PCI DSS is an industry-mandated set of standards, not a government-imposed law. However, companies that do not comply with this standard may face heavy fines. Moreover, banks may terminate with non-compliant companies, making it impossible to accept credit card payments.
The steps businesses must take to protect payment information depend on the number of transactions they process. Companies with a big customer base will face much stricter requirements than small companies. Ultimately, PCI DSS requires businesses of all sizes to guarantee a minimum level of security.
CCPA
The California Consumer Privacy (CCPA) act was passed in 2018 and came into effect in January 2020. It covers a broader scope than the GDPR in terms of protecting private data. Consumers can view any information about them that companies have saved. They can also request a full list of the third parties who have received their information. CCPA also enables consumers to take legal action if a company violates these privacy policies, even if the violation does not result in a data breach.
CCPA compliance applies only to businesses with a gross annual revenue of over $25 million, that derive at least 50% of their revenue from the sale of personal customer information, or that receive, buy, or sell the personal data of at least 50,000 consumers.
SOX
The Sarbanes-Oxley (SOX) act is aimed at protecting companies and the general public from fraudulent activities and accounting errors in organizations. In addition, the act improves the accuracy of company reports and disclosures by setting deadlines for complying with the SOX rules.
The SOX standard makes sure that IT departments automate financial reporting and set up alerts on events that require closer attention. These alerts enable CEOs and CFOs to receive real-time reports on their companies’ financials.
IT teams are also responsible for properly retaining all financial records. Therefore, IT departments have to periodically backup any sensitive documents and data management systems to remain compliant with SOX regulations. They must also ensure they maintain full visibility into all digital systems in the company to make this more effective.
Top 4 Data Compliance Tips
Consider the following tips when you are planning to implement one of the data compliance standards mentioned above.
1. Train your staff
According to GDPR, employees must receive periodic security awareness training. This training ensures that your staff is informed about the regulations, company policies, and any legal requirements affecting their everyday role.
Organizations must prove that all staff are familiar with and understand the GDPR policies. Organizations need to provide evidence that they incorporate privacy and security into their daily business operations.
2. Create an incident response plan
Organizations subject to the GDPR must report on any breaches of personal data to the relevant authorities within 72 hours of identifying the incident (many other data privacy laws have similar requirements). Therefore, organizations must have a robust incident response plan in place to quickly respond to any incident.
The incident response plan should describe the steps you have to take in case of an event. An organization should define who is responsible for making decisions and managing the incident. An incident response plan can help inform staff, reduce the potential financial impacts of a major breach, enhance organizational structures, and improve relationships with customers and stakeholders.
3. Implement effective data compliance policy management
Traditional methods of corporate communication like emails make compliance impossible. A policy management system, on the other hand, is a simpler, centralized solution for creating, distributing, and storing important data policy documents.
A dedicated management system can effectively address the areas presenting the highest risk in terms of data security. It can also streamline internal security processes and help companies demonstrate their compliance with legal requirements. In addition, an effective policy management system can provide a consistent method for policy creation, add structure to corporate procedures, and simplify compliance monitoring.
4. Defend all access points
Organizations must ensure that all endpoints are adequately protected to achieve data compliance. However, unpatched systems are responsible for many data breaches. Patches and updates are essential to the discovery of new vulnerabilities. Attackers can exploit new vulnerabilities to break into an unpatched system.
Organizations need to show they are doing everything they can to secure their systems in order to demonstrate compliance with regulations. Organizations have to document every patch they implement because auditors may demand reports of applied patches. Patches keep your systems up to date, safe, and stable.
Conclusion
In today’s world, data compliance and security are essential for survival. The widespread regulations of compliance standards across the world enables businesses to review their security posture and implement effective strategies that will protect their companies from data breaches, and avoid fines for noncompliance with data privacy regulation.
Hey! If you liked this post, I’d really appreciate it if you’d share the love by clicking one of the share buttons below!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Data Platform Examples: What are the 3 major options?
URL: https://www.data-mania.com/blog/data-platform-examples-what-are-the-3-major-options/
Type: post
Modified: 2026-03-17
The business world is becoming increasingly data-driven, and modern organizations need to keep up with the latest trends to remain competitive. Big data may seem like a buzzword to some business professionals, but companies that understand and harness its power can gain greater insight into their operations. That’s where data platforms come in. Here’s more about data platform examples, their key components and the three top options on the market.
What Is a Data Platform?
A data platform is a highly sophisticated, scalable and often cloud-based central repository and processing tool for all the information belonging to an organization.
Data platforms handle various tasks regarding a company’s data, such as collecting, cleansing, transforming and applying it to generate valuable insights. Many companies have leveraged enterprise-level platforms to manage big data.
Data platforms should not be confused with business intelligence (BI) platforms. BI tools can improve a company’s decision-making, but data platforms can manage more information types and various information structures across a company. They are centralized, so they prevent silos and allow all departments in an organization to access the same stats for different purposes.
Some companies are even building data platforms in-house. This route often results in better privacy, consistency and enrichment. However, creating one requires a dedicated engineering team. Downstream systems, such as data lakes and warehouses, still may not reflect the same information as the platform’s source of truth.
What Are the Four Components of a Data Platform?
Data platforms can be complex and continuously evolve, especially as business needs change. However, they should have four critical components to be considered viable.
1. Ingestion
A data platform should be able to collect and import data for storage in a database or for immediate use. Organizations typically have many data sources, and a platform is handy because it automatically ingests information from multiple sources effectively and efficiently.
Organizations must identify a storage method before a data platform ingests information. Some common examples of storage methods include warehouses, lakes and even lakehouses.
2. Processing
Once the data is ingested by the platform and stored properly, it must be organized and manipulated to make it understandable.
A platform may use batch or real-time processing. Regardless of how a platform processes information, it must have the ability to manage structured and unstructured data types.
3. Analysis
There are a few types of data analysis methods, including quantitative and qualitative, statistical, textual, predictive, descriptive and diagnostic. A data platform must be able to analyze any information that is ingested or processed to provide organizations with insights.
A data platform would not be of much use without the ability to analyze information.
4. Presentation
The final component of a data platform is its ability to present any information in an easy-to-interpret fashion. It may do this differently depending on the organization’s needs.
For example, it may present relationships between data through visualizations, such as graphs or charts. When a platform gives information to company leaders, they should be able to understand it, draw conclusions from what is presented and make better decisions.
It’s also critical for data platforms to have other features, including scalability, flexibility, usability, security, compliance, automation and intelligence. Most platforms are classified as on-premise, hybrid or cloud-based.
It’s also beneficial if a data platform has anomaly detection capabilities. Without anomaly detection during data pre-processing or cleansing, a data platform’s algorithm may not function properly.
What Are the Three Major Data Platforms?
There are many types of data platforms on the market, making it challenging for companies to determine which one will suit their needs. Here are three of the major data platforms organizations will use.
Microsoft Azure
Many companies use Microsoft Azure for their cloud computing needs. It has over 200 applications and offers more than 1,000 technical capabilities for users. Azure relies on open-source coding so companies can use their existing code in various languages.
What’s nice about Azure is that any size business can use it and they only pay for what they use. Many organizations will use Azure if they want to implement a hybrid cloud model, which is becoming an increasingly popular choice in the IT community.
Amazon Web Services (AWS)
More commonly referred to as AWS, this platform comes with advanced analytics tools that help with all aspects of data management, from prep and warehousing to data lake design and SQL queries.
AWS offers several benefits to its users — it’s easy to use, flexible, scalable, cost-effective, secure and considered a high-performance data platform. AWS allows an organization to tailor applications, databases and other services to its unique needs, which is a vital feature.
Google Cloud
Google Cloud offers numerous big data tools to assist organizations with massive amounts of information. Leading companies like P&G, Ulta, Twitter, McKesson, Deutsche Bank and more use Google Cloud for their operations.
Google Cloud allows users to accelerate their digital transformation, make informed decisions, break down data silos and leverage the power of artificial intelligence (AI).
More Data Platform Examples
Aside from the three major data platforms outlined above, some other data platform examples may be worth exploring.
Data Platform Examples Worth Knowing
Apache Hadoop
Apache Hadoop, often shortened to Hadoop, is a well-known solution in the data science community. It is an open-source platform made up of various software utilities that handle big data and computing problems. It’s highly scalable, free and uses commodity hardware, which is inexpensive for organizations. Users can still manage their data effectively and process it efficiently using Hadoop services.
Matillion
Matillion is another data platform example that helps organizations manage raw data to draw valuable conclusions to inform decision-making. It is a cloud-based ETL tool that has experienced growth due to its beneficial features and capabilities.
Right Data
Right Data is another platform that supports the daily activities of modern data practitioners. Its comprehensive capabilities include data streaming, batch processing, wrangling, bulk migration, machine learning (ML) modeling and no-code pipelines for novices. Right Data’s platform is easy to use and can be a powerful asset for businesses.
Snowflake
Snowflake is another well-known data warehouse that processes and analyzes information. It’s built like a software-as-a-service (SaaS) product and runs on top of the three major data platforms listed in the section above. One notable Snowflake feature is its SQL query engine, which receives high praise from users.
Cloudera
Cloudera planted its roots in Apache Hadoop, but it can handle massive amounts of data. It’s reported that Cloudera users often store more than 50 petabytes in Cloudera data warehouses. Additionally, Cloudera’s DataFlow (CDF) feature is capable of analyzing and prioritizing information.
Leveraging Other Data Platform Examples in 2022
It’s commonly understood that the quality of data is just as important, if not more important, than the quantity. That’s why companies must use the right platform, which can offer end-to-end information management solutions.
Data platforms play a significant role in today’s modern business environment. They can help streamline management and allow organizations to identify trends and improve performance through better, data-driven decision-making.
Hey! If you liked this post, I’d really appreciate it if you’d share the love by clicking one of the share buttons below!
A Guest Post By…
This blog post was generously contributed to Data-Mania by Shannon Flynn. Shannon Flynn is a freelance blogger who covers business, cybersecurity and IoT topics.
You can follow Shannon on Muck Rack or Medium to read more of her articles.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Marketing Mix Modeling Explained
URL: https://www.data-mania.com/blog/marketing-mix-modeling-explained/
Type: post
Modified: 2026-03-17
In this quick introductory blog post, you’ll see what marketing mix modeling is. You’ll also learn how you can use it to increase the profitability of your company.
I first learned about marketing mix modeling while gathering a client testimonial from Kam Lee. For the record, the “mixture” of Kam’s marketing data science expertise and the startup strategies he learned from within my course, led him to hit $350k in his first year of business!
If you want to learn more about how he marketed his marketing mix modeling services (pardon the pun 🙂 ) to get such a terrific business outcome, I recommend you start by watching my free masterclass on the 4 Steps To Monetizing Data Expertise in Your Small Business.
Now that you know the backstory, let’s dig into marketing mix modeling…
What is a Marketing Mix?
A marketing mix is simply a mixture of features that you use to describe your product. It’s comprised of attributes that describe the core product and the product marketing features you adopt when taking the product to market.
A marketing mix is commonly defined as the 4Ps, which are:
Product
Place
Promotion
Price
When we talk about marketing mix, we are really talking about a particular point. This is identifying the exact mixture of Product, Place, Promotion, and Price. All that is responsible for producing the optimal number of sales.
You don’t have to use the 4Ps though. You could use the 7Ps. This is more commonly used when using marketing mix modeling to optimize the sale of services.
The big thing you need to know about the “marketing mix” is that it should contain features which are reflective of both your product strategy and your product marketing strategy. Those features should be directly relevant to how well that product sells.
If you’re interested in learning more about data product management, I encourage you to check out:
This blog post on what a data product manager does on a daily basis
This blog post on: A Self-Taught Data Product Manager Curriculum and
This free CV template for aspiring data product managers here.
What is Marketing Mix Modeling (MMM)?
Once you’ve defined the core product and product marketing attributes that you want to model within your “marketing mix” (in our case, the 4 Ps), you’ll undoubtedly want to evaluate how that mixture of features directly impacts profitability. “Marketing mix model” is the model you’d use to make that type of prediction.
Marketing mix modeling is simply the act of taking historical sales and marketing time series data, and using statistical machine learning methods to uncover relationships between your core product and product marketing features and sales.
And marketing mix modeling? It is simply the act of taking historical sales and marketing time series data. Then we use statistical machine learning methods. These methods will uncover relationships between your core product and marketing features (as represented by the 4Ps) and sales. Once you’ve ascertained those relationships, you’ll be able to predict future sales and tweak your product marketing accordingly.
The entire point of marketing mix modeling is to quantify direct relationships between your marketing mix and sales for your business.
How does Marketing Mix Modeling Increase Profitability?
Since the entire point of marketing mix modeling is to quantify direct relationships between your marketing mix and sales for your business, there is not a lot of room for conflating the issue.
Once you’ve identified statistically (and economically) significant relationships between your marketing mix and actual sales for the business, you’ll be able to reliably predict what marketing mix will produce even more sales. Then, just adjust the product marketing strategy to increase profits.
More sales with the same (or less) marketing spend results in increased profitability. It’s as simple as that.
Learning How to Implement MMM
As far as books, a lot of stuff out there is for non-technical marketing people. It’s not that helpful for actually learning how to do machine learning implementation of marketing mix modeling. In fact, from what I’ve seen online, bloggers tend to make the topic A LOT more confusing than it actually needs to be.
There aren’t really any online courses on this topic yet, but you can actually start learning to do it for free by looking at this training documentation over at R-Studio. And to learn how to implement it in Python, you may want to check out this free demo over on Kaggle too.
If you enjoyed this blog post, please share it with your friends using the share bar below.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Data Product Manager Resume Template To Land The Job!
URL: https://www.data-mania.com/blog/data-product-manager-resume-template/
Type: post
Modified: 2026-03-17
Today we are going to talk about the data product manager resume and what you need to do to land the interview and get hired – with as little effort (on your part) as possible.
Make sure to read to the end because that’s where I’m going to show you my Data Product Manager Resume Template and how to get your own plug-in-play version of it for free.
YouTube URL: https://youtu.be/CIUu1nm_oQ0
If you prefer to read instead of watch, then read on…
For the best data leadership and business-building advice on the block, subscribe to my newsletter below and I’ll make sure you get notified when a new blog installment gets released (each week). 👉
As far as why I’m qualified to give advice on data product management – I first started building and leading data products way back in 2012, and since then I’ve managed and delivered data and machine learning information products that have been purchased and consumed by over 1.7 million people.
If you’re new around here… Hi, I’m Lillian Pierson and I support data professionals in becoming world-class data leaders and entrepreneurs.
While, yes – I can just give you the data product manager template… But you also need to see how to use it. So let me walk you through that process first.
Step 1. Find a representative Data Product Manager job posting
To pick a representative job posting, you have to start by getting super clear on what you want and what you have to offer. Start by answering the following questions:
What lifestyle requirements must the job afford you in order for you to be happy in the role?
Any income requirements? What are your income requirements?
As for the aspects of the data product manager role, what do you love doing (and are great at)?
How about the aspects of the data product manager role? What do you not love doing (and aren’t great at)?
I will demonstrate.
Lifestyle & Income Parameters
I have lived on an island in Thailand for 8 years. Well, I am not going anywhere. So, I would only look at job postings that are listed as “remote” on LinkedIn.
I’m an American living on the US Dollar and working with US Companies, so I need to set the location for job posting as “United States”. Jobs in developing countries won’t cover my living expenses, even here in Thailand.
Experience & Expertise Parameters
Within data science, analytics, and product management, my areas of expertise center around:
Data Companies, Products, and Services
Ecommerce
Digital Marketing
Engineering
As far as things I can hang my hat on, those could include:
Machine learning
Professional engineer licensure
Successful entrepreneur for 9 continuous years
Product development experience
Proven track record for leading highly profitable product launches
A good representative of data product manager roles in the marketing space
A good representative of data product manager roles with ecommerce specialization
A good representative of data product manager roles in the data space
Notice how the representative job postings are all in sectors where I have already planted my flag?
Now I am going to jot down the links of job posts that seem like a good fit.
1st job: https://www.linkedin.com/jobs/view/2587650613
2nd job: https://www.linkedin.com/jobs/view/2571805791
3rd job: https://www.linkedin.com/jobs/view/2658840200
As for an example of job postings that would not be a good fit for me and my background…
An example of a job posting that is not representative of what I’d look for.
Obviously, I am not going to pick a job posting as representative if it is looking for a decade of full-time, on-the-job data governance work. I don’t even have a decade of full-time employment experience, sooo… I would aim towards a job posting that aligns with my strengths. Honestly, this posting looks a lot like they are looking for a data manager with a product-bent. It’s a real estate start-up, which isn’t in my subject matter expertise… so this Juno Data Product Manager position is not representative of what I would be looking for if I were looking for a data product manager job.
Step 2. Generate the resume keywords
I have no affiliation with this resume keyword tool, but it’s free and it mines job postings for key words for you so I am going to use it. The tool is Resume Worded and you can check it out for yourself here: https://resumeworded.com/job-description-keyword-finder
How this works is you just go over to the job description and copy it into Resume Worded to identify resume keywords. I added all three descriptions from all the jobs I like because I want to get a generalized view of keyword priorities.
The tool requires you to upload your resume to act as a template, so I just fed it blank resume template. Of course, it isn’t going to find matches between the job posting and a blank resume template, but I don’t care about that – all I am looking for is a generalized representation of keywords that are a priority. It says that it uses NLP to fill in gaps and group words effectively but I suggest you look at the right side of what it returns to make sure that it’s model provided accurate predictions.
Thee original results look like this:
But when I eyeballed the sample data I saw some groupings that were misaligned. Those are:
Digital products
Analytics products
I copy out the results of the tool, apply some inferences and come up with a list of 18 important keywords, in order of decreasing importance. I notate the context of the word’s usage in parenthesis below:
Teams
Analytics (teams, partners, providers, products)
Design (teams, roadmap, product, features, applications)
Customer (-facing, success, opportunities, focused)
Engineering
Stakeholders
Building
Machine Learning (products)
MBA
Marketing
KPI
Product Design
Product Roadmap
Features
Strategy
Partnership
Functions
Product Development
I’d love to hear from you in the comments below… tell me, what’s your favorite thing about the data product manager role?!
Step 3. Populate your resume and its theme
From here, knowing what to emphasize on your data product management resume should be no-brainer. You just need to detail the ways your professional experience embodies the key words, making sure to add relevant details on as many quantitative results you’ve generated. If you did something cool and that you’re proud of, but that thing is not related to the keywords, I wouldn’t include it.
I’ll show you a snippet of the example resume I created based on my experience and the keywords identified above….
Speaking of careers in data product management, I’ve published a self-taught curriculum for data product managers that you can check out here.
Step 4. Customize your resume theme for the company
Notice how “design” is the #3 keywords on the list above? That’s because product managers are expected to at least be proficient in basic aspects of design (and managing design teams). The reason that company-themed resumes can be especially helpful in landing a job as a product manager is because data product managers are expected to be design-proficient.
Like I said, I created a free data product manager resume template for you to use (get it at the bottom of this post), but in addition to adding your professional details, I suggest you also customize the CV according to the brand colors and font of the company you are applying to.
To identify exactly what those are, I use the following free Chrome extensions:
FontPicker
ColorPick Eyedropper
So for my example, I looked at the website of Demand Science and customized my CV to their branding. It came out looking like this:
If you want to take a closer look at my own data product manager cv, you can do so here.
Branding your resume to the company only takes a few minutes inside Canva! Good luck!
Share the Love…
Hey, and if you liked this post, I’d really appreciate it if you’d share the love with your peers by sharing it on your favorite social network by clicking on one of the share buttons below!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Crisis In The Feed: How Media Monitoring Protects Tech Startups
URL: https://www.data-mania.com/blog/crisis-in-the-feed-how-media-monitoring-protects-tech-startups/
Type: post
Modified: 2026-03-17
Why Negative Publicity Spreads Fast in Tech
Technology companies operate in highly visible environments. Product launches attract coverage. Funding rounds invite scrutiny. User feedback circulates in real time on social platforms and review sites. This visibility creates opportunity, but it also amplifies criticism.
Startups often rely on rapid growth strategies and lean teams. A limited communications infrastructure can leave organizations vulnerable when issues arise. A single viral post or critical article can influence investor confidence, customer trust, and hiring efforts. Media ecosystems reward speed, which means incomplete information may travel widely before corrections appear.
The Role of Continuous Monitoring
Media monitoring involves tracking brand mentions across news outlets, blogs, forums, podcasts, and social media platforms. Early detection allows leadership to assess tone, accuracy, and potential impact. Continuous tracking also identifies patterns rather than isolated comments.
Digital tools aggregate data from multiple sources and highlight spikes in conversation. Teams can monitor sentiment trends, track keywords, and flag emerging issues. Broadcast media monitoring expands this visibility by capturing coverage from television and radio, which can influence audiences beyond online communities.
Comprehensive tracking creates a clearer picture of how a company is portrayed. Leadership can differentiate between routine criticism and signals of a larger reputational shift.
From Detection to Strategic Response
Identifying negative publicity is only the first step. Effective response requires a structured approach. Startups should establish clear internal processes that define who evaluates coverage, who drafts statements, and who communicates with stakeholders.
Transparency is critical. Public statements should address concerns directly, clarify facts, and outline corrective actions when necessary. Silence may be interpreted as indifference, while overreaction can escalate minor issues.
Speed matters, but accuracy matters more. Teams must verify claims before responding. Coordinated messaging across executive leadership, customer support, and marketing ensures consistency. When employees understand the situation and the company’s position, internal alignment supports external credibility.
Protecting Investor and Customer Confidence
Negative publicity can affect funding timelines and partnership discussions. Investors monitor press coverage as part of risk assessment. Customers evaluate trustworthiness through reviews and headlines. Active monitoring helps leadership prepare for difficult conversations before stakeholders raise questions.
Proactive communication can also reinforce trust. Sharing updates about product improvements, security audits, or policy changes demonstrates accountability. Clear documentation of corrective measures shows that the organization learns from setbacks rather than dismissing them.
Long-term reputation management extends beyond crisis response. Regular analysis of media trends reveals recurring themes. Addressing root causes reduces the likelihood of repeated issues. Monitoring can also identify positive stories that deserve amplification, helping balance public perception.
Media scrutiny is an unavoidable aspect of growth. Structured monitoring and disciplined response strategies provide a safeguard against reputational damage. Organizations that track conversations, assess risks, and communicate transparently position themselves to manage criticism constructively. Look over the infographic below for more information.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Naysayers of Decentralized Web 3.0: What They’re Saying and Why?
URL: https://www.data-mania.com/blog/naysayers-of-decentralized-web-3-0-what-theyre-saying-and-why/
Type: post
Modified: 2026-03-17
If you’re like most data professionals, you are still scratching your head about the decentralized web, or “Web 3.0”. You are also thinking about whether it’s really here to stay. And while the buzz about decentralized web has been heating up lately, it has included quite a bit of criticism. These criticisms are from some big names in both data and tech. The likes of Elon Musk and former Twitter CEO Jack Dorsey have spoken out online about their skepticism of Web 3.0. They were questioning its legitimacy as well as the people who are really behind it. Where do these concerns stem from, though? The answer lies in the intended purpose of the decentralized web.
Curious? Read on…
What is Decentralized Web 3.0?
Web 3.0 is the latest iteration of the Internet. Existing roughly from 1990 until 2000, Web 1.0, was primarily basic HTML webpages with little interactivity. Web 2.0 is the Internet as we have known throughout the 2000s and 2010s. Google and Facebook, the world’s biggest tech companies, centralize and control Web 2.0. Web 3.0 aims to give ownership of the Internet back to the masses and make it a community-controlled space, much like it was in the 1990s. This is decentralization and it stems from cryptocurrency.
Cryptocurrencies are run through a public ledger managed by millions of individuals around the world, known as the blockchain. Various currencies are run slightly differently. But the biggest players in crypto today, such as Etherum, are built on blockchain technology. No banks, middlemen, corporations, or financial institutions get to control decentralized finance. It is all in the hands of individuals. This can be both an advantage and a weakness. But the ideology behind decentralized finance and cryptocurrencies is a major force behind the decentralized web.
True Decentralization
Former Twitter CEO Jack Dorsey is a proponent of decentralization but a critic of Web 3.0. His reasoning highlights one of the most substantial concerns that is growing around Web 3.0. Dorsey has pointed out that the decentralized web will never actually exist. That’ll happen as long as venture capitalists, limited partnerships, and other corporate entities are funding Web 3.0 projects.
You don’t own “web3.”
The VCs and their LPs do. It will never escape their incentives. It’s ultimately a centralized entity with a different label.
Know what you’re getting into…
— jack⚡️ (@jack) December 21, 2021
The problem is that as long as these powerful corporate parties are funding the decentralized web, they can impose regulations and restrictions on developers. These motions could force developers to do things that go against the philosophy of the decentralized web. For example, a corporate sponsor may want to collect data on users which they could sell without a user’s knowledge or permission. This is one of the greatest issues with Web 2.0, which the decentralized web aims to solve.
A Question of Security with Decentralized Web
One of the oldest and most common concerns with cryptocurrency is vulnerability to hackers. The same concerns extend to Web 3.0 on an even larger scale. This is a direct result of the decentralized nature of Web 3.0 and crypto. When everyone is running a currency, for example, and everyone can see every transaction ever made, how is that data protected?
Security essentially falls into the hands of individuals. Some see this as a necessary benefit. Those familiar with cybersecurity regulations and best practices are used to creating their own digital security measures. They know their rights and the methods they can use to keep hackers out of their data and devices. However, for most people cybersecurity is a complicated concept that they leave to antivirus software developers. The skyrocketing rates of phishing attacks are proof enough that the average Internet user is vulnerable to cyber-attacks.
Would a decentralized web really be better for most people? Centralization puts control in the hands of a few powerful entities, but a centralized system can also provide some measure of standardized protection over users. A decentralized web would be essentially unregulated, which makes security a far greater issue.
Decentralized Web – Issues With Blockchain
While Elon Musk’s Tweets about Web 3.0 have been less thorough than those of Jack Dorsey, his actions related to the decentralized web reveal at least one major concern. The Tesla CEO made headlines in May of 2021 when he abruptly halted Tesla’s acceptance of Bitcoin as a form of payment for the company’s popular electric cars. Musk explained in a Twitter thread that the decision was due to the massive environmental impact of Bitcoin mining.
Musk’s announcement draws attention to a major issue with blockchain and decentralization. Bitcoin mining is essentially the process of verifying Bitcoin transactions. “Miners” all over the world do this by running extremely high-power computers that solve complex puzzles to validate transactions in blockchain. These computers draw monumental quantities of energy, most of which comes from fossil fuels, which are detrimental to the environment. Today, Bitcoin consumes hundreds of terawatt hours of electricity.
If the decentralized web is going to be run in a similar fashion, built on blockchain and the processing power of billions of computers, sustainability needs to be addressed. The emissions and carbon footprint of blockchain today will not be sustainable in the long term, certainly not long enough for Web 3.0 to truly become a reality.
Web 3.0: Innovation or Buzzword?
The decentralized web is on the verge of being an innovation in data privacy, but Web 3.0 is in many ways a failed attempt to achieve that. Funding remains a threat to true decentralization. Moreover, security and sustainability shortcomings have crippled the adoption of the technology itself. Once these core issues can be solved, though, the decentralized web could become the future of the Internet, one where users have control over their own data and can truly browse freely.
More To Explore…
If we’ve got you scratching your head with all this talk on Web 3, we invite you to uncover your most high-potential data superpower by taking our free Data Superhero Quiz. It’s a fun, 45-second experience that will show you the most powerful data career path for you given your skillsets, passions, and personality.
Also, I’d like to encourage you to save your seat for our upcoming Story Hour, live on LinkedIn – March 9, 10 am ET, where our former client, Stephen Taylor, will share the exciting story of how he sold his data consulting business to work as a CIO for Vast Bank, where he launched the first ever Crypto Bank in the United States!
Hey! If you liked this post, I’d really appreciate it if you’d share the love by clicking one of the share buttons below!
A Guest Post By…
This blog post was generously contributed to Data-Mania by Shannon Flynn. Shannon Flynn is a freelance blogger who covers business, cybersecurity and IoT topics.
You can follow Shannon on Muck Rack or Medium to read more of her articles.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Part 1 – Data Strategy Skills – The Ultimate Uplevel for Business-focused Data Professionals with Lillian Pierson
URL: https://www.data-mania.com/blog/part-1-data-strategy-skills-the-ultimate-uplevel-for-business-focused-data-professionals-with-lillian-pierson/
Type: post
Modified: 2026-03-17
In this episode, Lillian touches on the exponential growth the data science industry has had in the past few years and shares how hard it was for her to get useful data content online when she first started. That’s part of why she is interested in helping others and making knowledge available to everyone.
If you want to know more or get in touch with Lillian, follow the links below:
Weekly Free Trainings: We currently publish 1 free training per week on YouTube! https://www.youtube.com/channel/UCK4MGP0A6lBjnQWAmcWBcKQ
Becoming World-Class Data Leaders and Data Entrepreneurs Facebook Group: https://www.facebook.com/groups/data.leaders.and.entrepreneurs
LinkedIn: https://www.linkedin.com/in/lillianpierson/
The Data Entrepreneur’s Toolkit: A recommendation set for 32 free (or low-cost) tools & processes that’ll actually grow your data business (even if you still haven’t put up that website yet!). https://www.data-mania.com/data-entrepreneur-toolkit/
Listen on Apple Here: bit.ly/df-apple
Listen on Spotify Here: bit.ly/df-spotify
Watch it on YouTube: https://youtu.be/nBU0dZHHHB8
Discover your inner Data Superhero!
Most of the time, custom advice is all you need to achieve both your dream salary AND the satisfaction that you crave from your data career.
In our free, fun, 45-second data career path quiz, you’ll uncover your inner Data Superhero type and get personalized data career recommendations that directly align with your unique combination of data skills, personality and passions.
Take the Data Superhero’s Quiz today!
Get the Data Entrepreneur’s Toolkit
There’s always that data professional who starts an online business and hits 6-figures in less than a year. Now? It’s your turn and we’re ready to help get you there with our Data Entrepreneur’s Toolkit (designed to help you get results for your data business fast).
It’s our favorite 32 tools & processes (that we use), which includes:
Marketing & Sales Automation Tools, so you can generate leads and sales – even in your sleeping hours.
Business Process Automation Tools, so you have more time to chill offline, and relax.
Essential Data Startup Processes, so you feel confident knowing you’re doing the right things to build a data business that’s both profitable and scalable.
Download the Data Entrepreneur’s Toolkit for $0 here.
Execute Upon the Data Strategy Action Plan
This is our crowd-favorite data strategy product. No long video trainings, no books to read, no needless theory. Just clear, concise guidance on what your next data strategy steps should be, starting today.
It’s a step-by-step checklist & collaborative Trello Board planner for data professionals who want to get unstuck & up-leveled into their next promotion by delivering a fail-proof data strategy plan for their data projects.
There are also 2 bonus guides, if you need help improving communications with your senior executives and stakeholders
And, it comes with a bonus, members-only community, if you’d like a private sounding board for getting valuable input from other data strategists.
Start executing upon our Data Strategy Action Plan today.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## 4 steps to selecting an optimal analytics tool
URL: https://www.data-mania.com/blog/4-steps-to-selecting-an-optimal-analytics-tool/
Type: post
Modified: 2026-03-17
If you’re responsible for selecting an analytics tool to help your data project or product achieve a strong ROI, then this article is for you. That’s because I’m about to share with you a battle-tested 4-step process for planning a data-project and exactly what you need to do to select the optimal analytics tool for boosting the ROI of your next data project.
In case you’re new around here, I’m Lillian Pierson and I support data professionals to become world-class data leaders and entrepreneurs. As far as why I am qualified to teach on how to select an optimal analytics tool, so far I’ve educated over 1.3 Million people on data science and AI, I’ve consulted for 10% of Fortune 100 companies, and I’ve been delivering technical and strategic plans since 2007 for organizations as large as the US Navy.
Data tools are only ever as effective as the decision-making of the people who select them.
What’s in here…
If you’re reading this then you probably know that data tools are only ever as effective as the decision-making of the people who select them. Data project success propels a data career forward, and if you’re looking to land a promotion into a data leadership position, this article is for you.
The main four points covered in this article are:
My proprietary STAR framework for leading profit-forming data projects
How to leverage my proprietary STAR framework to plan profit-forming data projects;
The real reason why most data projects fail and what you can do to protect yours of course;
My top 3 data selection tips so that you can pick the analytics tool that’s sure to boost your project’s ROI
Spoiler alert: This article also comes with a video demonstration I created that shows you how to select an optimal analytics tool. It’s over on Logi Analytics’ website here. I will be directing you to the full training on analytics tool selection to get you all of the above-promised 👆 deliverables.
First let me ask you a question…
Let’s first start off with a fun game.
I want you to imagine that you have access to an analytics tool that you can embed directly into software solutions AND that is flexible enough to immediately provide your customers the self-service access to the data they need and want, all without excessive wait times or costly overhead spent on inefficient computing structures and processes.
How would you use that? How do you think that would improve your company’s bottom line? I would love it if you’d share your thoughts in the comments below.
Don’t worry if you’re not sure how to use an awesome analytics tool like that 👆 to increase your business’ bottom line. It’s not your fault, because if you’re like most data professionals, then you haven’t been trained on how to connect the cost of data operations to actual hard numbers in the business (ie; profits and revenues). The good news is that the rest of this post will get you started on how to do that…
No Data Strategy = Epic Data Project Failures
Actually, back in 2013, the senior leaders in the world’s most prestigious cancer treatment center also thought that they had found a “holy grail” of data tools. And they believed that, armed with the predictive AI, they would finally be able to eradicate cancer once and for all.
Four years later though, all they had seen was $62 Million in sunk costs, no results, and a slew of outraged oncologists who were happy to go public with defamatory remarks about the data product that had done them so much harm. The tool was Watson for Oncology and the client was M.D. Anderson.
EVERY ANALYTICS TOOL IS NOT THE SAME. And sometimes, it’s not even the tool’s fault that a project fails. Sometimes, the root cause of the failure is in the decision-making of the person who chose to use it in the first place.
So please, take me seriously when I say, EVERY ANALYTICS TOOL IS NOT THE SAME. And sometimes, it’s not even the tool’s fault that a project fails. Sometimes, the root cause of the failure is in the decision-making of the person who chose to use it in the first place.
But, if you let this happen to you or have been on the losing side of a data project, let me start by telling you – IT’S NOT YOUR FAULT.
With all of the online data science training courses out there, and all of the universities out there offering analytics degrees, the one thing that most of these data science training institutions don’t teach is Data Strategy. Data strategy is imperative to generate return on investment from data projects and products. And what you’re about to get is the data strategist’s cliff notes on data tool selection. You can save your company from epic failures like what M.D. Anderson had with Watson for Oncology.
Introducing the STAR framework
I developed this 4-step framework out of a decade of experience in delivering consulting services for some of the world’s largest organizations.
The STAR Framework For Analytics Tool Selection and Consulting
The STAR goes like this:
S – Survey the Industry (TL;DR You spend time reviewing relevant use cases and case studies)
T – Take stock and inventory of your organization (TL;DR You collect and generate documentation that describes all aspects of your company. This involves surveys, interviews,and requests for information)
A – Assess your organization’s current state conditions (TL;DR Identify gaps, risks, opportunities, and select an optimal data use case given specifics for your company)
R – Recommend a strategy for reaching future state goals (TL;DR Define a strategic plan for successfully implementing upon that data use case)
* If you want to go in for deep-dive training on the STAR framework, go ahead and check out this post here.
For now, what you need to know about the STAR Framework is, you would want to rinse, wash and repeat every 18 months.
That means, if you create a data strategy plan today or within the next 6 weeks, you need to go back and revisit, update, and maybe expand that within about 18 months because of the nature of our industry – ie; the rapid changes we see across the data industry.
Where an Analytics Tool Selection Fits In
Analytics tools selection fits in the “R – Recommendations” phase of the STAR framework. That’s because you can’t pick the right tool until you’ve clearly identified the use case. You also need to know which vendors and existing technologies you already have on hand to fulfill that use case. You’d identify both of these in the first three steps of the STAR framework. Then, select a tool when you’re ready to make recommendations….
The Real Reason Most Data Analytics Projects Fail
I recently did a poll over on LinkedIn:
Many of the respondents answered this question correctly.
The real reason that most data projects fail is a lack of careful data project pre-planning. And the following data tool selection method is going to take you a long way in prudent project pre-planning so you can avoid selecting the wrong data tool for the job.
How to Use the STAR Framework to Select the Perfect Analytics Tool: Leading Profit-Forming Data Projects
Please note, I offer an entire product that shows the detailed approach for applying this framework. But there is not enough space in a single blog post to cover the entire thing.
So, what I’ve done is to focus only on how to use the STAR Framework. I’ll teach you how to select an optimal analytics tool. Head over to Logi’s website and study through to the end of this training. Then, you’re going to get my full demonstration of how to use this process. I’ll teach you how to go about selecting the perfect analytics tool for your data project.
Spoiler alert: It involves Fuzzy-MCDM…
🎁 You’re also going to get my top 3 data selection tips! Pick the analytics tool that’s sure to boost your project’s ROI.
CLICK HERE TO WATCH THE DEMONSTRATION ON THE LOGI ANALYTICS WEBSITE
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## 4 Types of Data and How to Migrate it to the Cloud
URL: https://www.data-mania.com/blog/4-types-of-data-and-how-to-migrate-it-to-cloud/
Type: post
Modified: 2026-03-17
Curious to know what types of business data are out there? Keep reading to learn more about the 4 types of data and how to migrate it to the cloud.
YouTube URL: https://youtu.be/BQRTXiltqJg
If you prefer to read instead of watch, then read on…
For the best data leadership and business-building advice on the block, subscribe to my newsletter below and I’ll make sure you get notified when a new blog installment gets released (each week). 👇
What Is Data Migration?
Data migration involves the transfer of data from one data storage system, format or computer system to another. Organizations choose to migrate their data for various reasons—these include adopting a third-party cloud vendor, upgrading or replacing equipment like servers, consolidating websites, maintaining infrastructure, upgrading software, moving applications or databases to a new environment, and merging with another company.
In a modern IT environment, data migrations commonly involve moving data to the cloud. In this article I’ll cover 4 types of data that exist in almost every organization, and show how to build a winning cloud migration strategy for each one.
Types of Business Data
Structured Data
Structured data, also known as quantitative data, is highly ordered and is easy for Machine Learning algorithms to decipher. It is typically managed using Structured Query Language (SQL), often in a relational database that allows users to quickly create and access structured data.
Structured data may include names, dates, addresses and payment card numbers, which can pose liabilities. Compared to unstructured data, structured data is easier to use for ML algorithms, business users and a variety of tools.
Applications, websites and databases can function more efficiently if the structured data is close, so they are often stored in the same location as the data (or both are placed in the cloud). This allows users to access data remotely through an application.
Unstructured Data
Unstructured data is not structured according to a predefined schema or data model, but it does have an internal structure. This data can include rich media such as images and video in a wide range of formats, and can be stored in non-relational databases (i.e. NoSQL). The data is stored in the native (original) formats of the data files—the data stays undefined until it is required.
Additional advantages of unstructured data include faster collection rates and flexible storage in a data lake (priced on a pay-by-use basis). In the modern data ecosystem, unstructured data is often processed and enriched using external APIs, such as data pipeline APIs, AI services, and video APIs.
When you move data from a file system to the cloud, you have to make sure the file metadata and ACLs are all preserved, so you can use your existing permissions to access the data. Migration tools can help minimize the issues associated with migrating unstructured data. It is important to prioritize your data by analyzing your content to determine the migration approach.
Semi-Structured Data
Semi-structured data lacks a strict structural framework and cannot be organized in a relational database, but it does retain some structure. This includes open-ended (unstructured) text arranged by subjects. For example, emails can be sorted according to semi-structured categories like sender, recipient and date.
Advantages of semi-structured data include support for hierarchical data to simplify complex relationships, avoidance of complicated translations of object lists into a relational database, and serialization of objections in a lightweight library.
Sensitive Data
Sensitive data is information that requires strict data protection and cannot be accessed without special permissions. This includes both physical and digital data, which is stored and used restrictively to ensure privacy and comply with legal obligations.
According to most data regulations, sensitive data is defined as any information that cannot be disclosed without authorization—this includes personally identifiable information (PII). Data is usually protected both in transit and at rest using encryption.
Developing an Effective Strategy for Migrating Data to the Cloud
The following steps can help you build an effective data migration strategy for the cloud.
Objective Assessments
It is important to know your existing infrastructure, database and software schemas in order to effectively plan your migration strategy. Start by evaluating business use cases of a data lake. Consider applying security and prioritizing the data or applications you want to move first. This will help inform the effort, timeframe and cost of migration.
Proof of Concept on a Subset of Data
Before you commit to a new cloud provider, it is recommended you test the waters by developing a proof of concept (POC). The POC will help you compare and validate performance, features and network issues. Test your workload to establish insights about the cloud services provided (such as storage) and security requirements (e.g. controls), and evaluate the scaling of clusters.
Production Build
Once you have verified that the cloud provider and migration model meet your requirements, you can begin the actual migration process. You should move your data and applications to the cloud according to a phased approach, taking into account:
Infrastructure (how to migrate storage and compute, networking, sizing and scaling)
Data ingestion retooling (to move date from an on-prem platform to the data lake in the cloud)
Data security and access governance
Cloud resource usage
Inventory of on-prem data
Translation of data transformation pipelines to cloud mechanisms
Strategy for migrating applications (i.e forklift or rewrite)
Migration of historical data
Data lake management
Post-Production
Once you’ve successfully re-hosted your applications and data, you can optimize their performance by automating processes in your new infrastructure. Use an automatic testing framework, perhaps adopting an infrastructure-as-code (IaC) approach, in order to streamline the deployment process. Manually double-check the more critical aspects of your infrastructure, such as performance, security and compliance.
Data migration projects can be tricky and require a whole lot of planning in order to overcome data migration challenges. Whether you’re looking to learn more about data migration basics or even learn about cloud migration, consider this video I did about Evergreen Data Migration Strategy as a guide to data migration and everything you must know in order to do it successfully. Check it out here.
Adapting Your Strategy to Different Types of Business Data
A data migration strategy is not one-size-fits-all. Let’s see how to adapt your strategy to each of the 4 types of business data described above:
Structured data—
For structured data, prefer to use automated tools offered by your cloud provider. All cloud providers have automated systems that can take structured data systems, in particular databases, assess them for migration compatibility, and move them reliably to the cloud.
Unstructured data—
It is important to evaluate if your unstructured data is used by production applications, and how. If the data is commonly accessed by production applications and is mission critical, consider an online migration. In this process, on-premises data sources are continuously synchronized with the cloud. Another factor is the size of the data—if the dataset is very large, you can use storage appliances to physically ship the data to your cloud provider without having to transfer it over a WAN.
Semi-structured data—
for semi-structured data, integrity is an important consideration. Perform checksums to ensure the data hasn’t changed when being copied from source to destination. If possible, move entire VMs or file systems to the cloud. This is the best way to preserve the integrity of the data.
Sensitive data—
when transferring sensitive data, it is important to evaluate if the cloud environment is sufficiently secure to meet your security and compliance requirements. If not, you can perform on-the-fly data masking. This involves modifying sensitive parts of your dataset as they are copied to the cloud environment. For example, you can mask customer names, social security numbers or other personally identifiable information (PII) to avoid having to meet compliance requirements in your cloud environment.
I hope this will be helpful as you plan your successful data migration to a public cloud environment.
If you want me to do all the heavy-lifting for you, you can get my evergreen analytics strategy framework. It comes with the 44 sequential action item steps that you need to take in order to create a fail-proof data strategy plan for your company. It’s called the Data Strategy Action Plan.
The Data Strategy Action Plan is a step-by-step checklist & collaborative Trello Board planner for data professionals who want to get unstuck & up-leveled into their next promotion by delivering a fail-proof data strategy plan for their data projects.
Start executing upon our Data Strategy Action Plan today.
Also, I have a free Facebook Group called Becoming World-Class Data Leaders and Entrepreneurs. I’d love to get to know you inside there, so I hope you can join our community here.
Hey! If you liked this post about the types of business data, I’d really appreciate it if you’d share the love with your peers! Share it on your favorite social network by clicking on one of the share buttons below!
NOTE: This blog post contains affiliate links that allow you to find the items mentioned in this article. While this blog may earn minimal sums when the reader uses the links, the reader is in NO WAY obligated to use these links. Thank you for your support!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## What Is Synthetic Data and Why Is It Critical for MLOps and Computer Vision?
URL: https://www.data-mania.com/blog/what-is-synthetic-data-and-why-is-it-critical-for-mlops/
Type: post
Modified: 2026-03-17
In your steps towards a data-driven AI approach, this blog post will expose you to the following concepts – what is synthetic data, what is its importance to MLOps and how it could impact computer vision.
What Is Synthetic Data?
Synthetic data is information generated by a man-made process, not by real events. A variety of algorithmic and statistical methods can generate synthetic data. Training machine learning models use synthetic data as an alternative to real datasets, which can be costly and time consuming to collect.
Benefits of using synthetic data include scaling up data at low cost, creating data that adheres to specific conditions (for example covers specific edge cases), and overcoming data privacy and data protection regulations such as GDPR.
Synthetic Datasets Use Cases
Data is a critical part of any machine learning initiative. Diverse industries use synthetic data to speed up AI projects:
Cybersecurity—synthetic data can be used to train models to detect rare events like specific cyber attack techniques.
Automotive—synthetic data is used to create simulated environments for computer vision algorithms used in autonomous vehicles, and testing safety and collision avoidance technologies.
Healthcare—scientists are creating synthetic genomic data that can help speed time to market for new drugs and treatments.
Financial services—synthetic time-series data makes it possible to train algorithms on rare events and exceptions, without compromising privacy.
Media—synthetic data can be used to train recommendation algorithms for products or content without using real customer data.
Gaming—synthetic data is helping develop new forms of interaction including augmented reality (AR) and biometric detection.
Retail—synthetic data can help retailers simulate how items are placed in a store, to enable better automated detection of products on a shelf.
Importance of Data-Centric AI for MLOps and ML Engineering
Machine Learning Operations (MLOps) is a set of practices for deploying and maintaining production ML models efficiently and reliably. However, there are challenges to running a model after deployment:
Latency issues—ML engineers must consider how to run the model efficiently in production to provide a positive user experience. In some cases this can be challenging because end-user devices have limited computing power.
Fairness and bias—bias can easily creep into ML systems if left unchecked. Constant, close inspection is essential for maintaining a system’s fairness and minimizing bias.
Data drift—the real world is dynamic, so models trained on static data sets quickly move out of sync with changes affecting real world data.
Data-centric machine learning is an approach that keeps the ML model static while continuously improving datasets that can better simulate the real world. This approach is more effective than model-centric ML, where engineers tweak the model while training it on static data sets, which were often of low quality.
Combined with synthetic data, data-centric ML helps address the main challenges of maintaining machine learning models. Synthetic data can help prevent model bias, by augmenting data to ensure sufficient diversity and randomness. It can also minimize data drift, by ensuring training data is adaptable to changing real world conditions.
Data-centric decision-making and synthetically generated data provide major advantages for MLOps teams. Adopting data-centric ML shifts team’s focus to building data-driven pipelines that can improve AI performance by feeding models with fresh, high quality data.
How Can Synthetic Data Generation Help Computer Vision?
Collecting diverse, real-world data with the necessary characteristics when building visual data sets is often time-consuming and prohibitively expensive. Correct annotation is essential after collecting data points to ensure accurate outcomes. The data labeling process often takes months and consumes precious resources.
Synthetic data is programmatically generated data. So, there’s no need for manual collection or annotation of data. The annotations can be highly accurate and the synthetic data highly realistic, supplementing the otherwise insufficient real-world data. Synthetically generated datasets can also represent real-world diversity more accurately than some real data sets.
One popular application for computer vision is realistic image generation—research in this field has driven advances in GAN technology like the NVIDIA CycleGan, StyleGANm, and FastCUT models. These GANs can synthesize highly accurate images using only public datasets and labels as input.
A major issue with datasets sourced from the real world is the prevalence of biases. For example, sourcing rare (but possible) events may be difficult but is crucial for building an accurate image generation model. One practical example is an autonomous vehicle’s computer vision system, which must be able to predict and interpret various road conditions that may rarely occur in the real world (i.e., car accidents). Another example is visualizing rare diseases for medical imaging purposes.
Deep learning computer vision algorithms can train on synthetic images and videos (for example, car accidents in various circumstances, weather, lighting conditions, and environments). These data sets offer a fuller range of possible conditions and events, making the computer vision model more reliable and improving the safety of self-driving cars.
Conclusion
In this article, I explained the basics of synthetic data and showed how it can solve key challenges of machine learning operations:
Bias—synthetic data can generate data that is more balanced and representative of the real world.
Data drift—synthetic data can be easily adapted to changing real world conditions.
In addition, I described how synthetic data is transforming computer vision initiatives by enabling, for the first time, automatic creation of rich image and video data.
I hope this will be useful as you take your first steps towards a data-driven AI approach.
Hey! If you liked this post, I’d really appreciate it if you’d share the love by clicking one of the share buttons below!
A Guest Post By…
This blog post was generously contributed to Data-Mania by Gilad David Maayan. Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Samsung NEXT, NetApp and Imperva, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership.
You can follow Gilad on LinkedIn.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## High ROI advertising in a post-cookie world EXPLAINED
URL: https://www.data-mania.com/blog/advertising-in-a-post-cookie-world/
Type: post
Modified: 2026-03-17
You don’t need to be a marketing expert or data scientist to see firsthand the shift in mindset among consumers in the online space. In this post-cookie world, the masses are no longer willing to sit back and accept the way that their information is handled.
If we are honest about it, did consumers really understand the extent to which their habits and preferences were being used? Maybe, maybe not.
Note: This article was sponsored by SORT™, but all opinions are my own and I would never recommend a product that I don’t truly believe in myself!
If you prefer to read instead of watch then, read on…
Subscribe to my email newsletter below for the best data leadership and business-building advice on the web, and I’ll notify you when a new blog gets published 👇
What we know for sure is that privacy in 2022 has become paramount and a non-negotiable for online consumers. It’s not news that tracking cookies are in the process of being banned across the internet. Although advertising in this post-cookie world is still new to many, media companies were quick to jump on the band wagon.
Browsers such as Firefox and Safari have already phased out third-party cookies. Google Chrome, which holds almost 70% of the market share (as of June 2022) is coming up quickly behind them with its own declaration of being rid of third-party cookies by the end of 2023. Advertisers, marketers, and publishers are unsurprisingly concerned about what that will mean for the future of advertising.
In the post-cookie world, what opportunities will businesses have to maximize their return on investment (ROI)? As we know, cookies advertising has been used by traditional advertisers to successfully track conversions. Without using cookie tracking, how can they qualify or measure their advertising efforts and if they are working or not?
The question of the hour though is what does the future of advertising look like post cookies?
Consumers Want Data Privacy Solutions
The root of this upheaval in privacy regulations is the consumer’s need and want for more data privacy. And, why wouldn’t they?
There is nothing wrong with the concept of wanting to have your personal data safeguarded.
If anything, we could stand to be grateful for this awakening. Regulators across the world, within countries and industries, are putting into practice privacy laws to fulfill this need – which is more than a need, it’s a right.
Some examples of this are GDPR, short for General Data Protection Regulation in Europe, Data Privacy Laws in the US, and California have also come out with similar data privacy regulations they’re calling CCPA (California Consumer Privacy Act) As always, the big players have set the precedent. It is only a matter of time before smaller nations follow suit.
New Privacy Safe Advertising
How can advertisers now adhere to new guidelines and regulations surrounding data protection and privacy whilst still seeing ROI? Consequently, advertisers are in need of “privacy-safe” solutions. A solution that will allow them to see results and track conversions, but that does not infringe on the data privacy rights of consumers online.
Now, you might be wondering what privacy safe really means, or at least how it looks in real situations. Here are some characteristics that will allow you to quantify whether or not an advertising solution is privacy safe or not.
Personal Identifiable Information or PPI collection is the gathering of information that can help to distinguish one person from another, information such as a social security number, date of birth, or simply their name. Clearly, PPIs are not privacy safe and the collection of such data infringes on these regulations set out by the likes of GDPR and CCPA.
However, there is an exception to this rule.
First-party data is any personal information that has been given willingly and knowingly by the visitor of the website. Examples of this would be newsletter sign-up forms. So we can see the biggest difference here is by using third-party cookies, data is collected without knowledge or without expressed consent.
The best thing you can do to safeguard your business is to make sure that your brand is compliant with data privacy laws. A bit of a no-brainer, right?
Consumer Sentiments on Privacy Safe Advertising Solutions
Undertone and Lucid carried out a survey of 1,000 participants aged between 18-70 from the US. The purpose of this survey was to establish whether consumers really care and whether a brand goes the extra mile in terms of protecting the data privacy of their website and their website users.
Their findings give a very clear picture of how consumers really feel about their privacy, but not only that, it provides vital information about what consumers now expect from brands in the post-cookie world.
74% of respondents would like advertisements to have a clearly visible seal guaranteeing that the brand is not tracking.
87% of respondents said they have noticed when an advertisement follows them around. Of those, 46% find it suspicious, 41% think it is creepy and 40% are annoyed by it.
83% of people are unhappy that they do not know if a brand is tracking them.
53% of consumers favor brands that protect their privacy.
Source; Undertone article: “New Survey Reveals that Consumers Want Digital Ads to Carry a “Privacy Guaranteed” Seal” Dec 2021
The Solution for Privacy Safe Advertising In a Post-Cookie World?
We know that traditional advertising works by collecting PII about users and their past behaviors in order to cluster them into similar groups by their interests. Once you know the individual’s interests, you can then target them at mass with advertising. The issue with this approach is that it directly infringes on the consumer’s data privacy.
A solution that Data Mania can recommend is Undertone’s SORT ™ Technology. It is the only advertising solution that we’ve seen that checks all the privacy safe boxes, so to speak.
SORT ™ stands for Smart Optimization of Responsive Traits. It is the only cookieless targeting solution that allows advertisers to reach their audience at the exact moment that they are most receptive to seeing an ad. It does not use any cookies whatsoever and it doesn’t track any personally identifiable information. Lastly, it sees only real-time cookie lists, and data signals and never any Information about the user’s past behaviors.
Furthermore, it uses proprietary machine learning to predict customer mindset in real-time based solely on cookieless signals, all without browser limitations or the device limitations that traditional advertising solutions are currently facing.
Here are a couple more benefits of using SORT ™ by Undertone:
Complies with GDPR and CCPA
Certified cookieless by Neutronian
Consumer-friendly
No integration or opt-in needed by advertiser, publisher or consumer
Out-performing cookie-based tactics by up to 2X
Be sure to visit the SORT on Undertone’s website to learn more and check out the case studies while you are there.
If you enjoyed this post, spread the word by sharing it on your favorite social network by clicking on one of the share buttons below!
For the new and aspiring data entrepreneurs reading this, don’t forget to check out my masterclass ‘4 Steps to Monetizing Data Skills’, where I’ll show you how to repackage and market your data skills through your own business.
Note: This article was sponsored by SORT™, but all opinions are my own and I would never recommend a product that I don’t truly believe in myself!
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## The Importance of Education and Mentorship in Data
URL: https://www.data-mania.com/blog/the-importance-of-education-and-mentorship-in-data/
Type: post
Modified: 2026-03-17
In the world of data science, skills are constantly changing and evolving. Lillian shares insight into how mentorship can help you to develop and hone your skills while you expand your knowledge. The conversation also covers the need for a data leader to have a strong understanding of the company’s goals and objectives, as well as an idea about where their company should be going in order to stay competitive in today’s digital world.
If you want to know more or get in touch with Lillian, follow the links below:
Weekly Free Trainings: We currently publish 1 free training per week on YouTube! https://www.youtube.com/channel/UCK4MGP0A6lBjnQWAmcWBcKQ
Becoming World-Class Data Leaders and Data Entrepreneurs Facebook Group: https://www.facebook.com/groups/data.leaders.and.entrepreneurs
LinkedIn: https://www.linkedin.com/in/lillianpierson/
The Data Entrepreneur’s Toolkit: A recommendation set for 32 free (or low-cost) tools & processes that’ll actually grow your data business (even if you still haven’t put up that website yet!). https://www.data-mania.com/data-entrepreneur-toolkit/
Listen on Apple Here: https://podcasts.apple.com/gb/podcast/hub-spoken-data-analytics-chief-data-officer-cdo-strategy/id1350941579?ls=1&mt=2
Listen on Spotify: https://open.spotify.com/show/07R8OX5jhSbq44IcyjDJ5V?si=mjnrP-vwRuOiC8JDcxXsaQ
Discover your inner Data Superhero!
Most of the time, custom advice is all you need to achieve both your dream salary AND the satisfaction that you crave from your data career.
In our free, fun, 45-second data career path quiz, you’ll uncover your inner Data Superhero type and get personalized data career recommendations that directly align with your unique combination of data skills, personality and passions.
Take the Data Superhero’s Quiz today!
Get the Data Entrepreneur’s Toolkit
There’s always that data professional who starts an online business and hits 6-figures in less than a year. Now? It’s your turn and we’re ready to help get you there with our Data Entrepreneur’s Toolkit (designed to help you get results for your data business fast).
It’s our favorite 32 tools & processes (that we use), which includes:
Marketing & Sales Automation Tools, so you can generate leads and sales – even in your sleeping hours.
Business Process Automation Tools, so you have more time to chill offline, and relax.
Essential Data Startup Processes, so you feel confident knowing you’re doing the right things to build a data business that’s both profitable and scalable.
Download the Data Entrepreneur’s Toolkit for $0 here.
Execute Upon the Data Strategy Action Plan
This is our crowd-favorite data strategy product. No long video trainings, no books to read, no needless theory. Just clear, concise guidance on what your next data strategy steps should be, starting today.
It’s a step-by-step checklist & collaborative Trello Board planner for data professionals who want to get unstuck & up-leveled into their next promotion by delivering a fail-proof data strategy plan for their data projects.
There are also 2 bonus guides, if you need help improving communications with your senior executives and stakeholders
And, it comes with a bonus, members-only community, if you’d like a private sounding board for getting valuable input from other data strategists.
Start executing upon our Data Strategy Action Plan today.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## 3 Affordable Tools for Successful Data Forecasting in 2021
URL: https://www.data-mania.com/blog/affordable-tools-for-successful-data-forecasting-in-2021/
Type: post
Modified: 2026-03-17
Looking for best options for affordable tools for successful data forecasting in 2021? Read on to see our top 3 picks!
Developments in AI and machine learning have made data analytics platforms more powerful than ever.
The following are some of the best, and most affordable, tools on the market for data scientists wanting to build predictive models or create forecasts.
Not only are these tools affordable, but they also support a wide variety of features that make them a good fit – whether you need visualization tools, an easy-to-configure ML pipeline or a platform that can quickly connect to existing data sources…
Introducing the 3 most affordable tools for successful data forecasting in 2021:
Qlik Sense
A complete data analytics platform. General-purpose complement to the company’s business intelligence platform, QlikView.
Features include its proprietary associative analytics engine, a suite of data visualization tools and AI technology.
The platform offers cloud support, but can also be used offline with a local device.
One plan. Price is $30 per month per user, billed annually. A free trial is available.
Qlik Sense is a complete data analytics platform, built to be the general-purpose complement to the company’s business intelligence platform, QlikView.
Features of Qlik Sense include a proprietary associative analytics engine, a suite of data visualization tools and AI technology.
The platform offers cloud support, but can also be used offline with a local copy of the software.
Qlik Sense has two main editions — the Business edition for small teams and the Enterprise edition for organizations with multiple departments — and several pricing plans.
The Business edition has one plan, which costs $30 per month per user, billed annually. There are two plans for the Enterprise edition — Analyzer, which costs $40 per month per user, and Professional, which costs $70 per month per user.
Enterprise plans offer a few additional features not in the Business edition, including data alerts, managed app access and larger app sizes.
For businesses and individuals that want to test the software before committing to a subscription, a free trial is available.
RapidMiner
RapidMiner is an open and extensible cloud-based data science platform.
The platform is best for data scientists who want to use ML algorithms to analyze data and create predictive models.
Like Qlik Sense, it also comes with a suite of visualization tools.
The platform is designed to be accessible to both data scientists and general users.
For individuals, RapidMiner is highly affordable. The browser-based RapidMiner Go costs $10 per month. The offline RapidMiner Studio is free, and comes with a 30-day trial of RapidMiner Studio Enterprise.
Enterprise plans, however, are significantly more expensive, starting at $5,000 per month per user, according to Capterra data.
RapidMiner is an open and extensible cloud-based data science platform.
The platform is best for data scientists who want to use ML algorithms to analyze data and create predictive models, but is built to be accessible to both experienced data scientists and general users. If you want a data forecasting and analytics tool with a gentle learning curve, RapidMiner will likely be a good fit.
Like Qlik Sense, it also comes with a suite of visualization tools, which will help users create graphs, charts and reports that break down the insights they’ve uncovered.
Other features will help you streamline some of the more frustrating parts of data analysis, For example, TurboPrep is RapidMiner’s tool for handling the task of data cleaning — which is necessary for ensuring data sets are ready for analysis.
For individuals, RapidMiner is highly affordable. The browser-based RapidMiner Go costs $10 per month. The offline RapidMiner Studio is free, and comes with a 30-day trial of RapidMiner Studio Enterprise.
Enterprise plans, however, are significantly more expensive, starting at $5,000 per month per user, according to user data from Capterra.
Datagran.io
ML and AI analytics tool designed for data scientists and general-purpose users.
Primarily built for business analytics.
Features help users put analytics into practice quickly. For example — a visual environment allows you to design an ML pipeline with minimal to no coding. Easy exporting of visuals and analysis to third-party tools, like Slack.
Four pricing tiers. Free is free, Individual is $50 per month billed annually, Team is $100 per month billed annually and Enterprise plan pricing varies based on need.
Because prices are consistent no matter the number of users you’ll have, this product is likely best for individuals who want a free solution, or mid-size teams that can’t afford per-user billing.
Datagran is a machine learning and AI analytics tool designed for data scientists and general purpose users.
The platform is primarily built for business analytics, meaning that it may not be as useful for individuals outside the business world.
Part of what makes the tool unique is its features designed to help users put new analytics pipelines into practice quickly.
For example, the platform includes a visual environment that allows you to design an ML pipeline with minimal to no coding.
The visual environment also includes tools for easy exporting of visuals and analysis to third-party apps, like Slack and Twilio, and data formats like CSV.
The platform has four pricing tiers — Free, Individual, Team and Enterprise. Free is free, Individual is $50 per month billed annually, Team is $100 per month billed annually and Enterprise plan pricing varies based on need.
These prices are consistent no matter the number of users you’ll have, meaning that this product is likely best for individuals who want a free solution, or mid-size teams that can’t afford per-user billing.
The Best Affordable Tools for Data Forecasting
These tools are some of the best available if you need an affordable solution for data analytics and forecasting. Some, like Qlik Sense and Datagran, will be better for business applications, while RapidMiner will be a good fit for anyone who needs an easy-to-learn tool for general use.
A Guest Post By…
This blog post was generously contributed to Data-Mania by Shannon Flynn. Shannon Flynn is a freelance blogger who covers business, cybersecurity and IoT topics.
You can follow Shannon on Muck Rack or Medium to read more of her articles.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## What does a data product manager do? 3 types of work I do
URL: https://www.data-mania.com/blog/what-does-a-data-product-manager-do-3-types-of-work-i-do/
Type: post
Modified: 2026-03-17
Curious about what a data product manager does on a daily basis? Sweet! I’ve been managing data products since 2012 and, in this post, I’m going to show you the 3 types of work I do on a daily basis.
Be sure to read to the end because that’s where I am going to show you a cool hack for creating a rockin’ company-themed data product manager CV that is sure to get you a call back.
YouTube URL: https://youtu.be/5T1MZls8fIo
If you prefer to read instead of watch then, read on…
As far as why I’m qualified to give advice on managing data products, like I said – I’ve been managing data products since 2012. I have built and managed data science products that have served over 1.7 million data professionals so far. And, I’m actively managing 6 data products right now, after having already brought them to market.
If you’re new around here… Hi, I’m Lillian Pierson and I support data professionals in becoming world-class data leaders and entrepreneurs.
What Is A Data Product Manager?
The easiest way to explain this is to show you this Venn diagram.
The data product manager is really a hybrid between a product manager and a product data scientist. This means there’s a good bit of data product management; however, there’s also a good bit of data science involvedt.
Generally with data product management, data is the product – either data resources or data expertise. You then use data science and data analytics to actually manage and improve the product overtime.
Caveat: I own a small business and have been running that business for almost a decade. All of the products that I’m managing are either owned by my company or by a client.
This matters mostly in terms of what I’ve seen with respect to teams and in how much time traditional data product managers spend in meetings and in communications with teams. Because I own my own business and I work remotely, we don’t have meetings in my business, which I like!
What Does a Data Product Manager Do?
I’ve broken the work down into three main categories:
Metrics & Strategy
Launch
Products
The metrics inform the strategy, and the strategy drives growth in both product and product launch.
Below is a pretty extensive mind map which I created that plots out each of the aspects of these three main categories of work. But it’s a little bit more complicated because my business runs according to “seasons”, so the requirements around these categories shift according to the seasons of our business.
I’m going to break down for you what I’m doing on a daily basis.
I’d love to hear from you! In the comments, tell me a little bit about what you do on a daily basis as a data professional.
What Do I Do As A Data Product Manager?
Looking at what I actually do on a daily basis as a data product manager, it depends on the stage of the lifecycle that my products are in.
1. Work Breakdown by Product
In this type of work allocation, we are updating our course Python for Data Science, central training, one and two that are owned by LinkedIn Learning and I’m the instructor. That’s an information data product that requires data science expertise in order to build. I used to do everything associated with developing that course and I built it from scratch by myself.
But nowadays, my business has grown to the point that I’m not able to do all the implementation work myself and run the business (which I learned this year while rewriting Data Science for Dummies – the second product that I’m managing.)
Allocating time for each product
Data Science for Dummies is owned by Wiley and I rewrote it a second time since 2014. It’s done and we are in the launch phase. I hired a launch manager that’s why this takes 5%.
The reason why Python for Data Science is taking 5% of my time is because I hired a data scientist to come in and help me with building out the curriculum and it’s an active development. I had to minimize the amount of time I spent there so I could focus on the Data Creatives & Co. course which is not a data product. Python for Data Science is a data product, as is Data Science for Dummies.
Data Creatives & Co. is a course that helps data professionals, supporting them to hitting six figures in the first 12 months of their own data business. It’s data intensive but it’s more of a course designed for data professionals to help them with their new businesses. I am spending 90% of my time on that right now – it’s already developed and we’re in the launch phase.
The reason I didn’t spend much time on Python for Data Science is that I spent the whole year rewriting Data Science for Dummies – which required me to actually write it. (gasp!)
Python for Data Science is client work, so I have delegated a lot of the work to another data scientist. This way I can focus on my main revenue generator for my business, the Data Creatives & Co., which is my signature course since it’s owned by my company.
What I work on a daily basis changes according to the seasons of my business and I’m just going to cover two seasons.
2. Work Breakdown by Sales + Leads Season
I’m spending about 40% of my time in launch efforts – managing the launch and planning the launch of products. 20% of my time in managing my team, and 15% of my time doing product planning and development.
All of the decisions in my business are governed by data analytics and insights. That’s how – beyond data products – I’m using data analytics and making data-informed decisions in all aspects of our product management.
I spend about 5% of my time generating customer feedback and speaking to customers, just getting ideas for how I can improve our products to make them even better. It’s really important to keep a bead on who our customers are and what their needs are.
If you own your own business, it’s definitely important to know your customers and keep improving your products on an iterative basis.
Lastly, because I’m an entrepreneur, the other 20% is allocated to “other”.
3. Work Breakdown by Visibility + Nurture Season
When we are in visibility and nurture season, things are dramatically different.
I spend about 30% of my time on actual product development and 20% in managing my team.
The 15% is spent on working on the delivery systems for products or delivering services (which is not a product management role). And because the goal of the business during this season is visibility and nurture, I spend 10% of my time doing collaborations – ie; doing podcasts, live events or guest posting.
The 5% then is spent for customer feedback – just speaking with customers and looking for ways to create new products or improve the products I have.
Lastly, 20% is for “other” – just for running the business.
If you like this post on What a Data Product Manager does on a Daily Basis, then you’ll probably want to check out the video I made on creating the perfect Data Product Manager Resume.
What Does a Data Product Manager Do on a Daily Basis?
Are you wondering now what do I do as a data product manager, daily? Read below to find out and to see for yourself whether you have the same calling!
The 3 Types of Data Product Management Work I Do on A (Near) Daily Basis
As you recall, we have broken down the type of work that data product managers do into three categories:
1. Metrics and Strategy
In this part, I’m mostly responsible for all the metrics and strategy. My team collects the data that I need on a weekly basis, and I use that data to inform strategies moving forward. Also, I have a collection of tools that I use to generate analytics so we can easily see what is working and what’s not working.
Metrics Tracking and Analytics
Once a week I pop in and look at our metrics and analytics. A lot of them are basically describing leads and sales for the business – generating answers to the questions, “what marketing channels are producing leads and sales?”, “why?”, and “which ones are not performing well and why?”
Finance
I also look at finance. I have a team member collecting the data. I’ve got tools and I also have another team member who manages our finance requirements.
Competitive Analysis
I look at all of these metrics and analytics on Tuesday of every week and then I’m continually doing competitive analysis just because we’re always in the process of developing something – whether it be content, products, programs or services that are bringing leads and sales to the business.
I’ve got a variety of tools that I use to get information even about partnerships. I use data to inform and make sure I’m making a good decision on everything that I’m in charge of with our business.
A/B Testing
I do A/B testing on sales pages and my team also does A/B testing for me insie our email tool.
Market Research
I also always do market research – that’s includes competitive analysis and more. I have tools to do this so there’s no need to collect the data raw because tools are there to provide those answers for you.
Strategy Development
Lastly, I do strategy development. By looking at metrics and analytics on a weekly basis, I update our strategy to include more of what’s working and less of what’s not working. I also do testing to explore potential traction in new markets or new channels.
In the image, the nodes are mostly orange. That indicates that these are mostly all data aspects of the role. Strategy development is more of a traditional product management requirement.
2. Launch
Launch work is a pretty important part of product management and having a business. We are aiming for two or three launches per year.
Requirements Planning
I do the requirements planning and then I hand things off to my team in terms of launch marketing requirements.
Marketing
We have copywriters and content managers. The subject matter expertise for data and the entrepreneurship expertise comes from me.
I create a piece of source content and then hand it off to my team for formatting, repurposing, and preparing it the way that it needs to be consumed along our channels. Our content manager publishes the content.
Conversion Medium
Conversion medium would be for launch assets – my launch opt-in, my sales pages, forms and funnels – and I and my team work on this one. It just depends on how much time I have, how many things I’m doing.
Events
For the events, that’s all me because I’m the business owner so I need to show up and show my face.
Data Collection
Data collection is done by the team, but they collect the data and then I see how things performed in terms of conversion, open rates, sales, and leads. I then can make improvements in the next launch cycle.
3. Product
Product is the last category of work. By now we’ve already covered what products we are actively working on.
Just for this context, these products are not new and we’re in version three or higher for all of our products. We’re more in the improvement mode rather than like raw development mode.
Validation
The first thing to do is always validating your product. This is more of a data aspect than a product management task. Validation requires metric analysis and then ultimately sales. It’s looking at conversion rates and stuff, so that’s more data.
Product requirements
Because it’s my business, it’s my vision for the products, so then the requirements come from me. But that’s a more traditional product management requirement.
Design
I do not do my own design. I can do some design, but I have a Professional Designer and a Web developer. My designer creates the aesthetic, the brand, the colors, the fonts, the layouts. The designer sets it up all for me so I can just replicate what she’s doing if I need to design something. I don’t design because I’m not a designer and I have a web developer that helps implement the tech stuff on the backend. I coordinate with my team – I just tell, for example, the designer, what I like and what I don’t like. All the templates and base assets need to be done professionally by a designer. And then my team can use them to populate and create products out of the templates.
Testing
I test my products and my team also tests them. In our case, we already have users because this is version three, so we get feedback from those users on a continual basis and that equals data which we then cycle back through to make improvements to the products on the next round.
Improvements
The majority of our products are data products, data science products, and information products. The improvements to data science products are done by data scientists. I have another data scientist working with me at this point to help me with some things. For my business mentorship and information products – that’s 100% me!We have a group where I’m bringing in a data scientist because I don’t have enough time to work on all of my data products myself. Data science products are way easier to find help with because there are tons of data professionals out there available to build data science implementation curriculums. On the other hand, our business products contain content that’s actually coming straight from my brain, so it’s a lot harder to delegate.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## How long does it take to learn how to code?
URL: https://www.data-mania.com/blog/how-long-does-it-take-to-learn-how-to-code/
Type: post
Modified: 2026-03-17
How long does it take to learn how to code? Not long (at all). In this brief post, I’m going to pretend as if I was learning to code from the very beginning. I’ll show you the easiest, fastest way to go about learning to code. Make sure to read to the end of this post. That’s where I am going to share some great places where you can go to get real-life practice applying your new coding skills once you’ve learned them.
YouTube URL: https://youtu.be/fHa9xb5-JLE
If you prefer to read instead of watch, then read on…
For the best data leadership and business-building advice on the digital block, subscribe to my newsletter below. I’ll make sure you get notified when a new blog installment gets released (each week). 👇
As far as why I’m qualified to give advice on learning to code, I learned to code back in 1987 (at the ripe old age of 8) and subsequently in my “coding career” I’ve taught over 1.3 Million professionals how to code in Python. Don’t believe me, you can see the latest editions of my courses over on LinkedIn Learning here.
If you’re new around here… Hi, I’m Lillian Pierson and I support data professionals in becoming world-class data leaders and entrepreneurs.
How long does it take to learn how to code? You get to decide…
Now, obviously since I specialize in teaching people to learn how to code in order to do data science, the method I am going to teach you here will be slanted towards learning to code so that you can do work as a data professional.
Don’t worry if you already know you want to become a software developer, this 5-step method I’m about to show you is transferable to learning any other programming language or stack of languages as well.
If I could start over, in all honesty, I would learn to code the very same way I learned how to use Python for data science back in 2012. In this post, I am showing you the exact 5 step approach I used and would use again if I had to.
You’d be surprised at the amount of flexibility you have in determining how long it will take you to learn to code. If you choose a lofty goal, you can spend years learning to code and never feel like you’ve mastered it. I don’t recommend that approach. Instead, choose an attainable goal and set a deadline for when you will achieve it.
Of course, you want to make sure that you’re not learning just for the sake of learning. You want to make sure that what you learn is actually in alignment with your aspirations, correct? So, you’ll want to start with your end objective in mind and then reverse engineer how you can get there.
Let me illustrate with a fictional example…
Step 1: Find something you want to do
Imagine that you’re looking for a job and you know that you believe in the power of data to transform businesses and improve lives. So, because you believe that this would be a fun, fulfilling and rewarding role, you decide you’ll look for a job in “Data Monetization” on LinkedIn.
At the top of the results, you see that Pinterest is hiring for “Head of Analytics and Data Science, Monetization”… so you tap into that job listing and notice that – surprisingly – the job does not have a minimum requirement for coding experience.
https://www.linkedin.com/jobs/search/?currentJobId=2516585365&keywords=data%20 monetization
As you can see, the only mention of coding in the job description is that you have “Hands-on knowledge of SQL and Python or R”.
Yes, you read that right. They are requesting that you know how to use 2 languages SQL and either Python or R.
Now, in this hypothetical situation, you don’t know anything about how to code at all… so you’re probably not going to get this exact job with Pinterest. But, if you learn to code now, you can probably get a similar role with another company later on – after you’ve learned. So, let’s go with it.
I’d love to try to help you out in figuring out your best learning goals for you. Tell me a little bit about your time constraints in the comments below, and I promise to suggest a reasonable time commitment for learning to code.
Step 2: Find your learning instrument
The next thing you need to do is to decide what you want to learn first. Don’t try to learn more than 2 languages at a time, it will just slow down your learning momentum.
Don’t try to learn more than 2 languages at a time, it will just slow down your learning momentum.
Since you don’t know anything about either of these languages, go ahead and check to see if one language is a prerequisite for another.
You Google “is there a prerequisite for learning Python?” Also, “is there a prerequisite for learning SQL?” You discover that the answer to both of these questions is “no”.
Next you need to find out which of these will be the easiest to learn…
So you Google”what is easier python or sql?” The resounding answer from the internet is that SQL is easier, so you decide to learn to do SQL first, and then Python.
Next, you need to figure out how long it will take you to get “hands-on” learning experience with SQL. The easiest way to do that is just to go over to Udemy and find a well-rated course that tells you exactly how long it will take you to complete it. When I say a well-rated course on Udemy, I mean you want it to have at least 100 ratings of 4.4 stars or above.
When I say a well-rated course on Udemy, I mean you want it to have at least 100 ratings of 4.4 stars or above.
If you can find a course that’s relevant to the job description or industry you’re aiming for then that’s even better. Let’s look back at this Pinterest job posting real quick.
From the listing, you can see that they are looking for someone to Liaison with the Head of Monetization for Engineering and Product, and you know they’re a social media company, so… if you can find a SQL course related to SaaS products, that would be great.
You go over to Udemy and search beginner courses on ‘SQL “Product”’. Low and behold, at the top of the results you find a Beginners SQL course that will show you how to use SQL to analyze product data and inventory data. It’s well-rated and relevant. Bingo!
Learn Business Data Analysis with SQL and Tableau
It includes 4-hours of lecture material. So, you probably want to give yourself 8 to 12 hours to watch it and work the examples. As a rule of thumb: When using video courses to learn how to code – give yourself at least 2x – 3x the duration time of video lectures, to apply and practice what the course is showing you.
As a rule of thumb: When using video courses to learn how to code – give yourself at least 2x – 3x the duration time of video lectures, to apply and practice what the course is showing you.
We are going to create a milestone goal for you next. But while we are over in Udemy, let’s find a relevant Python course you can learn from too. Since the job is to be the head of analytics and data science, you know you need to know how to use Python for something related to that. So you search Udemy beginners courses on “data analytics python”.
The top result comes in as 15.5-hour video course on “Machine Learning, Data Science and Deep Learning with Python” The ratings look good, but the only problem is that – When I first discovered this course, the price was set to $89.99. At $89.99, it’s pricey for an online coding course. But when I went in to grab a closer screenshot a few hours later, you can see the price had already dropped to $14.99! Pro-tip: Don’t pay more than $9.99 for Udemy courses. Most of the best Udemy courses go on sale for one week per month. And the price will be set to $9.99 then. If that’s not the price when you first go to look, just pay attention to when Udemy has sales so you can snag the course at a small fraction of the price then.
Pro-tip: Don’t pay more than $9.99 for Udemy courses. Most of the best Udemy courses go on sale for one week per month and the price will be set to $9.99 then. If that’s not the price when you first go to look, just pay attention to when Udemy has sales so you can snag the course at a small fraction of the price then.
This Python course lasts for 15.5 hours, so you probably want to give yourself 45 hours to take and complete the course.
Step 3: Commit to clear learning goals
Congratulations – You’ve already done most of the heavy lifting for planning your initial syllabus for learning to code. Now you just need to set some goals and stick to them.
If you have a full-time job and a personal life then, don’t be too ambitious. Honestly, if you can fit in 5 or 10 hours per week to take coding courses and practice what you learn, that’d be great.
An awesome way to fit in the learning and get paid to learn to code is to see if you can get your employer to approve the course for on the job training. I did that when I learned Python back in 2013, and it was great. I got free training courses, paid to learn, and real-life business projects to apply the skills to as soon as I completed the courses.
An awesome way to fit in the learning and get paid to learn to code is to see if you can get your employer to approve the course for on the job training. Give that a shot.
But, let’s say that learning to code on the job is not an option for you. A realistic schedule might look like this then:
12 hours – Learn SQL Basics
Deadline: 2 weeks from now
45 hours – Learn Python for Data Science Basics
Deadline: 7 weeks from start date
What this really comes down to is that you can use video courses to teach yourself. This includes the basics of how to code in SQL in Python in about 9 weeks, after work and on the weekend.
Speaking of low-cost learning resources for learning to code in data science, I’ve published many free coding tutorials on how to do that. I will leave a link to a few of the more popular ones below:
CUSTOMER PROFILING AND SEGMENTATION IN PYTHON | A CONCEPTUAL OVERVIEW AND DEMONSTRATION http://data-mania.com/blog/customer-profiling-and-segmentation-in-python/
CONJOINT ANALYSIS IN R: A MARKETING DATA SCIENCE CODING DEMONSTRATION http://data-mania.com/blog/conjoint-analysis-in-r/
HOW TO BUILD A RECOMMENDATION ENGINE IN R | A MARKETING DATA SCIENCE DEMO http://data-mania.com/blog/how-to-build-a-recommendation-engine-in-r/
Also my Python for Data Science course are linked below and will work for beginners if you start with Part 1.
Python for Data Science Essential Training, Part 1
Python for Data Science Essential Training, Part 2
Building A Recommendation System With Python
Step 4: Follow thru on your commitment
If you’re following along with the advice in this post then you’ve either set really realistic goals for yourself, or you’re actually going to get paid to learn to code for free.
Both of these arrangements are highly desirable. But you’ll need to make sure you actually take the initiative to follow-thru on your commitment. Obviously, it will be easier to follow through if your job allows you to learn these skills as part of your job. But if that’s not the case, then you may want to pick someone in your life to remain accountable to. Just pick a best friend, partner, or maybe your spouse and give them a copy of your learning plan. Then set a time once per week where you report to them on the progress you’ve made towards completing that plan. Not only will that help you remain accountable and committed, it will also help you learn to communicate technical things to a (presumably) non-technical person.
Step 5: Apply what you learned
The fifth and final step in your learning to code journey should always to be to practice using what you learn. It will always be better if you can think up real-life applications for the skills you’ve learned. And honestly, if you are already a knowledge professional, there are usually an abundance of opportunities. These opportunities include applying Python to automate some of your daily work; thus, freeing even more time for you to learn more.
But if you really can’t think of any place where you can use your newfound coding skills in your real-life, then that will probably be remedied once you’ve done a few practice projects. The good news is that there are tons of fun and interesting projects online you can use to practice your new coding skills. Below, I will place links to some practice projects. Some of which can be made relevant to the example we’ve been working through in this post.
Humanitarian Open Street Map
DataKind
Tech For Campaigns
Peruse this Reddit thread on where to find ‘real problems’ to practice coding in data science
About your “learning to code” goal…
This is not a one and done thing. If you get a few courses and practice problems done, technically you will have learned to code. Of course, there is a lot more to learn, especially if you want to be a professional coder. But, learning to code is a rise, wash, repeat cycle. The good news is that you’ve just gotten a clear repeatable process you can use to learn any coding skill you so choose.
Continuing education is a lifelong process. You’ll never learn it all and you’ll never be done. That’s why you don’t need to fret about being a newbie… Just start today and keep going – you’ll be at the expert level sooner than you know!
If you like this small training that answered the questions: how long does it take to learn to code? And you think you might have some interest in learning to code so you can get a job in the data sector, then you’d probably really like the free guide I created, called “A Badass’s Guide To Breaking Into Data”. It’s a 52-page e-book. It also details some of the best data courses I recommend for learning coding skills that data professionals need.
Also, I have a free Facebook Group called Becoming World-Class Data Leaders and Entrepreneurs. I’d love to get to know you inside there, if you’d like to apply to join here.
Hey! If you liked this post, I’d really appreciate it if you’d share the love with your peers! Share it on your favorite social network by clicking on one of the share buttons below!
NOTE: This blog post contains affiliate links that allow you to find the items mentioned in this video and support the channel at no cost to you. While this channel may earn minimal sums when the viewer uses the links, the viewer is in NO WAY obligated to use these links. Thank you for your support!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Omnichannel Analytics and Channel Scoring for MORE SALES AND LOWER CHURN
URL: https://www.data-mania.com/blog/omnichannel-analytics-and-channel-scoring-for-more-sales-and-lower-churn/
Type: post
Modified: 2026-03-17
Ever heard about Omnichannel Analytics and Channel Scoring? For all you data professionals out there who are looking to make their break in the marketing analytics space, you’ll want to make sure you’re hip to “channel scoring” because it’s one of those powerful yet oh-so underutilized marketing data practices around.
In this article, I’m going to tell you what channel scoring is, how it’s helpful and how to get it done in 5 simple steps.
YouTube URL: https://youtu.be/dokBZ73XieQ
If you prefer to read instead of watch then, read on…
For the best data leadership and business-building advice on block, subscribe to my newsletter below and I’ll make sure you get notified when a new blog installment gets released (each week). 👇
Who am I to tell you about omnichannel analytics and channel scoring? Well, we’ve been using channel scoring in my business, data mania, since 2017. I also just wrote a whole section about it this week while working to update my book, Data Science For Dummies 3rd Edition.
Hi, I’m Lillian Pierson and I support data professionals to become world-class data leaders and entrepreneurs.
Channel scoring pretty much assumes that you have more than 1 marketing channel or what we call “Omnichannel.” We’re also going to assume in this article that you know what a channel is.
If you need to get up to speed on what a channel is or what omnichannel analytics is – I recommend you first watch the video I did about Omnichannel analytics. It’s actually a prequel to this post. Check it out here.
What is Channel Scoring?
Channel scoring is the practice of analyzing your company’s current sales and marketing data to identify where your customers are coming from, and then assign a score to each of those “channels” based on how well the channel is converting leads and sales for your business.
To represent your findings, you create a channel that visually displays the current importance of your various channels, relative to one another.
When we’re talking about omnichannel analytics and channel scoring, we’re always talking about sales and leads for your business, not anything related to vanity metrics or having a popular social media account. We’re talking about the performances of your marketing campaigns and sales efforts that your company has invested in, with respect to leads and sales – that’s your ROI.
With channel scoring, you would create a channel scorecard which visually shows your findings on one channel against another for a bird’s eye view.
Benefits of channel scoring:
Improve your sales and marketing strategy
Improve your ROI
Identify the underperforming channels
Figure out what’s working and what’s not
Make improvements on those non-performing channels while still garnering great results from other channels
Here on my channel, we’re all data professionals but we’re not all marketing data professionals. You may have experience in scoring all types of things that you could be scoring, like retail outlets, distribution chains, or logistics scoring, etc. so I’d love to hear from you, tell us in the comments:
What type of data-intensive scoring methods do you currently have experience with?
There are a number of ways you can go about scoring your sales channels, but I created a simplified 5-step approach, just to give you a quick snapshot here:
Step 1: Map Your Channels
Itemize all the different channels that generate sales, those are your sales and marketing channels.
Marketing Channels – the channels by which people become aware of your products and services to warm them for sales.
Sales Channels – where the sale is actually made as well as the point of distribution.
Step 2: Score Your Channels
Evaluate each of those channels against one another. Score them out based on the number of sales and leads that are generated from the marketing channels. Important metrics you can use to help you score your channels out may include:
Customer lifetime value. You can use the traditional approach where you use averages or you can get sophisticated and bring in machine learning to do predictive customer lifetime value estimates.
Customer reviews and satisfaction metrics
Upsell, downsell, and subscription renewal rates
Ticket volume
Customer profitability
What you’re really trying to do here is build a profile of your marketing channels. The main goal is to help you understand the quality of the customers that are coming through each of your channels. Even if you were to score each channel against one another, just looking at these metrics alone can be incredibly valuable in terms of improving your marketing strategy. That’s because when you start looking into these metrics and take a deeper dive into why things are happening the way they do, you will uncover all kinds of opportunities that you can use to supercharge what you’ve got going on in your business today and make it even more powerful. You can also identify what’s not working and try to figure out how to improve it. You can also change your marketing strategy based on your findings.
Channel scoring just takes things a few steps forward.
Step 3: Create a Channel Scorecard
Channel Scorecard is a visual representation of your analytical findings. It is a communication and summarization tool. Summarize your findings for each of the metrics by creating a scorecard for each channel.
Step 4: Define a Customer Avatar for Each Channel
You need to get some behavioral analytics that describe people’s preference and behavior on each of your marketing channels. Generally, it involves going into the actual marketing channel and using their built-in analytics. That is, unless you have a sophisticated marketing analytics recording tool like Keyhole. But for all intents and purposes, you can generally get away with using the in-platform analytics provided on most of the social media channels or through Google Analytics.
This information will start giving you an idea of people’s preferences and what they’re really looking for in your company.
Spoiler Alert: It generally isn’t the same thing on each of your different marketing channels – which is why you have to go into your channel analytics to see what is performing well, what people are loving on each of your channels and then figure out your strategy from there.
Another important part about developing an avatar in each of your channels is, you have to think about your existing customers and consider their personal attributes. Then, make some educated guesses about what types of customers fit into each of the channels in your channel portfolio.
Step 4: Tweak Your Sales and Marketing Strategy
Looking at this customer avatar along with the channel scorecard for each channel, decide what changes you can make to improve channel performance, so that it better supports your company’s overall sales strategy and goals.
This is a scorecard I created for my channel’s scores for March 2021. This is just an example of what your channel scorecard might look like after you’ve completed the 5-step process described above. In this example, we looked at LinkedIn, Search, Instagram and Email. Watch the video featured above to know how I scored each of these channels in more detail.
Unfortunately there is no exact cut-and-dry formula to use for assigning a score to a particular channel. You really need to get into your channel numbers and account for which channels are generating the most leads and sales. These metrics should be weighted in importance. Then, look to see how that success is being reflected in the channel data, in terms of customer engagement statistics with your channels. Based on these numbers for each channel, you need to then assign a relative score for all of your sales and marketing channels.
How Channel Scoring is Helpful
Customer acquisition: When you fine-tune your marketing strategy so that it aligns better with your customer desires and expectations along each channel, your marketing ROI will immediately increase. That is going to improve brand trust. It’ll also make it easier for your company to make sales from within those channels. Hence, it’s lowering the cost of customer acquisition, which is definitely a good thing.
Customer retention: Fine-tuning your sales and marketing strategy so that your company keeps on pulse with changes and evolution of its customer desires will help keep your existing customers coming back for more – driving an up-tick in repeat purchases and word-of-mouth marketing.
New product or service development: By using omnichannel analytics and channel scoring in the way discussed above, you’ll have a much more granular view of your customer and his or her preferences. This perspective is, of course, helpful in designing products and services that your customers need, want, and adore.
If you liked this article on using omnichannel analytics and channel scoring to improve your marketing strategy and increase ROI, you’d probably get a lot from my data strategy action plan. It’s a step-by-step checklist & collaborative Trello Board planner for data professionals who want to get unstuck & up-leveled into their next promotion by delivering a fail-proof data strategy plan for their data projects.
Start executing upon our Data Strategy Action Plan today.
You may also love it inside our Data Leader and Entrepreneur Community on Facebook. It’s chalked full of some of the internet’s most up-and-coming data leaders and entrepreneurs who’ve come together to inspire and uplift one another.
Join our community here.
Hey! If you liked this post, I’d really appreciate it if you’d share the love with your peers! Share it on your favorite social network by clicking on one of the share buttons below!
NOTE: This description contains affiliate links that allow you to find the items mentioned in this article and support the channel at no cost to you. While this blog may earn minimal sums when the reader uses the links, the reader is in NO WAY obligated to use these links. Thank you for your support!
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## What is Data Modeling – Data Modeling vs Data Analysis 101
URL: https://www.data-mania.com/blog/what-is-data-modeling-data-modeling-vs-data-analysis-101/
Type: post
Modified: 2026-03-17
The disciplines and subdisciplines of data are complex and often overlaid. Data analysis, engineering, and science are foundational concepts, while data modeling refers to the process of mapping data at conceptual, logical, and physical levels.
Data modeling overlaps with data science, engineering, and analysis, but its angle is probably more towards the engineering side of things. Namely, data modeling creates visual maps and references that allow data practitioners to visualize a data system.
Data analysis involves interpretation, critical thinking, and other analytical techniques to derive meaning. Modeling is the process of connecting data systems together, e.g., connecting a point-of-sale device to a CRM, or a sales database to a stock system.
What is Data Modeling?
Data modeling is the process of creating maps, graphs, or diagrams that visualize the relationships between data. In a data project, development of models is early and the project’s goals and architectures are the bases for their designs. Any virtual data project requires data models are required for virtually any data project which requires different systems to talk to each other in a structured manner.
The content and format of the diagrams themselves will vary with the project needs and architecture. Most database models are either:
Relational models, which provide well-structured, logical connections between different tables. These are simple to work with but have fixed schema.
NoSQL models, which are essentially schema-less. These are more intensive to model but allow for the creation of joins while allowing some data to remain nested.
Graph models, which are excellent for mapping one-to-one and one-to-many relationships in networks. The resulting data structure is heavily nested.
Data typically models in a relational database using structured query language (SQL), utilizing traditional table formats to store information. However, noSQL modeling uses collections of documents and is generally much more flexible. Graph databases are another possibility which heavily nests and suits interconnected network-based data, e.g., traffic networks, social media, or other digital networks.
There are other types of data modeling, such as traditional hierarchical modeling, object-oriented models, which use class hierarchies and associated features, and dimensional data models, which are frequently for business intelligence (BI).
3 Stages
Data modeling has these three stages:
Conceptual
Logical
Physical/technical
Conceptual data models are broad and abstractive. Here, data engineers, analysts, and other data practitioners will work together to overview the problem. For example, a brick-and-mortar high street store might want to integrate their point-of-sale data with their online store and logistic and distribution systems. Connecting these systems will allow the shop to recognize online customers when they shop in-store and refer in-store customers to the online store if something is out of stock and vice-versa.
At conceptual level, the three main components should be drafted (POS, online store, and distribution system) with the main entities (customers and products).
Logical data models add primary keys, attributes, and relationships. For example, customers will be broken down by attributes such as customer IDs, names, addresses, emails, etc. Products will contain their product IDs, location, category, etc. Assignment on nullability and optionality are at this stage.
Physical models then transfer these models onto the specific architecture and add foreign keys, data types, metadata, and everything else required to make the systems functional and communicable.
What Is Data Analysis?
Data analysis has a much wider, more general remit than data modeling. In fact, you could argue that most people conduct some level of data analysis in their daily lives – our brains are analyzing data constantly. Without data analysis, data is just a static entity. It needs to be processed and understood to mean anything.
In a business context, data analysis involves everything from analyzing sales trends and data to tracking customers and analyzing audience demographics or financial metrics. As a result, enterprise-level companies will employ a wide range of different data analysts.
At a fundamental level, there are probably six core types of data analysis:
Causal Analysis
Descriptive Analysis
Exploratory Analysis
Inferential Analysis
Mechanistic Analysis
Predictive Analysis
It’s often necessary to transform and clean data before loading it into dashboards and suites for analysis. Data engineers might handle the cleaning and transformation of data. Data analysts are perhaps more skilled in statistics and mathematics than programming or database management.
The job role of a data analyst is more client-facing – they work closely with the business or organization to analyze data to solve specific business problems.
Data analysis involves everything from visualization, clustering, exploration, classification, regression, and simulation modeling. The result of data analysis forms conclusions and builds solutions.
Data Analysts and Data Modeling
The concepts of data analysis and data modeling do not always exist in isolation from each other.
However, analysis is not really required when models are created to solve a simple, practical task (e.g., connecting brick-and-mortar POS databases to online store databases). Data doesn’t have to be analyzed to be modeled in a database, though it should obviously be appropriately clean and correctly validated.
The store requires analysis if it wants to query that database and compare in-store customers to online customers. This might involve querying the databases and retrieving appropriate data for insight.
The data modeling process involves data analysts heavily, but it really depends on the specific project in question. Data analysts will need to understand the database that the business is using so they can launch the required queries.
Summary: The Difference Between Data Analysis and Data Modeling
Data is hugely diverse and intersects with practically every digital system on the planet. Business, organizational, or other commercial contexts use data modeling to frequently connect different architectures or build new architecture from scratch to solve problems.
On the other hand, data analysis involves everything from querying databases to analyzing machine learning models. While data modelers lean towards the engineering side of things, data analysts lean towards mathematics and statistics. For example, a data analyst may have very little knowledge of database architectures. Conversely, those involved in data modeling will likely require an in-depth understanding of databases.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## A Self-Taught Data Product Manager Curriculum – Best Books to Read to GET THE JOB
URL: https://www.data-mania.com/blog/self-taught-data-product-manager-curriculum/
Type: post
Modified: 2026-03-17
You’re a data professional who’s curious about possibly stepping up into a data product manager position? Amazing! Keep reading so you can discover what the role is all about, the best books to read to become a self-taught data product manager and land the job, as well as what superpowers you’ll want to develop before seeking that role.
In today’s post, we’re going to talk about 3 books in particular:
Product Management’s Sacred Seven
Designing Data-Intensive Applications, and
Cracking the PM Interview
This content is also available in video format:
But if you prefer to read instead of watch, then read on…
A quick side note about why I’m covering this topic – I’ve recently been inspired by the evolving “Data Product Manager” role. Back in 2017, I came out with a series which I turned into an ebook called “A Badass’s Guide to Breaking Into Data.”
This ebook went viral. Lots of people got the book and it helped them make the transition to getting into the data professions. So, I wanted to start working towards developing something like this for Data Product Managers and this is the first installment.
I’ve got a challenge for you real quick. Stop reading here and in the comments below tell me your best answer to the question: “What is a data product manager?”
Answering the question, “What is a data product manager?”
The definition of “Data Product Manager” is nebulous, especially when we seek to compare it against the “Product Manager” and “Data Professional” roles.
Are “Product Managers” a type of data professional?
Not really! The “Product Manager” role, in general, is a very data-intensive role. In fact, some people would categorize it as a role within the “data professionals” spectrum. That said, being a former data consultant to 10% of Fortune 100 companies and a certified product manager myself, I have my doubts about that classification.
Why? Well, product managers are ALWAYS required to have subject matter expertise within the industry in which they are managing products – but product managers don’t always manage data products, so product managers do not always have data expertise.
Are “Data Product Managers” a type of data professional?
Maybe. Sometimes, it depends on what’s expected of them…
Let’s take an example from the crypto industry. You can’t be a Web 3 Product Manager and know nothing about blockchain technology!
The same goes for SaaS products and data products. If you’re managing data and AI products, then you could say you work in the data industry as a product manager, but that doesn’t exactly make you a data implementation professional, right?
In fact, you may NOT have ever built a data solution in your life, but you could still be working as a manager of a data product. You could still legitimately call yourself a “Data Product Manager”.
If you’ve never built a data solution, are you really a “data professional”? That’s up for debate! Let me know your opinion in the comments below.
Opinions aside, product managers manage all aspects of development and launch for a company’s products. This includes things like ideation, research, design, development, performance evaluation, and launch – and that’s just for starters.
Now, let’s talk about becoming a self-taught Data Product Manager.
Data Product Managers (DPM) are expected to cover all the same types of responsibilities as product managers, and then some. Where a product manager uses data to guide their decision-making in terms of product development and launch, a data product manager often uses data more deeply.
A DPM often goes deeper into the data science and predictive analytics to guide and govern all of the decisions around the product. So, instead of stopping at the data analyst level in terms of evaluating data on a product, a DPM might actually be asked to build machine learning models and use sophisticated data science algorithms to uncover deeper insights that will then inform product development decisions, launch decisions and overall product strategy.
In short, a Data Product Manager is a product manager (1) who manages data products, and (2) who has a sophisticated working knowledge of data science, data engineering and machine learning, and (3) who is able to uncover deep data insights related to their products, to help make better informed and educated decisions about product development, product launch, and product strategy.
It’s often a more data-intensive role than other types of product manager roles.
Oh and for the “self-taught” part – that is self-evident, no? (pardon the pun 😉 )
The Best Books To Get the Data Product Manager Job
If you want to become a self-taught data product manager, you definitely need to read each of the following titles.
1. Product Management Sacred Seven
I love this book for so many reasons. One of the things I love about this is the fact that it is so modular. You can basically pick up in the area of your interest and learn so much from the pages of this book. This book spills the tea on everything from tech business strategy, to pricing, to data privacy.
I really think it should be called “The PM’s Bible” just because the information it contains is so incredibly valuable.
Just to put a little perspective on the value of this book, I’ve spent over $50k on business coaching and courses related to growing my own business – and we’ve hit multiple six figures in my data business and helped other new data entrepreneurs do the same in the first seven months of their businesses. And even with all of that, I’ve seen stuff inside this book that was truly just “ninja shite.” It just totally blew my mind. I can’t say enough good things about this book!
In terms of what others have to say – it has 393 reviews on Amazon with a 4.8 star rating. It is a new book, and the gist of the book is basically this:
The authors themselves are already accomplished seasoned PMs themselves. They ended up surveying and interviewing 67 product managers from the world’s finest companies across 4 different continents. They took all of that research findings and they basically broke it down into an essential framework for what makes a truly great product manager.
They found 7 core pillars that distinguish an average product manager from a truly great and exceptional one. Those are:
Product design
Economics
Marketing & growth
Psychology
UI/UX
Law & policy
Data Science
The book covers each of those topics in-depth, and within each of these pillars, it shares insider strategies developed from within the walls of the world’s most innovative tech companies.
What it is NOT
IT’S NOT a book that you pick up to learn how to craft your resume or to answer interview questions. It doesn’t show you how to get a job, prepare for an interview or get up to speed in order to land a PM role…This book is for PMs who want to go from GOOD to GREAT.
The authors are Parth Detroja, Neel Mehta, and Aditya Agashe. If you want more from them, they’ve got a few other books and they’ve also developed something called Product Alliance which is a program that helps people land jobs as product managers. Their other book is entitled “Swipe to Unlock,” a business + tech strategy primer, which is an extremely reputable book about getting skilled up in business and technology strategy.
Why it’s valuable to aspiring DPMs
The topics in this book are pretty sophisticated if you don’t have a solid product management background. Even if you just read through this book and you only takeaway half of it, I think that doing so will help you develop a perspective that would be immensely helpful to you as you are building out your career and skillsets as a data product manager.
2. Designing Data-Intensive Applications
If you are a data professional and want to become a self-taught data product manager, I recommend you read this book before trying to make the transition. The reason that I love this book is because it is an excellent high-level overview of the data engineering and software engineering requirements that go into building data-intensive solutions. Of course, it’s a very popular book amongst data professionals and at this time, it has 1,852 reviews with 4.8 star rating on Amazon.
The author of this book is Martin Kleppmann and he has a blog and an 8-series course that can be used as a companion.
Why this book is vital to the success of self-taught data product managers:
If you’re coming into a company and you’re a PM for a technical product, then you need to have a good understanding of how all the technology works in order to properly support your teams of engineers and designers. You really need to understand the consistency of the tech that makes your product.
But if you’re coming in as a data product manager, then it will absolutely be assumed that you understand the data systems and the data technologies that support those data-intensive solutions, right?
The thing is I know enough data professionals to know that’s not always the case. Some people come in with an data analyst background, others have spent years building data visualizations. If you haven’t had the chance to get into the nuts and bolts of software and data engineering that supports data-intensive predictive applications, this is as good a time as any to make sure that you’re up to speed on how the technologies work. This understanding is a pre-requisite to becoming a DPM.
3. Cracking the PM Interview
This book is all about how to get a job as a product manager, data product managers included. What I really love about this book is that it gives you an insider perspective into product management. It also gives you tips on what to look for in companies that you might potentially want to work for. It really helps you to understand what types of companies would be a good fit for you given your personality and your ambitions, and what types of companies would not.
In terms of what other people are saying, it’s got 1,288 reviews so far with a 4.5 star rating. It’s overwhelmingly popular.
Like the PM’s Sacred Seven, this is a book that’s been derived from expert surveys and interviews. It’s a compilation of well-experienced, highly esteemed product managers sharing the stories of their careers. That includes how they landed a job and got promoted, and what their experience was like in various companies.
The book is full of great takeaways.
The authors help extrapolate the core details. For example, if you know you want to move up the career ladder and get promoted quickly, the authors recommend that you seek a product management job in a start-up environment first.
The book is full of interview questions and guidance on how you should answer those questions, as well as resume before and afters.
Why this book is important to read for anyone who’s considering becoming a data product manager.
You don’t want to go into a new job flying blind. You don’t want to take a job at a company that looks cool from the outside, until you really understand the culture and the nuances of working as a PM at that company. This book really helps you understand what it is actually like to work as a DPM in all five big tech companies. It also includes information about all kinds of awesome startups! This will help you to avoid getting yourself into troublesome situations or landing a job that doesn’t make you happy.
The authors are Gayle Laakmann McDowell and Jackie Bavaro.
They’ve also written another highly esteemed book called “Cracking the PM Career”. They also have some career guidance or coaching books for software engineers. It may be worth reaching out to them if you’re considering becoming a data product manager. Also, it’s worth it if you’d like that extra bit of guidance from world-renowned leaders.
More resources to get ahead…
Get Income-Generating Ideas For Data Professionals
Are you tired of relying on one employer for your income? Are you dreaming of a side hustle that won’t put you at risk of getting fired or sued? Well, my friend, you’re in luck.
This 48-page listing is here to rescue you from the drudgery of corporate slavery and set you on the path to start earning more money from your existing data expertise. Spend just 1 hour with this pdf and I can guarantee you’ll be bursting at the seams with practical, proven & profitable ideas for new income-streams you can create from your existing expertise.
Learn more here!
Take The Data Superhero Quiz
You can take a much more direct path to the top once you understand how to leverage your skillsets, your talents, your personality and your passions in order to serve in a capacity where you’ll thrive. That’s why I’m encouraging you to take the data superhero quiz.
This free and super-fun 45-second quiz is all about you and how your personality type aligns with the very best career path for you. It’s fun, free and it will provide you personalized data career recommendations, complete with potential roles that fit your unique skills and passions, as well as salaries associated with those roles.
Take the Data Superhero Quiz today!
Share It On Twitter by Clicking This Link -> https://ctt.ac/p0dfU
Watch It On YouTube Here: https://youtu.be/InVNrvMJzMM
NOTE: This blog post contains affiliate links that allow you to find the items mentioned in this video and support the channel at no cost to you. While this channel may earn minimal sums when the viewer uses the links, the viewer is in NO WAY obligated to use these links. Thank you for your support!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## How Does Blockchain Support Data Privacy and Storage Security?
URL: https://www.data-mania.com/blog/how-does-blockchain-support-data-privacy-and-storage-security/
Type: post
Modified: 2026-03-17
Have you ever thought of the question, “how does blockchain support data privacy?” In this post, you will learn how blockchain technology does that to both data privacy and security.
As the world becomes highly interconnected and data-driven, concerns regarding data privacy and data storage security arise. According to Statista, global data creation will reach an estimated 181 zettabytes (ZB). To put the figure in perspective, a single ZB is the equivalent of around 250 billion DVDs.
With the unbelievable amount of data already being generated, it’s no surprise that more technologies are emerging to provide storage and security options. One notable example is blockchain technology, originally the topic of a research project in the 1990s.
Because more industries are adopting blockchain, you may be wondering how blockchain technology works, how it supports data privacy and what security measures it offers.
Continue reading to learn more about blockchain, its role in protecting data and the ins and outs of blockchain data security.
An Overview of Blockchain Technology
In simple terms, a blockchain is a distributed ledger or database shared across multiple nodes within a computer network. If a blockchain is used as a database, it can store information electronically in various digital formats. A blockchain is not the same as a traditional database because the data it stores is structured differently.
Think of it this way — a blockchain collects data in groups, known as blocks. Once the blocks reach full storage capacity, the blocks close, link to the previous block and form a chain using cryptography. This is how it’s earned its name, “blockchain.”
Other databases typically store data in tables. A blockchain’s data structure is timestamped and set in stone, which is one reason why anything that leverages blockchain technology is so secure and reliable.
Blockchain is most commonly associated with the cryptocurrency market. In 2009, blockchain technology came to the forefront. It was specifically used for Bitcoin, the market’s most popular and highly valued cryptocurrency.
Since then, blockchain applications have increased in number — it’s now used for smart contracts, non-fungible tokens (NFTs), decentralized finance (DeFi) apps and more. Different data types can be stored on a blockchain, but thus far, it’s most commonly used for secure, decentralized transactions.
The concept of blockchain technology can be perplexing to the average person. However, experts believe it will shape the future of business, especially in a more globalized world.
How Does Blockchain Support Data Privacy?
Now that you understand how blockchains form, it’s time to dive into blockchain security and what makes it unique. Really, how does blockchain support data privacy?
First, it’s important to address that the inherent structure of data on the blockchain provides a layer of security. Since each block is connected to all of the blocks before and after, it makes it extremely challenging for hackers to tamper with a single block. In other words, a hacker would need to change one block containing data and all other blocks on the blockchain to go unnoticed.
Next, the records on a blockchain are stored using cryptography, as mentioned above. Blockchain users receive a private key assigned to their transactions, which acts as a digital signature. If any records are compromised, the signature is rendered invalid, and the user is notified via the peer network.
To sum up, there are three key blockchain data protection properties to be aware of, including:
Public audit
Immutable data storage
Secure timestamping
However, like most technologies, blockchain is not perfect — it does have its shortcomings.
Potential Limitations of Blockchain
So, does blockchain provide confidentiality? Well, yes and no.
It’s commonly understood that not all blockchains are created equal. There are public and private blockchains and the two types differ in ways that can affect their levels of security. For example, public blockchains may not store confidential data securely, meaning a business may not want to use this type of blockchain.
With a blockchain, different network configurations can employ certain components, meaning that there will be various security risks an individual or company may face.
Another limitation of blockchain is that they are not entirely immune to fraud or cyberattacks. There’s certainly no shortage of hacks in the crypto world that dominated headlines, where people lost millions of dollars.
Common blockchain attacks include code exploitation, stolen keys and social engineering. For example, hackers may infiltrate an employee’s computer and compromise sensitive business data.
Understanding Blockchain Data Privacy and Storage Security
Overall, blockchain is regarded as a generally safe and secure technology. Many large, publicly-traded corporations, including IBM, Microsoft, Oracle, Intel and Goldman Sachs, leverage blockchain technology and see how promising it is.
However, it’s critical to understand the possible downsides of using blockchain technology. Suppose an individual or business takes extra cybersecurity measures to compensate for blockchain’s shortcomings. In that case, they may benefit from enhanced security and data protection.
Hey! If you liked this post, I’d really appreciate it if you’d share the love by clicking one of the share buttons below!
A Guest Post By…
This blog post was generously contributed to Data-Mania by Shannon Flynn. Shannon Flynn is a freelance blogger who covers business, cybersecurity and IoT topics.
You can follow Shannon on Muck Rack or Medium to read more of her articles.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Hidden Danger In Greater Data Privacy
URL: https://www.data-mania.com/blog/hidden-danger-in-greater-data-privacy/
Type: post
Modified: 2026-03-17
What are the risks in data privacy and how can we ensure a more ethical use of data? In today’s article, I’ll be discussing data privacy, data ethics, and the complicated logistics that make the answer to this question so hard to answer.
I’m also going to share a personal story about the nightmare outcomes I’ve had to experience when I tried to use FB ads to grow my small business.
YouTube URL: https://youtu.be/lV08dioQcw4
If you prefer to read instead of watch then, read on…
For the best data leadership and business-building advice on block, subscribe to my newsletter below and I’ll make sure you get notified when a new blog installment gets released (each week). 👇
For some perspective on why I’m qualified to speak about data privacy – I’ve been a data professional for a decade, and an online entrepreneur for 9 years…
At this point I’ve helped educate 1.2 Million + data professionals on AI, and I’ve helped new data entrepreneurs get profitable fast in their own online businesses…
So you could say, I know a thing or two about both data science and data businesses, which is what companies like Facebook actually are.
My name is Lillian Pierson and I support data professionals to become world-class data leaders and entrepreneurs.
When it comes to data privacy, the biggest game-changer is Apple’s iOS 14 software update – a data privacy where Apple put a full stop to user tracking for any persons using iOS 14 or above. With this, Apple software will no longer report the user ID of their customers or any of their activities.
Consumers can sigh in relief with the open assurance that all of their activities are no longer being recorded, tracked, and shared on the open market unless they give their permission. Forced data privacy is coming!! Hooray, right?! Unfortunately, within a data-intensive ecosystem like the internet, things are not always as they seem. You really need to dig a bit deeper into what’s happening with personal data, technology businesses and data businesses. My goal for this article is to help spread awareness about what’s currently happening under the hood with respect to late-breaking data privacy developments.
The Problem: Danger
I think we can all agree that – YES, we need more data privacy. Corporations and the government spying on us sucks! It all started off on 9/11 when the towers came down and the US government got the impetus it needed to start spying on its citizens and the rest of the world.
Then came Edward Snowden, the whistleblower, who revealed that the government is spying on everyone and that the FBI is collecting personal data from companies like Microsoft.
If you’re not familiar with what I’m talking about, there are some great YouTube videos out there that you can search for, such as:
Safe and Sorry Terrorism Mass Surveillance
Ethical Insights Big Data and Privacy Navigating Benefits Risks and Ethical Boundaries
App Tracking Sucks
3rd Party Risks Sucks
And I can wholeheartedly say that I agree with all of the fundamental tenets of all these videos.
I just want to stop here and say that I 100% agree that free data collection usage and resale of personal data is both dangerous and unethical.
The Problem: Power Inequity
Like most of you, I believe that data and technology companies have way too much power. For an insider glimpse on the dangers of Facebook, let me share my personal story.
Just a few days ago, Facebook decided that in order for me to access my Instagram account with almost 100K followers, I need to scan my face and provide them with biometric data in hopes that their bots will recognize me and give me access to my data. If there’s any sort of hold up, I will not have access to my data nor I can download it out of Instagram, because the account will be gone. And there’s no way to get a hold of anyone at Facebook in order to request my data which actually belongs to me, according to GDPR. So, I believe that by making me give my biometric data in exchange for data that I own which I put in their platform, is against the law…but there’s nothing I can do about it.
The grievances I have with Facebook are so much worse than that, but of course because of the power of inequity, I can’t do anything about it.
I HATE FACEBOOK and for good reason.
Besides the fact that they’re requesting my biometric data, they also have a bunch of scammers who go in there and create false accounts and take my photos, then actually scam people in America for thousands of dollars, using my images. Last time it happened, I had over 20 people report the account but Facebook didn’t take it down. In fact, they said that it doesn’t violate their community guidelines. The same thing happens on Instagram where people are creating false accounts using other people’s images and Facebook doesn’t care. I’ve asked them to verify my account multiple times. But I guess I’m not cool enough for them, so they won’t do it.
In fact, they would probably verify me if I spent enough money on Facebook ads, which I can’t actually do.
This is the real issue I had with them – back in 2020, I had ads running and I tried to use my account to log in to something called “picmaker.com,” then my Facebook account got hacked and they took it down. So, I submitted my passport and all other stuff to Facebook’s automated engine but it wouldn’t give me my account back. The ads are still running and I couldn’t get to my business manager. I tried every which way, but there was no way for me to get a hold of a person to check if my ads have also been taken down. It turned out that they didn’t.
When I got my personal account back through a Facebook employee (who manually submitted a ticket inside the company), they then banned my business manager, but not without removing the $600 in advertising fees that they run on my credit card without my consent. I tried to dispute this through my credit card company, but they said it happens all the time. Facebook blocks people out and runs up their bills and there’s nothing we can do.
So…
Is FaceBook unethical? 100% YES!
Are they “spying” less data literate people might say? So, yes, they’re spying.
Are they unfair? 100% yes, they’re totally unfair.
Are they criminally negligent? I think so.
Should Apple cut off FB from getting data from apps on your phone? My answer is: I don’t know…
Now the reason is really complicated and I don’t know if I want to pay the price for that. Also, Apple wasn’t without it’s underlying motives…
Before I explain further, I’d love to hear from you.
Tell me in the comments – do you think FB is completely unethical, why or why not?
The Solution
Let’s go back to the nuts and bolts of data privacy – yes, I want total data privacy but NO, THERE’S NO EASY SOLUTION.
It really requires a “Risk / Benefit Analysis.” Data privacy is a lot like data governance, the looser the restrictions on data privacy, the more unethical behavior is gonna be happening across the internet. But if your data privacy laws and restrictions are too tight, then the internet really can’t run very well.
To do that, you have to understand the structures of these technology businesses because they are the relevant actors here.
While these are all technology businesses, their business models are completely different. The most important of these today are:
Hardware (that comes with software) product companies
This is Apple, Samsung, and the like. These hardware companies aren’t currently focusing on monetizing the personal data that they have on their users, but that’s not to say they won’t in the future. Instead of advertising to their customers on other platforms, they’ve built their own ecosystem in which they do indeed use personal data to advertise their products to their users.
Data companies
This is Facebook, Google, Tiktok, Snapchat and the like. These companies sell access to our personal data, including data that is collected through software on Apple and Samsung phones.The only way companies like these can stay in business is because of the value of the data they collect from their users. Unlike hardware companies like Apple, they don’t have much of a need to advertise their own products, since they already have the users. So they monetize by offering the chance for other businesses to advertise within their ecosystem.
Going back to Apple’s iOS14 update – in their highly contentious, politicized play, Apple recently thrust itself into the limelight as a data privacy champion in the eyes of most. On its face, it would seem like Apple is standing up for the people with respect to data privacy laws, but when you look deeper at it, is Apple really against using personal data to advertise to their consumers? No – Apple still continues to use its customers’ personal data to advertise to its own ecosystem.
Furthermore, by launching this attack against Facebook, Apple has hurt its own customers and partners in at least two big ways:
Hurting Apple application developers’ bottomline
Obviously if you pay $1,000 or more for a cellular phone, you want it to come with access to really awesome applications, right? As any iPhone user knows, Apple does not play friendly with any non-Apple technology, so you can’t download apps from Google Play – Google’s application store for all Android users – you have to get your applications only from Apple’s App Store. Well, the developers of those applications need customers so that they can generate revenue and then reinvest a significant amount of that money into improving their applications. But where are most of these revenue-generating customers coming from? They come from ads that are run on data businesses like Facebook, Google, Snapchat, etc. Without these ads, how will application development companies stay in business?
Decreasing Apple iOS app quality
To turn a profit, and subsequently reinvest in developing the best iOS applications possible, iOS app developers need customers. By cutting off user identification reporting, Apple’s own app developers can no longer get the conversion tracking data they need to run ads to get more customers. This is going to result in fewer customers for them. This then leads to decreasing revenues, and subsequently lower quality standards for the application they develop. Maintaining a great software product costs money. When you lose a leads source, you get decreased sales and have less money to use for product maintenance. This change can’t be good for Apple customers who are already trapped within the Apple ecosystem and unable to use any applications from within the open source Google Play store.
So, is Apple a savior? Meh.
It almost seems like Apple cut off it’s own nose to spite its face, doesn’t it? While Apple’s recent move means more privacy for its users, it also means less relevance with respect to advertisements those users see. It represents a complete loss of the ability to do any real-time advertising optimization. No one I know wants to see more ads for things they don’t care about. Who out there has not discovered courses, products, or services that they absolutely adore but would never have known about if it wasn’t for the precision at which Facebook was able to match your interest and passions with various marketplace offers?
If this whole discussion on personal data collection and resale is news to you, then you’d probably be super interested to hear the story of how one company is buying personal data and reselling to the tune of $450K per month. Check it out here.
The Cost Of Data Privacy
Here is what stricter data privacy laws going to cost you:
Expect to pay by being forced to repeatedly consume the same annoying, spammy ads that you have absolutely no interest in seeing ever again. Relevant advertisers will no longer be able to find you, and the ads that do make it through will not be optimized to conform with your desires, expectations, and preferences.
No targeted ads = higher cost per customer.
Higher cost per customer means higher prices.
As rollouts like these affects more and more platforms. This means the platforms and their advertisers are going to have less and less data to use in evaluating what’s working and what’s not. Current estimates are that 30-40% of the conversion tracking data will be lost by the platforms. This includes all of the businesses, brands, and advertisers that use them. Imagine paying $1000 to run an ad. In return, you don’t get any information about how that ad performed, or whether it actually generated any leads or sales for your business whatsoever. Who would do that? For professionals who make their living helping companies create value from data, the enormity of problems this “cookieless society movement” will create should be beyond obvious.
For data professionals, these changes are like taking food right out of our mouths.
No more free Facebook, Google, IG, what have you – someone has to pay. If you don’t allow advertisers to pay for the service for you, then you’ll ultimately have to pay yourself.
So unfortunately, data privacy isn’t a simple matter.
One last thing, the data privacy officer is the data pro. The person is responsible for managing data privacy issues like we just discussed. It is one of the 50 emerging data roles I report on in my Data Superhero Quiz. This is a fast, fun, 45-second quiz for data pros! It aims to help you uncover the optimal role for you given your passions, skillsets and personality.
Take the Data Superhero Quiz Today!
Hey! If you liked this post, I’d really appreciate it if you’d share the love with your peers! Share it on your favorite social network by clicking on one of the share buttons below!
NOTE: This description contains affiliate links that allow you to find the items mentioned in this article and support the channel at no cost to you. While this blog may earn minimal sums when the reader uses the links, the reader is in NO WAY obligated to use these links. Thank you for your support!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Proven Evergreen Data Migration Strategy for Data Professionals Who Want to GET PROMOTED FAST
URL: https://www.data-mania.com/blog/evergreen-data-migration-strategy/
Type: post
Modified: 2026-03-17
Evergreen data migration strategy – what does it have to do with you getting promoted as a data professional? LOTS, actually. Well, depending on your role or where you’re trying to go, of course.
By the end of this article, you’ll have a pretty solid idea of how to build an evergreen data migration strategy for your company and how to get strategy building experience that is sure to attract the right type of attention from business leaders.
Whether you’re new to the data industry or like me, and have been at it for over a decade… It is vitally important for you to make sure that the work you’re doing on a daily basis is actually benefiting your company’s bottom line. It’s not that difficult for you to conceptually bridge that gap either.
How do I know? Well, to date I’ve educated over 1 Million data professionals on AI, so you can say I know a thing or two about data science…
That and I’ve been delivering strategic plans since 2008, for organizations as large as the US Navy, National Geographic, and Saudi Aramco.
If you prefer to read instead of watch, then read on…
THE STAR FRAMEWORK
Our entire data strategy will be centered around my proven evergreen data strategy framework – called STAR.
By applying this framework to your strategy building efforts, you can be sure you’re building an effective data strategy plan, despite changes in market condition – hence this being an EVERGREEN DATA STRATEGY.
ANOTHER beauty of the STAR framework is that it is completely vendor agnostic – and actually prompts you to dig deeper to explore and discover the most efficient data technology solutions given your company’s specific needs.
It’s comprised of the following 4 phases..
1. S – Survey the industry –
This is where you go around and do a ton of research looking at all the different use cases and case studies, and considering how those might fall into place with your organization’s current set up.
2. T – Take stock & inventory of your organization –
Collect or generate all sorts of documentation that describes the state of your organization as it currently is. This documentation could include:
Surveys
Interviews
Requests for information
3. A – Assess your organization’s current state conditions
Identify gaps
Identify risks
Identify opportunities
Select an optimal data use case given specifics for your company
4. R – Recommend a strategy for reaching future state goals –
Make recommendations and develop a strategic plan for reaching future state goals. This is where you’ll lay out all of your recommendations and requirements in order to implement the use case.
Like I said, we are applying MY STAR framework to a data migration strategy building efforts – so essentially here, our use case has already been chosen for us – it’s DATA MIGRATION.
Although we’ve already been assigned a use case in this situation – we still need to apply the STAR framework so we can make sure we have all the project planning in place and that the strategy is feasible for the long term – say, 18-month future.
Let’s start looking at how the STAR framework would be applied to a data migration use case.
PHASE 1: S – SURVEY THE INDUSTRY
You’ll need to go around and collect a ton of research use cases that document other companies’ experiences with data migration projects. You do this in order to gather ideas of what’s actually possible and what’s most feasible for your company given its current technology set-up.
I’ll give you a head start on where to look for these. Here’s a whole set of data migration case studies by Foresight Group International.
If you’ve got a data migration use case that comes to mind, it would be awesome if you would link to it, or just name it and describe it in the comments. That way, our community can grow and help each other out along the way.
Now, for many of the steps and elements that we’ll be plugging in the STAR framework, I’m actually leaning heavily on the Data Migration Project Checklist – this is a migration checklist that was developed by Experian to help data leaders to utilize their Pandora technology. Nonetheless, it’s very good. Check it out here.
As I mentioned earlier, I strive for all of my content to be completely vendor-agnostic – which of course is a big reason why I recommend you to survey the industry and look at use cases and case studies before trying to decide what technologies to use.
PHASE 2 – T- TAKE STOCK & INVENTORY OF YOUR ORGANIZATION
Generally, this information-gathering phase will involve surveys, interviews and requests for information.
There are 5 main categories of information you’ll need to collect:
Let’s dig a little deeper into what you’d want to inventory if you were preparing to develop a data migration strategy
Specifications – You’ll need a target mapping specification. This documents high-level objects and relationships that will be linked during the migration.
Policies:
You’ll need a configuration management policy – this policy will document how data migration project resources will be managed. You’ll need to have this handy because it’s gonna be needed for reference in the execution.
You’ll also need security policies – these document the security restrictions across the organization.
Lastly, you’ll need any existing data migration policy documentation.
Agreements – Make sure to collect all 3rd Party agreements especially if they pertain to vendors and the requirements these vendors are covering
Documentation – You’ll need to collect any relevant training documentation and existing data dictionary
Assessment Findings – If your company has done a pre-migration assessment, you want to take a look at that so make sure you grab it, if not, you’ll have to produce one now.
Hey, if you’re liking my data strategy framework, you’d probably love the video I did on How to create an evergreen analytics strategy framework. Check it out!
PHASE 3: A – ASSESS YOUR COMPANY’S CURRENT STATE CONDITIONS
This is where you need to assess as it currently is in order to uncover any gaps, risks or opportunities as they may exist for your company today.
This is generally where you’ll Identify opportunities and select an optimal data use.
But here, of course, our use case has been prescribed to us as data migration. You’ll want to make sure that you’re thoroughly assessing these elements when building an evergreen data migration strategy. These elements include:
Gaps, Risks & Opportunities
You’ll need to produce a preliminary structured task workflow. This will help you identify:
Gaps in budget and skill sets
Gaps in knowledge or on-hand training resources
Gaps, opportunities and risks within data skill sets, tools and resources
Privileges and Securities
You’ll need to assess what type of security issues are likely to arise during your data migration project. You’ll also want to look at your access rights and make sure you have everything you need, if not, you’ll need to put requests into place
Processes
You’ll need to create the following processes:
Data quality management process – you’ll need to decide what type of processes you need to put in place in order to preserve your data quality rules as you work in the data migration project.
Risk management process – you have to decide what measures you need to put in place in order to record and resolve risks as they occur.
Project Management
You need to do the following:
Project estimates – you need to estimate how much time you have and how long it takes to complete the data migration.
Target mapping assessment and retirement strategy
You need to have a plan in place on how to educate users about where to retrieve or access their data once the old system is decommissioned.
Assess relevant stakeholders on any issues that could arise among them
Produce conceptual and logical models – these communicate and define the structure of the legacy and target environments. You want to make sure that these models are logical and easy to understand because they serve as fundamental communication tools between all team members and stakeholders.
Engineering
You’ll have to take a look at the hardware and software in order to assess and evaluate how you can make optimal use of your company’s existing data technologies, skill sets and data resources.
PHASE 4: R – RECOMMEND A STRATEGY FOR REACHING FUTURE-STATE GOALS
This is where you’re going to produce a lot of deliverables for your data strategy action plan. These are the following:
Baseline Recommendations
Production hardware and software requirements
Project estimates – time & budget
Milestone goals
Key project resources – people, tools, data access
Strategic Assets
Proposed update to data dictionary
Standard project document templates – create project documentation such as risk register, issue register, acceptance criteria, project controls, job descriptions, project progress report, change management report, etc.
Stakeholder communication plan
Training plans – will help you ensure that all the relevant team members are properly trained before asking them to perform the work
Data migration execution strategy
Retirement strategy
Structures & Workflows
Project delivery structure – this will probably resemble a standard waterfall approach: Analyze, Design, Build, Test and Launch.
Recommendations for your structured task workflow
Specifications
Target mapping design specification – this would be a complete source-to-target specification, down to the attribute level
Interface design specification
Data quality management specification
Draft Policies
Configuration management policy – this document where data migration resource materials will be stored so they can be easily and quickly retrieved and easily during execution phase
Recommended data migration policy
Security policy – detailed resolutions to any security or data access issues you identified during the assessment phase
Draft Agreements
Service level agreements
Recommended 3rd Party supplier agreements and requirements
Congratulations, we have covered each of the four phases of the STAR framework. Now, what I recommend you do is go ahead and pull all of these together and start putting into place all of the pieces you need to produce an evergreen data migration strategy, then show it to your boss.
Look, even if they don’t let you take the lead on the project or if they take all the credit, if you continue on like this – going the extra mile to use data and company resources to produce transformative business results – then,
(1) you’re gonna build up a kick-ass CV that you can use to score a better job or
(2) you’ll be recognized and promoted within your current company
Either way, that’s a WIN-WIN for you!
If you want my 44-action item plan for building a fail-proof data strategy that works for every data use case under the sun (including the evergreen data migration strategy, as you’ve just seen here)… you should definitely check out the Data Strategy Action Plan.
The Data Strategy Action Plan is a step-by-step checklist & collaborative Trello Board planner for data professionals who want to get unstuck & up-leveled into their next promotion by delivering a fail-proof data strategy plan for their data projects.
Start executing upon our Data Strategy Action Plan today.
Share It On Twitter by Clicking This Link -> https://ctt.ac/pT1ld
Watch It On YouTube Here: https://youtu.be/1yeKVcVyyN4
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## The difference between Metrics, KPIs and Key Results
URL: https://www.data-mania.com/blog/difference-between-metrics-kpis-key-results/
Type: post
Modified: 2026-03-17
What are the differences between Metrics, KPIs and Key Results? If you’re out here swinging terms like these, but you’re not actually 100% confident on what they mean, this post is for you! Keep reading to learn more about the difference between metrics, KPIs and key results, full details on what each of these important data terms means, as well as when to use them and when to refrain to improve your data lingo!
If you prefer to read instead of watch, then read on…
First things first: what does it mean to be data-driven?
At an organizational level, data-drivenness is about building tools, abilities, and, most crucially, a culture that acts on data. Individually, it’s about being empowered to make more informed decisions every day.
Becoming data-driven is a journey. If you possess most (or at least some) of these qualities, there’s a good chance you’re moving in the right direction:
You don’t like making decisions without a clear understanding of what the data is saying.
You’re not one to rely on anecdotal or peer evidence, or a gut feeling. You use data as the core part of your decision-making process, and won’t move forward until you have data to support your decisions.
You’re adaptable and willing to change. Being data-driven means you understand that your strategy will CONSTANTLY be changing depending on what the data says. You’re okay with this and thrive in a fast-paced, ever-changing environment.
You don’t shy away from using data tools.
You may need some help with the numbers from time to time. The reality is not everyone is an analyst. But, you ensure you have an arsenal of tools that help you manage your data and act on it. Tools that are proactive, simple, and personal.
If you’re still reading this, nodding your head, and thinking “yes, that’s me to a tee!” – let’s get into some key terms that should be in every data-driven individual’s lexicon.
The Difference Between Metrics, KPIs, and Key Results
To kick-off our discussion on the difference between metrics, KPIs, and key results – let’s start by defining metrics, then move into KPIs, then key results. Stick with us to uncover the rhythm and reason behind that ordering.
What are Metrics?
According to Oxford, a metric is simply a system or standard for measurement. A metric can be defined by one or more measures. Essentially, a metric is a communication tool, where we’re all agreeing upon the same standard.
Metrics are defined by something called unit value. The unit value is simply the quantitative value that a unit of measurement takes.
Let me illustrate with two examples.
First, let’s use a simple spatial example.
The United States is approximately 2800 miles wide. The unit value for this would be 2800, and the metric describing the width of the country would be miles.
Pretty simple, right?
Now let’s look at a more realistic example, one you might see on a day-to-day basis as a data professional.
Let’s assume you’re a Data Product Manager, and you’re reporting that your application had 50,000 daily active users.
The unit value in this scenario would be 50,000 – and the metric would be daily active users. As I mentioned before, a metric can also be calculated from more than one measure.
Going back to our spatial example, the United States contains 2.43 billion acres. Of course, the unit value here would be 2.43 billion, and the metric would be acres.
But with this example, acre could be a straight-out measure, OR it could be a calculated metric.
To calculate acreage, you would times length by width in feet, divided by 43,560 square feet per acre. Keep in mind, with this example you could either simply receive a report of the measurement of acreage OR you could calculate it using the formula I just provided, making it a calculated metric.
Looking back at our more practical example, let’s return to the Data Product Manager. Imagine now that the application has a 19% retention rate. The unit value here is 19%, and the metric is the retention rate. The retention rate is calculated as follows:
Customers at the end of the calculated period minus new customers, all divided by customers at the start of the calculated period, multiplied by 100.
Now that is definitely a calculated metric!
Now that you know exactly what a metric is, we can start looking at the difference between metrics, KPIs, and key results.
What are KPIs?
According to kpi.org, a KPI is a critical key indicator of progress toward an intended result.
Just like metrics, KPIs are also a communication tool, but they’re used to indicate progress towards a desired result.
Sounds pretty clear-cut, right? Let’s explore how you’d use this with a business example.
The first thing you need to realize is that you DESIGN a KPI in order to gauge your progress towards your desired result.
In order to design a good KPI, you have to start first by defining its five core ingredients.
Business Objective: the desired business result.
Unit of Measure: the metrics you use to describe the progress you’re making towards the desired business result.
Current Unit Value: the value that your metric assumes today.
Target Unit Value: the value your metric assumes, once you’ve reached the desired business result.
KPI Title: you should title your KPIs according to the metrics you’ve used to measure them.
Let’s dive into an example of a KPI.
If we take the retention rate example, imagine that your target unit value was 19%. But the current unit value falls short at just 17%. In this case, the desired business result hasn’t been met.
If you’re a Data Product Manager, you’re going to be getting a thumbs down from your superiors in terms of progress results!
Any discussion about the difference between metrics, KPIs, and key results would be impossible without first introducing you to the concept of starting unit values. Let’s dig deeper into what exactly that is next, shall we?
What are Key Results?
Finally, let’s look at key results. Key results are very similar to KPIs except for one fundamental difference – that being the start unit value.
We’ll use the KPI discussion above to illustrate the difference between key performance indicators and key results.
Just like KPIs, you’ll also need a business objective (AKA, your desired business results) as well as a unit of measure (the metric you use to describe progress towards the business results).
But with key results, instead of using the current unit value, you’ll replace that with a starting unit value. Starting unit value is the value your metric assumes at the start time of the period you begin taking action towards reaching your desired business result.
They’ll also be a target unit value, which is the value of the metric when you’ve reached your desired business objectives.
The title is also a little bit different with key results than with KPIs. With key results, the title describes the entire transformation you’re trying to achieve for the business.
Going back to our retention rate example, let’s say our start value is 15% and our target value is 19%. In this case, the key result title would be “increased retention rate from 15% to 19%”.
All of these data terms become incredibly important when you want to put together a data strategy. It’s important you have a deep understanding so you can craft a well-executed data strategy plan, help increase business revenue for your company, and WOW your superiors.
To help you do all that and more, be sure to check out The Data Strategy Action Plan. It’s a step-by-step checklist and Trello board planner for data professionals who want to get unstuck and up leveled into their next promotion by building a fail-proof data strategy for their data projects!
Get The Data Strategy Action Plan Here
Share It On Twitter by Clicking This Link -> https://ctt.ac/fPgdl
Watch It On YouTube Here: https://youtu.be/9GDYqeFQZII
NOTE: This description contains affiliate links that allow you to find the items mentioned in this video and support the channel at no cost to you. While this channel may earn minimal sums when the viewer uses the links, the viewer is in NO WAY obligated to use these links. Thank you for your support!
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## GPT-3 AI Examples – The Good, The Bad and The Ugly AF
URL: https://www.data-mania.com/blog/gpt-3-ai-examples-the-good-the-bad-and-the-ugly-af/
Type: post
Modified: 2026-03-17
In a world of GPT-3 AI-generated content, are writers even needed? In a recent business experiment, I set out to answer this question.
If you’re wondering, who am I to tell you anything about GPT-3 AI? Well, I’m Lillian Pierson, and I help data professionals become world-class data leaders and entrepreneurs – to date I’ve trained over 1 million data professionals on the topics of data science and AI. I’m a data scientist turned data entrepreneur, and I’ve been testing out GPT-3 AI for about 3 months now in my data business, Data-Mania.
If you prefer to read instead of watch then, read on…
For the best data leadership and business-building advice on the digital block, subscribe to my newsletter below and I’ll make sure you get notified when a new blog installment gets released (each week). 👇
As a data entrepreneur, I spend a TON of my time, energy, and financial resources on creating content. From podcast episodes to YouTube scripts, to emails and social media posts, content creation eats up a huge chunk of my week.
So when I heard about GPT-3 AI copy services, I was curious to know: would this be a useful tool in my business?
Would I be able to 10x my content production rates? Replace freelance writers?
Rather than simply buying into the online hype, I wanted to conduct my own research – and today, I want to share it with you. Whether you’re a data entrepreneur, data professional, or simply a fellow data geek who LOVES reading about the smartest AI companies, read on to get the full scoop on GPT-3 AI and how I believe it will shape the content writing industry.
In this article, we’ll cover:
What is GPT-3 AI?
The pros of GPT-3
The cons of GPT-3
3 guidelines to use GPT-3 while maintaining brand integrity
Will GPT-3 change the content writing industry?
Let’s get started.
What is GPT-3 AI?
First of all, what is GPT-3 AI? GPT-3 is a model for human-like language production written in Python. It uses large amounts of texts crawled from the web to create similar, but unique content. Since it was developed by OpenAI and released for public use in June of 2020, there have been TONS of data entrepreneurs creating SAAS products that run off of GPT-3.
Some of the most common GPT-3 AI content services are Copy.ai and WriteSonic. I conducted my experiment using Writesonic.
Pros of GPT-3 AI
Alright, let’s start with the good.
Great for Product Descriptions
During my experiment, I have to say I was genuinely impressed by the product description snippets I was able to create using Write Sonic’s GPT-3 AI service.
All I needed to do was input the name of my product (in this case, it was my free Data Superhero Quiz) as well as add product info such as features and benefits. All I did was copy and paste some bullet points from my sales page and I was good to go.
And wow! With the click on a button, I had ten high-quality product descriptions to pull from. The service was even suggesting some features and benefits I hadn’t even thought of.
Unique and Anonymous
A big pro to using GPT-3 AI content is that everything it spits out is completely unique. There’s no need to worry about plagiarized content. Also, the service is totally anonymous – no one will know you’re using AI so there’s no need to worry about being judged.
Good ROI on Your Time and Money
After reviewing the product descriptions created by Writesonic, I have to admit I liked them a lot better than the ones I’d written myself. Considering the fact they’d taken me a good 10-20 minutes to write, PLUS I’d purchased templates for $50 to speed up the process of writing them, the GPT-3 AI content is clearly better value. I had dozens of descriptions within just 30 seconds.
Overall, if you are looking for a tool to help you quickly and easily create short content snippets (i.e. product descriptions!) you should definitely add a tool like Copy.ai or Writesonic to your toolbox.
Cons of GPT-3 AI
While I had some successes with GPT-3 AI, I also had some total failures.
Lacks context
Unfortunately, GPT-3 is not great at generating content if it doesn’t have the context directly from you.
I tried playing around with its article writing mode, which is still in beta.
Essentially, you give it an outline and an introduction, and then it returns the entire article with all of the body copy.
While technically the information may be factually correct, it lacks context. It won’t have the context needed for YOUR particular audience, so it won’t be intelligible.
Information without context about WHY it matters to your customers is useless. They need to know why they should care and how what you’re sharing will actually have an impact on their life. Without that, you’re simply producing content for the sake of content, and adding to the noise.
In some cases, it gets things wrong.
While in some cases the information might be garbled and lacking context, in other instances, the content GPT-3 AI provides could be flat out wrong.
GPT-3 AI will lack the nuances about your industry that come naturally to you.
For example, when I was using Writesonic’s article mode, one of the headings was “What are the obligations of a Data Processor?”
However, the body copy that GPT-3 produced did NOT correlate with the appropriate heading. Rather than telling me the obligations of a Data Processor, it gave me content about the role of a Data Protection Officer.
It brought up a totally different point. And while it may be related, if you had actually used this content on the web, it would’ve reduced your credibility and put your brand in a bad light.
In short, I would straight up AVOID GPT-3 AI for article-writing or long-form content. You could potentially use it as a research tool, to help you uncover relevant topics you may not have thought of, but always be sure to dig deeper into those topics and not rely on what GPT-3 gives you.
3 Guidelines To Make the Most of GPT-3
Here are three recommendations and safety guidelines for you to use in order to make sure that you’re protecting your brand integrity and the quality of the content you produce when working with GPT-3.
Review GPT-3 AI Content Carefully
GPT-3 is going to create a TON of content for you. It’s up to you to pick and choose what is valuable, and to make sure everything is factually correct and appropriate.
Add Personalization
Whatever content that GPT-3 gives you, you need to improve on it, add to it and personalize it for your brand. You know your customers better than anyone else. I recommend seeing GPT-3 as more of a content research tool than as something to produce finished copy.
Add Context
No one on this planet needs more random information. What we need is meaning and context. So while the creators of GPT-3 are correct in saying it produces ‘human-like text, it’s not able to add the context readers need in order to create meaning in their lives.
Content without context doesn’t compel readers to take action based on what they’ve read – all it does is overwhelm them. Information for the sake of information simply adds to the noise – which is something all of us online content creators should be trying to avoid at all costs
Listen to All Content Aloud
And last, but not least, rule number four is to listen to your end text aloud.
You want to make sure that whatever content GPT-3 AI spits out, you’re listening to out loud so you can make sure it’s conversational and flows nicely. It’ll also be an opportunity to double-check everything is factually correct.
My favorite tool to do this is a TTS reader.
By following these guidelines, you’ll be able to ensure that you can safely increase your content production WITHOUT harming your brand’s reputation.
Will GPT-3 change the game for writers?
After reviewing the results from my business experiment, I STILL believe that there is a need for highly skilled content writers. However, the rise of GPT-3 AI demonstrates how AI is certainly changing the content marketing landscape.
While I do believe GPT-3 may replace low-level, unskilled writers (who, let’s be real, probably shouldn’t be pursuing writing in the first place) businesses will continue to require writers who can deliver nuance, context, and meaning to their customers.
At best, GPT-3 will become a tool that helps smart writers speed up their writing process and make their lives easier. They may use GPT-3 content as a starting point from which they can create highly personalized and meaningful content.
At worst, the web could become flooded with GPT-3 AI generated that only adds noise to the already crowded internet, significantly contributing to the overwhelm people are already experiencing when trying to find high-value information online.
In order to create long-form, meaningful content, GPT-3 AI content tools still have a long way to go, but they show promise as a tool to speed up businesses’ content workflows.
Get the Data Entrepreneur’s Toolkit (free)
If you love learning about this GPT-3 tool, then you’re also going to love our FREE Data Entrepreneur’s Toolkit. It’s designed to help data professionals who want to start an online business and hit 6-figures in less than a year.
It’s our favorite 32 tools & processes (that we use), which includes:
Marketing & Sales Automation Tools, so you can generate leads and sales – even in your sleeping hours
Business Process Automation Tools, so you have more time to chill offline, and relax.
Essential Data Startup Processes, so you feel confident knowing you’re doing the right things to build a data business that’s both profitable and scalable.
Download the Data Entrepreneur’s Toolkit for $0 here.
Share It On Twitter by Clicking This Link -> https://ctt.ac/dMiJ4
Watch It On YouTube Here: https://youtu.be/Hs9c8IRN5ys
NOTE: This description contains affiliate links. This will allow you to find the items mentioned in this video and support the channel at no cost to you. While this channel may earn minimal sums when the viewer uses the links, the viewer is in NO WAY obligated to use these links. Thank you for your support!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Data Pipeline Design And Build 101
URL: https://www.data-mania.com/blog/data-pipeline-design-and-build-101/
Type: post
Modified: 2026-03-17
Curious about how to get started with data pipeline design and build processes? Here’s what you need to know…
What Is a Data Pipeline?
A data pipeline enables you to move data from a certain source to another destination. The pipeline transforms and optimizes the data and ships it in a state suitable for analysis. It includes steps that aggregate, organize, and move data, typically using automation to reduce the scope of manual work.
A continuous data pipeline usually phases through the following tasks:
Loading raw data into a staging table for interim storage
Transforming the data
Adding the transformed data to the destination reporting tables
This basic process may change according to the use case and individual business requirements and needs. Data pipelines are used for a variety of business processes including big data analytics, machine learning operations (MLOps), data warehouses, and data lakes.
Data Pipeline Design Process
A data pipeline design is a process designed for shifting data from one place to another. The process can change depending on each scenario. For example, a simple data pipeline can include mainly data extraction and loading, while a more advanced pipeline can include training datasets for artificial intelligence (AI) machine learning (ML) use cases.
Here are key phases typically used in data pipeline design processes:
Source—you can use various data sources for your pipeline, including data from SaaS applications and relational databases. You can set up your pipeline to ingest raw data from several sources using a push mechanism, a webhook, an API call, or a replication engine that can pull data at regular intervals. Additionally, you can set up data synchronization at scheduled intervals or in real-time.
Destination—you can use various destinations, including an on-premises data store, a cloud-based data warehouse, a data mart, a data lake, or an analytics or business intelligence (BI) application.
Transformation—any operation that changes data is associated with the transformation process. It may include data standardization, deduplication, sorting, verification, and validation. The goal of transformation is to prepare the data for analysis.
Processing—this step applies data ingestion models, such as batch processing to collect source data periodically and send it to a destination system. Alternatively, you can use stream processing to source, manipulate, and load data as soon as it is created.
Workflow—this step includes sequencing and dependency management of processes. Workflow dependencies can be business-oriented or technical.
Monitoring—a data pipeline requires monitoring to ensure data integrity. Potential failure scenarios include an offline source or destination and network congestion. Monitoring processes push alerts to inform administrators about these issues.
Automated pipeline deployment
Keep in mind that a pipeline is not static. Over time, you will have to iterate on pipeline stages to resolve bugs and incorporate new business requirements. To do this, it is a good idea to keep your entire pipeline and all the tools it comprises using infrastructure as code (IaC) templates. You can then establish an automated software deployment process that updates your pipeline whenever changes are needed, without disrupting the pipeline’s operation.
Types of Data Pipeline Tools
Various data pipeline tools are available depending on the purpose. The following are some of the popular tool types.
Batch and Real-Time Tools
These batch data pipeline tools can move large volumes of data at regular intervals, known as batches. Batch tools can impact real-time operations. That is the reason most people usually prefer these tools for on-prem data sources and use cases that don’t require real-time processing.
Real-time extract, transform and load tools process data quickly and are suited to real-time analysis. They work well with streaming sources.
Open Source and Proprietary Tools
Open source tools use publicly available technology and require customization based on the use case. These tools are usually free or low-cost, but you need the expertise to use them. They can also expose your organization to open source security risks.
Proprietary data pipeline tools suit specific uses and don’t require customization or expertise to maintain.
On-Premises and Cloud Native Tools
Traditionally, businesses stored all their data on-premises in a data lake or warehouse. On-premise tools are more secure and rely on the organization’s infrastructure.
Cloud native tools can transfer and process cloud-hosted data and rely on the vendor’s infrastructure. They help organizations save resources. The cloud service provider is responsible for security.
Best Practices for Data Pipeline Design and Build
Manage the Data Pipeline Like a Project
Viewing data pipelines as projects, like software development pipelines, is important for making them manageable. Data project managers must collaborate with end-users to understand their data demands, use cases, and expectations. Including data engineers is also important to ensure smooth data pipeline processes.
Use a Configuration-Based Approach
You can reduce the coding workload by adopting an ontology-based data pipeline design approach. The ontology (configurations) helps keep the data schema consistent throughout the organization—this approach limits coding to highly complex use cases that a configuration-based process cannot address.
Keep the Data Lineage Clear
The data continuously changes when applications evolve, with teams adding or removing fields over time. These constant changes make it difficult to access and process data. Labeling the data tables and columns with logical descriptions and details about their migration history is crucial.
Use Checkpoints
Capturing intermediate results during long calculations and implementing checkpoints is useful. For instance, you can store computed values using checkpoints and reuse them later. This method helps reduce the time it takes to re-execute a failed pipeline. It should also make it easier to recompute data as needed.
Divide Ingestion Pipelines into Components
Data engineers can benefit from accumulating a rich source of vetted components to process data. These components offer flexibility, allowing data teams to adapt to changing processing needs and environments without overhauling the entire pipeline. You must ensure continued support for your initiatives by converting the technical benefits of the component-based approach into tangible business value.
Keep Data Context
You must keep track of your data’s context and specific uses throughout the pipeline, allowing each unit to define its data quality needs for various business use cases. You must enforce these standards before the data goes into the pipeline. The pipeline is responsible for ensuring the data context is intact during data processing.
Plan to Accommodate Change
Usually, the data pipeline frequently delivers data to a data warehouse or lake that stores data in a text format. When you update individual records, this can often result in duplicates of already delivered data. You must have a plan to ensure your data consumption reflects the up-to-date records without duplicates.
Conclusion
In this article, I explained the basics of data pipeline design and build. I also provided some essential best practices to consider as you build your first pipeline:
Manage the data pipeline like a project with an iterative development process
Use a configuration-based approach and plan for future changes
Keep data lineage clear and keep the context of data throughout the pipeline
Use checkpoints to capture intermediate results, enable error checking and recovery
Divide ingestion pipelines into components, to ensure easier updates of pipeline elements
I hope this will be useful as you take your first steps in a data pipeline project.
Hey! If you liked this post, I’d really appreciate it if you’d share the love by clicking one of the share buttons below!
A Guest Post By…
This blog post was generously contributed to Data-Mania by Gilad David Maayan. Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Samsung NEXT, NetApp and Imperva, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership.
You can follow Gilad on LinkedIn.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Marketing Data Warehouse – Why every online business needs one
URL: https://www.data-mania.com/blog/marketing-data-warehouse-why-every-online-business-needs-one/
Type: post
Modified: 2026-03-17
If you’re at a small- or medium-sized company that attracts most of its customer online, you NEED a marketing data warehouse – like, yesterday. In this brief article, we’re going to explain what data warehousing is, how it benefits business, and how it improves ROI for marketing operations specifically.
Introduction to Data Warehousing
Cloud-based technologies have taken the business world by storm; allowing companies to easily store and retrieve valuable data about their products, customers, and employees.
This data can be then used to make key business decisions. Based on the insights uncovered by Global Marketing Insights, the Data Warehousing Market is expected to grow at over 12% CAGR (Compound Annual Growth Rate) between 2019 to 2025.
Several global enterprises turned to Data Warehousing to organize their data that streams in from corporate operation centers and branches all over the world. A Data Warehouse is defined as a system that stores data from a company’s Operational Databases along with its external sources. Data Warehouses are different from Operational Databases because they store historical information. This simplifies the analysis of data over a specific period of time. Data Warehouse platforms can also sort data based on varying subject matter, such as business activities, customers, and products.
Understanding the Need for a Data Warehouse
While all of lines of business can be improved with proper data warehousing, marketing operations are particularly ripe for the picking when it comes to the ROI of a Marketing Data Warehouse project.
Data Warehousing has become increasingly more significant since it allows companies to:
Improve their Bottom Line: Data Warehouse platforms help business leaders quickly access their organization’s historical activities and assess successful or unsuccessful initiatives from the past. This allows executives to identify where they can adjust their strategy to maximize efficiency, increase Sales, and decrease costs to improve the bottom line.
Ensure Consistency: Data Warehouses are programmed to apply a uniform format to the accumulated data. This makes it easier for corporate decision-makers to share and analyze data insights with their colleagues and around the world. By standardizing data from different sources, you can improve overall accuracy and reduce the risk of error in interpretation.
Make Better Business Decisions: Successful business leaders develop data-driven strategies and seldom make decisions without understanding the facts first. Data Warehousing improves the efficiency and speed of accessing different datasets. It also makes it easier for decision-makers to draw insights that can guide the Marketing and Business Strategies that make them stand out.
Understanding How Data Warehousing boosts ROI
Here are a few ways Data Warehousing can help you create an influx for better ROI for your organization:
1) Customized Marketing Campaigns
A common reason for the failure of a Marketing Campaign is that it isn’t relevant. If your Marketing Campaigns aren’t created with the needs of your target audience in mind, they will eventually go bust. For instance, if you are showing your kids’ wear ads to bachelors or if your email campaign about buying your new product is sent to subscribers who have already purchased the product. Such Marketing efforts are simply a waste of time and resources. It is important that you analyze Marketing data to Create Marketing Campaigns with customized ad copies, offers, and landing pages, along with helping you scope out channels on which you can run those campaigns.
2) Understanding Your Audience Better
You can get a better understanding of what your target audience expects from your brand with effective data analysis. This can help you customize your Marketing Strategies accordingly. You can also merge your customers’ data from multiple offline and online sources to find what you exactly need to do to bring in more customers to your business. This allows you to create more compelling content and marketing Campaigns like a perfect Marketer.
3) Choosing the Best Channels for Promotion
Marketing data reveals the needs, expectations, and preferences of your potential and existing clients. It also allows you to determine which channels are best for your brand to do the promotion to engage with a larger audience. These insights can help you deliver your brand’s vision statement and message to the target audience and convince them to reach out to you.
4) Improved Connection with your Potential Customers
With the constant influx of Marketing data to your Data Warehouse, you can make optimizations. These can be in both your campaigns and strategies in real time. This ensures optimal performance on various channels. This also creates an influx for a better ROI making its way to you.
5) Boost the Decision-Making Process
Armed with the right data at the right time, you can make integral business decisions accurately and timely. Result-oriented and fast decision-making is the primary focus of boosting Marketing ROI. Faster decisions boost your productivity and lead to faster actions in no time. Increased productivity can save you millions in operational costs.
6) Manage Your Budget
To thrive in the marketing world, you need to make the best use of your limited budget. Big Data can help you invest your Marketing budget in the right target audience and channels. It will also help you to deliver a high Marketing ROI. With the right data at your fingertips, you can easily scale the use of channels that deliver better ROI. Additionally, you can also easily discard the campaigns with a poor Conversion Rate. This allows you to ensure the highest return on an investment made.
If you like all this talk on getting the best marketing ROI for your business, be sure to check out this video on how this Marketing Data Scientist Made $370K in just 18 months!
Conclusion
This blog talks about Data Warehousing and its impact on ROI (Return on Investment) in Marketing Data Science. It also discusses the importance and benefits of a Cloud Data Warehouse before jumping into the various ways Data Warehousing can boost ROI.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Do The 4 Types of Data Analytics Even Matter in 2021
URL: https://www.data-mania.com/blog/do-the-4-types-of-data-analytics-even-matter-in-2021/
Type: post
Modified: 2026-03-17
Here’s the thing about the 4 types of data analytics – If you’re new to the data field then you may be curious about the 4 types of data analytics, but if you’ve been around a while – you may be wondering why people are still talking about them. If either of these are you, then stay tuned because I’m about to answer those questions in this post.
I’m also going to share with you an awesome example – that you can pattern after – of the classic “4 types of data analytics” topic DONE RIGHT!
YouTube URL : https://youtu.be/cGAFFgIBgQ0
If you prefer to read instead of watch then, read on…
For the best data leadership and business-building advice on the digital block, subscribe to my newsletter below and I’ll make sure you get notified when a new blog installment gets released (each week). 👇
Get to Know Me…
In case you’re new to the community – Hi, I’m Lillian Pierson and I help data professionals transform into world-class data leaders and entrepreneurs…
I’ve just spent the last 6 months both refreshing and rewriting my third edition of Data Science for Dummies, and the discussion about the 4 types of data analytics needed to get weaved in there somehow, so I decided to cover this topic now.
I’ve been a full-time data professional for the last 9 years, 8 of which have been through my own data business… For now – I wanted to teach you a little about data analytics and also give you a data industry insider perspective on how to better position yourself in the online data sector.
I am not going to spend much time defining the analytics types because it’s simple and I assume you already know… the important part of this post is the part about why they don’t actually matter, but for clarity purposes, let’s have a look at the each type of analytics first…
Descriptive Analytics
Descriptive – Describe what happened in the past
Descriptive analytics are built off of current and historical data sets and they answer the questions: “what,” “when,” “how many,” and “where” something happened.
It is the most basic type of data analytic
The deliverables include: Ad hoc exploration and reporting or even canned reports
Diagnostic Analytics
Diagnostic – Why it happened in the past
It is commonly used in engineering and in the sciences, they basically answer questions like:
“Why did something happen?”
“How will we know if it happens again?”
They’re great for diagnosing problems in systems or in sub components of processes.
I’d love to hear from you about your experience working with any of the 4 types of data analytics. Let me know in the comments!
Predictive Analytics
Predictive – Uses correlation to predict what will happen in the future if nothing changes
It is more in the domain of the data scientists or analysts because you use correlation analysis to uncover correlation between variables
It is built on top of both current and historical datasets
They use statistical and mathematical methods to predict future events or trends
Prescriptive Analytics
Prescriptive – What will happen in the future if we take this prescribed course of action
This is the domain of data scientists who predicts what will happen in the future if they take a prescribed course of action
It is usually built on top of a whole series of:
Random testing
Experimentation
Optimization
Recommendations
The biggest thing you need to take away from this whole 4 types of analytics thing is that:
You can’t accurately predict the future if you don’t have an accurate picture of what happened in the past. – Albert Einstein
What this means is – to create prescriptive and predictive analytics, you must have accurate descriptive and diagnostic analytics first.
If you’re enjoying this discussion on the 4 types of analytics then you’d love my video on the Top 5 Reasons Not to Become a Data Analyst. Check it out here.
Why the 4 Types of Analytics Don’t Matter
The thing about any conversation you’d have with anyone (offline or online), is the fact that you need context and relevance in order for any sort of information to have any meaning whatsoever. Because information for the sake of information just completely SUCKS.
Honestly, there is so much noise on the internet now in the data space, that it’s completely overwhelming. It could push people away from even looking at online communities for new and fresh information, just because so many people are saying the same thing – and finding something truly valuable and meaningful is like finding a needle in a haystack.
Now, you do not want to contribute to the noise when discussing the 4 types of data analytics. This topic is still relevant, of course, and you still need to know them. If you’re a data professional then, you gotta bake in the “WHY” into that conversation. What you don’t want to do is repeat the same generic thing that you’ve heard other people say, because it’s not novel, it’s not new and it doesn’t really need to be said again. Not only does it contribute to the noise problem within the data community, but it’s also a waste of your precious time – and really, your time is the most valuable asset you have. So, when you take the time to share something online, just make sure that it’s actually meaningful and helpful to your intended audience.
“Types of Data Analytics” Discussion Done Right
When I say this – I mean that these conversations are done in a meaningful way with lots of context and purpose, so it’s not just rehashing the same information, but it helps you apply that information to actually get some sort of results in your career or data business (if you’re an entrepreneur).
First, let’s look at my buddy Ken’s video on “The 4 Types of Sports Analytics Projects”
What I love about this video is, it talks about the types of analytics projects that you can do WITHIN THE SPORTS INDUSTRY in order to attract paid opportunities to work as a data analytics professional. This is good because it is applied to a specific industry which is sports. It has a purpose – to help people get experience and work as a sports analyst. And it incorporates the 4 types of data analytics without mindlessly rehashing what they are and then not giving an outcome. If you want to create content about the 4 types of data analytics, try to do something applied like Ken’s example here.
Another cool example I found was by a company called Retalon. Retalon is an award-winning provider of advanced retail predictive analytics & AI solutions for supply chain, planning, merchandising, inventory management, pricing optimization, with a transformational approach to the retail industry.
How are they using this conversation to get traction for their data business?
Well, by targeting and narrowing it down to their ideal client. Not by posting the same old, same old – but by getting super specific with their data expertise and the content they are publishing around that expertise. They have retail clients in the fashion industry, so instead of creating a blog post on the 4 types of data analytics – which has been covered everywhere by everyone – they apply the 4 types of analytics to a specific industry that their business supports – fashion. And guess what – Google Search is awarding them for the usefulness of their content by giving their post a high ranking and sending traffic.
Why Talk About the 4 Types of Data Analytics in 2021
This is how and why it would make sense to talk about the 4 types of data analytics in 2021. It is simply because the data field is pretty darn mature compared to what it was 10 years ago. So, we need to keep maturing, progressing and evolving in our conversations about data analytics or data science. We should also learn how to use these super powers to make the world a better place.
This video is not really about the 4 types of analytics at all. It’s about having meaningful, relevant conversations about analytics or data science. It’s important because the conversations about those two topics actually have an impact on the people who are involved. This content itself was meant to add value in terms of helping other data professionals. This help includes getting their message out to the world and getting more traction in their career. This can be done by being more specific and more contextual with their communications.
Now, if you’ve enjoyed this meta discussion about data analytics, then I’m pretty sure you’re gonna love my data superhero quiz.
It is a super-fun, 45-second quiz. The quiz is about you and how your personality type aligns with the top 50 data roles that companies are actively hiring for.
Take the Data Superhero Quiz Today!
Also, I’d love to see you inside my free Facebook community for data professionals who are working to become world-class data leaders and entrepreneurs.
Join our community here.
Hey, and if you liked this post, I’d really appreciate it if you’d share the love with your peers by sharing it on your favorite social network by clicking on one of the share buttons below!
NOTE: This description contains affiliate links that allow you to find the items mentioned in this article and support the channel at no cost to you. While this blog may earn minimal sums when the reader uses the links, the reader is in NO WAY obligated to use these links. Thank you for your support!
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Introduction To Business Analytics
URL: https://www.data-mania.com/blog/introduction-to-business-analytics/
Type: post
Modified: 2026-03-17
Introduction to Business Analytics – ever wonder what business analytics are? To the average person, the concept of business analytics may seem foreign, complex and ambiguous. And it’s with good reason — without extensive knowledge of the subject, business analytics can quickly become overwhelming.
There are plenty of online resources you can use to better educate yourself about business analytics and possible career opportunities in the field.
Below is a brief introduction to business analytics and some more information about the industry. Let’s explore more details about business analytics to improve your understanding of the subject and some examples of skills necessary to obtain a career in this critical field of interest.
YouTube URL: https://youtu.be/ot1tcZEvuxc
If you prefer to read instead of watch then, read on…
For the best data leadership and business-building advice on the digital block, subscribe to my newsletter below and I’ll make sure you get notified when a new blog installment gets released (each week). 👇
Business analytics is the science of using data to draw conclusions and gather insights that offer value to a company or industry. The data collected is used to build mathematical models that help professionals make data-driven business decisions.
Essentially, data surrounds us in every aspect of life. Information is constantly being shared to and from organizations in the form of transactions and other exchanges. Whether you’re using devices like smartphones or purchasing items at a grocery store, data is everywhere.
Specific types of data help drive business decisions, such as descriptive, predictive, and prescriptive. These three types of data make up the data that offer value to businesses. Here’s some more information about these types of data:
Descriptive: helps make sense of the data available and draws conclusions from the past
Predictive: looks forward and predicts trends likely to occur in the future
Prescriptive: assists analysts when prescribing actionable steps to reach business goals
What Are Business Analytics & Who Build Them
Some of the job titles associated with business analytics are business analyst, data scientist, modeler, quantitative analyst, data business analyst and business analyst manager. All of these jobs are responsible for working with large data sets and finding trends and patterns within them. Companies are looking to hire business analysts to spend time working with data to use it to inform their business decisions, whether it’s taking steps to increase revenue or decrease costs.
Now, what’s the difference between business analytics and data science?
Business Analytics vs. Data Science
One of the main differences between the two topics regarding data is that data science focuses on using coding and other methods of cleaning and sorting data into manageable sets.
Data scientists rely on computer science skills to accomplish their tasks, while business analysts use statistical concepts to analyze data.
While business data analysts focus on outcomes for their clients, data scientists can draw conclusions from data without focusing on any specific results.
Business analysts typically work in the health care, finance, and marketing industries, whereas data scientists work in e-commerce, manufacturing and machine learning industries. These are only examples, however, and both of these roles can cross into different sectors.
Learn more about the differences of each role here: Data Analyst vs. Data Scientist.
Roles & Responsibilities of a Business Analyst
Listed below are some of the roles and responsibilities a business analyst must undertake while providing valuable information to their clients.
Data interpretation
Data visualization
Storytelling
Analytical reasoning
Math and statistics skills
Written and verbal communication skills
Business analysts must plan, monitor, price and create detailed reports which include their top takeaways from data sets.
Analysts must be comfortable working with data in software like Microsoft Excel, Python and R programming language. Many businesses are making a digital transition to the cloud, and cloud computing is a skill business analysts should know.
Let’s discuss some of the specific skills business analysts need to perform well in their roles.
Business Analyst Skills
Because business analysts are needed in multiple industries, it may be worth looking into getting a bachelor’s or master’s degree specializing in analytics. However, alternatives to the traditional education system can help someone grow their knowledge of business analytics.
The open online course platform, Udemy, has many courses available to those interested in business analytics. It’ll be crucial for those interested to consider taking these core courses to fine-tune their skills and prepare for an entry-level position in business analytics. These courses serve as supplements that can make someone attractive to hiring managers.
Fundamental Analytics Skills: Intro to Business Analytics
In this introduction to business analytics course, students will learn the basic concepts and best practices of business analytics. They will also learn to identify analytical methods, master fundamental concepts and understand differences between predictive and prescriptive analytics.
Turn Data to Insights: Complete Introduction to Business Data Analysis
Students enrolled in this course have the opportunity to learn more about the actual analysis taking place. Students will be exposed to drag and drop techniques to master analytics, rather than using confusing formulas, macro or VBA. Understand how to turn data into actionable steps businesses can take to fuel future growth.
Python Data Analysis: 12 Easy Steps to the Python Data Analysis, the Beginner’s Guide
Python is a frequently used programming language in the world of business analytics. In the course, students will learn the ins and outs of Python and learn how automation helps improve efficiency. Students will learn how to use Python to deliver valuable insights to clients, so they’re ready when they enter the industry.
These are a few examples of the courses available. Udemy has plenty of other classes to choose from, depending on the student’s existing knowledge.
Entering the Business Analytics Industry
If you’re interested in becoming a business analyst or a data scientist, the field of analytics is proliferating, and more companies are looking for individuals capable of providing them with valuable insights. Consider enrolling in a four-year program or take a look at the courses listed on Udemy. The analytics field shows plenty of promise for the future.
Watch this video on the data analyst career path to learn everything you need to know about how to become a data analyst.
If you like this introduction to business analytics article and you want to consider taking this career path, you’re probably wondering if this role would suit you well. Find that out by taking the data superhero quiz! It’s a free and super-fun 45-second quiz that will help you uncover your inner data superhero. By that, I mean it will help you uncover the optimal data career path for you given your skill sets and personality types. After taking it, you’ll get personalized data career recommendations as well as all sorts of information about various data roles and compensation statistics related to those roles.
Take the Data Superhero Quiz today!
NOTE: This description contains affiliate links that allow you to find the items mentioned in this blog and support the channel at no cost to you. While this channel may earn minimal sums when the viewer uses the links, the viewer is in NO WAY obligated to use these links. Thank you for your support!
A Guest Post By…
This blog post was generously contributed to Data-Mania by Shannon Flynn. Shannon Flynn is a freelance blogger who covers business, cybersecurity and IoT topics.
You can follow Shannon on Muck Rack or Medium to read more of her articles.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Tech for Good: Helping Developing Countries Through Innovation
URL: https://www.data-mania.com/blog/tech-for-good-helping-developing-countries-through-innovation/
Type: post
Modified: 2026-03-17
When we as data professionals engage with developing populations and help them access better technologies, we also benefit by gaining exposure to new strategies of application and implementation, develop problem-solving skills, generate positive press or media for our organizations, and learn from radically different perspectives and life experiences. In this blog post, you’re going to get a slew of best practices that you can use to guide you in developing a tech for good initiative.
Why Your Social Initiative Needs a Smart Design
We all know that technology advances create seismic changes, not only within communities but for countries and entire international ecosystems. However, these advances stem from resources and hubs of innovation that naturally cluster within countries of influence and wealth.
Oftentimes, those places considered “developing countries” don’t benefit from new technologies for years or even decades after they are available in more affluent places. Because of this, a huge opportunity exists for technological leaders and entrepreneurs to help upskill developing countries through innovative technology advances.
It’s probable that you’ll create unwanted adverse effects if your technological initiative is implemented poorly, whereas technological initiatives that are well designed and delivered enable developing countries to advance in critical areas of importance.
When done well, using tech for good can be a hugely important way of developing a country’s economic landscape as a whole as well as creating social good. Poor implementation of technological initiative may probably create unwanted adverse effects. However, well-designed and delivered technological initiatives enable developing countries to advance in critical areas of importance.
Social Impact and Developing Countries: A Snapshot
Social impact refers loosely to something’s effects on people, society, and communities, and specifically the pursuit of creating positive changes for those groups. Tech for good has become a forefront issue of interest in recent years. Many organizations, corporations, and countries that have influence and agency are pursuing (or pressured to pursue) methods of creating social impact for countries and populations that do not have the same access to resources. Doing this via technological means is a significant way of distributing agency and ability.
What exactly is a developing country? World Population Review defines a “developing country” as one that fits a set of multiple criteria developed by the United Nations. This assessment is referred to as the Human Development Index (HDI). The HDI looks at metrics such as the country’s economic growth, average life expectancy, population health, the state of education, and quality of life scores to develop a composite rating.
However, HDI is just one instrument to gauge a country’s development status. Another metric commonly employed is to calculate a country’s nominal gross national income (GNI) per capita. We use that figure sometimes as a quick alternative assessment to estimate a country’s level of development relative to others.
Ways Tech Can Be Deployed to Alleviate Common Problems in the Developing World
You can apply technology to just about any aspect of human existence. Because of this, using tech in specific ways to enact social impact is just about as broad. However, there are a few main focus areas that exist within the majority of tech-driven social impact projects.
EdTech Tech for Good
Education is one large area where technology can be applied to make a widespread difference for developing countries. This could take the form of strengthening internet connection, infrastructure, and capabilities for school facilities. It might look like supplying teachers, school systems, or institutions with computing hardware or goods. Developing and producing technologies or devices that are adapted to particular needs or specific environmental elements can be another way technological innovation can strengthen education in developing areas.
Increasing capacity for collecting insights and conducting data-driven decision making is another way technology can help developing countries. Equipping developing countries with more robust data capabilities and better intelligence training and frameworks can allow them to apply their resources more effectively, and improve the caliber of the strategic decisions their leaders make in directing the country. These tools can also be a helpful resource for its private sector.
Healthcare Tech for Good
Medicine, health, and disease prevention is another area where technology advances can create huge advantages for developing countries. Drugs, medical procedures and instruments, better training and public health awareness, and more can create fundamental changes for entire communities and people groups. As an example, treatments for diseases or medical conditions that historically ravaged populations have saved countless lives, impacted economies, and changed realities for huge numbers and even entire generations of people.
Technology’s impact on industry can also be far-reaching and create significant advancements for developing countries. Introducing new agricultural techniques and equipment, advanced machinery for various business types, better computing devices and software systems for managing businesses, and more can all help developing countries increase their production and efficiency and build their economies.
Transportation Tech for Good
Transportation is another area in which technology can create a lasting impact. Better alloys, building materials, manufacturing methods, design strategies, and know-how can contribute to better infrastructure. Improving public transportation methods and functionality can increase efficiency and make commuting and travel more available for more people. Green energy sources, more efficient vehicles and machinery, and other advances can help developing countries lessen pollution. They also reduce their reliance on crude oils and external regimes, and better protect their local flora and fauna.
Helping Well: Considerations for Responsible Tech Assistance Initiatives
If you as a data professional, technology entity leader, or entrepreneur are interested in developing an initiative to help bring your technology to a developing country, keep a few principles in mind while you are designing your endeavor.
As alluded to in the introduction, not every attempted technological social impact initiative (especially when exerted by an outside body) has gone well. In fact, trying to manufacture social impact through technology can often create ill effects on local people. It also affects organizations, populations, cultures, and more when not carried out in a thoughtful, well-researched, thought-out way. Consider a few when thinking or planning of a technological social impact intervention of any kind.
Be Ready for Unforeseen Effects and Unintended Consequences
Introducing new technologies into a process, a population, or a location will almost always create change. Hopefully, it will create the anticipated positive change. But too often, the introduction of new technologies into ecosystems formerly devoid of that technology creates unintended side effects that are difficult to predict. These can sometimes be benign or insignificant. But in some cases, they can be harmful or detrimental. It’s extremely important to do homework and take a slow, diligent approach to technological introduction. Your project should entail time, assessment, and unbiased observation that can monitor undesired effects.
Be Aware of International Standards
Standards for various aspects of technology differ from place to place. It’s naive to bring technology into a country that might be governed by different regulations or work according to different standards without researching first. Whether electrical realities like voltage or power grid considerations, differing data protection laws, or otherwise, make sure you have a strong understanding of any relevant technological realities that may affect your project when you deliver technology to a different country.
Be Mindful of Local Societal Norms, and (when possible) Support Demographics that Are Commonly Suppressed
In many parts of the world, stigmas or prejudices exist. They’re also often quite different than they may have been in your country of origin. For example, women are treated as secondary citizens in many places even though they can make capable innovators and partners in any industry or field if given the opportunity. Be aware of how the culture you’re entering operates and views different groups of people. And when in your power, champion social change to help those that don’t have the freedom or agency they should.
Take a Learning-First Posture
It’s easy for outsiders that come from more “advanced” societies or privileged life circumstances to assume they know best. However, the best technological social impact initiatives take a humble posture when introducing new technologies to developing countries. There are many reasons that make this the most appropriate way to conduct technological interventions. Some include preserving dignity for local people. It also includes making sure the opportunity to learn from that developing country’s way of life or existing methods remain forefront for your implementation team.
The Benefits to Your Team, Brand, and Product
Helping tackle a technological deficiency in a developing country can be an involved project. Ensure to design with care and a listening-first posture. This is to avoid the potential stumbling blocks and unhelpful outcomes alluded to above. You should not enter into these types of projects lightly – It’s important to do your homework. Make sure the project you have in mind is sustainable and something you can see all the way through.
Engaging with this type of technological project can produce several solid benefits mentioned at the beginning of this article. These include the following:
A unique opportunity to strengthen your team’s culture. It also provides strong team building experience as they engage in the implementation process. The process can be in a foreign country or with a different people group.
A noteworthy initiative to share with your audiences, consumers, and stakeholders that can benefit your brand.
Opportunities to surmount unforeseen challenges and think creatively to navigate differences in resources, social norms, device and technology availability, and needs.
A chance to think critically about your product, brand, and team that can help you strengthen any or all of these elements going forward.
The Future of Tech in Developing Countries
There will always be disparities amongst various countries around the world. This also applies to their ability to develop or harness cutting-edge technology within their borders. Through well-designed and implemented technological initiatives delivered by external bodies to create social impact, we can leverage technology worldwide. This also means we can create meaningful change for some of the most vulnerable populations on the planet.
A social impact project could provide you, your company, or your team with invaluable experience. Finally, it gives you growth opportunities, and helps you strengthen your product and your brand.
A Guest Post By…
This blog post was generously contributed to Data-Mania by Ryan Ayers.
Ryan Ayers is a researcher and consultant within multiple industries including information technology, blockchain and business development. Always up for a challenge, Ayers enjoys working with startups as well as Fortune 500 companies.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## What Is Data Content? How to Use It to Grow a Data Business
URL: https://www.data-mania.com/blog/what-is-data-content-how-to-use-it-to-grow-a-data-business/
Type: post
Modified: 2026-03-17
What is data content and can it help you grow your data business? If you’re a data professional who is looking to expand your network and business, this post is for you! Discover how content can help you grow your own data business.
Even if you don’t know it by name, you’ve probably seen examples of content marketing before. It does a lot of the heavy lifting in public relations and search engine optimization.
Choosing the right kind of content to offer or sell is vital; so, when data, digital design or information services are your primary products, you need to think outside the box and showcase them in a way that translates well to the audio- and visual-heavy world of the internet.
What Is Data Content and Its Primary Types
There are two primary types of content you can put to work for your data business: paid and free content. Paid content is information products like e-books, reports, white papers, software, ads, online courses or market research.
On the other hand, free content forms the heart of your marketing campaign because it proves to your audience and prospects that your whole lineup of data products is valuable and worth signing up or paying for. Some 87% of surveyed marketers say their company under-utilizes the data at their disposal and that means their content marketing efforts are almost certainly not as effective as they could be.
The Different Types
The following are several types of data content and how they can work for your brand.
1. Coding Demos
Coding and product demos help attract both new startups with IT needs and mature companies looking for products to help them scale. Furthermore, it also helps businesses see how a professional coder, product suite or automation tool will take their workflows to the next level.
However, not all coding demos offer the same level of content marketing value. One of the biggest mistakes is making the demo all about the product instead of the value it brings to the prospect’s operation.
This is where data science can help hone the perfect coding or product demo. Companies and their marketing partners should be exploring the questions and pain points frequently experienced by their target market, as revealed by keyword search data. By doing so, they can tailor product demos to different types of professionals and the sectors they represent.
Coding demos include:
Walkthroughs of enterprise-level software
Demonstrations of research processes or data forensics procedures
Showing how automation or machine learning products apply to competitive industries
Displaying graphic design or backend coding experience using digital mockups
Good demos of coding or software products should showcase the techniques used to safeguard clients’ intellectual property or personal data.
2. Blog Articles
Writing and publishing blog posts are some of the best investments professionals can make. To add, publishing regularly shows that the organization has its finger on the pulse of the industry, has advice and insights at the ready, and is committed to supporting its products with supplementary documents, guides and insights.
Types of blog posts include:
How-to guides
Checklists
Year-end roundups
Guest blog post
Infographics
Industry insight articles
Personal anecdotes and reviews
Blog posts are a great way to dial in your audience and find motivated buyers, but do note that conducting keyword research on what they are searching for lets you cast a wide net for customers who want what you have.
You can launch an especially successful content marketing campaign using blog posts if you take special care to target keywords your competitors aren’t utilizing.
Additionally, blog posts help brands grow effectively because they’re free. Whether you’re a data scientist with a proven track record or a contractor specializing in interior design, a blog post helps show off your skills, intuition, work ethic, and up-to-date knowledge about your sector.
3. Social Media Posts
Worldwide usage of social media has been on an uninterrupted upward trajectory for years, and the global pandemic caused a further 58% increase in people using these platforms.
The chief strength of social media posts is their versatility. So, data science can help PR specialists create a winning campaign. Content types may include:
How-to guides
Memes and funny content
Educational or research-based posts
Advance warning about upcoming sales and promotions
Contests and collaborations
Testimonials
YouTube and Facebook are the most popular, with 81% and 69% of U.S. adults using them, respectively. In addition, their audiences roughly represent the general population in age, gender and racial diversity.
The rest of the social networks have market shares in the same general ballpark as one another. Hence, you could potentially shift your spending from one network to another as you learn more about your audience.
4. YouTube Videos
As of February 2021, 81% of U.S. adults used YouTube regularly while on the one hand, about 95% of individuals aged 18-29 use YouTube, along with 91% of 30- to 49-year-olds and 49% of those over 65.
Some of the most successful video campaigns on YouTube fall into categories like these:
Ongoing vlogs
Product reviews
Walkthroughs and how-to videos
Funny animal memes or videos
Product features or unboxings
Educational content
Like other forms of content, YouTube videos position your brand as an authority in your sector. Moreover, they can also showcase your company’s products and give prospects on the fence a gentle push by showing them off in the real world.
5. Content-as-a-Product
Content-as-a-product, also called information products or data products, is paid-for information or a subscription that helps empower or transform your clients.
Here are some examples of content-as-a-product:
Online and continued learning programs
Photographs or stock images
Competitor or market research
Apps and software programs
E-books, white papers, PDFs and other longform texts
Think of YouTube and other free content as a preamble for the content you’ll sell. Content-as-a-product helps brands grow because it demonstrates that your insights and datasets are valuable enough to pay for.
Content products are also useful because they can target specific audiences and demographics or drive organic traffic through internet searches.
What is Data Content – The Business Growth Secrets
Data businesses have lots of opportunities to grow their client base using content marketing. These companies are also best positioned to realize a great ROI for their efforts since information determines what that content looks like, who will most benefit from it, and on which platform it will fare best.
How can content help your data business?
With the varied types of data content, pretty sure you can find the ones that fit most for your business. No matter what type of content you use though, what’s most important is the value of the information you share to existing and potential clients. So ultimately, that’s how content can help your data business grow.
A Guest Post By…
This blog post was generously contributed to Data-Mania by Shannon Flynn. Shannon Flynn is a freelance blogger who covers business, cybersecurity and IoT topics.
You can follow Shannon on Muck Rack or Medium to read more of her articles.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## What Is Clubhouse App? Walkthrough & Tutorial (Next Big Social Media)
URL: https://www.data-mania.com/blog/what-is-clubhouse-app-walkthrough-tutorial-next-big-social-media/
Type: post
Modified: 2026-03-17
What is Clubhouse App? Let me guess. You’ve been hearing LOTS about Clubhouse and you’re wondering what all the fuss is all about? In today’s article, I’m going to show you the inside and out of this application – and let you in on a secret about how I’m using it to grow my data career.
YouTube URL: https://youtu.be/XQWHBoKyDRo
If you prefer to read instead of watch then, read on…
For the best data leadership and business-building advice on the digital block, subscribe to my newsletter below and I’ll make sure you get notified when a new blog installment gets released (each week). 👇
First things first, why am I qualified to tell you anything about the Clubhouse app? Well…
I’m on the application and I have hosted a room there,
I’m using it to develop and build positive outcomes in my data career, and
It’s a social app, and judging by the 650k data professionals who are currently following me across all my channels, you can say I know a thing or two about social media.
Hi, I’m Lillian Pierson and I help data professionals transform into world-class data leaders and entrepreneurs…
Why Clubhouse
The answer to that is – relationships, relationships, relationships
To be honest, as a techie, I’m not the most outgoing person – but recently, I’ve really come to understand the value of organic and authentic relationship building with other leaders in the data space.
You know what they say, “it’s not what you know but who you know”
When I talk about relationship-building, I am NOT talking about:
Growing an audience
Getting followers
Driving traffic
These things are essential, especially if you’re a data entrepreneur but I don’t think the Clubhouse app is the place to do that because you can’t have conversations inside the app – there’s no messaging option and there’s no link sharing as well. So if you want to drive traffic, it would be very difficult, since you can only link to Twitter and Instagram inside it.
What the application is perfect for is – developing true, organic, authentic relationships with other leaders in the industry.
I’m literally talking about getting to know people and looking for ways to help them without expecting anything in return. Not the type of transactional relationships that happen when people get to know someone, only so that they can ask for something from them later.
And really, Clubhouse is just perfect for this sort of thing.
I’ve heard of people using it to get clients, but I’ve only been using it to get to know some of the most innovative people in the data space and get exposure to new people that are doing incredible things in technology that I didn’t know about before.
Here’s one trick to connect and build relationships – when you’re listening to someone talk, go to their other profiles on social media and reach out to them by providing feedback about the topic they shared on Clubhouse and maybe ask them if they need help in hosting a room next time.
About Clubhouse App
A tiny bit about Clubhouse – it was launched in April 2020 and it’s only for iPhone users at this point. It’s also kind of exclusive because each user only gets a few invites.
Some people think that the Clubhouse app is only doing an exclusivity hack to get people to want it more, but it’s not actually that. It’s because they want to have a stable application and having a lot of users may cause it to crash down.
The rooms are coming out with guests like Elon Musk which happened in early February and everyone just got crazy…
If you’re curious about what the fuss is all about, no doubt that this is the hottest new social media application. I would love to hear from you – What are your thoughts about the whole Clubhouse app concept? Sound cool or you need to know more? Tell me in the comments below.
Let’s take a look at the inside of Clubhouse app together…
The Hallway
When you open up the application, you’re going to see the hallway. Along the top, you’ll see icons for navigating the applications and you’ll see the title of rooms that are scheduled inside the clubs you are following.
When you scroll down, you’ll see the rooms that are currently active – they may or may not be from the clubs you follow. Clubhouse recommends them based on things like – who you’re following and the interests you’ve selected when you set up your profile. You’ll also see the number of moderators and the number of participants.
Now, if you’re really looking to get a chance to speak, then you’ll want to be part of a smaller room with more moderators. But if you’re there to learn, you’ll want to be part of a bigger room with less moderators, because chances are, they have a pretty juicy guest there spilling the tea.
If you don’t see anything being recommended to you by Clubhouse and you want to see what else is happening, just hit the “explore” button to see what other conversations you can join in.
Setting Up Your Profile
To set up your profile, you just need to click on the face icon and you’ll have a chance to fill out the information about you, your professional interests, your passions and why you’re there. I also want to add a little credibility statement and a little claim-to-fame, to help people understand that I’m there for business and not just for fun.
One thing you need to know about the bio, it’s searchable within the application so if you want to attract like-minded data professionals, make sure to use the keywords that reflect your interest within the field. I definitely recommend putting in a call-to-action to tell people what they should do next to get to know you better. Since you can’t add any links, you can ask them to send you a DM either on Twitter or Instagram as your CTA.
Also, please note that when you follow a club, it doesn’t necessarily mean that you’re a member of it. They actually need to invite you to be a member after you follow them, but you can still listen to the conversations inside their club even if they don’t invite you.
Scheduling a Room
You DON’T need to be a member of a club or own a club, in order to schedule a room. Schedule a room and start inviting friends and have conversations that can actually pick up some serious weight inside of Clubhouse without having been so vested as to actually own a room.
You would just need to press the calendar icon on the app and then start filling out the fields. If you’re trying to pick up some steam and get attendees pushed to your event, I would suggest that you add extra moderators and use relevant keywords in the event name. Using relevant keywords will help Clubhouse understand what the event is about and the topic inspiration so that they can recommend it to other data professionals.
Searching Inside the App
You can search for users and clubs inside of Clubhouse. You can search according to username or keywords. If you search for keywords, profiles and clubs that have those keywords will appear and you can then follow those that are most interesting to you.
In the explore page, you can also see various conversations that are grouped according to topics, i.e., Tech, Startups, Marketing, etc. and that’s where you can find more people of your interest.
Setting Your Interests
You can set up your interests in your profile setting so you can get personalized recommendations. You can then start getting to know all the incredible people that are inside the app and start having conversations and building relationships with them.
If you’re liking this article on how to grow your data career opportunities via Clubhouse, then you’d probably love a recent episode I did on Data Analytics Consulting Rates in 2021 for New Data Freelancers – 2X Your Rates Overnight. Watch it here.
Let’s also take a look inside the rooms…
Inside the Room
Warning: Don’t record anything inside of Clubhouse, or you’ll probably get banned pretty quickly.
When you go inside the room, you’ll see the “stage area” where the title of the event is, as well as the people inside. Underneath the stage area, you’ll see more of the people who were followed by the speakers. This is Clubhouse’s way of trying to increase connectivity across the network – by showing you who to follow if you like the speakers inside the room. You’ll see some icons next to the participant’s photo and here are what they mean:
Green asterisks – moderators
Party poppers – new Clubhouse users
Hands up – people who raise their hands to speak or ask questions
Others icons:
Peace sign – leave the room
Plus sign – invite other people inside the room
And that is Clubhouse in a nutshell. It’s actually pretty intuitive and I’m sure you’ll be able to learn how to navigate inside it really quickly.
If you found this intro primer on Clubhouse entertaining and are looking for other insider secrets on how to 10x your data career, I invite you to discover your inner data superhero quiz.
It’s a free and super-fun 45-second quiz that’s all about you and how your personality type aligns with the very best career path for you. It’s fun, free and it will provide you personalized data career recommendations, complete with potential roles that fit your unique skills and passions, as well as salaries associated with those roles.
Take the Data Superhero Quiz today!
Oh and – if you liked this article, go ahead and show the love by sharing it to your friends and leaving a comment telling me what you like most about the Clubhouse concept and why.
NOTE: This description contains affiliate links that allow you to find the items mentioned in this article at no cost to you. While this blog may earn minimal sums when the reader uses the links, the reader is in NO WAY obligated to use these links. Thank you!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Web Analytics Job – Omnichannel Analytics Basics to GET THE JOB
URL: https://www.data-mania.com/blog/web-analytics-job-omnichannel-analytics-basics-to-get-the-job/
Type: post
Modified: 2026-03-17
I’m sure you’ve seen tons about “omnichannel analytics,” when you’re looking at web analytics jobs in the marketing data space, but you may not be sure what those are or how they’re useful in increasing sales and marketing ROI.
Worry not because in this article, I’m going to give you the low-down on omnichannel analytics with respect to how to use them to fine tune the performance of an omnichannel marketing strategy, and up your chances for landing that job.
YouTube URL: https://youtu.be/gNp6O3Mv2LA
If you prefer to read instead of watch then, read on…
For the best data leadership and business-building advice on block, subscribe to my newsletter below and I’ll make sure you get notified when a new blog installment gets released (each week). 👇
Who am I to tell you about omnichannel analytics and marketing strategy? Well, I’ve been using channel analytics to drive my business strategy that has taken our business to multiple-six figures in revenue and grown our community to over 650k data professionals so far.
Hi, I’m Lillian Pierson and I support data professionals to become world-class data leaders and entrepreneurs.
In all honesty, the data professional in me wants to jump straight into building channel scorecards from your omnichannel analytics, but that’s putting the cart before the horse. But if you’d rather do that, you can check out the video I did on Omnichannel Analytics and Channel Scoring for more sales and lower churn. These multi-channel marketing techniques will help get you more leads in no time. Check it out here.
Let’s start by defining what a channel is…
What is a “Channel”?
When it comes to scorable omnichannel analytics, when we use the word “channel” we are either talking about sales channels or marketing channels.
Sales Channels are the channels through which your company generates sales & distributes products or services. They could include:
Website
Email List
Brick and Mortar Store
Sales Calls
Live Events
Marketing Channels are the channels through which people are made aware of your products and services. It’s where your company generates leads AND sometimes warming people up for the sale. Examples of marketing channels are:
Website Traffic from SEO
Website Traffic from LinkedIn, Instagram, Facebook
Live Events
Referral Sites
Paid Ads
What does it mean to be omnichannel?
Omnichannel is a marketing approach that is centered around meeting your customers (and prospective customers) exactly where they are at. An omnichannel approach assumes that you have several channels through which you market to customers, and that you’ve identified what your customers want to see on each channel. Through omnichannel marketing, you’re able to show up and provide your customers different experiences on a channel-by-channel basis; thus allowing you to cater to your customers’ expectations and personalize their experience with your company based on where they’re interacting with it.
An omnichannel presence allows you provide your customers different experiences on a channel-by-channel basis.
If you think of marketing as a user – everyone goes to each channel for different reasons. For example, you don’t go to Instagram with the same intention you go to LinkedIn, Twitter or Facebook.
So when you’re building your channel strategy, you want to make sure that your marketing strategy and your content is reflective of the reason your customers are showing up there. It’s not a one-size-fits-all solution. You actually have to look at the analytics inside each of the different channels to figure out what the customers are expecting from you in each of those touch points.
Speaking of omnichannel… aren’t we all pretty much omnichannel in social media marketing nowadays? In the comments below, I’d love it if you’d tell me:
Which is your favorite social media platform and why?
What is Omnichannel Analytics?
Omnichannel analytics are analytics that demonstrate your customers’ interests and expectations along each of your sales and marketing channels. They also help tie your channel marketing efforts to a direct business ROI in terms of leads and sales.
Just a quick tip on what tools you can use to generate these types of analytics:
Segmetrics – I use Segmetrics for sales analytics – but it also is very good for putting a dollar amount on leads and sales you generate through various marketing channels – so it works for both.
Google Analytics – You can also use Google Analytics and Google Data Studio for omnichannel analytics with respect to both sales and marketing analytics.
There are tons of tools out there, but the trick is to find one that is affordable to your budget and doesn’t take too much downtime to set up. Segmetrics is both of those for me so it’s my absolute fave analytics tool ATM.
Omnichannel analytics for sales channels
Here in my business, I used Google Data Studio where it shows the conversion rates for the sales page:
And here in Segmetrics, are the sales analytics that are reflective of the actual sales that are made straight off of each marketing channel:
Just to put things in perspective for you, all of our marketing is organic and we don’t have paid ads running, so there’s no need to calculate ads cost against revenue to figure out the true performance of each marketing channel. But if your company is running ads, then you want to make sure that you subtract the ads cost from the revenues generated from each channel just because that would ultimately impact the actual ROI of the channel.
If you are loving all this talk about how to use data to inform strategy, then I think you’ll love the video I did on Evergreen Data Strategies. Watch it here.
Omnichannel analytics for marketing channels
Now, I just wanted to go over some of the data with you inside of my backend just to help you understand how it works.
So, looking at the sales that were generated on my website, they didn’t just come from nowhere. Most of them are coming from our marketing channels that we used as funnels to convert potential customers from cold audience members to customers.
Our website sales came from:
Google Search (SEO)
LinkedIn
Instagram
Twitter
YouTube
Referrals
And it’s important to look at those channels because:
(1) Potential customers have different expectations and preferences for how they want to engage with your brand at its different touch points – and you want to demonstrate an awareness of that.
AND
(2) Double down on what’s working, fix what’s not – If you look at the marketing channel analytics you can optimize and double down on what’s working and cut out what’s not.
Makes sense right?
So now after reading this article and when you’re scrolling through those web analytics job descriptions and see that they’re looking for someone with experience in “Omnichannel Analytics” – you’ll know what that means.
If you’re digging this article on omnichannel analytics, then you’re going to love the web analytics tools I share over inside my FREE Data Entrepreneur’s Toolkit.
It’s complete coverage of the 32 Tools & Processes we used to hit multiple six-figures in my data business, Data-Mania.
Download the Toolkit for $0 here.
You may also love it inside our Data Leader and Entrepreneur Community on Facebook. It’s chalked full of some of the internet’s most up-and-coming data leaders and entrepreneurs who’ve come together to inspire and uplift one another.
Join our community here.
Hey, and if you liked this post, I’d really appreciate it if you’d share the love with your peers by sharing it on your favorite social network by clicking on one of the share buttons below!
NOTE: This description contains affiliate links that allow you to find the items mentioned in this article and support the channel at no cost to you. While this blog may earn minimal sums when the reader uses the links, the reader is in NO WAY obligated to use these links. Thank you for your support!
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Top 5 Best Data Lineage Tools in 2022 Pros & Cons
URL: https://www.data-mania.com/blog/top-5-best-data-lineage-tools-in-2022-pros-cons/
Type: post
Modified: 2026-03-17
Data is arguably the most valuable resource today. As beneficial as it can be, though, it can be misleading if people don’t understand its context and history. The best data science processes need the best data lineage tools. Read about the top 5 best data lineage tools in 2022 and their respective PROs and CONs.
Data lineage tools record and visualize where data came from, how it changed, where it moved and why. This context can help data scientists find errors, get a better understanding of metadata and change processes more effectively.
Here’s a comparison of the top 5 best data lineage tools in 2022 with their PROs and CONs available today to help you make the most of your data.
1. OvalEdge
OvalEdge describes itself as a data catalog and governance toolset, and it includes more than just data lineage functionality. It organizes and indexes data, offers summaries and marks data relationships on top of normal lineage mapping. OvalEdge also makes governance easier, thanks to custom definitions, data quality rules and reporting tools.
You can download Windows and Linux versions of OvalEdge or use it on the cloud. Plans start at $15,600 a year, which breaks down to roughly $260 a month per author user. While that may be affordable for businesses, individual users may not be able to afford it.
Pros
Helpful organizational tools
Custom governance controls
Easy collaboration
Compatible with many third-party integrations
Easy to use
Cons
No encryption or decryption functionality
May be too expensive for non-business users
2. MANTA
Another one of the best data lineage tools for 2022 is MANTA. MANTA’s lineage tools focus on three solutions: data governance, DataOps and cloud migrations. Automation drives the platform, including automation tools for scanning, lineage mapping, impact analysis and regulatory compliance. Considering data workers spend 44% of their time on manual tasks, all that automation is helpful.
MANTA’s target audience is medium-sized businesses to enterprises, so it may not suit smaller teams or hobbyists. Consequently, its pricing also varies because it matches customers’ unique needs.
Pros
Extensive automation
Intuitive
Fits virtually any data ecosystem
Helps manage the entire data pipeline
Cons
Not suitable for smaller teams or individuals
Unclear pricing
3. Alation
Scalability and flexibility are crucial for data lineage tools, and Alation specializes in these areas because it’s entirely cloud-based. Being cloud-first has many advantages, with some government agencies saving hundreds of millions by using the cloud. Alation promises similar benefits, claiming to save 211 workdays by automating data classification and more.
Alation automates data cataloging, classification and stewardship, and it offers advanced insights and automatically flags potential issues.
Pros
Cloud-native
Automates much of the data lineage and management process
Advanced data analysis tools
Active data governance
Cons
Unclear custom pricing
Managing automation tools can be complex
4. Octopai
Octopai is another one of the best data lineage tools available in 2022. Like Alation, Octopai is completely cloud-based and focuses on automation, citing how 90% of data teams take hours to weeks to conduct impact analysis. Octopai automates that analysis, as well as metadata extraction, data discovery, cataloging and lineage mapping.
This platform makes it easier to gather metadata from all sources, improving your data quality. However, some people say its interface isn’t as helpful as it could be, and it doesn’t publicly list its pricing.
Pros
Cloud-based
Comprehensive metadata management
Streamlined, effective search processes
Ready out-of-the-box
Seamless data migration
Cons
Hidden pricing
UI can be clunky
Not as easy to use as other options
5. Kylo
This data lineage tools comparison wouldn’t be complete without at least one free option. Kylo is one of the best free data lineage tools, featuring self-service data ingesting, preparation, metadata discovery and monitoring. A visual-heavy, simple interface makes this platform so straightforward, even the least experienced users can understand it.
Kylo may not have as many automation features as other options, but its lack of a price tag makes up for that. Since it’s open-source, it’s also easy for users to create new integrations and features.
Pros
Free
Open-source
Easy to use
Data governance and security tools
Cloud-based
Cons
Not as feature-rich as other tools
Lacks the support of more enterprise-focused options
Get the Best Data Lineage Tool for You
Deciding on which of these is the best data lineage tool for you depends on your specific needs and goals. Once you know what you need and know what each option has to offer, you can make the most informed choice.
Data lineage tools are crucial as data pipelines become more complex. Choosing the right one will help you make the most of your data.
Hey! If you liked this post, I’d really appreciate it if you’d share the love by clicking one of the share buttons below!
More resources to get ahead…
Get Income-Generating Ideas For Data Professionals
Are you tired of relying on one employer for your income? Are you dreaming of a side hustle that won’t put you at risk of getting fired or sued? Well, my friend, you’re in luck.
This 48-page listing is here to rescue you from the drudgery of corporate slavery and set you on the path to start earning more money from your existing data expertise. Spend just 1 hour with this pdf and I can guarantee you’ll be bursting at the seams with practical, proven & profitable ideas for new income-streams you can create from your existing expertise.
Learn more here!
Take The Data Superhero Quiz
You can take a much more direct path to the top once you understand how to leverage your skillsets, your talents, your personality and your passions in order to serve in a capacity where you’ll thrive. That’s why I’m encouraging you to take the data superhero quiz.
This free and super-fun 45-second quiz is all about you and how your personality type aligns with the very best career path for you. It’s fun, free and it will provide you personalized data career recommendations, complete with potential roles that fit your unique skills and passions, as well as salaries associated with those roles.
Take the Data Superhero Quiz today!
A Guest Post By…
This blog post was generously contributed to Data-Mania by Shannon Flynn. Shannon Flynn is a freelance blogger who covers business, cybersecurity and IoT topics.
You can follow Shannon on Muck Rack or Medium to read more of her articles.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Do What You Love Doing | Lillian Pierson
URL: https://www.data-mania.com/blog/do-what-you-love-doing-lillian-pierson/
Type: post
Modified: 2026-03-17
MEMORABLE QUOTES FROM THE EPISODE:
[00:13:05] “If you’re an implementation person and you love implementing, that’s awesome because you don’t have to learn the people skills, you don’t have to become a leader, so on and so forth. And you can still land jobs.”
HIGHLIGHTS FROM THE SHOW:
[00:12:08] Everybody wants to break into data science but nobody is willing to appreciate.
[00:22:43] Do you have to know what skill sets you’re working with?
[00:25:40] How can you get more information from your stakeholders?
[00:27:23] What a day in the life of a Data entrepreneur is like?
[00:32:11] How are you managing your time on a day to day basis?
[00:41:48] How is your experience been working with the coach?
If you want to know more or get in touch with Lillian, follow the links below:
Weekly Free Trainings: We currently publish 1 free training per week on YouTube! https://www.youtube.com/channel/UCK4MGP0A6lBjnQWAmcWBcKQ
Becoming World-Class Data Leaders and Data Entrepreneurs Facebook Group: https://www.facebook.com/groups/data.leaders.and.entrepreneurs
LinkedIn: https://www.linkedin.com/in/lillianpierson/
The Data Entrepreneur’s Toolkit: A recommendation set for 32 free (or low-cost) tools & processes that’ll actually grow your data business (even if you still haven’t put up that website yet!). https://www.data-mania.com/data-entrepreneur-toolkit/
Listen on Apple Here: https://apple.co/37jP8pT
Listen on Spotify Here: https://spoti.fi/2TOpjej
Discover your inner Data Superhero!
Most of the time, custom advice is all you need to achieve both your dream salary AND the satisfaction that you crave from your data career.
In our free, fun, 45-second data career path quiz, you’ll uncover your inner Data Superhero type and get personalized data career recommendations that directly align with your unique combination of data skills, personality and passions.
Take the Data Superhero’s Quiz today!
Get the Data Entrepreneur’s Toolkit
There’s always that data professional who starts an online business and hits 6-figures in less than a year. Now? It’s your turn and we’re ready to help get you there with our Data Entrepreneur’s Toolkit (designed to help you get results for your data business fast).
It’s our favorite 32 tools & processes (that we use), which includes:
Marketing & Sales Automation Tools, so you can generate leads and sales – even in your sleeping hours.
Business Process Automation Tools, so you have more time to chill offline, and relax.
Essential Data Startup Processes, so you feel confident knowing you’re doing the right things to build a data business that’s both profitable and scalable.
Download the Data Entrepreneur’s Toolkit for $0 here.
Execute Upon the Data Strategy Action Plan
This is our crowd-favorite data strategy product. No long video trainings, no books to read, no needless theory. Just clear, concise guidance on what your next data strategy steps should be, starting today.
It’s a step-by-step checklist & collaborative Trello Board planner for data professionals who want to get unstuck & up-leveled into their next promotion by delivering a fail-proof data strategy plan for their data projects.
There are also 2 bonus guides, if you need help improving communications with your senior executives and stakeholders
And, it comes with a bonus, members-only community, if you’d like a private sounding board for getting valuable input from other data strategists.
Start executing upon our Data Strategy Action Plan today.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Marketing Mix Modeling Algorithms and Variables
URL: https://www.data-mania.com/blog/marketing-mix-modeling-algorithms-and-variables/
Type: post
Modified: 2026-03-17
For data professionals who are looking to work in marketing data science, knowing marketing mix modeling algorithms, variables and methods is non-negotiable. That’s why we are dedicating this blog to introducing the algorithms and variables you’ll want to use to implement the marketing mix modeling (MMM) approach.
Now, if you aren’t quite sure what MMM is exactly, I recommend you first start by going back and reviewing last week’s blog post here: Marketing Mix Modeling Explained.
I first learned about marketing mix modeling while gathering a client testimonial from Kam Lee. For the record, the “mixture” of Kam’s marketing data science expertise and the startup strategies he learned from within my course, led him to hit $350k in his first year or so of business!
The 4 Most-Common Features for MMM
The 4 most-common features measured within a marketing mix model are:
Product
Place
Promotion
Price
If this is your first exposure to these variables, just know that these are the “4Ps of marketing”. They’re household staples for product marketers, and they’re actually quite useful as features in MMM. We’ll use them as part of the “marketing mix” in the discussion that follows.
What is Marketing Mix Modeling?
Again, if you aren’t quite sure what MMM is exactly, I recommend you go back and review last week’s blog post on Marketing Mix Modeling Explained.
Marketing Mix Modeling TL;DR Version
Basically, for MMM, you want to take those 4 Ps and evaluate how that mixture of marketing attributes directly impacts profitability. The model you’d use to make that prediction (or set of predictions) is what’s commonly referred to as a “marketing mix model”.
And marketing mix modeling? That’s simply the act of taking historical sales and marketing data, and using statistical methods to build a model from that data in order to uncover statistically (and economically) significant relationships between your marketing mix and sales.
Once you’ve ascertained those relationships, you’ll be able to predict future sales and tweak your company’s marketing plans accordingly.
What’s the Catch? – Inserting 4 Ps into Marketing Mix Modeling Algorithms
The thing is though, you can’t just plug the 4 Ps into marketing mix modeling algorithms and be done with it.
First you need to know which are the best marketing mix modeling algorithms, and then you need to understand how the 4 Ps behave, as well as what to choose for explanatory and response variables for a MMM model. Let’s take a look….
Marketing Mix Modeling Algorithms
In all honesty, there aren’t that many marketing mix algorithms out there. The most common approaches include multiple linear regression and Bayesian methods.
Multiple Linear Regression
The most common type of machine learning algorithm that’s used in MMM is multiple linear regression. If you’re not proficient with multiple linear regression, feel free to peruse the following training and coding demonstrations:
On the Data-Mania Blog
A 5-Step Checklist for Multiple Linear Regression
A Demo of Hierarchical, Moderated, Multiple Regression Analysis in R
Our Courses / Books
Multiple Linear Regression – in Python for Data Science @ LinkedIn Learning
Chapter 4: Math, Probability, and Statistical Modeling – in Data Science for Dummies with Wiley & Sons Publishers
Bayesian Methods
There are some limitations to using multiple linear regression for MMM. For example, if you’re working with sparse data, you’re at risk of model-overfitting if you use regression. Also, as you’re about to see in our discussion of MMM variables, there is quite a bit of dependency between variables that go into a marketing mix. Since that dependence defies model assumptions for multiple linear regression, you may be forced to take another approach.
In these cases, you can try out Bayesian statistics to model your marketing mix. The advantage of taking a Bayesian approach is that it allows you to interject your domain expertise to guide the model in the most logical direction. If you’d like to learn more using Bayesian modeling for MMM, I suggest you read this article here.
As always with machine learning, predictive success is directly correlated with how well you understand the data you’re modeling. With that, let’s turn to the variables you’d use in MMM and how those variables behave.
The Response Variables Are Pretty Obvious…
Since you are using MMM to predict and optimize profits, the main response variables you’d want to consider are:
Number of Sales
Sales Revenue ($)
Explanatory Variables for Modeling the 4 Ps
Let’s do a quick overview of what explanatory variables would be appropriate to represent each of the variables in a MMM model.
Product
This refers to the product that is being sold.
How Product data behaves
Factors that impact how the product variable behaves include product quality, ease-of-use (ie; “usability”), and buyer expectations vs customer satisfaction. Basically – if your product sucks, then it doesn’t matter how you price or promote it – your buyers aren’t going to be happy, sales will falter, and selling it will eventually tarnish your company’s brand.
If you’re selling services, you could technically use a “service package” as the product here. In that case, you’ll probably want to extend out to a 7Ps approach: By adding variables that represent process, people, and physical evidence.
Explanatory variables you can use to represent Product in your MMM
MMM variables you can use to model Product in your mix include (in order of decreasing impact on total sales):
Product quality
in terms of constituency – % of a desirable attribute
when it comes to “durability” – product life span in days
in terms of conformance to manufacturing requirements – risk priority number
Product newness (days on the market)
Price
The price variable is simply the price at which the product sells.
How the Price variable behaves
There are lots of different pricing strategies out there, but the main thing to remember here is that you don’t want to be in a race to the bottom.
The price should reflect the value that the product provides its buyer, as well as how much supply there is to meet its demand…
Instead of lowering prices, look for ways to increase the value of the product and improve your marketing messaging to enhance the product’s positioning.
Generally, as prices increase, sales volumes decrease – so your distribution numbers (as represented by “place” variables) would decrease, but you could end up getting an increase in sales revenues anyway…. That’s one of the reasons it’s important to include both price and distribution in the marketing mix.
Also, when you drop prices you tend to get more sales, but the buyers are generally higher maintenance customers – this actually erodes the profitability of the product, because you’ll get more customers that all tend to require more support services which will have to be paid for out of the operations budget.. In this case your distribution numbers (as represented by “place” variables) could go up, but your price would be down and the overall profitability of this product for your company would suffer.
Obviously, this is something you’ll want to avoid…
If all this talk on how price impacts product marketing has you scratching your head, I recommend you check out this video blog here: Omnichannel Analytics and Channel Scoring for MORE SALES AND LOWER CHURN
Explanatory variables you can use to represent Price in your MMM
MMM variables you can use to model Price in your mix include (in order of decreasing impact on total sales) :
Unit Cost ($)
Spending/Customer ($/PP)
Product Discount ($)
Place
With respect to the place variable, we’re really talking about the place where the sale was made and the product is distributed to its buyer.
How the Place variable behaves
If you have a digital business, then “place” would be equivalent to the sales channel.
So if you have a digital business, then “place” would pretty much be equivalent to the sales channel.
If you don’t know what I mean by “sales channel”, make sure to checkout this video blog here: Omnichannel Analytics and Channel Scoring for MORE SALES AND LOWER CHURN
But if you’re in retail and have a brick-and-mortar store, along with an ecommerce store… place would designate the actual location where the sales and product distributions are made.
Explanatory variables you can use to represent Place in your MMM
MMM variables you can use to model Place in your mix include (in order of decreasing impact on total sales) :
Distribution Volume – How widely is the product being distributed?
Distribution per unit time
In terms of “Distribution” – Number of units purchased total
In terms of “Distribution” – Number of units purchased per location
Promotion
Promotion technically refers to how your company makes potential customers aware that the product is available.
How Promotion data behaves
Promotion often includes things like organic marketing, paid ads, press releases, and how your brand appears in search engine results. Promotion is the vehicle by which these tactics are communicated to customers in order to produce an increase in sales.
There are other approaches to building MMMs, and these involve extending out your marketing mix to include additional features. Well, this is the simplest. Let’s go with it…
Explanatory variables you can use to represent Place in your MMM
MMM variables you can use to model Promotion in your mix include all activities that increase product awareness and sales. Some examples are as follows (in order of decreasing impact on total sales) :
Number of promotions
Cost per promotion ($)
TV ads spend ($) – traditional
Print ads spend ($) – traditional
Outdoor campaign spends – traditional
Facebook and Instagram ad spend ($) – new, digital
Website traffic volumes – new, digital
Paid search spend ($) – new, digital
Closing Thoughts on Learning How to Implement MMM
As far as books, a lot of stuff out there is for non-technical marketing people. It’s not that helpful for actually learning how to do machine learning implementation of marketing mix modeling. In fact, from what I’ve seen online, bloggers tend to make the topic A LOT more confusing than it actually needs to be.
Marketing Mix Modeling Algorithms and Variables…
Some resources I can recommend for digging deeper into marketing data science and marketing mix modeling algorithms include:
Hands-On Data Science for Marketing: Improve your marketing strategies with machine learning using Python and R
Marketing Data Science: Modeling Techniques In Predictive Analytics With R And Python
[My course on how to build recommendation systems – does not cover MMM] Building A Recommendation System With Python
There aren’t really any online courses on marketing mix algorithms, variables, and techniques yet, but you can actually start learning to do it for free by looking at this training documentation over at R-Studio. And to learn how to implement it in Python, you may want to check out this free demo over on Kaggle too.
If you enjoyed this blog post, please share it with your friends using the share bar at the bottom of this page.
More resources to get ahead…
Get Income-Generating Ideas For Data Professionals
Are you tired of relying on one employer for your income? Are you dreaming of a side hustle that won’t put you at risk of getting fired or sued? Well, my friend, you’re in luck.
This 48-page listing is here to rescue you from the drudgery of corporate slavery and set you on the path to start earning more money from your existing data expertise. Spend just 1 hour with this pdf and I can guarantee you’ll be bursting at the seams with practical, proven & profitable ideas for new income-streams you can create from your existing expertise.
Learn more here!
Take The Data Superhero Quiz
You can take a much more direct path to the top once you understand how to leverage your skillsets, your talents, your personality and your passions in order to serve in a capacity where you’ll thrive. That’s why I’m encouraging you to take the data superhero quiz.
This free and super-fun 45-second quiz is all about you and how your personality type aligns with the very best career path for you. It’s fun, free and it will provide you personalized data career recommendations, complete with potential roles that fit your unique skills and passions, as well as salaries associated with those roles.
Take the Data Superhero Quiz today!
NOTE: This blog post contains affiliate links that allow you to find the items mentioned in this video and support the channel at no cost to you. While this channel may earn minimal sums when the viewer uses the links, the viewer is in NO WAY obligated to use these links. Thank you for your support!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## 9 Fast Measures to Stop Hackers From Stealing Your Data!
URL: https://www.data-mania.com/blog/prevent-web-scraping-9-fast-measures-to-keep-your-data-safe/
Type: post
Modified: 2026-03-17
Are you wondering how you can prevent web scraping? Keep reading to know more about web data scraping! Learn the fastest ways to stop hackers from stealing data on your website.
Web scraping is a technique that consists of extracting data from web pages in an automated way. It is based on the indexing of content. It can also be on the transformation of the information contained in web pages into intelligible duplicate information. This information can then be exported to other documents such as spreadsheets.
The people in charge of this crawling task, called scraping, are the so-called bots or crawlers. They are robots that are dedicated to automatically navigate through web pages, collecting data or information present in them.
The types of data that can be obtained are very varied. For example, there are tools that are responsible for price mapping, i.e., obtaining information on hotel or travel prices for comparison sites. Other techniques such as SERP scraping are used to find out the first results in search engines for certain keywords.
Data scraping is used by most large companies. Perhaps the clearest example is Google: where do you think it gets all the information it needs to index websites? Its bots continuously analyze the web to find and classify content by relevance.
Protecting your data from Data Scraping
Data scraping is a practice that continues to raise some eyebrows, as it is considered unethical in some quarters. In the end, in many cases, it is used to obtain data from other web pages. Its main goal is to replicate them in a new one through the use of an API. In some cases, it could lead to copying or duplication of information.
Also, these bots can be designed to navigate automatically through a website, even creating fake accounts. Hence, on many websites, you will see the typical captcha to confirm that you are not a bot.
On the other hand, the automatic extraction of information can create problems for the analyzed web pages, especially if the crawling is done on a recurring basis. Think that Google Analytics or other web metrics sites collect visits from bots. Therefore, if crawlers continuously visit a website, it could be affected and harmed by these “low quality” visits and lose ranking.
But all these are moral rather than legal issues. What does the General Data Protection Regulation (GDPR) say?
This law establishes new data protection and internet crime prevention data. The regulation states that the fact that a web page is public, accessible or indexable does not imply, in any way, that its data can be extracted.
This technique is only allowed in the following cases:
They are publicly accessible sources or the data are collected for the purpose of general public interest.
The interest of the data controller prevails over the right to data protection.
The tracked person is tracked with their consent.
Therefore, in case of a complaint, it must be demonstrated that the information is in the general public interest. It should be according to Article 45 of the GDPR, or the right of the controller to collect the data must be weighed.
In addition, web scraping cannot be used to infringe intellectual property law or the right to privacy of individuals. An example of this is through practices such as identity theft.
If you’re loving this whole discussion on how to prevent web scraping and you’re wondering how we can ensure the ethical use of data, then you’d probably be super interested to know more about data privacy and security. I did a video about the hidden danger in greater data privacy where I discussed the ethical insights of big data and privacy, navigating benefits risks and ethical boundaries, and overcoming hidden risks in a shared security model. Check it out here.
How can I prevent web scraping?
Web data scraping is a technique that can cause damage to crawled websites, especially if it is used continuously. One of the most direct consequences is the alteration of visitor data by the bots. This damages the perception that Google has of the website in relation to the bounce rate, time per visit, etc.
In addition, depending on the data collected, web scraping could be an act of unfair competition or infringement of intellectual property rights. For example, websites that copy content directly from Wikipedia or other websites, or stores that duplicate the product descriptions of others.
Furthermore, a website can also be scraped for other malicious purposes that fall under the scope of the right to privacy, for example, companies that scrape emails, phone numbers, or social network profiles in order to sell them to third parties.
If you want to prevent web scraping on your website, we recommend following these tips:
1. Using cookies or Javascript to verify that the visitor is a web browser.
As most web scrapers do not process complex javascript code, to verify that the user is a real web browser you can insert a complicated javascript calculation into the page, and verify that it has been correctly computed.
2. Introduce Captchas to make sure that the user is a human.
It is still a good measure to eliminate robot visitors; although lately they have become more sophisticated and manage to bypass them.
3. Set limits on requests and connections.
You can mitigate scrapers’ visits by adjusting the number of requests to the page, and connections; since a human user is slower than an automatic one.
4. Obfuscate or hide data.
Web scrapers crawl data in text format. Therefore, it is a good measure to publish data in image or flash format.
5. Detecting and blocking known malicious sources.
Locate and block access to known site scrapers, which may include our competitors, and whose IP address could be blocked.
6. Detecting and blocking site scraping tools.
Most tools use an identifiable signature to detect and block them.
7. Constantly update the HTML tags of the page.
Scrapers are programmed to search for certain content in the tags of the web page. Frequently changing the tags by introducing, for example, spaces, comments, new tags, etc. can prevent the same scraper from repeating the attack.
8. Using fake web content to trap attackers.
If you suspect that your information is being plagiarized, you can publish fictitious content and monitor its access to discover the scraper.
9. Inform in the legal conditions section about the prohibition of web scraping on your site.
Preventing web scraping attacks is difficult because it is increasingly difficult to distinguish scrapers from legitimate users. That is why the companies most exposed to plagiarism of their content, such as online stores, airlines, gambling sites, social networks, or companies with content that is subject to intellectual property, among others, must reinforce the security measures of their content published on the Internet. Remember how important it is to keep your data protected on the Internet to avoid spam, phishing, and other computer crimes.
If you like this article on how to keep your data secure from hackers and wondering what kind of data role this would fall into, a data privacy officer is a potential role I report on in my Data Superhero Quiz. This is a fast fun, 45-second quiz for data pros, to help you uncover the optimal role for you given your passions, skillsets and personality.
Take the Data Superhero Quiz today!
Also, I have a free Facebook Group called Becoming World-Class Data Leaders and Entrepreneurs. I’d love to get to know you inside there, if you’d like to apply to join here.
Hey, and if you liked this post, I’d really appreciate it if you’d share the love with your peers by sharing it on your favorite social network by clicking on one of the share buttons below!
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Create an Evergreen Analytics Strategy Framework So You Can LEAD WITH CONFIDENCE
URL: https://www.data-mania.com/blog/how-to-create-an-evergreen-analytics-strategy-framework/
Type: post
Modified: 2026-03-17
Evergreen Analytics Strategy Framework is a proven process for making triple sure that your data projects will be a success. Tell me, how many times have you wished you had something like this to ensure that your projects become successful?
Read this article and you’ll know all the ingredients that go into creating an evergreen analytics strategy framework, AND I’ll even give you my own proven framework that you can start using today, if you so choose.
Whether you’re new to the data industry or like me, and have been at it for over a decade… It is vitally important for you to make sure that the work you’re doing on a daily basis is actually benefiting your company’s bottom line. It’s not that difficult for you to conceptually bridge that gap either.
How do I know? Well, to date I’ve educated over 1 Million data professionals on AI, so you can say I know a thing or two about data science…
That and I’ve been delivering strategic plans since 2008, for organizations as large as the US Navy, National Geographic, and Saudi Aramco.
YouTube URL: https://youtu.be/qJE1cTdpZAU
If you prefer to read instead of watch, then read on…
To create an evergreen analytics strategy framework, you first need to understand what it is and how it’s beneficial – so let me break that down for you real quick.
WHAT IS AN EVERGREEN DATA STRATEGY?
An evergreen strategy is a strategy for CONTINUALLY generating new and/or improved revenue and profits from your business’s investment into data skill sets, technologies and resources.
To be “evergreen”, a strategy needs to be adaptable enough to perform and continually drive results and meet its objectives despite rapidly changing market conditions.
WHY DO YOU NEED AN EVERGREEN DATA STRATEGY?”
The competitive landscape is changing so incredibly fast, especially in the data space. In order to keep up and stay competitive, businesses need to make sure that they have evergreen strategies in place.
Now, if you don’t plan for this type of change, then it will be very easy for you to create systems that will rapidly become obsolete and your company would have lost most of its investments into the data initiative.
IF THAT HAPPENS, YOU DON’T WANT TO BE THE ONE WITH THE FINGERS POINTING AT YOU.
That’s why you need to make sure that your data analytics strategy is “evergreen”, meaning it makes provisions to ensure that your company:
Stays nimble enough to keep up with the speed of innovation
Remains adaptable to current market conditions
Stays up-to-date, timely, and relevant with respect to how it leverages its data resources, tech, and skillsets to generate new or improved revenue streams.
Actually, let’s stop a second here – I want to hear from you…
Have you ever worked on any type of evergreen strategy? Data analytics or otherwise? Tell us about it in the comments!
WHAT IS AN ANALYTICS FRAMEWORK?
An analytics framework is an overarching, reusable process that you can leverage to ensure that your company’s data initiatives are squarely meeting its business goals, and that the company’s data operations are directly in support of the business in reaching its business vision.
An analytics project helps you develop your data projects and data initiatives in a way that directly meets its objectives.
What it does
It helps you create direct focus not only for yourself but for other team members that are supporting your company’s data initiatives. You could think of it as helping to keep your company’s data eyes on the prize – the prize being the new and improved business profits.
Why you need it
An analytics framework is super important in protecting the business’s ROI into data projects. It does the following:
Prevent spinning wheels – it happens when data implementation people get so focused on the details of the implementation and configuring the solution, that they forget to keep their eyes on the core business objective that the work is satisfying. So, it’s kind of like “putting the cart before the horse.” If you can manage to keep your eyes in focus directly centered on how your work actually increases business profits for your company, that is going to help you find the clearest and most direct path to reaching that goal.
Reduce waste – it helps to break down silos between data workers and data resources. It’s the situation where you have multiple people working to solve the same problem in different ways. Well, you only need one person to find the solution to the problem and share it with the team, right? So, an analytics framework helps you reduce wastage in terms of data man hours, you could say.
Speaking of directing focus and reducing waste in your data projects, I did a whole video on 8 steps you can take today to increase your data science process lifecycle. Check it out here.
Pulling it all together – “WHAT IS AN EVERGREEN ANALYTICS STRATEGY FRAMEWORK?”
To put it simply, it is a framework you can use to develop and maintain an evergreen analytics strategy for your company.
To develop one, you need to step back from your daily work and think about your process for a bit.
Can you think of a 4 or 5 phase process that you can use repeatedly and consistently to make sure that the data projects your company is paying for is actually successful?
When I say successful, I mean that, either directly or indirectly increasing the bottom line of the business.
What about if you look as incrementally as only the data projects you’re working on? What steps could you take to make sure that the work you’re doing on a daily basis is actually improving the bottom line for your company?
With this, I would start with brainstorming and then trying to develop a process. Within the process, you would detail each step that’s required to make sure that your plan is fail-proof. That’s how you go about creating an evergreen analytics strategy framework.
Now, of course there’s a good degree of iteration as you’re working through this process and as you work over the years, you will refine your process, make it better, more repeatable and also more fail-proof.
In all honesty, a good framework comes from years of experience. Now, if you wanna bypass all the years of iterations and improvements, you can, of course, use my analytics framework – its called the STAR framework.
MY STAR FRAMEWORK
1. S – Survey the industry
This is where you go around and do extensive research looking at all the different use cases and case studies, getting yourself up to speed with all of the various use cases across the industry and which ones might be most effective in getting your company results considering its current set-up. When I say current set- up, I mean, in terms of the people it has in place to implement as well as the data resources it has and the technologies. Since you’re a data worker, you’re intimately aware of these aspects, so there’s really no one better than you to be out there surveying the industry and trying to identify optimal use cases for implementation at your company.
2. T – Take stock & inventory of your organization
Collect or generate all sorts of documentation that describes all of the various aspects of operations inside your business. You definitely don’t want to limit yourself to only data operations, because they aren’t really the core business operations that need to be optimized. You’ll need to take a more holistic view of your company and its most urgent needs. The tools you can use in order to take stock and inventory in your company include things like:
Surveys
Interviews
Requests for information
3. A – Assess your organization’s current state conditions
This is essentially where you take all of the documentations that you have collected in the previous phase, and start evaluating and identifying gaps or areas of opportunity. You also need to consider imminent risks that could be mitigated through implementing the right data use case. After doing a super thorough evaluation of your business and its operations, you would go ahead and hone in on that ideal optimal use case that’s gonna give the biggest bank for your company’s buck. Now, this use case needs to be based on your company’s existing resources. You need to make sure that you’re creating as much value as possible from it’s existing technologies, skill sets and data resources.
4. R – Recommend a strategy for reaching future state goals
This is the phase where you’ll go ahead and create a strategic plan for implementing that lowest hanging fruit data use case.
One of the beauties of the STAR framework is that, it’s essentially a process where you can RINSE, WASH and REPEAT every 18 months to make sure that your company has a proven evergreen data analytics strategy that it’s following and that’s working to increase the company’s bottom line.
We have covered a ton here in terms of what an evergreen analytics strategy framework is and why it’s essential to the success of data projects.
But of course there is more to it…
If you want me to do all the heavy-lifting for you, you can get my evergreen analytics strategy framework that comes with the 44 sequential action item steps that you need to take in order to create a fail-proof data strategy plan for your company. It’s called the Data Strategy Action Plan.
Start executing upon our Data Strategy Action Plan today.
NOTE: This description contains affiliate links that allow you to find the items mentioned in this video and support the channel at no cost to you. While this channel may earn minimal sums when the viewer uses the links, the viewer is in NO WAY obligated to use these links. Thank you for your support!
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## How to find new NFT Projects: 6 metrics you MUST evaluate
URL: https://www.data-mania.com/blog/how-to-find-new-nft-projects-6-metrics-you-must-evaluate/
Type: post
Modified: 2026-03-17
Are you interested in how to find new NFT projects?
NFTs are sweeping the Internet, but what makes new NFT projects a smart investment? Much of the hype surrounding NFTs (or non-fungible tokens) is focused on the extreme prices at which some rare assets are sold. With non-stop buzz about one NFT trend or another, it seems that there is a new NFT project launching every other week. How can data professionals take part in the NFT action without accidentally putting money into an asset that isn’t as valuable as it seems? Read on to identify 6 metrics for evaluating new NFT projects. Use them to get a clear perspective on the value of just about any NFT.
How to Find New NFT Projects Using The 6 Metrics for Evaluation
So, how to find new NFT projects by the way? Here are 6 metrics for evaluating new NFT projects below, in numerical order. Please note, the order in which these 6 metrics are presented does not reflect their relative importance.
1. Creator Prominence
Just like with physical artwork or content, the prominence of an NFT’s creator has a direct impact on the value of their work. Creators who have sold NFTs for high values in the past are more likely to do so again, especially if they have generated a dedicated following.
Of course, all creators have to start somewhere. It is absolutely possible for a small creator’s work to skyrocket in value after they become more well-known. Whether or not this is likely to happen can be estimated with thorough research into the creator, their background, and how dedicated they seem to be to creating valuable content.
2. Estimated Market Capitalization
The market cap of an NFT can be determined by multiplying the total supply of the NFT by its average price. As a general rule, higher market cap NFTs are likely to be more established and lower risk. However, financial experts strongly recommend using a variety of tools and sources for liquidity and pricing research, particularly when working with crypto.
It is also important to note that the value of NFTs is largely dependent on audience tastes and trends. You can bet on a new NFT project becoming popular or you can invest in one that is already booming with the anticipation that it will maintain that popularity.
3. Community and Unique Holders
Connected to estimated market cap is an NFT project’s community. The number of unique holders of a certain NFT can tell potential investors a lot about its value and appeal. Holders and fans of NFT projects often gather on platforms. These platforms include Twitter and Discord to connect and chat about a certain token.
A larger community will naturally garner more attention and buzz, which is highly beneficial for the NFT’s value. Finding a community that you personally connect and identify with can also make investing in a new NFT project more enjoyable and rewarding.
4. Rarity
Rarity is an important metric to assess. Scarcity is the reason that NFTs have value to begin with. So, a less rare NFT needs high demand in order to be a good investment. Similarly, a one-of-a-kind NFT has inherently higher value than a project with numerous tokens. Even beyond general uniqueness, an NFT might have certain attributes that are particularly rare.
This is the concept behind many play-to-earn games, such as the massively popular CryptoKitties. This game focuses on trading and breeding randomly generated cartoon cats. Each cat is a unique NFT, but those with rare characteristics are worth more in the game.
5. Floor Price
Floor price refers to the lowest price that an NFT is sold for. This is essentially the baseline minimum market price of a project’s tokens. Higher floor prices correspond to a higher value project. When investing in a new NFT project, especially if it is your first one, a good goal is to find a project that strikes a balance between high value and attainable floor price. While projects with higher floor prices are worth more, they are more difficult to get into because they are more expensive. At the same time, a project with a particularly low floor price is less likely to yield a desirable return on investment.
6. Function and Taste
The final metric to consider when assessing a new NFT project is its intended function as well as your personal taste. These are more subjective than other metrics but just as important. Investing in NFTs is about more than making money. There are vibrant, enthusiastic communities and cultures centered around each project. Choosing one that you are personally excited about will make the investment more valuable and rewarding to you.
In terms of function, NFTs take a myriad of shapes. Some are pieces of digital artwork. Many others are in-game items for video games or collectibles with physical rewards attached to them. A musician, for example, might sell an NFT of their album art. This can also include an exclusive physical t-shirt to go with it.
How to Find New NFT Projects: Next Steps for Investing in New NFTs
Finding a new NFT project to invest in is exciting, but choosing a project is only the first step. Different NFTs run on different blockchains and different cryptocurrencies. Potential investors will need to carefully research the project they are interested in to make sure they have the right kind of crypto and a wallet that is set up accordingly. You will also need to find a trustworthy marketplace to buy and sell NFTs. Moreover, make sure that your cybersecurity measures are up to date.
Make sure to do plenty of research but also remember to have fun. Investing in NFTs is investing in the technology of the future. This means, you get to be part of history while having a unique digital experience.
Parting thoughts on how to find new NFT projects: Start by investing your time, not your money
If I’ve got you scratching your head with all this talk on how to find new NFT projects using the 6 metrics for evaluation, that probably means you’re interested in making smart, high-ROI investments.
One of the absolute smartest, highest ROI investments you can make is the investment you make into yourself and your data career.
In that case, you probably don’t realize that one of the absolute smartest, highest ROI investments you can make is the investment you make into yourself and your data career. If this piques your interest, then I invite you to invest 1-hour of your time into joining our free masterclass where our Founder, Lillian Pierson, will teach you exactly how to take your data expertise and turn it into a 6-figure business (or side hustle), practically overnight. This is a limited-edition masterclass, so don’t miss this chance to take it for free – before we change our minds.
More resources to get ahead…
Get Income-Generating Ideas For Data Professionals
Are you tired of relying on one employer for your income? Are you dreaming of a side hustle that won’t put you at risk of getting fired or sued? Well, my friend, you’re in luck.
This 48-page listing is here to rescue you from the drudgery of corporate slavery and set you on the path to start earning more money from your existing data expertise. Spend just 1 hour with this pdf and I can guarantee you’ll be bursting at the seams with practical, proven & profitable ideas for new income-streams you can create from your existing expertise.
Learn more here!
Take The Data Superhero Quiz
You can take a much more direct path to the top once you understand how to leverage your skillsets, your talents, your personality and your passions in order to serve in a capacity where you’ll thrive. That’s why I’m encouraging you to take the data superhero quiz.
This free and super-fun 45-second quiz is all about you and how your personality type aligns with the very best career path for you. It’s fun, free and it will provide you personalized data career recommendations, complete with potential roles that fit your unique skills and passions, as well as salaries associated with those roles.
Take the Data Superhero Quiz today!
Hey! If you liked this post, I’d really appreciate it if you’d share the love by clicking one of the share buttons below!
A Guest Post By…
This blog post was generously contributed to Data-Mania by Shannon Flynn. Shannon Flynn is a freelance blogger who covers business, cybersecurity and IoT topics.
You can follow Shannon on Muck Rack or Medium to read more of her articles.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Evergreen Analytics Strategy Frameworks – Collaboration Between Women In Data x Lillian Pierson
URL: https://www.data-mania.com/blog/evergreen-analytics-strategy-frameworks/
Type: post
Modified: 2026-03-17
Evergreen Analytics Strategy Frameworks – Collaboration Between @WomenInDataOrg x @StrategyGal // Create an evergreen analytics strategy framework so you can LEAD WITH CONFIDENCE. Struggling with how to build out an analytics strategy? If you’re in the strategy development phase of your projects and want to develop an evergreen strategy for data management (or even machine learning products!) watch the video to learn the EXACT steps you’ll need to take to set yourself up for success. This video was created for data professionals to give you a head start on creating data strategies; whether that be an AI strategy or just a general data strategy, you’ll learn how you can craft an organizational framework so your data projects produce greater business ROI. If you’re looking to learn more about data analytics for managers, this video will also serve as a great primer. This training was brought to you by Lillian Pierson, CEO of Data-Mania, LLC.
Watch It On YouTube Here: https://youtu.be/qJE1cTdpZAU
Discover your inner Data Superhero!
Most of the time, custom advice is all you need to achieve both your dream salary AND the satisfaction that you crave from your data career.
In our free, fun, 45-second data career path quiz, you’ll uncover your inner Data Superhero type and get personalized data career recommendations that directly align with your unique combination of data skills, personality and passions.
Take the Data Superhero’s Quiz today!
Get the Data Entrepreneur’s Toolkit
There’s always that data professional who starts an online business and hits 6-figures in less than a year. Now? It’s your turn and we’re ready to help get you there with our Data Entrepreneur’s Toolkit (designed to help you get results for your data business fast).
It’s our favorite 32 tools & processes (that we use), which includes:
Marketing & Sales Automation Tools, so you can generate leads and sales – even in your sleeping hours.
Business Process Automation Tools, so you have more time to chill offline, and relax.
Essential Data Startup Processes, so you feel confident knowing you’re doing the right things to build a data business that’s both profitable and scalable.
Download the Data Entrepreneur’s Toolkit for $0 here.
Execute Upon the Data Strategy Action Plan
This is our crowd-favorite data strategy product. No long video trainings, no books to read, no needless theory. Just clear, concise guidance on what your next data strategy steps should be, starting today.
It’s a step-by-step checklist & collaborative Trello Board planner for data professionals who want to get unstuck & up-leveled into their next promotion by delivering a fail-proof data strategy plan for their data projects.
There are also 2 bonus guides, if you need help improving communications with your senior executives and stakeholders
And, it comes with a bonus, members-only community, if you’d like a private sounding board for getting valuable input from other data strategists.
Start executing upon our Data Strategy Action Plan today.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## What is a data leader & how you can become one (even if you don’t have a STEM degree!)
URL: https://www.data-mania.com/blog/what-is-a-data-leader/
Type: post
Modified: 2026-03-17
In today’s post, you’re going to get quick answers to the following questions: (1) what is a data leader? And (2) how to become a data leader?
There’s a BIG myth going around about data leadership that I’ve been seeing lately.
That myth?
That you need a degree in STEM, or tons of data science implementation experience in order to secure a position in data leadership.
Whatever misconceptions you may have, or whatever you’ve heard before – I want to make it clear…you do NOT need either of those things to be a data leader!
So yeh, like I said, today you’re going to get a quick definition answering the question “what is a data leader?” and then I’m going to share with you a case study that illustrates how one of my readers managed to become one (without a STEM degree or any serious professional data implementation experience).
If we haven’t met before, I’m Lillian Pierson. I’m a data strategist and the founder of Data-Mania, and it’s our mission to help other data professionals get ahead in their careers by becoming data leaders. To date, I’ve trained over 1.7 million workers in data science in partnership with LinkedIn Learning and Wiley.
Part of what prompted me to address this was a comment I received on Instagram the other day. Someone asked, “do you think that in order to be a good data leader you should first master data itself, through data science?”
And my answer to that question is “yes – but also no”.
Let me explain.
I have seen many amazing data leaders out there that never actually practiced data science – they didn’t build models or implement solutions.
What is a data leader, anyway?
A data leader is a highly data-competent leader whose job it is to make sure a company’s data projects are profitable and performing well.
This is key, so I’ll repeat it again:
A data leader is a highly data-competent leader whose job it is to make sure a company’s data projects are profitable and performing well.
I’ve also defined it over here, as:
A leader or manager of data projects on behalf of your employer
An online thought leader in the data industry
A leader of data projects within a business that you own
A leader of data projects for client’s who’ve retained your data consulting services
What sort of competencies do you need to become a data leader?
Being a data leader requires data competency and knowledge from a wide range of consulting and leadership expertise as well. Relevant experience includes:
Data strategy
Data competency (ie; data science, data engineering, AI, data storytelling, data management, data governance, data privacy, AI ethics, etc.)
Organizational leadership
Project management
Thought leadership
In order to become a data leader, you definitely need to understand the ins and outs of how data science works, all the algorithms, all the caveats, etc.
But that’s not all you need to know.
You do have to know statistics and a good bit about computer science. And you certainly need to understand the principles of data storytelling and analytics design. You also need to understand the ins and outs of data science, AI, data engineering, data management & governance, and more…
But on top of all that, you need to have good leadership and project management skills. It’s a management position after all, so you should have a good grasp on managing and leading a team.
And last but certainly not least, you need to understand strategic and tactical planning.
So, no – just doing data science is not the way to move into a data leadership role.
What you actually need is to know data science, and then broaden your range of expertise to support you in a higher function within a data leadership role.
What you actually need is to know data science, and then broaden your range of expertise to support you in a higher function within a data leadership role.
Case Study – Landing a Data Leader Role Without a STEM Degree
Let me illustrate this with a little story about one of my readers.
I want to introduce you to a gentleman named Tim. In reality, I’ve changed his name and other key details for the sake of anonymity, but this story is 100% true.
Tim attended a liberal arts college and pursued a degree in Government Studies. After finishing his studies, he went on to work in – you guessed it, government. He also went on to learn project management and get his certification. In his first big role out of college, he landed as a position as a business analyst. Specifically, he landed a role as a business analyst within the IT department.
Tim did a stellar job in this position, and his superiors were impressed, so they promoted him to be a senior analyst within the IT department. In these roles, Tim got to know IT from a business perspective, which plays a pivotal role in this story of his.
And keep in mind, on the side, Tim has been up-leveling his project management skills and working on those credentials.
So here we have Tim who has great people skills, great project management skills and understands the business of IT, and the next thing we know, Tim’s been promoted again – this time to be an IT Team Leader.
After some time in this position, Tim decided it was time to exit the public sector and move into the private sector, where he became a management consultant working in technical project management. Through his years in IT roles, Tim started taking an interest in data science.
IT projects tend to be very data-intensive and there’s a lot of overlap between IT and data science, so he decided to dig into data and diversify his skill set, once again. He approached data science from a self-study perspective, purchasing my book, Data Science For Dummies, and taking online courses to learn how to implement data science. Tim fell in love with data science and was eager to bring this expertise into his career.
Fast forward a few years, and I get in touch with Tim. You want to know where good ol’ Tim’s at now?
Tim is suddenly Chief Data Officer at Ritzy Karton, a luxury hotel chain (FICTITIOUS NAME ALERT: remember this case study has been anonymized ???? )
What are his duties?
Tim now spends his days building AI programs, managing leadership relationships, and managing teams of data professionals to make sure that projects are delivered on time and under budget, and then, of course, doing data strategy and oversight.
To me, Tim’s story is an incredible one.
Because trust me, it’s HARD to snag a data leadership position (even harder than a data science position in my opinion).
The difficulty is reflected in the pay scale. According to Glassdoor, the average salary for a data leadership position is $236,000 a year.
Tim was able to secure a position like this with his liberal arts degree, using his people skills, his sharp project management skills and by learning data strategy skills.
He didn’t spend years implementing data science – his career wasn’t about that. It was about understanding the ins and outs of IT, being great with people, finessing situations, inspiring and motivating team members, and developing strategies to deliver projects that are actually profitable for businesses.
Imposter Syndrome and How It Holds Us Back
I’ve spent years being an engineer and a data scientist and I know firsthand how it feels to beat yourself up about “not knowing enough”. You think you just need to learn a new programming language, you just need to get better at this methodology, that you just need to master deep reinforcement learning or whatever the latest trend in data science is and then you’ll be enough. You’ll get promoted, you’ll move up a rung on your data career and you’ll finally get the recognition and salary you deserve.
Spoiler alert: it doesn’t work like that.
The thing about imposter syndrome is that the more you know, the more you realize how much you don’t know.
There will ALWAYS be new data implementation skills to learn. Data is an ever-evolving industry.
The key to advancing your data career is not to get caught up in a continuous cycle of online courses and doing and learning more, more, and more implementation skills. The key is to diversify your skillset and learn the skills needed to become an effective data leader. Skills like project management, leadership, team management, and data strategy.
“What got you here won’t get you there” and this couldn’t be more true when it comes to your data career.
I share all this to educate my fellow data professionals so that you can know what to focus on in order to grow. In order to advance. There’s a popular saying, “what got you here won’t get you there” and this couldn’t be more true when it comes to your data career. Taking more courses on Udemy and diving into new programming languages will not help you snag a data leadership position.
I’m also sharing this for all of you out there who are interested in data strategy but DON’T have that data science and implementation background. If you are a business analyst, a business intelligence or analytics specialist, you too can be a data leader.
All you need to do, my dear data professional, is take your exceptional data literacy skills, and focus on up-leveling your people skills, leadership skills, project management skills, and diving into data strategy. Easier said than done, I know… But with a little effort every day, you’ll be leading data projects before you know it!
More resources to get ahead…
Get Income-Generating Ideas For Data Professionals
Are you tired of relying on one employer for your income? Are you dreaming of a side hustle that won’t put you at risk of getting fired or sued? Well, my friend, you’re in luck.
This 48-page listing is here to rescue you from the drudgery of corporate slavery and set you on the path to start earning more money from your existing data expertise. Spend just 1 hour with this pdf and I can guarantee you’ll be bursting at the seams with practical, proven & profitable ideas for new income-streams you can create from your existing expertise.
Learn more here!
Take The Data Superhero Quiz
You can take a much more direct path to the top once you understand how to leverage your skillsets, your talents, your personality and your passions in order to serve in a capacity where you’ll thrive. That’s why I’m encouraging you to take the data superhero quiz.
This free and super-fun 45-second quiz is all about you and how your personality type aligns with the very best career path for you. It’s fun, free and it will provide you personalized data career recommendations, complete with potential roles that fit your unique skills and passions, as well as salaries associated with those roles.
Take the Data Superhero Quiz today!
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Getting Started with Marketing Mix Modelling: Step-By-Step
URL: https://www.data-mania.com/blog/getting-started-with-marketing-mix-modelling-step-by-step/
Type: post
Modified: 2026-03-17
Curious about how to get started with marketing mix modelling? Read this blog post to learn about the marketing mix modelling step-by-step fundamentals and its benefits.
Marketing mix modelling (MMM) has been around since the 1980s and was invented as a means to track and analyze fluctuations in sales performance.
The marketing mix can involve various factors ranging from digital channel spend (e.g., Facebook and TikTok ads) to print media, TV and radio advertising, etc.
These are added to the ‘mix’ with campaigns, sales and promos, and external factors like seasonality. The general idea is to create a sales and marketing model immersed in the real world. With MMM, forecasts are enriched by context and interlinked as part of a whole.
Numerous businesses from the Fortune 500 use MMM already, and Think With Google promotes it as a “time-tested method for measuring marketing impact.”
The end goal of MMM is to optimize marketing budgets and actions to drive sales. Marketing budgets range between 5% and 15% of a company’s total budget, though this can push 20% for newer businesses. Optimizing spend with MMM vastly increases the leverage of those budgets.
So, how do you get started with marketing mix modelling?
The Benefits of Marketing Mix Modelling
First, let’s examine the benefits of marketing mix modelling.
1: Non-Reliant on Tracking
Broadly speaking, MMM is most similar to marketing attribution, which seeks to attribute the results of various marketing campaigns to the touchpoints involved.
Marketing attribution relies heavily on user tracking, which has become more difficult in today’s privacy-centric internet universe.
In contrast, MMM uses internal and external data that doesn’t rely on user tracking to paint an overarching picture of marketing channels, actions, and external factors.
As a result, it’s considerably more grounded in reality than marketing attribution and doesn’t suffer from changes to regulation.
2: Combines Marketing With External Data
MMM is often described as an ‘art form’ as it weaves different marketing and sales factors together into a holistic model.
Specifically, MMM integrates multi-channel marketing actions with external factors, from inflation to temperature, location, current affairs, and custom events like Christmas and new year. This provides businesses with a nuanced method for examining sales and marketing inputs and outputs.
3: Integrates Product Changes
MMM helps businesses describe how internal factors affect sales outputs. One example is a product change. If a business changes its product offerings, MMM helps marketers delineate changes in outputs from other marketing activities.
In other words, MMM helps answer questions like “is this drop and sales due to product changes or something internal, or with our channel performance?”
Marketing Mix Modelling: Step-By-Step Fundamentals
Data is fundamental to marketing mix modelling. In a nutshell, standard marketing mix models use internal and external data to perform multiple linear regression.
Here’s the basic process:
1: Collect Data
Step one is invariably collecting data. Marketing mix models need a steady stream of historical marketing and sales time series data.
Around two to three years should do the trick. It’s best to have daily data rather than weekly on monthly data. You can interpolate your datasets to obtain daily data if you only have weekly or monthly data.
The first model should contain a small number of variables. For example, total marketing spend combined with main keyword search trend is a good choice, enabling marketers to evaluate how those two foundational factors affect sales.
External data is often obtained from open-source or public databases. For example, if you’re selling ice cream, you may want to add daytime temperatures to isolate marketing actions’ relationship with spikes in hot weather.
2: Engineer
Next, transform and clean the data. All data needs to be in the right format, missing values handled, etc. Finally, assess and remove outliers if necessary.
It’s often necessary to create new variables via feature engineering. Linear regression has specific requirements that the data must fulfill to create an accurate and efficient model. Always check data with statistical tests.
Engineering the data is time-consuming, but producing an efficient model is worth it in the long run and working with bad data is sure to cause headaches.
This is a granular process that requires rigorous attention to detail. Small issues in the data can turn into big issues in the model.
3: Model
Building the model is the fun bit. As mentioned, most MMM models use multiple linear regression.
The model predicts past performance. Training the model to accurately predict fluctuations in past data should, in theory, replicate that accuracy when exposed to real data. Finding patterns with explanatory power is the goal.
Once patterns are discovered, and accurate predictions are made on past data, you can start optimizing the model for deployment and use.
4: Optimization and Use
Once a “minimum viable model” has been built and tested on past performance, it’s time to simulate marketing actions. By running simulations based on the model, it’s possible to predict future outputs.
In marketing mix modelling, the majority of the work goes into building an accurate model with plenty of useful factors. It’s like building any other model – the hard work goes into construction.
Once the resulting model displays some promising forecasts, you can build confidence and optimize spending and strategy to boost sales. MMM has the capacity to explain simple actions, such as increasing ad spend on one channel, but it can also expose nuances in how external data affects performance, whether that’s inflation, COVID-related disruption, or adverse weather.
Summary: How to Get Started with Marketing Mix Modelling
By using an MMM model to learn and predict, businesses can boost sales while unlocking vital insights into their overall operational strategy.
One of the best ways to learn marketing mix modelling is inside a simulation. This way, marketers can use real data from realistic scenarios to learn about MMM. Building your own model is much easier after learning inside a simulator.
MMM is a powerful tool in the repertoire of any marketer or data scientist and is great to add to one’s CV and resume. Once the model is up and running, it can provide years of use. MMM is the marketing gift that keeps on giving.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Cloud Security Consulting Services: Key Benefits, Trends & Important Cloud Strategy Trends for 2023
URL: https://www.data-mania.com/blog/cloud-security-consulting-services-key-benefits-trends-important-cloud-strategy-trends-for-2023/
Type: post
Modified: 2026-03-17
Organizations can ensure that their data and applications are secure when using the cloud by leveraging cloud security consulting services. Through in-depth assessments of existing security protocols, expert advice on improving controls, and guidance with respect to compliance standards, these specialized consultants provide an extra layer of protection for companies utilizing cutting-edge solutions.
Cloud based security services provide a valuable deal to organizations looking to keep their systems safe and secure. Professional consultants offer the implementation of firewalls, encryption tools, identity management solutions, and more to maximize protection.
In addition, they may be called upon for incident response procedures during crises or to deliver security training courses for employees to create an environment impervious to malicious intrusions.
Increased Adoption Of Multi-Cloud And Hybrid Cloud Environments
Adopting a multi-cloud or hybrid cloud environment provides many advantages to organizations.
Flexibility and Scalability
The flexibility and scalability of cloud environments allow for easy expansion and contraction of resources as business needs grow. This flexible computing infrastructure enables organizations to respond more quickly and cost-effectively to changing customer demands, market conditions, and other unpredictable disruptions.
Disaster Recovery and Business Continuity
Multi-cloud or hybrid cloud environments are ideal for disaster recovery scenarios since they allow organizations to distribute their data across multiple regions, ensuring that important information is backed up in multiple locations. This provides peace of mind for a natural disaster or other unexpected events. The organization can quickly recover its systems without too much disruption in service or revenue.
Leveraging Unique Features and Capabilities
Each provider offers unique features and capabilities that may be beneficial to an organization’s business needs. By leveraging the strengths of different providers, companies can access a wider range of options and capabilities.
Cost Management
Organizations can optimize costs in a multi-cloud or hybrid cloud environment by taking advantage of competitive pricing, specialized offerings, and different pricing models, which are available through each provider. This allows them to tailor their usage and budgets accordingly.
Security and Compliance
Multi-cloud or hybrid cloud environments provide an additional layer of security that is not possible with traditional in-house infrastructure since the organization can spread its data across multiple providers and regions for backup purposes. Additionally, cloud providers typically adhere to rigorous compliance standards that certain industries may require.
Reduced Vendor Lock-in
As opposed to using a single provider, organizations can reduce their risk of vendor lock-in by utilizing multi-cloud or hybrid cloud environments. This allows organizations to take advantage of the most cost-effective and reliable cloud-based security services available, without worrying about being tied down to one vendor.
Greater Focus On Data Privacy And Compliance
Cloud-based security services allow businesses to create a secure digital environment. Companies can ensure their data is well-protected and governed in accordance with relevant regulations by having cloud computing and data privacy experts on their teams.
Data Privacy
Data privacy is critical for organizations. With increasing cyber threats, it is crucial to have a comprehensive security plan in place to protect digital infrastructure. Cloud security consultants help businesses build secure data systems and processes, and develop systems for identifying and responding to potential threats.
Compliance
Cloud security consulting services can help companies remain compliant with various regulations including GDPR, HITECH, HIPAA, and more. By assessing the security of a company’s digital infrastructure, experts can identify any potential issues that could lead to violations and ensure compliance with applicable regulations.
Regular Risk Assessments
Regular risk assessments are critical for ensuring continued data privacy and compliance. Cloud security service providers help businesses stay ahead of the game by providing ongoing risk assessment services to identify emerging threats and areas of vulnerability. Additionally, these services can help businesses develop strategies for mitigating risk and ensuring data privacy and compliance in the long term.
More Emphasis On Container Security
Cloud security consulting services provide organizations with the expertise and guidance needed to ensure their cloud environments are secure. With a rapidly evolving technology landscape, companies need to leverage the capabilities of multiple cloud providers while also using advanced automation and orchestration tools to increase efficiency and reduce costs.
The rise in multi-cloud and hybrid cloud environments has increased the emphasis on container security. Containers can isolate applications and services, making them more secure than traditional virtual machines. By leveraging cloud-based security services, organizations can implement to protect their containers from malicious attacks and other malicious activities.
The advantages of utilizing cloud security consulting services are numerous, from increased security to cost savings. Security consultants can help organizations ensure that their cloud environments comply with applicable laws, regulations, industry standards, and best practices. Additionally, they can guide on using cost-saving features, such as automated patching, monitoring, and log management.
Growing Interest In Zero Trust Security
In recent years, many organizations have adopted a zero-trust security approach. This involves adopting a “never trust, always verify” approach. In this approach, each user and device must be individually identified and verified. This is accomplished through multi-factor authentication, while networks are segmented and resources are micro-segmented to contain threats.
Security tools, such as encryption and data loss prevention are also employed to protect data, while regular monitoring helps identify any areas of risk. With the rise of sophisticated cybercrime, zero-trust security has become increasingly popular among those looking for better assurance against online threats.
This type of security emphasizes preventive measures, requiring users to prove their identity before accessing the system by using multi-factor authentication and other techniques. You may also further implement network segmentation and micro-segmentation to enhance your cloud security.
Many organizations offer specialized cloud security consulting services to help you adopt this approach. These services involve assessing your existing infrastructure and developing a customized strategy that meets your specific requirements. The consultants will also help you deploy the appropriate tools – such as data vaulting solutions, encryption protocols, and more, to ensure maximum protection for your system.
Cloud Threat Intelligence And Incident Response
Cloud-based security service providers are essential for enterprises looking to protect their data and systems against threats. An effective solution requires using threat intelligence feeds, incorporating machine learning and artificial intelligence to identify potential risks, forming incident response teams, and communicating any incidents that occur.
Threat Intelligence Feeds are a great way to stay informed about potential threats. By combining data analysis from multiple sources, you can better understand cyber threats and how to best defend against them. Companies can also use this intelligence to identify malicious actors and respond accordingly.
Machine learning and artificial intelligence are key components of cloud security consulting solutions. These technologies can help identify anomalies and potentially malicious activity and detect the different types of suspicious files. This can pinpoint malicious behavior across networks and systems, allowing for proactive defense against cyberattacks.
In addition to threat intelligence feeds and technology solutions, incident response teams are valuable in defending against cyber-related threats. An incident response team responds to security incidents and ensures that appropriate measures are taken. They should be able to analyze the incident, determine its cause, and take steps to mitigate any damage or loss of data.
It is also essential to communicate incident response activities in order to keep all relevant personnel informed. This could include informing stakeholders about the status of a security incident. It also includes reporting to stakeholders on steps taken to mitigate the risk & informing customers of any data loss or damage. This ensures that everyone involved is aware of the actions taken, creating accountability for each team member.
Wrapping Up
In conclusion, cloud-based security services are valuable for enterprises looking to protect their data and resources. The benefits of having professionals consult on their various security strategies and needs go beyond the surface level. By using the advances in cloud computing can further improve the longevity, scalability, and efficiency of an organization’s business strategies.
Organizations should also identify meaningful trends in the industry and proactively invest in key technology initiatives. Enterprises that take action sooner than later on these opportunities will perfectly position themselves for long-term success.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## The Top 10 Data Breach Types and How to Safeguard Yourself
URL: https://www.data-mania.com/blog/the-top-10-data-breach-types-and-how-to-safeguard-yourself/
Type: post
Modified: 2026-03-17
Businesses handling data must always be on guard against data breaches – but individuals can also take measures to protect themselves. Data Breach Types can range from minor mistakes to large-scale attacks, and any leaks can result in severe consequences. Data breaches are widespread, with 39% of UK businesses reporting a cyber attack in 2022.
In this article, legal experts at Graham Coffey & Co. Solicitors will discuss the top 10 data breach categories individuals should be aware of, and how to implement robust safeguarding measures to reduce the risk of a breach of your data.
Data Breach Types – Human Error
Simple human mistakes are a leading cause of data breaches. Instances include emails with personal information sent to the wrong recipient or misplaced physical documents. Although these occurrences may not lead to severe breaches, it is crucial to mitigate any risk. Ensure data controllers understand their roles in protecting personal data and provide training for staff handling information.
Inadequate Control Procedures
Many businesses do not realise that altering personal data without permission can be considered a breach under certain circumstances. Breaches do not always result from hackers, but from businesses failing to establish proper data security measures.
Businesses designated as ‘data controllers’ under GDPR or the UK’s Data Protection Act 2018 must understand their responsibilities to protect stored or processed data, as failure to do so can lead to serious consequences.
If a breach exposes an individual’s data that results in a leak of sensitive information or tangible loss, they may be entitled to claim compensation. Anyone who suffers distress and financial loss due to a data breach should consult a data breach compensation solicitor for advice.
Password Guessing
In some cases, accessing private data requires little more than guessing a password or trying common variations. Data Breach Types can be caused by stolen credentials, which are the most frequent cause of data breaches, according to AAG.
Using simplistic and predictable passwords makes it easier for cybercriminals to gain unauthorised access to personal or professional accounts. To minimise this risk, create strong, unique passwords by mixing uppercase and lowercase letters, numbers, and special characters. Consider using a password management solution to store and produce passwords, and enabling multi-factor authentication (MFA) whenever it is available.
Unsecured Networks
The rise of remote work has led to increased reliance on cloud-based servers – but accessing data through unsecured networks, such as public WiFi, opens the doors to hackers.
Businesses should use secure cloud storage providers and ensure network security for remote data access. You should avoid connecting via public WiFi whenever possible.
Physical Theft
Stolen or misplaced devices can result in unauthorised access to personal data. To minimise this risk, enable device encryption, use password protection, and install remote wiping capabilities on all devices containing sensitive information. Additionally, maintain physical security measures and keep your devices secure at all times.
Phishing Data Breach
Cybercriminals employ deceptive emails or websites to lure users into revealing sensitive information or installing malware. To avoid falling for phishing scams, verify the legitimacy of emails, especially those requesting sensitive information or containing suspicious links.
You should educate yourself about phishing tactics and implement robust email filtering and security systems to minimise the risk.
Ransomware Data Breach
In a ransomware attack, a hacker locks a computer system and demands payment to release it. The breach itself and the potential sharing, selling, or use of the accessed information can have severe consequences.
Preventing ransomware involves employee awareness of phishing risks, installing firewalls, and providing necessary training to avoid mistakes that may lead to ransomware attacks.
Malware
Malware includes any software that allows hackers to access and control a device. Similar to ransomware, malware can serve various purposes, including stealing data or security credentials. In the event that an infiltrated device belongs to a network, the intruder might access additional devices within the system and potentially acquire passwords, allowing them to access accounts and data without being noticed.
In order to protect against malware, use dependable antivirus software, keep all software current, avoid clicking on suspicious links, and abstain from downloading files or applications from unconfirmed sources.
Public Wi-Fi Usage
Public Wi-Fi networks lacking proper security measures leave devices and data vulnerable to hackers who can intercept information transmitted across these networks. When using public Wi-Fi is necessary, safeguard your personal data by utilising a virtual private network (VPN) and turning off file-sharing features on your device.
Unauthorised Third-Party Access
Sharing login credentials or allowing unauthorised third parties to access sensitive data can lead to data breach types. Implement strict access controls, follow the principle of least privilege, and regularly review and update user permissions. Additionally, educate those with whom you are in contact about the dangers of sharing credentials and the importance of maintaining account security.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Churn Rate Analysis Using The GraphLab Framework – A Demonstration
URL: https://www.data-mania.com/blog/churn-rate-analysis/
Type: post
Modified: 2026-03-17
In this article, you’re going to learn what customer churn rate analysis is and get a demonstration of how you can perform it using GraphLab. Churn is very domain specific, but we’ve tried to generalize it for purposes of this demonstration.
What is Churn?
Churn is essentially a term that is used to describe the process where the existing customers of a business stop using – and cancel payment on – the business’s services or products. Churn rate analysis is vital to businesses which offer subscription-based services (like phone plans, broadband, video games, newspapers etc.)
Questions to Answer Within a Churn Rate Analysis
Some of the questions that need to be addressed within a churn rate analysis are –
What is the reason for the customers to churn?
Is there any method to predict the customers who might churn?
How long will the customer stay with us?
Let’s look at two cases –
Case 1 – A Department store has transactional data which consists of sales data for a period of one year. Now we need to predict the customers who might churn. The only problem is that the data is not labelled, hence supervised algorithms will not work. Many of the real-world data sets are not labelled. Predicting customer churn in this type of setting requires a special package known as GraphLab create.
Case 2 – Consists of data which has labels to indicate whether a customer churned or not. Any supervised algorithm such as Xgboost or Random Forest can be applied to predict churn.
Since Case 2 is simple and straightforward. This article will primarily focus on Case 1 i.e. data sets without labels.
Case 1 – Using Churn Rate Analysis To Predict Customers Who Have A Propensity To Churn
In this particular scenario, we shall be using the GraphLab package in python. Before proceeding to the churn rate analysis tutorial, let’s look at how GraphLab can be installed.
Installation
1. You need to sign up for a one year free academic license from here. This is purely for understanding and learning for GraphLab works. If you require the package for Commercial version, buy a commercial licence.
2. Once you have signed up for GraphLab create, you will receive a mail with the product key.
3. Now you are ready to install GraphLab. Below are the requirements for GraphLab.
4. GraphLab only works on python 2. If you have Anaconda installed you can simple create a python 2 environment with the following commands.
Now activate the new environment –
5. If you are not using Anaconda, you can install python 2 from here
6. Once you have python 2 installed. It’s time to install GraphLab. Head over here to get the installation file.
There are two ways to install GraphLab –
Installation Method A
Using the GUI based tool to install it.
Download the installation file from the website and run it.
Enter your registered email address and the product key you received via email. And boom you are done.
Installation Method B
The second method to install GraphLab is via the pip package manager. Type the following commands to install GraphLab using pip –
pip install --upgrade --no-cache-dir https://get.graphlab.com/GraphLab-Create/2.1/your registered email address here/your product key here/GraphLab-Create-License.tar.gz
Using GraphLab to Conduct Churn Rate Analysis
Now that Graphlab is installed, the first step involves invoking the GraphLab package along with some other essential packages.
import graphlab as gl
import datetime
from dateutil import parser as datetime_parser
For reading in the CSV files we shall be using the SFrames from the GraphLab package.
sl = gl.SFrame.read_csv('online_retail.csv')
Parsing completed. Parsed 100 lines in 1.39481 secs.
------------------------------------------------------
Inferred types from first 100 line(s) of file as
column_type_hints=[long,str,str,long,str,float,long,str]
If parsing fails due to incorrect types, you can correct
the inferred type list above and pass it to read_csv in
the column_type_hints argument
------------------------------------------------------
Finished parsing file C:\Users\Rohit\Documents\Python Scripts\online_retail.csv
Parsing completed. Parsed 541909 lines in 1.22513 secs.
The file is read and parsed. Let’s have a look at the first few rows of the data set.
sl.head
From the above snippet of data set it’s apparent that the data set is transactional in nature, hence converting the SFrame to a time series is the preferred format. Before proceeding further, let’s convert the invoice date which is in the string format to date time format.
sl['InvoiceDate'] = sl['InvoiceDate'].apply(datetime_parser.parse)
Let’s confirm if the date is parsed in the right format as required.
sl.head
The invoice date column has indeed been parsed to the right format. The next step involves creating the time series with the invoice date as the reference.
timeseries = gl.TimeSeries(sl, 'InvoiceDate')
timeseries.head
The invoice date column has successfully been converted into a time series data set. Since, we don’t necessarily have a train-test data set. Let’s split the existing data set into a train and validation set.
train, valid = gl.churn_predictor.random_split(timeseries, user_id='CustomerID', fraction=0.7, seed = 2018)
This should split the existing data into 70% training and 30% validation. Before training the model on the train data set. We need to sort out a few things –
We need to define the number of days after which a customer is categorised as churned,in this case it is 30 days.
Since we need to look at the effectiveness of the algorithm, we need to set a date limit until which the algorithm trains on.
These two actions are accomplished with below code.
churn_period = datetime.timedelta(days = 30)
churn_boundary_oct = datetime.datetime(year = 2011, month = 8, day = 1)
Phew, finally let’s train the model.
model = gl.churn_predictor.create(train, user_id='CustomerID',
features = ['Quantity'],
churn_period = churn_period,
time_boundaries = [churn_boundary_oct])
Here we are using only ‘quantity’ column as a dependent variable. Along with the churn_period and time_boundaries.
PROGRESS: Grouping observation_data by user.
PROGRESS: Resampling grouped observation_data by time-period 1 day, 0:00:00.
PROGRESS: Generating features at time-boundaries.
PROGRESS: --------------------------------------------------
PROGRESS: Features for 2011-08-01 05:30:00
PROGRESS: Training a classifier model.
Boosted trees classifier:
--------------------------------------------------------
Number of examples : 2209
Number of classes : 2
Number of feature columns : 15
Number of unpacked features : 150
+-----------+--------------+-------------------+-------------------+
| Iteration | Elapsed Time | Training-accuracy | Training-log_loss |
+-----------+--------------+-------------------+-------------------+
| 1 | 0.015494 | 0.843821 | 0.568237 |
| 2 | 0.050637 | 0.856043 | 0.496491 |
| 3 | 0.062637 | 0.867361 | 0.445855 |
| 4 | 0.074637 | 0.871435 | 0.410984 |
| 5 | 0.086639 | 0.876415 | 0.386890 |
PROGRESS: --------------------------------------------------
PROGRESS: Model training complete: Next steps
PROGRESS: --------------------------------------------------
PROGRESS: (1) Evaluate the model at various timestamps in the past:
PROGRESS: metrics = model.evaluate(data, time_in_past)
PROGRESS: (2) Make a churn forecast for a timestamp in the future:
PROGRESS: predictions = model.predict(data, time_in_future)
| 6 | 0.094639 | 0.878225 | 0.369549 |
+-----------+--------------+-------------------+-------------------+
Hooray, the model has finished training. The next step involves evaluating the trained model. Since we have already split the data into train and validate, we need to evaluate the model on the validation set and not the training set. The model has been trained until the 1st of August 2011. And the churn time has been set to 30 days. We set the evaluation date to 1st September 2011.
evaluation_time = datetime.datetime(2011, 9, 1)
metrics = model.evaluate(valid, time_boundary = evaluation_time)
PROGRESS: Making a churn forecast for the time window:
PROGRESS: --------------------------------------------------
PROGRESS: Start : 2011-09-01 00:00:00
PROGRESS: End : 2011-10-01 00:00:00
PROGRESS: --------------------------------------------------
PROGRESS: Grouping dataset by user.
PROGRESS: Resampling grouped observation_data by time-period 1 day, 0:00:00.
PROGRESS: Generating features for boundary 2011-09-01 00:00:00.
PROGRESS: Not enough data to make predictions for 321 user(s).
Metrics
{'auc': 0.7041731741781945, 'evaluation_data': Columns:
CustomerID int
probability float
label int
Rows: 1035
Data:
+------------+----------------+-------+
| CustomerID | probability | label |
+------------+----------------+-------+
| 12365 | 0.899722337723 | 1 |
| 12370 | 0.899722337723 | 1 |
| 12372 | 0.877351164818 | 0 |
| 12377 | 0.877230584621 | 1 |
| 12384 | 0.879127502441 | 0 |
| 12401 | 0.877230584621 | 1 |
| 12402 | 0.877230584621 | 1 |
| 12405 | 0.182979628444 | 1 |
| 12414 | 0.90181106329 | 1 |
| 12426 | 0.877351164818 | 1 |
+------------+----------------+-------+
[1035 rows x 3 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'precision': 0.7741573033707865, 'precision_recall_curve': Columns:
cutoffs float
precision float
recall float
Rows: 5
Data:
+---------+----------------+----------------+
| cutoffs | precision | recall |
+---------+----------------+----------------+
| 0.1 | 0.732546705998 | 0.997322623829 |
| 0.25 | 0.753877973113 | 0.975903614458 |
| 0.5 | 0.774157303371 | 0.922356091031 |
| 0.75 | 0.801939058172 | 0.775100401606 |
| 0.9 | 0.874345549738 | 0.223560910308 |
+---------+----------------+----------------+
[5 rows x 3 columns], 'recall': 0.9223560910307899, 'roc_curve': Columns:
threshold float
fpr float
tpr float
p int
n int
Rows: 100001
Data:
+-----------+-----+-----+-----+-----+
| threshold | fpr | tpr | p | n |
+-----------+-----+-----+-----+-----+
| 0.0 | 1.0 | 1.0 | 747 | 288 |
| 1e-05 | 1.0 | 1.0 | 747 | 288 |
| 2e-05 | 1.0 | 1.0 | 747 | 288 |
| 3e-05 | 1.0 | 1.0 | 747 | 288 |
| 4e-05 | 1.0 | 1.0 | 747 | 288 |
| 5e-05 | 1.0 | 1.0 | 747 | 288 |
| 6e-05 | 1.0 | 1.0 | 747 | 288 |
| 7e-05 | 1.0 | 1.0 | 747 | 288 |
| 8e-05 | 1.0 | 1.0 | 747 | 288 |
| 9e-05 | 1.0 | 1.0 | 747 | 288 |
+-----------+-----+-----+-----+-----+
[100001 rows x 5 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}
The Evaluation metrics such as AUC, precision and recall score are displayed in the report. However all the metrics can be obtained in a GUI.
time_boundary = datetime.datetime(2011, 9, 1)
view = model.views.evaluate(valid, time_boundary)
view.show()
PROGRESS: Making a churn forecast for the time window:
PROGRESS: --------------------------------------------------
PROGRESS: Start : 2011-09-01 00:00:00
PROGRESS: End : 2011-10-01 00:00:00
PROGRESS: --------------------------------------------------
PROGRESS: Grouping dataset by user.
PROGRESS: Resampling grouped observation_data by time-period 1 day, 0:00:00.
PROGRESS: Generating features for boundary 2011-09-01 00:00:00.
PROGRESS: Not enough data to make predictions for 321 user(s).
PROGRESS: Making a churn forecast for the time window:
PROGRESS: --------------------------------------------------
PROGRESS: Start : 2011-09-01 00:00:00
PROGRESS: End : 2011-10-01 00:00:00
PROGRESS: --------------------------------------------------
PROGRESS: Grouping dataset by user.
PROGRESS: Resampling grouped observation_data by time-period 1 day, 0:00:00.
PROGRESS: Generating features for boundary 2011-09-01 00:00:00.
PROGRESS: Not enough data to make predictions for 321 user(s)
We can pull out a report on the trained model using.
report = model.get_churn_report(valid, time_boundary = evaluation_time)
print report
+------------+-----------+----------------------+-------------------------------+
| segment_id | num_users | num_users_percentage | explanation |
+------------+-----------+----------------------+-------------------------------+
| 0 | 435 | 42.0289855072 | [No events in the last 21 ... |
| 1 | 101 | 9.75845410628 | [Less than 2.50 days with ... |
| 2 | 80 | 7.72946859903 | [No "Quantity" events in t... |
| 3 | 51 | 4.92753623188 | [No events in the last 21 ... |
| 4 | 51 | 4.92753623188 | [Less than 28.50 days sinc... |
| 5 | 44 | 4.25120772947 | [Greater than (or equal to... |
| 6 | 36 | 3.47826086957 | [No events in the last 21 ... |
| 7 | 32 | 3.09178743961 | [Less than 2.50 days with ... |
| 8 | 24 | 2.31884057971 | [Sum of "Quantity" in the ... |
| 9 | 22 | 2.12560386473 | [Greater than (or equal to... |
+------------+-----------+----------------------+-------------------------------+
+-----------------+------------------+-------------------------------+
| avg_probability | stdv_probability | users |
+-----------------+------------------+-------------------------------+
| 0.897792713258 | 0.0240167598568 | [12365, 12370, 12372, 1237... |
| 0.69319883166 | 0.100162972963 | [12530, 12576, 12648, 1269... |
| 0.757627598941 | 0.0904122072578 | [12432, 12463, 12465, 1248... |
| 0.859993882623 | 0.070536854901 | [12384, 12494, 12929, 1297... |
| 0.792790167472 | 0.0859747592324 | [12513, 12556, 12635, 1263... |
| 0.25629338131 | 0.135935808077 | [12471, 12474, 12540, 1262... |
| 0.866931213273 | 0.034443289173 | [12548, 12818, 16832, 1688... |
| 0.632504582405 | 0.121735932946 | [12449, 12500, 12624, 1263... |
| 0.824982141455 | 0.0968270683383 | [12676, 12942, 12993, 1682... |
| 0.0796884274618 | 0.0453845944586 | [12682, 12748, 12901, 1667... |
+-----------------+------------------+-------------------------------+
[46 rows x 7 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
The training data used for the model along with the features created for the data can be viewed by.
Model.processed_training_data.head
print model.get_feature_importance()
+-------------------------+-------------------------------+-------+
| name | index | count |
+-------------------------+-------------------------------+-------+
| Quantity||features||7 | user_timesinceseen | 62 |
| Quantity||features||90 | sum||sum | 24 |
| __internal__count||90 | count||sum | 20 |
| Quantity||features||60 | sum||sum | 15 |
| Quantity||features||90 | sum||ratio | 14 |
| Quantity||features||7 | sum||sum | 13 |
| UnitPrice||features||90 | sum||sum | 12 |
| UnitPrice||features||60 | sum||sum | 12 |
| Quantity||features||90 | sum||slope | 11 |
| Quantity||features||90 | sum||firstinteraction_time... | 11 |
+-------------------------+-------------------------------+-------+
+-------------------------------+
| description |
+-------------------------------+
| Days since most recent event |
| Sum of "Quantity" in the l... |
| Events in the last 90 days |
| Sum of "Quantity" in the l... |
| Average of "Quantity" in t... |
| Sum of "Quantity" in the l... |
| Sum of "UnitPrice" in the ... |
| Sum of "UnitPrice" in the ... |
| 90 day trend in the number... |
| Days since the first event... |
+-------------------------------+
[150 rows x 4 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
The last and final part in the exercise is to predict which customers might churn. This is done on the validation data set.
predictions = model.predict(valid, time_boundary= evaluation_time)
predictions.head
The values given in the 2nd column are the probability that a user will have no activity in the churn period that we defined earlier (30 days), hence the probability for the customer to churn. You can obtain the prediction for the first 500 customers by using
predictions.print_rows(num_rows = 10000)
You can adjust the number of predictions to be displayed using
num_rows
Conclusion
Now that we have discussed a way to calculate churn with unlabelled data, it’s your turn to use the methods discussed to experiment with the GraphLab package.
And if you enjoyed this demonstration, consider enrolling in our course on Python for Data Science over on LinkedIn Learning.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## UX in Marketing: Why User Experience Is Key to Product Marketing Success
URL: https://www.data-mania.com/blog/ux-in-marketing/
Type: post
Modified: 2026-03-17
When you think of user experience (UX), you generally do not think of product marketing. While – on the surface – they may appear to be two separate domains, but upon closer analysis UX plays an integral part of successful product marketing. In this blog, we’ll explore the dynamic of UX in marketing, and how you can leverage analytics to capitalize on product growth from the synergies between personalization and UX in marketing.
The term “user experience” (UX) is often associated with improving adaptability across different devices. However, UX encompasses much more than that.
It is a seamless integration of design, content, and strategy that evokes an emotional response in users while catering to their preferences and fulfilling their needs.
In this article, we will delve deeper into user experience and discuss its crucial role in achieving product marketing success.
In addition to looking good and working well, we’ll explore how a user-centric approach to design can influence – not just visitor interaction – but can also foster lasting brand loyalty.
The Vital Role of User Experience in Product Marketing Success
Picture yourself walking into a physical store with poorly organized products, harsh lighting, and confusing aisle layouts. It’s highly likely that you would get frustrated and leave in no time, vowing never to return.
The same principle holds true in the digital world. A website that is poorly designed, with confusing navigation, slow loading speeds, and inconsistent branding, can quickly discourage visitors and increase bounce rates.
User experience encompasses the entire online journey a user takes when interacting with your brand. It includes the first impression they get upon landing on your website, as well as how easily they can navigate and find information, make a purchase, or engage with your content.
UX is all about understanding and meeting the needs, wants, and challenges of users. It involves creating a positive emotional connection with users while addressing their preferences and pain points.
Source: Unsplash
The Impact of UX in Marketing Effectiveness
User experience is vital in influencing customer behavior and improving conversion rates in product marketing. When users have a positive interaction with your website or digital materials, they tend to stay longer, explore further, and ultimately take desired actions like making a purchase or subscribing to a newsletter.
Here’s where UX in marketing fits in: A well-designed interface that is easy for users to navigate and understand increases the likelihood of achieving their desired goals. By implementing clear calls-to-action, responsive design, and smooth navigation, you can minimize obstacles in the user journey, ultimately leading to higher conversion rates.
Conversely, a complex or confusing interface can frustrate users and cause them to abandon your website or platform, thus undermining your marketing efforts.
By focusing on providing a seamless and enjoyable experience, product marketers can effectively reduce bounce rates and increase the likelihood of users completing desired actions. This, in turn, contributes to the overall success of product marketing campaigns and strategies.
The Synergy Between UX in Marketing and Personalization in Product Marketing
Personalization has become a powerful tool in captivating audience attention and fostering engagement. The link between UX in marketing and personalization is intricate, as both strive to customize interactions according to individual preferences and needs.
Through the use of advanced analytics and tracking user behavior, product marketers can gain valuable insights into user preferences, browsing history, and demographic information. This data then enables them to create personalized experiences, such as recommending relevant products or curating content based on individual interests.
A seamless user experience, paired with personalized features, cultivates a profound sense of relevance and connection. This fosters understanding and appreciation in users, resulting in heightened engagement and a profound emotional bond between individuals and brands.
→ RELATED POST: Leveraging Content Marketing for Startup Growth: What Every New Founder Needs to Know (Incl. Tech Startup Marketing Budget Details)
Measuring Impact of UX in Marketing: The Intersection of Analytics and Product Marketing Strategy
In the realm of product marketing, measuring performance is crucial for quantifying campaign effectiveness and refining strategies. The utilization of user experience metrics offers invaluable insights into the triumph of product marketing endeavors.
Tracking metrics such as time on page, click-through rates, conversion rates, and bounce rates on your website will provide a thorough understanding of how users engage with your digital assets. By carefully monitoring these metrics, product marketers can identify areas that require enhancement, pinpoint obstacles in the user journey, and adjust their strategies accordingly.
The convergences between UX in marketing are mutually beneficial. While good UX improves marketing outcomes, insights gathered from data-driven product marketing can inform and guide refinements to UX design.
By continually iterating and refining the user experience, product marketers can ensure that they stays in line with the changing needs and expectations of their audience. This approach ultimately leads to greater success in their marketing endeavors.
Interesting UX Statistics
In the high-stakes game of product marketing, UX isn’t just the ace up your sleeve—it’s the entire deck. Dive into these stats and witness the UX effect on modern marketing.
More than 53% of users will abandon a webpage despite its relevance to their search if it takes a while to load.
After a poor user experience, 88% of users are less inclined to return.
More than 92% of the entire population of internet users access the internet through a mobile phone. Or, only one out of 10 people uses a desktop device to access the Internet and social media.
Mobile users make up more than 60% of eCommerce sales globally. The revenue gathered from these users reached $2.2 trillion in 2023!
Only 55% of businesses conduct user experience testing.
Wrapping Up
User experience is a fundamental aspect of effective product marketing strategies. It goes beyond just the visual appeal and ease of navigation. It encompasses the skillful creation of seamless interactions that evoke emotional connection and cater to users’ preferences and needs.
The user’s first impression upon entering your website sets the tone for their entire experience. From there, the art of personalized and data-driven optimization guides their journey, shaping their behavior and ultimately driving conversion rates.To truly harness the transformative power of UX in your product marketing endeavors, consider partnering with industry experts who excel in crafting captivating user experiences. Digital Silk, a leading agency specializing in corporate web design services, is primed to elevate your digital presence to new heights.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## 100 Days Of Generative AI Challenge – A Free Generative AI Learning Path & Community
URL: https://www.data-mania.com/blog/generative-ai-learning-path/
Type: post
Modified: 2026-03-17
Generative AI frameworks, APIs, models, and tools are being released faster than even the most astute data professional can keep up. After some discussion with members inside our private substack community, we’ve decided to host a 100 Days Of Generative AI challenge. With this free collaborative challenge, we’re providing a generative AI learning path for you to use to get up-to-speed on AI engineering, prompt engineering, and generative AI, in general.
To be clear, this challenge is meant to be completely collaborative. We are here to support and encourage one another in the process of getting upskilled and functional as builders of generative AI applications. You are welcome and encouraged to join, participate, and contribute in any way that you can.
I’m Lillian Pierson, the founder of the Data-Mania blog here – and I’m building this curriculum plan out of the very limited amount of time I have to research generative AI requirements and resources. There are gaps in the generative AI learning path provided below. That’s because I simply haven’t had time to conduct deep research in those areas. If you know good resources to fill those gaps, or any gaps that you identify in the generative AI learning path below, then please leave your suggestions in the comments and I will go back and add those to the list later.
Caveats On The Generative AI Learning Path Shared Below…
Two things to note here:
I am a data product manager, so I sourced these requirements from LinkedIn job listings for both AI engineers and data product managers. Some of the recommendations are from more traditional data requirements (ie; SQL, Tableau, and Python), nonetheless – those are still base requirements for effectively managing generative AI products, so I have included them.
AI engineering is quickly bifurcating into 2 branches. ML engineering (this is not new) and AI engineering (this is an emerging sub-discipline). To keep the time-to-value low here, within this generative AI learning path, I’m only making recommendations for the AI engineering route. Please read this post if you want more information regarding the differences between these sub-disciplines.
One last thing you need to know about the generative AI learning path shared below… It’s under development. I’ll be updating it on a regular basis as we all work together in the challenge to support each other’s professional development and growth. Please check back often for changes.
You’re Invited To Join Our 100 Days Of Generative AI Challenge (optional)
The mission of the 100 Days Of Generative AI Challenge is simple: To create a robust, supportive, and collaborative community that’s dedicated to supporting fellow data professionals in the learning and building of generative AI products and features.
Taking the courses that are prescribed in the generative AI learning path below is a great start, but there’s much to be said for accountability and networking support that’s only available inside of communities like the one we’ve set up for this challenge.
The community for this challenge will be hosted over on LinkedIn. Guidelines for sharing your work and supporting others will be provided within that group itself. If you would like to join this 100 Days Of Generative AI Challenge, please join our substack below and you will be automatically emailed with information on how to join the free LinkedIn community. (if you’re already part of our substack community, the details for joining this group were already sent to you in a past email titled: Join The Free 100 Days Of Generative AI Challenge)
And without further ado, let’s take a look at the generative AI learning path recommendations [LAST UPDATED Sept 8, 2023]
The Free Generative AI Learning Path
Be sure to start by reading the following articles:
The Rise of the AI Engineer
The New Language Model Stack
After you’ve read those and developed a broad understanding of the requirements space for generative AI, next it’s time to start working through the generative AI learning path recommendations. Here are the recommendations I’ve come up with so far from my networking and independent research efforts.
Learning Path Legend
Each course recommendation is marked with:
A time estimate – estimated hours until completion, and
A color token to indicate the difficulty of technical pre-requisites.
Pre-Requisite Color Token Legend
🟢 = BEGINNER – Basic Python Only (if that…)
🟡 = INTERMEDIATE
REQUIREMENT: LLM APIs (Base Essentials)
LLM Foundation Model APIs:
OpenAI
Anthropic
Cohere
Learning
ChatGPT Prompt Engineering for Developers (OpenAI) (5 hours) 🟢
Building
Large Language Models with Semantic Search (Cohere) (8 hours) 🟢
REQUIREMENT: LLM API Frameworks (Base Essentials)
LLM API Frameworks:
Langchain
LlamaIndex
Learning
LangChain for LLM Application Development (5 Hours) 🟢
Building
LangChain: Chat with Your Data (8 Hours) 🟢
Example Project: A Hands-on Journey to a working LangChain LLM Application 🟡
REQUIREMENT: A grasp of AI, Large Language Models (LLMs), and prompt engineering, including Chain-of-Thought (CoT) prompting and Self-Consistency in CoT (Base Essentials)
Learning
Generative AI with Large Language Models (AWS – 16 hours) 🟢 – This course is taught by my friends, Chris Fregly and Antje Barth.
Building
Building Systems with the ChatGPT API (5 hours) 🟢
REQUIREMENT: Implementing Generative AI Solutions / Builds AI prototypes (Base Essentials)
Learning
Generative AI with Large Language Models (AWS – 16 hours) 🟢 – This course is taught by my friends, Chris Fregly and Antje Barth.
Building
How Business Thinkers Can Start Building AI Plugins With Semantic Kernel (5 hours) 🟢
Building Generative AI Applications with Gradio (8 hours) 🟢
AI For Good Specialization (? hours) 🟢
Hugging Face NLP Course (64 hours) 🟡
REQUIREMENT: Python For Data Science / Jupyter Notebooks
Learning
ChatGPT Prompt Engineering for Developers (OpenAI) (5 hours) 🟢
LangChain for LLM Application Development (5 Hours) 🟢
Building
Building Systems with the ChatGPT API (5 hours) 🟢
Building Generative AI Applications with Gradio (8 hours) 🟢
LangChain: Chat with Your Data (8 Hours) 🟢
REQUIREMENT: Tableau
Learning
Tableau 2022 A-Z: Hands-On Tableau Training for Data Science (9 hours) 🟢 – This course is by my friend, Kirill Eremenko.
Building
😬 This is a resource gap – please leave a comment with a suggestion if you have a recommendation for a good resource that you have used yourself and that we can use to fill this gap.
REQUIREMENT: SQL (with certification)
Learning
SQL for Data Science (14 hours) 🟢 – This course is by my friend, Sadie Lawrence.
Building
😬 This is a resource gap – please leave a comment with a suggestion if you have a recommendation for a good resource that you have used yourself and that we can use to fill this gap.
REQUIREMENT: AWS and Microsoft Azure Cloud + ETL Pipelines To Support Generative AI Products
Including AWS Glue (Cloud ETL)
Learning
AWS Cloud Skill Support: skillbuilder.aws 🟢
AWS Innovate Online Conference – AWS provides plenty of free training and support on this topic within these regular conference events.
Building
AWS Skill Builder Challenges 🟢
A Tour of Google Cloud Hands-on Labs 🟢
Additional resources
AI Safety Newsletter
What would you add to this generative AI learning path?
You can help a lot of people by making suggestions and providing feedback on the generative AI learning path that I’ve shared here. Please share your tips on how we can improve it by submitting a comment on this blog post.
And again, if you want to participate with us in the free accountability community then please drop your dets in the form below and you’ll get an email with the details you need to join.
I hope to see you in there with us!
Warmly,
Lillian Pierson
ABOUT ME:
I am Lillian Pierson. I have 18 years of experience launching and developing technology products and delivering strategic consulting services. Additionally, I’ve also managed the development and launch of dozens of e-learning products; Products that educate learners on how to apply data science, data strategy, and business strategy to increase profits for their companies. To date, the products I’ve managed have been consumed by ~2 million learners and have generated over $6M in revenue for my clients.
I have launched over 40 products globally, delivered in 4 different languages. My products & go-to-market strategies have supported organizations as large as Walmart, Amazon, Microsoft, Dell & the US Navy. In fact, over the last 10 years I’ve supported 10% of Fortune 100 companies. Industries I’ve supported include Software as a Service, education, ecommerce, media, technology consulting, government services, finance, environmental consulting, oil & gas, and banking.
Besides my extensive business background, I’m also an accomplished data scientist & engineer, having held licensure as a Professional Engineer since 2014.
Lillian Pierson: CV
Lillian Pierson: Product Portfolio
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## 5 Surprisingly Simple Strategies For How To Manage IT in a Hybrid Work Environment
URL: https://www.data-mania.com/blog/how-to-manage-it/
Type: post
Modified: 2026-03-17
Now, more than ever, it’s important to know how to manage IT in a hybrid work environment. Changes in modern working environments have had a dramatic impact on how technology is managed within organisations.
Consider this; five, even 10 years ago, your operations may have consisted of desktops within an office environment that was centrally managed by an information technology team. However, due to the impacts of major global events such as pandemics, war, or even climate impacts, you’ve had to transform your business approach. Now, instead of a series of desktops in a workplace, you’re dealing with clusters of laptops that are often working in remote environments that are not necessarily familiar to the network administrator.
If you’re undertaking a Master of IT Management or a similar qualification that teaches how to manage IT, you may be considering how businesses manage IT infrastructure in these newly emerging hybrid environments. Let’s explore how technology is critical for the success of a business, and how an organisation can adopt some simple strategies to really transform their business approach so that they are prepared for the issues of tomorrow, today.
Technology is Critical for Business Success
It’s hard to believe now, but 20 years ago, companies like eBay and Amazon were relatively small. At the time online shopping and eCommerce were in their infancy – the ideas of bright young developers that hadn’t fully developed. However, as time has gone on, the Internet has become a fierce battleground for online sales and web shopping.
In recent years, retail eCommerce sales have topped 6 trillion US dollars annually, according to a recent study by Statista. No matter whether your business is an online retailer, a services provider, or you simply manage file transfers, knowing how to manage IT effectively has become critical for the success of your business. Gone are the vast majority of businesses managed by paper and books. The modern enterprise has gone digital – they are supported by software suites, enabling collaboration, conferencing, and improved business efficiencies.
Technology is no doubt going to be critical in the years ahead. As the world races to adapt to emerging cyber concerns and a changing environment, understanding how to manage IT, and how technology may change, is a great way to prepare yourself and your organisation for the challenges ahead.
Simple Strategies For How To Manage IT In A Hybrid Work Environment
This article is a simple primer, but the following strategies are readily available to help you get started with planning how to manage IT for your distributed company.
A Hybrid Environment Can Present Challenges
No matter whether your business is on-site all the time or works remotely, a hybrid operating environment can provide unique challenges for IT infrastructure teams. Managing system security, data loss, and document management, amongst others, can be a challenging experience for many IT managers.
Strategy 1: Considerations of how you manage company app deployment and download strategy can be vital in managing and mitigating the cyber risks that may present to your organisation.
Consider the importance of a centralised app directory for businesses. Rather than allowing your employees to download any app they want, presenting a security risk, having a set of secure restricted applications can enable employees to do their job without increasing risk. Considerations of how you manage company app deployment and download strategy can be vital in managing and mitigating the cyber risks that may present to your organisation.
Enforcing a Cyber-Secure Mindset
Something else to really consider as a business that’s grappling with how to manage IT in a hybrid work environment: The enforcement of a cyber-secure mindset…
Strategy 2: Encourage your employees to question strange and suspicious emails.
Encourage your employees to question strange and suspicious emails. After all, with the relative rate of scam emails and SMS rising dramatically in recent years, having employees who are prepared for the eventual phishing attack is a great way to prepare your organisation for the future.
Additional strategies that should be considered include:
Strategy 3: The regular enforcement of password refreshes and
Strategy 4: Encouraging company employees to take on future learning opportunities such as enhanced cyber training.
Strategy 5: Running an organization-wide suite of practical tests to test things like cyber awareness to help patch holes at a human level.
We have all seen in recent years the impact of poor cyber mindsets at work. These have included the impacts of the leaks of more than 10 million customer records at Optus and Medibank respectively.
These attacks have a dramatic effect on customers; people are suddenly wondering whether they’re at risk of fraud or theft, which highlights just how important it is for modern businesses to consider the needs of their customers within their cybersecurity approach within they make plans for how to manage IT in a hybrid work environment.
Where Will the Future Take Hybrid Work?
As work transforms from a solely in-person experience, understanding what changes will impact organisations, as well as their customers is critical and also timely. Consider what has changed within the organisation that you work in.
Perhaps it is something as simple as a reporting strategy, maybe it’s something more complex, such as the devices that you use to connect and communicate with your fellow employees. There’s no doubt that hybrid work will be transforming the digital landscape in the years to come including the ways that employees and employers interact.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Voice Cloning Free Demo: Learn To Build Your Own Cloning Tool In Just 1 Hour
URL: https://www.data-mania.com/blog/voice-cloning-free-demo/
Type: post
Modified: 2026-03-17
Looking to learn how to do voice cloning free of charge? You’ve come to the right place.
It’s been a few short weeks since OpenAI released its new interactive audio-visual capabilities, and guess what – Spotify and OpenAI have already teamed up to pilot a voice translation service that uses generative AI to automatically translate podcasts into alternative languages, all without modifying the speakers voice in a noticeable manner. (1)
Even as an AI industry veteran, I have to say that the breakneck speed of new innovation is absolutely mind-blowing.
We’ve entered the age of voice-cloning
With OpenAI’s voice cloning capabilities the possibilities are endless (including getting to do voice cloning free of charge – well, practically free!). For example, here are just a few use cases that are in the works:
Disability Support: Giving a voice to individuals who have lost the ability to speak, enabling them to communicate in their original voice.
Entertainment: Dubbing movies or TV shows in various languages using the original actor’s voice.
Disability Support: Generative AI screen readers produce lifelike voices, enhancing the digital experience for the visually impaired.
Post-Production Editing: Fixing errors in live recordings or adding content in post-production without needing the original speaker to re-record. (I do this using Descript.ai, by the way)
The sky is the limit when it comes to new ways we can improve people’s lives while increasing business’s bottom-line with voice cloning.
The sky is the limit when it comes to new ways we can improve people’s lives while increasing business’s bottom-line with voice cloning.
That said, there’s a serious need for stringent ethical guidelines and safety mechanisms to prevent abuse and misuse of voice cloning capabilities.
Worried about safety?
Fear not! While generative AI startups like OpenAI and LangChain are on a race to develop and release as many mind-boggling generative AI capabilities as they possibly can, other genAI startups are on a race to construct guardrails to keep society safe from malicious misuse.
Case in point, Resemble.ai.
Resemble.ai has already successfully built an audio watermarking application that’s useful for verifying AI-generated audio without causing any distortion within the audio itself.
Here’s the basic physics behind their solution:
(Source: Resemble.AI)
Resemble.ai is already using their technology to safely build digital characters, smart assistance, speech localizers, and hyper-realistic voice clones that translate between up to 100 language translations (with custom dialect support!).
Love it or loathe it, the age of voice cloning is upon us.
👀 And if you’re a data professional who’s not been engaged in rapid upskilling recently, now is the time to catch up…
Voice cloning free demo: How to Build Generative Voice Clone Applications with OpenAI
Step into the new era of Generative AI, where voice isn’t just heard — it’s tailored, cloned, and brought to life!
We’re no longer just speaking to the future; we’re scripting it.
As OpenAI’s groundbreaking models mingle with cutting-edge voice tools, we’re no longer just speaking to the future; we’re scripting it.
** This voice cloning free training was delivered live and is now available here on-demand **
If you’re ready to be blown away by the marvel of generative voice clone applications, then grab your front-row seat at this electrifying demonstration.
In just 1 short hour, our technology and product experts will present a hands-on demo showing you how to build with OpenAI’s generative models that are harmoniously intertwined with voice processing tools.
What you’ll get:
OpenAI 101: Acquaint yourself with the nuances of OpenAI’s generative models, the powerhouse behind the most realistic voice clones today.
A Voice Cloning Deep Dive: Delve into the science of voice cloning, its potential applications, ethical considerations, and its role in today’s digital landscape.
A Generative Voice Cloning Free Demo: Experience a real-time demonstration as presenters create a voice clone, tweak its attributes, and integrate it into a functional application, all powered by OpenAI and SingleStoreDB.
Sign-up here anytime and get on-demand demonstration access.
🦄 To SingleStore: Massive thank you for sponsoring this training & post.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## No Code AI Tools: Build No-Code GenAI Apps Using AWS Bedrock
URL: https://www.data-mania.com/blog/no-code-ai-tools-build-genai-in-flowise/
Type: post
Modified: 2026-03-17
No code AI tools are hard to come by, especially if you’re looking to build generative AI applications. That’s why I’m so excited to be able to provide this free training where you’ll learn to build genAI apps on one of the best no code AI tools around – Flowise!
If you’re involved in GenAI development, the opportunities are staggering! But, for most traditional data professionals, there’s a significant challenge.
The Scope Of The Challenge
If you want to start building generative AI applications from scratch, you’re going to need to overcome some major hurdles, like:
The Skills Barrier: It can take roughly 4 years to cultivate the expertise needed to become a proficient deep learning engineer. Out of the numerous deep learning experts in the field, only 5,000 people have the prowess to build and train LLMs from the ground up. (1)
🤯 Training GPT-3 solely on an NVIDIA Tesla V100 GPU could take a whopping 288 years!
The Technical Constraints: As an example, training GPT-3 solely on an NVIDIA Tesla V100 GPU could take a whopping 288 years (2). Though spreading the computation over multiple GPUs can speed this up, the financial cost becomes a significant barrier.
The Economic Implications (3): To put it in perspective, training Meta’s Llama 2 with 7b and 70b parameters, respectively, required vast GPU hours. In monetary terms, the training cost is around:
~10b parameters: Approximately $150,000
~100b parameters: Around $1,500,000.
In short, to build a generative AI application from scratch, it’s not just the 4 years of learning you’ll need to do, but there’s also the significant time and financial investments you’ll need.
Looking For A Simpler Route?
However, there’s an easier path forward. Foundation models, such as those by OpenAI, Cohere, and PaLM, provide APIs that even someone with moderate Python skills can start building with.
These models come pre-trained, eliminating the enormous costs and expertise previously mentioned. It’s practically a plug-and-play method for generative AI development.
The Most Direct Path Of Them All: No Code AI Tools!
If you’re seeking an even more streamlined approach, enter the world of no code AI tools!
Why No-Code?
No-code platforms are revolutionizing the landscape. Even without in-depth coding or deep learning knowledge, these platforms provide an easy-to-use interface to build generative AI applications.
With respect to generative AI, no code AI tools are all about making advanced AI technologies accessible and practical for everyone.
⏰ Don’t Miss This Free Training: Building a NoCode AWS Bedrock LLM App on Flowise
🔹 This show-stopping new training is your golden ticket to step into the future of No-Code generative AI app development.
** This no code AI tool training was delivered live and is now available here on-demand **
🌐 Register Now!
Topic: How to Build a NoCode AWS Bedrock LLM App on Flowise
Date / Time: On-Demand Replay
Location: Sign-up here
Give us just 60 short minutes and you’ll witness and learn the intricacies of building NoCode AWS Bedrock LLM Apps on Flowise.
What you’ll get when you attend:
Witness cutting-edge no-code AI tools & integrations: Discover how the marriage between AWS Bedrock, Flowise AI, and SingleStoreDB is laying the groundwork for future of dynamic LLM applications.
Get hands-on training: Michael Connor, the eminent head of AWS Consumer Package Goods, will be delivering a demo that was live-recorded. Experience for yourself the magic of building LLM apps without any coding requirements! – all streamlined on SingleStoreDB.
Grow your data expertise: See the power of vector databases, learn the capabilities of AWS Bedrock, and get acquainted with Flowise AI’s intuitive drag & drop tool. Elevate your skills and stay ahead in the AI landscape.
Times ‘a ticking! ⏰
The clock is ticking before we take this free training down (or put it behind a paywall!). This is your chance to witness the next big thing in AI app development. Ensure you’re at the forefront of this technological revolution.
🌐 Register Now!
Don’t let this opportunity slip through your fingers. Get on board, learn from the experts, and embrace the future, today.
Enjoy!
Lillian Pierson, PE
Shop | Blog | LinkedIn
PS. Don’t miss this other free training we did showing how to build a voice cloning app using OpenAI.
Disclaimer: This blog post may include sponsored content or affiliate links and I may possibly earn a small commission if you purchase something after clicking the link. Thank you for supporting small business ♥️.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## WordPress Sales Funnel Conversion Rate Tracking As Easy As 1-2-3
URL: https://www.data-mania.com/blog/conversion-rate-tracking/
Type: post
Modified: 2026-03-17
I’ve got a story for you today… It’s time we connect the dots between your demanding data analysis tasks and your existing skillsets. But don’t worry, it won’t require you to break a sweat, and you might just learn a thing or two about conversion rate tracking and SLO funnels along the way.
Today I want to talk about a novel way I’ve discovered to unlock the power of Excel, SQL, NoSQL Python, and JavaScript, all right within your spreadsheet – it’s going to make your life SO much easier!
The day has come where you finally get the chance to say 👋 “see ya” to the daily data integration grind and…
Instead, get your groove on with a game-changing, new analytical workflow that’s certain to save you heaps of precious time.
I’m talking about a spreadsheet called Equals.
Last week I had the chance to have a quick coffee chat with Bobby Pinero, Cofounder and CEO of Equals. What he was telling me about his spreadsheet product was nothing short of mind-boggling, so I had to go check it out for myself…
You won’t believe what I discovered!
✨ Imagine connecting a spreadsheet directly with the data that your company has sitting in RedShift, Snowflake, MySQL, NoSQL, Stripe, QuickBooks or just about any other business system (or API) you can think of – and then being able to analyze that data using handy-dandy Excel formulas, SQL statements, Python or JS, directly within the spreadsheet interface!
✨ Imagine being able to use your existing Excel skills to build and publish real-time updating reports for stakeholders… or being able to set-it and forget-it, by automating this publishing workflow so all your daily / weekly / monthly reporting requirements go on autopilot while you prop up your heels and sip your latte. 🥤
✨Imagine dramatically decreasing the time and hassle involved in communicating the finding of your analyses by using an easy-peasy 1-click publishing workflow to send them directly to Slack, Google Slides or email!
That ^^ is the power of Equals!
It’s a spreadsheet that could do all the above, plus SO much more…
The truth is, though, my heart sunk when I saw how easy it is to integrate disparate data sources and generate real-time reporting inside Equals…
😱 Did I ever tell you about my conversion rate tracking debacle?
It got me thinking back to when I really needed a product like Equals and had to scrape by with manual reporting and guess-timation as my only option.
In 2019, I launched my very first self-liquidating offer (SLO) funnel. SLO funnels focus on audience-building, driving numerous customers through the sale of low-cost, high-value products.
The term “self-liquidating” comes from the idea that when you run ads to the funnel and the offer aligns perfectly with the needs of visitors driven by your traffic source, while the funnel pages are finely tuned for conversions, the revenue and leads generated cover the full cost of customer acquisition. In fact, you can even make a small profit, hence the “self-liquidating” label.
However, for a SLO funnel to succeed, you must achieve:
A 10% conversion rate for warm traffic
A 2.5% conversion rate for cold traffic (from ads)
Without reliable conversion rate tracking & reporting, scaling your funnel becomes a challenge, as you can’t confidently determine if the costs justify the returns.
Without reliable conversion rate tracking & reporting, scaling your funnel becomes a challenge, as you can’t confidently determine if the costs justify the returns.
Since I had built my funnel on WordPress using WooCommerce, I lacked proper conversion rate reporting for the funnel pages. Naturally, I turned to Google Analytics and Looker Studio in an attempt to obtain the necessary data.
Unfortunately, the presence of complex UTM parameters in my referral links made it a real headache to get reliable reporting.
I shelled out nearly $500 on a pre-built conversion-tracking dashboard, along with the setup services needed to get it running. It ended up being a colossal waste of both time and money, as the connections quickly fell apart, and I couldn’t trust the data.
So, I decided to dive into Google Analytics and attempt to build a manual tracking system myself. To be honest, it wasn’t much more helpful.
The conversion rate tracking aspect of this funnel posed one of the most significant challenges in bringing the entire product suite to market. It was a major downer. 😢
If only I had known about Equals back then…
If only I had known about Equals back then, I could have effortlessly solved this conversion rate tracking problem, as easy as 1-2-3:
Connect my accounts in Google Analytics and Google Ads, Twitter Ads, or LinkedIn Ads (I’d track ad sources separately, of course).
Create a spreadsheet that automatically updates with real-time data on:
Unique Page Views (#)
New Orders (#)
Conversion Rate, per page (%)
Average Order Value ($)
Once I nailed down my conversion rate, I could simply duplicate this page and break down the calculations by advertising platforms (e.g., Google, Twitter, and LinkedIn – this way, I could verify the cost of customer acquisition independently, regardless of what the ads management platforms were telling me 😉.)
Alas, hindsight is 20-20. Equals even has a built-in template for this exact requirement!
I could have just plugged in and been on my way in a matter of minutes rather than struggling for weeks…
But Equals is much more than just a conversion rate tracking and marketing analytics tool
Within Equals, data professionals can look forward to seamless data analysis using their favorite toolset, whether that be through Equals’ modern SQL editor or by scripting directly in Python, all within an environment that boasts effortless reporting and stakeholder management support.
And if you’re a business analyst, you can look forward to saving up to one week of every month by getting direct access to live updating spreadsheets and these automated reporting capabilities.
I kid you not, look at what Barry O’Mahony has to say:
Honestly, I’m floored by what they’ve built over at Equals, and I’d love to tell you more about it but I’d need more time… I promise to pick back up on this next week.
And in the meantime, I definitely encourage you to go take a free test drive of Equals – the only spreadsheet known to man that has built-in connections to any database, versioning, and collaboration support.
❤️ Proudly produced in partnership with Equals! ❤️
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Vector Embedding Example: Free Training On How-To Build LLM Apps
URL: https://www.data-mania.com/blog/vector-embedding-example/
Type: post
Modified: 2026-03-17
If you’re browsing the web looking for a powerful vector embedding example to help navigate you in your quest to build an LLM application, then you’re in the right place.
Within this blog post you’re going to get:
An introduction to what vector embeddings is,
A heads-up on cool new startup that’s using them to transform high-tech businesses all over the world, and
An invite on where you can go to get trained, quickly and for free!
In the ever-evolving technology and innovation landscape, few names resonate quite like Y Combinator. In the summer of 2023, the esteemed startup accelerator bore witness to a remarkable batch of Seattle-based startups.
Among them, Neum AI emerged as a prominent player, perfectly poised to revolutionize the data and AI technology landscape.
Founded in the vibrant city of Seattle, Neum AI carries a clear mission: to empower companies in maintaining the relevance of their AI applications by providing real-time updates and unwavering accuracy.
But what exactly fuels this mission? Let’s take a deeper look at the groundbreaking technology that underpins Neum AI’s vision — The Vector Embedding.
Unearthing the Power of AI Representation with Vector Embeddings
At the heart of Neum AI’s innovation lies the concept of Vector Embedding.
For those unfamiliar with this term, Vector Embedding is a fundamental technique in the world of AI and data analytics.
It enables the representation of complex data, such as words, phrases, or objects, in a format that machines can easily understand and work with.
Vector Embeddings Explained
Here’s where the magic happens: Vector Embedding assigns numerical vectors to data points in a high-dimensional space. These vectors capture the essence of the data, allowing AI algorithms to perform calculations and comparisons more efficiently.
In essence, Vector Embedding transforms abstract data into a tangible format that AI models can manipulate and analyze.
The Significance of Vector Embedding in AI Applications
Now, you might wonder, why is Vector Embedding such a game-changer in the realm of AI and data analytics?
Vector Embedding Example Use Cases Within AI Applications
Real-Time Insights: Vector Embedding facilitates real-time updates by enabling rapid data retrieval and processing. This means that AI applications can provide insights and recommendations instantaneously, keeping businesses ahead of the curve.
Enhanced Accuracy: With Vector Embedding, accuracy is paramount. It ensures that AI models can precisely capture the nuances of data, leading to more reliable predictions and decisions.
Efficiency at Scale: Vector Embedding simplifies the complexity of managing and updating large-scale data sets. This not only saves time but also reduces the associated costs, making AI applications more accessible to businesses of all sizes.
Neum AI: Shaping the Future of AI with Vector Embedding
Now that we’ve unveiled the power of Vector Embedding, it’s clear why Neum AI’s mission is so significant. They’re at the forefront of using this transformative technology to redefine how we approach AI applications in the real world.
Neum AI seamlessly integrates Vector Embedding into their platform, enabling data professionals, analysts, and software developers to harness the full potential of AI. By connecting data into vector databases and improving data pipelines through AI analysis, Neum AI ensures that businesses stay ahead in the rapidly evolving landscape of AI.
Free Vector Embedding Example Demo & How-To Training: Don’t Miss the Opportunity to Learn More!
Ready to dive deeper into the world of Vector Embedding , get a Vector Embedding example, and see its pivotal role in AI applications?
Join us for a free 1-hour training that’s set to transform your approach to AI.
This is a golden opportunity to stay ahead of the competition and unlock the full power of Vector Embedding in all your AI app-building endeavors.
🌐 Register Now!
** This vector embedding example how-to training was delivered live and is now available here on-demand **
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Equals spreadsheet dashboards: You won’t believe your eyes!
URL: https://www.data-mania.com/blog/equals-spreadsheet/
Type: post
Modified: 2026-03-17
Do you remember those late nights sifting through columns of data, getting lost in spreadsheets, and wishing there was an easier way to draw actionable insights? Or that time when you spent hours, maybe days, trying to set up a comprehensive dashboard for your business metrics, only to find it’s not as intuitive as you’d hoped? If only you’d known about Equals spreadsheet dashboards back then…
But look, we’ve all been there.
But today, I have some exciting news that could change the way you approach business intelligence forever.
Today, I have some exciting news that could change the way you approach business intelligence forever.
Introducing… Equals Spreadsheet Dashboards – “Business Intelligence, but Dead Easy.”
Imagine a tool that:
Offers instant auto-analyses of your dashboards and delivers up-to-date reports in a few clicks. 🤯
Allows you to effortlessly pull data from any source, with a built-in SQL Editor and Visual Builder.
Enables you to visualize findings with stunning charts that are updated automatically.
And what if you could schedule, compile, and deliver reports with impeccable timing? You can now!
Equals spreadsheet brings all this to the table and more. Its intuitive design and powerful features will transform the way you perform analyses and present findings.
There are a million reasons why you should care…
Here are just a few reasons that Equals’ Dashboards are a game-changer for data pros:
Simplicity: Equals promises the ease of creating a document. Yes, a dashboard as quick as drafting a doc.
Integration: Connect with numerous databases and 3rd-party tools without any hassle.
Real-time Analytics: With on-the-fly updates, make real-time decisions without waiting for manual data refreshes.
Effective Communication: Easily distribute your findings to teams or clients. Post directly to Slack channels, send emails, create slide decks, or even just share a link.
Trust & Validation: The platform is already being hailed as a game-changer by users. For example, Zeta’s co-founder, Kevin Hopkins, is already shouting from the streets about how it’s been transformative for their operations.
The raw power of Equals spreadsheet dashboards
We all need more efficient and intuitive ways to manage and analyze our data.
Equals spreadsheet Dashboards not only makes data analysis intuitive and easy, but it goes above and beyond all expectations by now offering a fresh perspective on business intelligence.
The days of feeling bogged down by data, overwhelmed and under-equipped can now become a thing of the past.
And instead enjoy streamlined, insightful data analysis and business intelligence reporting experiences — all by simply adding Equals Dashboards to your workflow.
Look, reading about it is one thing… but to truly grasp the potential and capability of Equals spreadsheet Dashboards, you’ve got to see it in action!
I strongly encourage you to head over to the Equals website and watch the product demo. Maybe even start a free trial and experience the power of Equals’ Dashboards yourself.
A revolution in business intelligence awaits you.
Produced in proud partnership with Equals! 🤍
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## The Future of AI Apps with React Native AI & Elegance SDK: A Free Training
URL: https://www.data-mania.com/blog/react-native-ai/
Type: post
Modified: 2026-03-17
With the tech world evolving at lightning speed, the demand for intuitive and smart applications has never been higher. Today, we’re pulling back the curtain on what goes into building AI-driven apps using React native AI and Elegance SDK.
Spoiler alert: I’ll also share a little something to get you started on this fascinating journey.
Building applications, especially with AI capabilities, can be daunting
The complex architectures, different programming languages, and libraries… and then ensuring it all comes together seamlessly… It’s A LOT! 😱
But what if I told you there’s a simpler, more efficient path to bringing your AI vision to life?
Before we get into specifics, let me lay out some foundations for you real quick…
Why React native AI for frontend development?
React isn’t just another JS library. It’s the epitome of building dynamic user interfaces.
Its component-based architecture ensures that the UI is efficient, as only the components that undergo changes are re-rendered.
The power of AI in modern application development
In today’s landscape, when you hear “AI”, it’d be easy to assume that someone is talking about OpenAI, Anthropic, Google Bard, or some other hot generative AI company / application… but not all AI applications are built of foundation models like that…
In the application development world, AI is deployed to help developers, product managers, and founders better understand user behavior, predict trends, and build a tailored experience.
For example, a music app predicting your favorite song, or e-commerce platforms suggesting products based on your browsing behavior…
Neither of these use cases are necessarily tied to generative AI models in anyway.
A brief intro to Elegance SDK
So, with respect to AI application development, the beauty of Elegance SDK is how elegantly it integrates AI into your apps.
Instead of writing hundreds of lines of code, it offers a suite of tools that can help you integrate AI models seamlessly, thus enabling you to focus more on the user experience and less on backend complexities.
By now, you might be wondering how all these elements come together. That’s exactly what I want to talk about next…
FREE TRAINING: How to Build a Full Stack AI App in React Native AI with Elegance SDK
Can you imagine building an app that not only offers cutting-edge features but also harnesses the power of AI, all without spending countless hours debugging and coding?
An app where the interface is sleek, user-friendly, and driven by React native AI…
An app that’s underlying engine is the potent Elegance SDK…
If this sounds like your wildest dream come true, then you’re in for a real treat today my friend!
If you’re eager to learn how to build a full stack AI application using React native AI and Elegance SDK, then I want to encourage you to sign up for this free training.
** This training was originally delivered live and is now available here on-demand **
Save Me A Space >>
Why is this training a game-changer for you?
Rapid development: The future belongs to those who can quickly iterate and adapt. By grasping the principles of rapid AI application development with React native AI and Elegance SDK, you’re not just learning to code; you’re learning to innovate faster.
Interactive learning: The “Books Chat” demo that we’ll be sharing isn’t just another passive tutorial. It’s a hands-on experience where you interact with data stored in SingleStoreDB, bringing theoretical knowledge into real-world application.
Collaborative code sharing: The code-sharing session is more than a simple walkthrough. It’s a collaborative space where you can dive deep into the code’s intricacies, ask questions, and get insights that’ll position you well ahead of many developers in the AI space.
I want you to visualize this – after the training, you sit down to build your AI app.
Instead of being bogged down by doubts and endless Google searches, you confidently navigate through the development process.
Your app interacts intelligently, processes data efficiently, and offers a user experience that’s nothing short of exceptional.
And when you showcase this project, whether in your portfolio or a business pitch, it speaks volumes about your prowess and vision!!
And when you showcase this project, whether in your portfolio or a business pitch, it speaks volumes about your prowess and vision!!
This isn’t just a training; it’s a transformative experience.
So, if you’re excited about taking a leap into the world of advanced AI application development and doing it the RIGHT way, this is your moment.
Join us for a riveting 1-hour training session.
👉 Click here to secure your spot now!
Remember, the AI revolution is not waiting. But with the right tools and knowledge, you can ride its wave to new career heights.
And if you like this type of training, consider checking out other free AI app development trainings we are offering here, here, and here.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## OpenAI & Kafka for IoT Streaming Data Analytics: Training + GitHub Tutorial
URL: https://www.data-mania.com/blog/openai-kafka-for-iot-streaming-analytics/
Type: post
Modified: 2026-03-17
Today I want to talk about getting in on the ground floor of any emerging technology, and how you can best position yourself for the type of extreme growth that naturally occurs when you’re able to show up at the right time, with the right skills, in the right industry. If you’ve already been using Kafka for IoT, that’s amazing! But today I’m going to take you one step further by providing you a stellar free training and GitHub tutorial on OpenAI & Kafka for IoT streaming data analytics!
I’m qualified to share about this topic because “getting in on the ground floor” of emerging technologies is one thing to which I can attribute much of my career success over the last decade or so…
Back in 2012, I happened to discover the “big data” trend, just before Drew Conway came out with the whole “data science is the sexiest job” thing.
At that time, I happened to also be working as an employee… building and analytics MVP, leading a BI strategy, and coding in Python to uncover insights from disparate datasets…
In other words, the work I was doing then perfectly positioned me to add-value to the data science conversation that was happening across the country (in USA)… There was almost no supply, surging demand, and very very few people online who were leading conversations in the data science space. Honestly, it was a heyday.
Over the years though, the industry has matured. Companies began taking more interest in data engineering. Millions of people have graduated with college degrees in data science (they did not even offer those 10 years ago btw!). Lots of people have spent an entire decade mastering the nth detail of implementing machine learning and deep learning algorithms…
If you’re looking to get an edge in data science or data analytics right now… well, I am sorry, but – get in line. BUT don’t fret, when one door closes, another opens…
How does all this ^^ affect you? Well, if you’re looking to get an edge in data science or data analytics right now… well, I am sorry, but – get in line.
There is so much competition from so many very experienced people, you’ve really got to have a very strong leg to stand on if you’re going to manage to stand out in the crowd.
But don’t fret, when one door closes, another opens…
The good news is…
While the traditional data professions are flooded with very experienced people who can implement the de facto methods that have proliferated across industries over the last decade…
The same is not true for generative AI and IoT. (ok, IoT is not new, but it’s also not as saturated on the supply side 😉)
In fact, last week I asked data professionals across several platforms about where they thought the next generation of data jobs would lie.
Each community responded with about the same opinion:
Most data professionals believe that generative AI is the most important sub-niche within the data professions, and then data analytics, and lastly IoT.
On a side note: That perspective was quite interesting to me given the IoT market value growth statistics that I shared with you last week – namely:
But I digress, my point here is that tens of thousands of really smart data people all share the same opinion that people with skills in generative AI, data analytics, and IoT will be positioned well to support future development needs of the organizations they support!
When we’re talking about generative AI specifically, the DEMAND IS EXTREMELY HIGH, THE SUPPLY IS EXTREMELY LOW… and the barrier of entry is probably the lowest it will ever be.
When we’re talking about generative AI specifically, the DEMAND IS EXTREMELY HIGH, THE SUPPLY IS EXTREMELY LOW… and the barrier of entry is probably the lowest it will ever be.
Even as a rookie with just basic Python skills, you can use Foundation Model APIs to start building generative AI applications… and with that capability, you can expect a lot of demand, a decent paycheck and quick growth opportunities as you up-level your data skills and experience.
There is no one-size-fits-all path to “success” in your career. Your goals, personality, passions and priorities all play a big part in your perfect career path.
But, if you’re eager to get in on the ground floor of something huge, so that you can reach senior-level ranks before the other data pros see you coming, then I encourage you to start learning and BUILDING today.
Even better news, I have a 60-minute one-and-done free training for you today that will show you exactly how to start building-out with all three technology types: generative AI, IoT, and data analytics.
⏳ [FREE TRAINING REPLAY] Build With OpenAI & Kafka for IoT Streaming Data Analytics
In just 60-showstopping minutes, you’ll get hands-on learning and a demo on how to harness OpenAI to build a real-time streaming analytics IoT application.
** This how-to training was delivered live and is now available here on-demand **
Topic: OpenAI & Kafka for IoT Streaming Data Analytics
It really doesn’t matter in what capacity you’re working, for data professionals and developers like us, it’s imperative that we regularly participate in continuing technical education…
But legacy IoT trainings are all rather dated, and certainly are not educating learners on how to utilize latest LLM technologies to build real-time IoT applications!
The good news? If you’re a builder who’s eager to harness the power of OpenAI with Kafka for IoT analytics, this training session is both FAST and FREE! You really won’t want to miss it!
What You’ll Get:
👉 An introduction to the latest tools and technology for real-time streaming analytics and Generative AI LLMs
👉 Step-by-step guidance on building robust IoT analytics applications with OpenAI and Kafka for IoT.
👉 Access to valuable code snippets and best practices to kickstart your own IoT analytics projects.
This 1-hour training session will provide you theory, hands-on learning with a coding demo, and a free GitHub tutorial!
You’ll walk away with practical knowledge from this lovely technical app-building tutorial.
CLAIM YOUR SPOT NOW before they decide to take the training down for good.
Gimme That Training >>
Check out this video and Github tutorial to access this free training on quickly building with OpenAI & Kafka for IoT streaming data analytics.
And if you like this type of training, consider checking out other free AI app development trainings we are offering here, here, here, and here.
Disclaimer: This blog post may include sponsored content or affiliate links and I may possibly earn a small commission if you purchase something after clicking the link. Thank you for supporting small business ♥️.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## NVIDIA Jetbot free training: Build cutting-edge AI robots with OpenAI chat controllers
URL: https://www.data-mania.com/blog/nvidia-jetbot-tutorial/
Type: post
Modified: 2026-03-17
Have you ever imagined a world where robots are seamlessly integrated with human-like intelligence, responding not just to pre-programmed commands but also engaging in real-time interactions? If you’re a developer or data professional, or even just someone with a keen interest in cutting-edge technology, I have some exciting news for you! In today’s post you’re going to see all the latest that NVIDIA is up to in the world of robotics, and even get access to a free tutorial on how to use NVIDIA Jetbot with OpenAI chat controller to build an AI robot!
There’s a revolution brewing at the intersection of robotics and artificial intelligence
The giants, NVIDIA and OpenAI, are leading the charge, offering advancements that were once only the stuff of science fiction.
NVIDIA’s recent robotics automation advancements
Take a look at just some of the things that NVIDIA has been up to in the AI robotics world lately 🤯
NVIDIA’s AI factories
Imagine a powerhouse of innovation where data centers are actively being transformed into intelligent hubs in order to pave the way for futuristic applications.
This is not a drill – NVIDIA, in collaboration with Foxconn, has embarked on a journey to build in-real-life ‘AI factories’!
This is not a drill – NVIDIA, in collaboration with Foxconn, has embarked on a journey to build in-real-life ‘AI factories’!
These aren’t your plain old vanilla data centers either; They’re vibrant ecosystems that run off of NVIDIA’s cutting-edge chips and software. They’re designed for the sole purpose of propelling applications like self-driving cars and smart cities to new height of intelligence.
These “AI factories” are a nexus where data from autonomous electric vehicles can be collected and assimilated via custom AI applications, to refine the software and ensure that the entire AI fleet becomes more intelligent with each iteration.
NVIDIA AI agents
NVIDIA isn’t stopping there either, of course.
They’ve also taken the reins of robotic education by building an AI agent that has built-in capabilities for imparting complex skills to robots.
Picture a robotic hand, elegantly spinning a pen with a finesse that rivals human dexterity:
Video Source: NVIDIA blog
No, this ^^ is NOT a scene from a sci-fi movie; it’s a reality that’s currently being delivered by NVIDIA AI agents. Just imagine the new possibilities where robots can learn, adapt, and evolve autonomously (and even master the fine art of a complex skill, like twiddling your thumbs 😅)!
The NVIDIA generative AI adventure is just beginning
NVIDIA has broadened its horizons by infusing its robotics platform with Generative AI and LLMs.
This marriage of technologies produces the ability for robots to interpret human language prompts, and use them to tweak AI models, thereby facilitating a fluid interface where modifications are a simple conversation away.
This breakthrough is a giant leap towards making AI models more versatile in detecting, segmenting, and even reprogramming. It’s a blazing trail that heads straight towards more advanced robotic functionalities.
The Monumental potential of NVIDIA coupled with OpenAI
NVIDIA coupled with OpenAI represents a synergy between AI and robotics which transcends all conventional boundaries.
The marvels that await us are not confined to just labs and tech expos either; They are the harbingers of an automated era, redefining the way we perceive robotics and AI.
Intrigued? I thought you might be.
Free Training: How to Build an NVIDIA Jetbot AI Robot with OpenAI Chat Controller
We’re providing a special 1-hour training event just for enthusiasts like you: “How to Build an NVIDIA Jetbot AI Robot with OpenAI Chat Controller.“
Hosted by Ayush Pai from Georgia Tech and Akmal Chaudhri, the Senior Technical Evangelist at SingleStore, this session promises a deep dive into the future of robotics and AI.
** This NVIDIA Jetbot tutorial was delivered live and is now available here on-demand **
Here’s what’s on the agenda:
Learn about integration techniques: Understand the nuances of integrating OpenAI Chat technology with NVIDIA Jetbot, its cutting-edge robot hardware.
A hands-on demonstration: Experience the marvel of the Nvidia Jetbot, equipped with an OpenAI Chat controller. Witness the future in the present!
Tips and tricks for advanced customizations: Every project is unique. Learn tips and tricks to modify the robot’s capabilities to suit specific needs, making your AI robot truly one-of-a-kind.
Keep your thumb on the pulse of AI robotics: Stay ahead of the curve by gaining invaluable insights into upcoming trends and understanding how they’ll redefine the future of the industry.
Save Me A Seat >>
Just a few reasons that you can’t afford to miss this event:
Stay ahead in the industry: The tech industry evolves at a rapid pace. Today’s innovations become tomorrow’s basics. To remain relevant and competitive, continuous learning and adaptation are crucial.
Learn to solve real-world problems: With the advancements in AI and robotics, there’s potential to address real-world challenges in novel ways. From healthcare to entertainment, the applications are endless.
Unlock new career opportunities: The demand for professionals who can work with AI and robotics is higher than ever! This training could be your steppingstone to a thriving career in the domain.
Sign Me Up >>
Your future self will thank you for seizing this opportunity.
So, without further ado, SIGN UP NOW for the free NVIDIA Jetbot tutorial.
Join us and discover how to shape the future with the combined power of NVIDIA Jetbot and OpenAI.
And remember, as the famous saying goes, “The best way to predict the future is to create it.”
Opportunities like this don’t come often. 🚀 Grab your seat now, before we take it down! 🚀
And, if you like this training, don’t forget to share it with a friend!
Pro-tip: If you like this type of training, consider checking out other free AI app development trainings we are offering here, here, here, here, and here.
Disclaimer: This blog post may include sponsored content or affiliate links and I may possibly earn a small commission if you purchase something after clicking the link. Thank you for supporting small business ♥️.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## AI For Wealth Management: 10 Genius Tips For Supercharging Your Savings
URL: https://www.data-mania.com/blog/ai-for-wealth-management/
Type: post
Modified: 2026-03-17
In today’s fast-paced digital world, harnessing the power of AI has become the key to unlocking immense potential for your savings. Imagine a future where your money works for you tirelessly, growing and multiplying while you focus on your passions. This dream is now a reality with the rise of cutting-edge AI technologies. This article delves into ten actionable ways to leverage AI for wealth management to supercharge your savings, ensuring a financially secure future. Let’s explore the future of AI for wealth management together.
The financial landscape is evolving, with AI leading the charge. AI algorithms analyze vast data, predicting market trends and suggesting the most profitable investment avenues. By understanding these technologies, you can make informed decisions that align with your financial goals. Stay updated on the latest AI-driven tools and platforms that provide real-time market insights, enabling you to make timely investment choices that yield maximum returns.
10 Genius Tips for Leveraging AI for Wealth Management
Tip 1: Automating Investments for Consistent Growth
AI-powered investment platforms offer automated solutions tailored to your risk tolerance and financial objectives. Automating your investments eliminates emotional decision-making, ensuring consistent growth over time. hese platforms leverage complex algorithms to rebalance your portfolio, optimize tax efficiency, minimize fees, and consider high-yield savings accounts, maximizing your long-term returns.
Tip 2: Personalized Financial Planning
Traditional financial planning often needs more personalization. Conversely, when using tools that implement AI for wealth management, they analyze your spending patterns, financial habits, and life goals to create customized savings and investment plans. These tailored strategies adapt as your circumstances change, guaranteeing a flexible approach to wealth accumulation. Stay connected with financial advisors who incorporate AI-driven tools into their services, ensuring your financial plan evolves with your needs.
Tip 3: Enhanced Bargain Hunting
AI-driven price comparison tools are like your shopping sidekick, continually scanning the digital marketplace for the best deals. These tools are equipped with algorithms that gather and analyze data from various online retailers to find the lowest prices and the best discounts for the products and services you desire. Whether you’re searching for a new gadget, clothing, or travel bookings, these AI for wealth management tools are adept at quickly sifting through vast amounts of information to ensure you get the most bang for your buck. Using them saves money and valuable time that would otherwise be spent on manual price hunting.
Tip 4: Personalized Savings at Your Fingertips
Online shopping has been revolutionized by AI’s ability to personalize your experience. AI algorithms can scrutinize your online shopping habits, preferences, and wishlist items to provide tailored discount offers and coupons. This personalization level means you receive deals on products that genuinely interest you. By presenting these offers right now, AI helps you make cost-effective purchase decisions. This enables you to save money and prevents unnecessarily spending on items not aligned with your interests or needs. It’s like having a virtual shopper who knows your style and budget.
Tip 5: Robo-Advisors for Smart Investments
Robo-advisors, powered by AI, provide cost-effective investment solutions. They assess your risk tolerance and investment horizon, creating diversified portfolios that align with your goals. These platforms continuously monitor market trends, adjusting your investments to capitalize on emerging opportunities. Utilize robo-advisors to enjoy hassle-free, expert-guided investments, ensuring your money grows intelligently.
Tip 6: Predictive Analytics for Informed Decision-Making
AI-driven predictive analytics analyze historical data to forecast market trends accurately. You can anticipate market movements by staying informed about these predictions, enabling strategic decision-making. Stay ahead of the curve by subscribing to AI-driven financial newsletters and platforms that offer real-time market analyses. With this knowledge, you can make timely investment choices, maximizing your savings.
Tip 7: Cryptocurrency Trading with AI Algorithms
Cryptocurrency markets are highly volatile, making them both lucrative and risky. AI algorithms analyze cryptocurrency trends, predicting price fluctuations with remarkable accuracy. By leveraging AI-powered trading bots, you can automate your cryptocurrency investments. These bots execute trades based on predefined algorithms, ensuring you capitalize on market movements while minimizing risks. Stay updated on the latest advancements in AI-driven cryptocurrency trading for profitable outcomes.
Tip 8: Real Estate Investment Insights
Real estate investments are significant financial commitments. AI algorithms analyze property market trends, rental yields, and economic indicators, providing valuable insights for investors. Stay informed about AI-powered real estate platforms that offer in-depth analyses and property recommendations. By making data-driven decisions, you can invest in properties with high appreciation potential and rental income, maximizing your real estate investments.
Tip 9: Credit Score Improvement – AI for Wealth Management and Your Credit Score
Using AI for wealth management can be a game-changer when it comes to improving your credit score. Your credit score plays a pivotal role in your financial life, impacting your ability to secure loans, mortgages, and credit cards. AI-driven credit score improvement services go beyond offering generic advice. They utilize advanced algorithms to analyze your financial history, pinpoint areas for improvement, and provide tailored strategies to boost your score.
Tip 10: Predictive Analytics – Anticipating Financial Trends
AI’s predictive analytics capabilities are a powerful tool in your savings arsenal. These systems analyze vast amounts of historical and real-time data to anticipate financial trends. By using AI for wealth management, you’ll be able to identify optimal times for big purchases, best compound interest investments, or debt repayments, whether it’s the stock market, real estate, or currency exchange rates. By heeding these predictions, you can make more informed financial decisions, potentially saving you from costly mistakes or capitalizing on lucrative opportunities. Predictive analytics acts as a financial crystal ball, helping you make the right moves at the right times.
Final Thoughts
Unlock the full potential of your savings with AI-driven strategies. Stay informed, automate investments, personalize your plans, and embrace predictive analytics. By leveraging the power of AI for wealth management, you’re not just saving; you’re growing your wealth exponentially. Seize control of your financial future today!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Ugly Generative AI Ethics Concerns: RLHF Edition
URL: https://www.data-mania.com/blog/generative-ai-ethics-rlhf/
Type: post
Modified: 2026-03-17
In this post we’re looking at one of the biggest generative AI ethics concerns I’ve uncovered in my recent research of large language models (LLMs), that of fairness implications involved in reinforcement learning with human feedback (RLHF).
But first, if you’re completely new to the world of generative AI and LLMs, just know that one of the more fundamental aspects of working with them involves fine-tuning LLMs.
Lucky for all genAI newbies out there, SingleStore has provided a free training on this exact topic!
Free Training: Using Google Vertex AI to Fine-Tune LLM Apps
Join us to learn more about Using Google Vertex AI to Fine-Tune LLM Apps!
Save My Seat >>
If you’re a developer, data scientist, or machine learning enthusiast looking to revolutionize your LLM applications… It’s time to stop scrolling and start soaring! 🚀
Join this exclusive training where we’ll unveil the unmatched power of Google Vertex AI!
This isn’t your average tech talk; it’s a transformative experience featuring cutting-edge presentations and jaw-dropping, hands-on coding demonstrations.
Here’s a sneak-peek into what you’ll get:
Deep Dive into Vertex AI: Grasp the nuts and bolts of Google Vertex AI and discover how to apply its core components to LLM applications.
Hands-On Coding Experience: Forget the yawn-inducing slide decks; witness and participate in a live coding demo, where the potential of Vertex AI will unfold before your eyes.
Optimization Masterclass: Uncover the secrets to fine-tuning LLM applications. You’ll walk away with actionable techniques and tools that make a difference.
Insider Insights: Exclusive tips and best practices from experts who’ve been there, done that, and are now willing to share their blueprint for success.
The best part? Whether you’re just starting out in machine learning or you’re an experienced developer, this event has something incredibly valuable for everyone.
** This training was delivered live and is now available here on-demand **
Spots are filling up faster than you can say “machine learning”!
Click the link below to reserve your seat and catapult your journey into leveraging Google Vertex AI for LLM applications.
👉 Sign Up Here 👈
My Generative AI Ethics Concerns Regarding RLHF
A few months ago I took the Generative AI with LLMs class on Coursera… and well – I’ve really been loving all that I’ve learned in that power-punched course.
Yesterday I was learning more about reinforcement learning with human feedback (RLHF), which is a method for fine-tuning LLMs in order to minimize the chance the model will produce “toxic” or “harmful” content. Within that learning, I found myself questioning the generative AI ethics around the process itself.
I’m going to share my learnings of the process below, but I want to raise one point first.
Hey, generative AI outputs aren’t perfect, but their builders have their hearts in the right place when it comes to ethical issues, and I see that generally reflected in the model outputs I get on a near daily basis.
I’ve used generative AI applications pretty darn heavily over the last 5 months… and I am incredibly impressed by the design engineers’ and product peoples’ ability to have launched products that are more or less producing unbiased and harmless content. Of course, there are exceptions, which I will discuss in later blog posts and in my upcoming books and courses, but for now… let me just say: Hey, generative AI outputs aren’t perfect, but their builders have their hearts in the right place when it comes to ethical issues, and I see that generally reflected in the model outputs I get on a near daily basis.
Here’s the process that’s used to collect and prepare human feedback for use in fine-tuning LLMs.
Fine-Tuning LLMs with Human Feedback Process
Here’s the basic process for (RLHF):
Choose an initial model.
Use a prompt dataset to generate multiple model completions.
Establish alignment criteria for the model.
Have humans rank the model’s output based on this criteria.
Gather all human feedback.
Average and distribute feedback across multiple labelers for a balanced view.
Feed that into the LLM to fine-tune its output so that they more closely align with human values.
Hairy Generative AI Ethics Concerns with Respect to RLHF
My point about questionable generative AI ethics with respect to RLHF comes down to this:
Addressing fairness in AI is challenging due to diverse global beliefs.
Gen AI companies say that they are selecting human labelers from a diverse pool, but… are they addressing their own personal biases in that selection process?
It’s difficult to form a consensus when belief systems—religious, political, or otherwise—often conflict, with completely irreconcilable differences.
Often due to differing religious beliefs, what is normal in one country, is completely illegal in others… (say, for example, women showing their hair in public).
No one can really come in deem one side correct, and the other wrong. People and societies are free to be who they are and do what they want to do. Irreconcilable differences like this abound!
If you’re building a technology that has the potential to elevate or destroy societies, opinions of people from all sides should be represented equally in the logic and reasoning outputs that these technologies are constructed to generate.
With AI set to revolutionize various sectors, it’s crucial to include diverse perspectives in its development to avoid perpetuating unfairness.
Early intervention is essential for a future that benefits everyone – but I do not recall myself or anyone that I personally know getting to have a seat at the decision-making table. Considering that these technologies are in the process of upending the digital world in irrevocable ways, and that these changes will impact the lives of our children and generations forth, the fact that the voices of everyday people, like me and you, are not at all considered … it doesn’t seem like right or fair generative AI ethics, IMHO.
Early intervention is essential for a future that benefits everyone – but I do not recall myself or anyone that I personally know getting to have a seat at the decision-making table. Considering that these technologies are in the process of upending the digital world in irrevocable ways, and that these changes will impact the lives of our children and generations forth, the fact that the voices of everyday people, like me and you, are not at all considered … it doesn’t seem like right or fair generative AI ethics, IMHO.
Wanna learn from the course too? Be my guest! Generative AI with LLMs class on Coursera…
Pro-tip: If you like this type of training, consider checking out other free AI app development trainings we are offering here, here, here, here, here, here, and here.
I hope you enjoyed this post on generative AI ethics, and I’d love to see you inside our free newsletter community.
Yours Truly,
Lillian Pierson
Shop | Blog | LinkedIn
PS. If you liked this post, please consider sending it to a friend!
Disclaimer: This post may include sponsored content or affiliate links and I may possibly earn a small commission if you purchase something after clicking the link. Thank you for supporting small business ♥️.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## 5 Powerful Techniques for Mitigating LLM Hallucinations
URL: https://www.data-mania.com/blog/llm-hallucinations/
Type: post
Modified: 2026-03-17
As we continue to learn how harness the power of Large Language Models (LLMs), we must also grapple with their limitations. One such limitation is the phenomenon of “hallucinations.”. That’s where LLMs generate text that is erroneous, nonsensical, or detached from reality. In today’s brief update I’m going to share 5 powerful techniques for mitigating LLM hallucinations, and…
As usual, at the end of this post, I’ll provide you a special event to a free live online training event where you can go for hands-on training for how to tackle the hallucinations problem in real life.
The problem with LLM hallucinations
The first problem with LLM hallucinations is, of course, that they’re annoying. I mean, it would be ideal if users didn’t have to go through all model outputs with a finetooth comb every time they want to use something the create with AI.
But the problems with LLM hallucinations are more grave.
LLM hallucinations can result in the following grievances:
The spread of misinformation
The exposure of confidential information, and
The fabrication of unrealistic expectations about what LLMs can actually do.
That said, there are effective strategies to mitigate these hallucinations and enhance the accuracy of LLM-generated responses. And without further ado, here are 5 powerful techniques for mitigating LLM hallucinations.
5 powerful techniques for detecting & mitigating LLM hallucinations
The techniques for detecting and mitigating LLM hallucinations may be simpler than you think…
These are the most popular methodologies right now…
1. Log probability
The first technique involves using log probability. Research shows that token probabilities are a good indicator of hallucinations. When LLMs are uncertain about their generation, it shows up. Probability actually performs better than entropy of top-5 tokens in detecting hallucinations. Woohoo!
2. Sentence similarity
The second technique for mitigating LLM hallucinations is sentence similarity. This method involves comparing the generated text with the input prompt or other relevant data. If the generated text deviates significantly from the input or relevant data, it could be a sign of a hallucination. (check yourself before you wreck yourself? 🤪)
3. SelfCheckGPT
SelfCheckGPT is a third technique that can be used to mitigate hallucinations. This method involves using another LLM to check the output of the first LLM. If the second LLM detects inconsistencies or errors in the output of the first LLM, then that could be a sign of a hallucination.
4. GPT-4 prompting
GPT-4 prompting is a powerful technique for mitigating hallucinations in LLMs.
Here are the top three techniques for using GPT-4 prompting to mitigate LLM hallucinations:
Provide precise and detailed prompts – This involves crafting precise and detailed prompts that deliver clear, specific, and detailed guidance to help the LLM generate more accurate and reliable text. This technique reduces the chances of the LLM filling in gaps with invented information, thus mitigating hallucinations.
Provide contextual prompts – Using contextual prompts involves providing the LLM with relevant context through the prompt. The context can be related to the topic, the desired format of the response, or any other relevant information that can guide the LLM’s generation process. By providing the right context, you can guide the LLM to generate text that is more aligned with the desired output, thus reducing the likelihood of hallucinations.
Augment your prompts – Prompt augmentation involves modifying or augmenting your prompt to guide the LLM towards a more accurate response. For instance, if the LLM generates a hallucinated response to a prompt, you can modify the prompt to make it more specific or to guide the LLM away from the hallucinated content. This technique can be particularly effective when used in conjunction with a feedback loop, where the LLM’s responses are evaluated, and the prompts are adjusted based on the evaluation
These techniques can be highly effective in mitigating hallucinations in LLMs, but be careful they’re certainly not foolproof!
5. G-EVAL
The fifth technique is G-EVAL. This is a tool that can be used to evaluate the output of an LLM. It can detect hallucinations by comparing the output of the LLM with a set of predefined criteria or benchmarks.
Interested in learning more about how to efficiently optimize LLM applications?
If you’re ready for a deeper look into what you can do to overcome the LLM hallucination problem, then you’re going to love the free live training that’s coming up on Nov 8 at 10 am PT.
Topic: Scoring LLM Results with UpTrain and SingleStoreDB
Sign Me Up >>
In this 1-hour live demo and code-sharing session, you’ll get robust best practices for integrating UpTrain and SingleStoreDB to achieve real-time evaluation and optimization of LLM apps.
Join us for a state-of-the-art showcasing of the powerful and little-known synergy between UpTrain’s open-source LLM evaluation tool and SingleStoreDB’s real-time data infrastructure!
Within this session, you’ll get the chance to witness how effortlessly you can score, analyze, and optimize LLM applications, allowing you to turn raw data into actionable insights in real-time.
Save My Seat >>
You’ll also learn just how top-tier companies are already harnessing the power of UpTrain to evaluate over 8 million LLM responses. 🤯
Sign up for our free training today and unlock the power of real-time LLM evaluation and optimization.
Pro-tip: If you like this type of training, consider checking out other free AI app development trainings we are offering here, here, here, here, here, here, and here.
Hope to see you there!
Cheers,
Lillian
PS. If you liked this blog, please consider sending it to a friend!
Disclaimer: This post may include sponsored content or affiliate links and I may possibly earn a small commission if you purchase something after clicking the link. Thank you for supporting small business ♥️.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## The Role and Potential of AI in Digital Marketing
URL: https://www.data-mania.com/blog/ai-in-digital-marketing/
Type: post
Modified: 2026-03-17
Like any other industry, innovation has been a consistent driving force in the realm of digital marketing. With the fast-paced and highly competitive nature of today’s business landscape, knowing how to stand out from the crowd is vital for the success of digital marketing campaigns. It enables organizations to connect with their audience and engage them in a way that caters to their unique needs, motivations, and beliefs. That’s why, today, we want to talk to you about the impact and role of AI in digital marketing.
This process is usually tedious and labor intensive, involving in-depth research and analysis to develop an effective strategy. However, Artificial Intelligence (AI) is an innovation that has completely transformed the digital marketing sector. The rapid advancements in Artificial Intelligence have helped revolutionize their approach to digital marketing, enabling a vast range of applications with near-infinite scope and scalability.
Getting Up to Speed with AI in Digital Marketing
Artificial Intelligence or “AI” refers to one of the most recent branches of computer science, which helps create intelligent machines and algorithms that can function autonomously, almost mimicking humans. AI-generated content is already taking the world by storm today, generating precise, targeted, and well-articulated copies for advertising and marketing purposes, among others. But, AI technology is not restricted to only content generation. It plays a critical role in optimizing and automating several aspects of business processes while also allowing marketing professionals to make highly customized content based on vast amounts of data-driven insights. Let’s look at the scope of application area around AI in digital marketing.
Scope of Application
Today, AI has a wide range of applications in digital marketing and offers quantifiable benefits to organizations that adopt it. Some of these include:
Customer Segmentation: Thanks to its vast computing power, businesses can leverage AI in digital marketing to create comprehensive customer profiles and segment their audience accurately. This helps them create highly personalized marketing campaigns based on their customers’ past behavior and interests. For instance, if a customer previously showed interest in a particular product, an AI-powered system will be able to entice the customer to purchase it or suggest similar products, increasing the chances of conversion.
Predictive Analysis: AI-driven predictive analytics has proven to be a massive boon for businesses today. The vast reserves of data that are accessed by such tools help them predict upcoming trends and customer behavior in specific markets and domains, as well as the efficacy of the marketing strategies implemented. This helps organizations preemptively adjust their approach and strategies to achieve better results.
Chatbots and Virtual Assistants: Effective customer support is vital to building and maintaining long-term relationships while achieving business sustainability. Since AI chatbots and virtual assistants do not have human limitations, they can provide 24×7 support to customers by answering queries, offering product or service recommendations, and guidance on usage. This ensures a seamless and engaging experience for users.
Content Creation: As mentioned earlier, businesses are today able to save massive amounts of time and resources by leveraging AI for content creation. From social media posts and news articles to product descriptions and more, today’s AI models are able to ensure accuracy, precision, and quality while also allowing businesses to discover best keywords to rank higher on search engine results pages (SERPs) like Google. While human editors might still be necessary for fine-tuning a piece of content and adding a personal touch, the effort required is usually minimal.
Optimizing Email Marketing: Email marketing campaigns have proven themselves to be vastly effective in generating leads and converting them into sales, as well as maintaining relationships with existing customers or attracting new ones. AI-driven processes are able to autonomously determine the best time for such emails, the kind of subject lines that attract attention, and even the kind of niche topics and subject matter that resonate with customers.
Creating Advertising and Bidding Strategies: Just like email marketing, timing plays a crucial role in advertising and bidding strategies. AI-driven programming helps adjust bidding strategies based on the analysis of real-time data, helping advertisements reach the correct and most relevant audience at the right time.
Benefits of AI in Digital Marketing
Now that we have discussed some of the many applications of AI in digital marketing, let’s look at what businesses stand to gain from its implementation:
Cost-Reduction and Efficiency: The use of AI in digital marketing has the ability to automate various sections of business processes across the sales and operations journey, including data analysis, lead scoring, and ad optimization. As such, it minimizes the need for human intervention in redundant and repetitive tasks, saving significant amounts of time and resources for a business. This, in turn, can be invested in more important aspects to further enable its growth and success.
Data-Driven Decisions: Informed decision-making plays a critical role in business success, but human errors often compromise the accuracy and effectiveness of the collected data. Since AI possesses the ability to analyze massive quantities of data at speeds far beyond human capacity, it becomes an invaluable source of information for managers and executives alike. Thanks to the abundance of insights and quantitative data and the lack of errors, they can make the most appropriate decisions at any time.
Enhanced Customer Experience: Customers today expect personalization at every step of their interaction with a business, from conflict resolution to answering queries or offering guidance. Free of human fatigue and inconsistencies, AI models can greatly enhance the customer experience by offering consistent and tireless support while doing away with wait times and missed opportunities.
Competitive Advantage: Adopting AI in digital marketing processes inherently boosts a business’s ability to adapt to changes while staying ahead of its competitors. In case of crisis situations, AI-driven business models are able to regain their composure much faster and address the core issues while offering resolutions immediately. They can also suggest minute improvements and adjustments based on the analysis of competitors so that organizations can see where they are lacking and quickly correct their courses.
Bulk, In-depth Analytics: Manual, human-driven analysis and research can often be prone to errors, missing out on important details that greatly affect the effectiveness of digital marketing campaigns. AI, on the other hand, is able to sift through vast and diverse reserves of data, identifying underlying patterns and trends that could have been missed by the human eye. The in-depth insights into customers’ behavior and their own marketing campaigns help create and implement more effective strategies than ever before.
Conclusion
AI has today become indispensable for the world of digital marketing, enabling consistency, accuracy, personalization, scalability, and data-driven insights and decision-making. While the need for human intervention will continue to decline, it will likely still be necessary to ensure quality and creativity while aligning with an organization’s core values and goals. AI in digital marketing, hence, is not about replacing humans but rather enhancing their capabilities and effectiveness. The future will only continue to look brighter as the technology evolves further, enabling more personalized and successful digital marketing campaigns.
As such, embracing AI in digital marketing is today no longer just an option but a necessity for businesses looking to survive and thrive for years to come. When implemented effectively, it will help businesses connect with their customers on a much deeper level and on a larger scale while ensuring the highest standards of accuracy and service. The key to it all is finding the right balance between AI and human expertise to ensure customer satisfaction and business success.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Custom GPT training – Learn to build your own custom GPT for free
URL: https://www.data-mania.com/blog/custom-gpt-training/
Type: post
Modified: 2026-03-17
As you may be aware, OpenAI recently unveiled a series of new products, including an improved version of GPT-4 and a groundbreaking marketplace for developers to build and monetize their own AI systems using OpenAI’s models. In today’s post you’re going to see why you need a custom GPT and where you can go to get a free custom GPT training.
This development deeply expands opportunities for developers and businesses to monetize data and AI.
Of all the products that they released, my favorite among them is the Generative Pre-trained Transformer (GPT)
OpenAI’s GPTs represent a significant advancement in the realm of conversational AI by providing a robust platform for developers to create custom AI systems. These systems are powered by OpenAI’s sophisticated models and can be published on an OpenAI-hosted marketplace, the GPT Store.
What makes GPTs particularly relevant for data professionals is their ability to democratize generative AI application-building. GPTs essentially make GenAI app development accessible even to people who don’t have extensive coding experience!
They offer a versatile range of applications, from answering complex data queries to integrating with proprietary codebases for code generation in line with best practices. This flexibility and power make GPTs an invaluable tool for data professionals who want to harness the latest in AI technology for use in building more innovative, efficient data solutions.
But, there’s a catch…
Standard GPTs are pretty severely limited in terms of data volume processing capacity and cost. The good news is that there’s a pretty straightforward way around these limitations.
Standard GPTs are pretty severely limited in terms of data volume processing capacity and cost. The good news is that there’s a pretty straightforward way around these limitations.
You’re invited to take this free custom GPT training wherein you’ll discover the secrets on how to break free from the constraints of standard GPT offerings
I am thrilled to announce our on-demand training on: “How to Build Custom GPTs Using OpenAI Functions“.
Sign Up Now For This Custom GPT Training >>
Here’s what’s in store for you within this custom GPT training
Join this custom GPT training to get free on-demand education where you’ll:
🔹 Learn to harness CSV datasets using SingleStoreDB GPT in real-world scenarios.
🔹 Master the art of dealing with dynamic data and larger datasets, so you can move beyond the limitations of custom GPTs with this custom GPT training.
🔹 Gain valuable insights on effective natural language processing with dynamic datasets.
🔹 Discover best practices for scaling AI applications with streaming data.
🔹Experience the power of SingleStoreDB GPT firsthand in our live demo. See how it enables cost-effective handling of large, constantly updating datasets!
Don’t miss this chance to elevate your AI expertise. Register today and be part of a community shaping the future of AI applications.
Join us and unlock the full potential of your AI endeavors!
Cheers,
Lillian Pierson
Shop | Blog | LinkedIn
P.S. Learn to build custom GPTs in this free, on-demand custom GPT training!
Refer a friend
Disclaimer: This email may include sponsored content or affiliate links and I may possibly earn a small commission if you purchase something after clicking the link. Thank you for supporting small business ♥️.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Bridging AI in the Cloud: How AWS Bedrock Enhances LLM Integration
URL: https://www.data-mania.com/blog/ai-in-the-cloud/
Type: post
Modified: 2026-03-17
In today’s rapidly evolving tech landscape, the fusion of AI with cloud computing is reshaping how we approach complex problems and solutions. Among the most significant advancements in this realm is the integration of Large Language Models (LLMs) with cloud infrastructure. This represents a pioneering move that is significantly enhancing AI’s capabilities. At the heart of this breakthrough lies AWS Bedrock – a powerful tool that is pivotal in harmonizing AI in the cloud.
This blog post delves into the critical role AWS Bedrock plays in elevating LLM integration. It offers a glimpse into a world where the boundaries of AI’s potential are continually expanding. As we navigate through the intricacies of this integration, we also invite you to join an enlightening learning experience – a free training session that illuminates the path for aspiring and seasoned professionals in AI in the cloud domain.
The Evolution of AI in the Cloud
The journey of AI in the cloud has been nothing short of revolutionary. From its nascent stages, cloud computing has offered a fertile ground for AI technologies to grow and flourish. Initially, the cloud served as a mere repository for data and a platform for basic computing tasks. However, as technology evolved, so did the capabilities of cloud platforms, transforming them into powerful engines capable of processing and analyzing vast amounts of data in real time.
This evolution paved the way for the integration of sophisticated AI models, particularly LLMs, into cloud infrastructure.
The integration of AI and cloud computing has unearthed new possibilities, allowing for more complex, scalable, and efficient AI applications. This transformative journey has not only democratized access to cutting-edge AI technologies but has also catalyzed a paradigm shift in how we view and utilize AI in the cloud.
Today, cloud platforms are not just hosting environments for AI; they are active participants in AI’s learning and evolution, offering unprecedented scalability, flexibility, and computational power.
The emergence of AWS Bedrock as a key player in this domain marks a significant milestone in this ongoing evolution. It represents a leap forward in how we harness the full potential of AI in the cloud by providing the tools and infrastructure necessary for seamless integration and deployment of advanced AI models.
As we delve deeper into AWS Bedrock’s role in this transformative era, it’s crucial to understand that the journey of AI in the cloud is an ever-evolving narrative; One that is continuously redefining the boundaries of what’s possible in the world of technology.
Understanding AWS Bedrock and Its Role in AI
AWS Bedrock stands at the forefront of technologies that bridge AI with cloud computing, a pivotal development in the field of AI in the cloud. As a comprehensive suite within Amazon Web Services (AWS), Bedrock is designed specifically for the deployment and management of LLMs. It provides an integrated environment that simplifies the complexities associated with LLMs, making it more accessible for developers and businesses alike.
The primary role of AWS Bedrock in AI is to provide a robust and scalable infrastructure that supports the integration and execution of LLMs. This is crucial, considering the enormous computational resources that are required by these models. Bedrock’s infrastructure is tailored to handle large volumes of data and complex computational processes, ensuring that LLMs function efficiently and effectively within the cloud environment.
Furthermore, AWS Bedrock addresses some of the most pressing challenges in AI deployment, including data privacy, model training, and resource optimization. It offers tools and services that ensure data used in LLMs is handled securely in order to maintain confidentiality and compliance with data protection regulations. This aspect is particularly vital for businesses that leverage AI in the cloud for sensitive applications.
Another key feature of AWS Bedrock is its ability to facilitate the scaling of AI applications. As the demand for AI-powered solutions grows, the ability to scale these solutions efficiently becomes increasingly important. AWS Bedrock enables users to scale their LLM applications seamlessly, adapting to varying workloads without compromising performance or security.
By providing a streamlined platform for deploying and managing LLMs, AWS Bedrock is not just enhancing the integration of AI in the cloud; it’s revolutionizing the way we develop, deploy, and interact with AI applications. It represents a significant leap in making advanced AI technologies more accessible and manageable, thus further democratizing the power of AI in the cloud.
Enhancing LLM Integration with AWS Bedrock
The integration of LLMs into the cloud has been a game-changer in the realm of AI. AWS Bedrock significantly enhances this integration, making AI in the cloud not just a possibility but a highly efficient and scalable reality.
One of the most notable contributions of AWS Bedrock to LLM integration is its ability to simplify complex processes. Typically, deploying LLMs in the cloud can be a daunting task due to their complexity and the extensive computational resources they require. AWS Bedrock streamlines this process by providing a user-friendly interface and tools that make it easier for developers to deploy, manage, and scale LLMs in the cloud environment.
Another critical aspect of AWS Bedrock is its optimization of resource utilization. LLMs are known for their intensive use of processing power and memory. AWS Bedrock addresses this by offering optimized cloud resources specifically designed for AI workloads. This means more efficient processing, reduced latency, and lower costs, all of which are essential for effective AI applications in the cloud.
AWS Bedrock also plays a significant role in facilitating real-time data processing and analytics, which is of course a cornerstone of effective LLM applications. This real-time capability allows businesses to harness the full potential of AI in the cloud, enabling them to make quicker, more informed decisions based on the insights generated by LLMs.
Moreover, AWS Bedrock provides robust security features, ensuring that the integration of LLMs into the cloud is secure and compliant with various data protection standards. This is particularly important given the sensitive nature of the data that’s often processed by AI applications.
In essence, AWS Bedrock not only simplifies the integration of LLMs into the cloud but also elevates the overall capabilities of AI in the cloud. It allows businesses and developers to harness the power of LLMs with greater ease, efficiency, and security, thus unlocking new possibilities in AI-driven solutions and applications.
Overcoming Challenges in AI Deployment
Deploying AI in the cloud comes with its unique set of challenges, from ensuring efficient resource utilization to maintaining data security and privacy. AWS Bedrock is instrumental in addressing these challenges, further solidifying its role as a crucial tool for AI deployment in cloud environments.
One of the primary challenges in deploying AI, especially LLMs, is the need for high computational power. These models process vast amounts of data, requiring significant computational resources. AWS Bedrock tackles this by providing scalable cloud resources that can be adjusted based on the demands of the AI application. This scalability ensures that AI in the cloud is not only feasible but also efficient, allowing for the handling of large-scale AI tasks without a compromise in performance.
Data privacy and security are other critical concerns in AI deployment. With the increasing emphasis on data protection regulations, it’s paramount to ensure that AI applications comply with these standards. AWS Bedrock offers robust security features, including encryption and compliance tools, making it easier for organizations to deploy AI in the cloud while adhering to stringent data protection laws.
Another challenge lies in the integration of AI with existing cloud infrastructure. AWS Bedrock simplifies this process through seamless integration tools, allowing developers to easily integrate LLMs with existing cloud services and applications. This ease of integration accelerates the deployment process and reduces the complexities typically associated with such integrations.
Finally, the cost of deploying and maintaining AI applications in the cloud can be prohibitive. AWS Bedrock addresses this by offering cost-effective solutions that optimize resource utilization, thereby reducing overall expenses. This cost efficiency is crucial for businesses that are looking to leverage AI in the cloud without incurring exorbitant costs.
Future of AI in the Cloud with AWS Bedrock
As we look towards the future, the integration of AI in the cloud is poised to become even more pivotal in driving innovation and technological advancement. AWS Bedrock is at the forefront of this evolution and it’s set to play a key role in shaping the landscape of AI applications and deployments.
The potential for AI in the cloud is vast, with AWS Bedrock leading the charge in unlocking new capabilities and applications. One of the future trends we can anticipate is the increasing use of AI for more complex, real-time decision-making processes. With its robust infrastructure, AWS Bedrock will enable AI systems to process and analyze data at unprecedented speeds, making real-time analytics and responses a reality in various industries.
Another exciting prospect is the democratization of AI. AWS Bedrock lowers the barrier to entry for businesses and developers wanting to leverage AI in the cloud. This accessibility means that more organizations, regardless of their size or technical prowess, can harness the power of advanced AI technologies to innovate and compete in the market.
Furthermore, we can expect to see advancements in AI’s self-learning capabilities. AWS Bedrock’s scalable and flexible environment provides the ideal platform for the development of more sophisticated AI models that can learn and adapt in real-time, continually improving their performance and accuracy.
The integration of AI with other emerging technologies is another area of potential growth. AWS Bedrock’s versatile and integrative nature will facilitate the convergence of AI with technologies like IoT, blockchain, and more, leading to the creation of groundbreaking solutions and applications.
In essence, the future of AI in the cloud is bright and full of possibilities, with AWS Bedrock serving as a catalyst for innovation and growth. As we continue to explore and expand the boundaries of what AI can achieve, AWS Bedrock will undoubtedly be a key player in driving these advancements, making AI more powerful, accessible, and impactful than ever before.
Conclusion
As we have explored in this blog, the integration of AI in the cloud is a dynamic and rapidly evolving field, with AWS Bedrock playing a crucial role in shaping its future. The advancements and capabilities brought forth by AWS Bedrock are not just enhancing the efficiency and scalability of AI applications but are also paving the way for new innovations and opportunities in the realm of AI in the cloud.
The synergy between AI and cloud computing, facilitated by AWS Bedrock, is more than just a technological advancement; it’s a transformative movement that is redefining the limits of what AI can achieve. From simplifying complex deployments to ensuring security and scalability, AWS Bedrock stands as a testament to the power and potential of AI in the cloud.
As we stand on the brink of this exciting new era, the opportunity to be a part of this transformation is within your grasp. Whether you’re a developer, a data professional, or simply an enthusiast of AI and cloud technologies, now is the time to dive in and explore the endless possibilities that AWS Bedrock offers.
Join Our Free Training Session
To further your understanding and skills in this groundbreaking field, we invite you to join our free training session on “Using AWS Bedrock & LangChain for Private LLM App Dev.”
This session will not only deepen your knowledge of AWS Bedrock and its applications but also provide you with practical insights into deploying and managing AI in the cloud.
Don’t miss this opportunity to be at the forefront of AI innovation. Click here to register for the free training and embark on a journey that will transform your understanding of AI in the cloud and open doors to new, exciting opportunities.
Pro-tip: If you like this training, consider checking out other free AI app development trainings we are offering here, here, here, here, here, here, here, here,here, and here.
🤍 A creative collaboration with SingleStore 🤍
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## AI Implementation In Business: What You Need To Know About Snowflake’s Pioneering AI Integration
URL: https://www.data-mania.com/blog/ai-implementation-in-business/
Type: post
Modified: 2026-03-17
Have you heard about the incredible progress that Snowflake has made with integrating AI and large language models (LLMs) into its platform? Snowflake’s integration of AI and LLMs showcases a leading example of AI implementation in business and is rapidly setting new standards in data cloud technology.
This recent adaptation by Snowflake marks a significant milestone in AI implementation in business. It demonstrates how LLMs can be applied and monetized very quickly in a practical business context.
Let’s take a deeper look under the hood here, shall we?
Advancing Business with AI: Snowflake’s Role in Pioneering AI Implementation in Business
Snowflake’s AI integration involves the use of generative AI and LLMs to enhance data-driven decisions and maximize the value of data that sits on its platform. For this use case, generative AI and LLMs are being deployed to identify the right data points, assets, and insights, thereby empowering teams to make maximum value from the data that’s sitting within their repositories.
But how did Snowflake manage to adapt so quickly, you ask? Strategic acquisition of course!
But how did Snowflake manage to adapt so quickly, you ask? Strategic acquisition of course!
Simply put, the strategic acquisitions made by Snowflake underline the importance of AI implementation in business, particularly in enhancing data analytics and decision-making processes.
Snowflake recently acquired three companies to bring advanced AI and deep learning to its Data Cloud. Those three companies:
Neeva: A search startup that leverages generative AI to enable users to query and discover data in new ways
LeapYear: A company that enhances Snowflake’s data clean room capabilities
Myst AI: An artificial intelligence-based time series forecasting platform provider
This move is part of Snowflake’s strategy to stay at the forefront of the AI trend; A trend which is expected to see a massive $1.3 trillion spend over the next decade (according to Bloomberg, June 2023)
Snowflake’s commitment to AI implementation in business is also evident in their recent launch of Snowflake Cortex, aimed at custom AI development for companies.
Snowflake’s AI integration is just one of hundreds of real-life cases where AI implementation in business is generating massive returns very quickly.
If you’re lacking in development skills that are required to integrate LLMs into your company’s applications, don’t feel bad. This is a very new space and there is still time for you to jump onboard and get ahead of the pack!
That’s one reason I’m so excited to bring to you this week’s incredible free learning opportunity: How to Launch ChatGPT LLM Apps in 3 Easy Steps
Free Training Invite: How to Launch ChatGPT LLM Apps in 3 Easy Steps
This training session will focus on the essentials of AI implementation in business, particularly on integrating ChatGPT LLMs into corporate applications.
Here’s what you’ll come away with:
See first-hand the potential of LLMs in enhancing data-driven decisions.
Get a simple 3-step process you can use for integrating LLMs into your applications
Learn industry best practices for developing and testing LLM apps.
Available on-demand, join us for a power-packed training session where you’ll learn how to get started building and launching ChatGPT LLM applications almost overnight.
Sign Me Up >>
This event is meticulously designed for developers, data professionals, and AI enthusiasts like yourself.
As always with SingleStore trainings, you’ll be getting hands-on knowledge straight from the experts.
Plus, don’t miss out on our live demo and code-sharing segment for a practical experience in deploying these sophisticated technologies.
This training is key to elevating your skills so you can stay at the forefront of AI application development.
Click here to reserve your seat now and take the first step on your journey to mastering ChatGPT LLM applications.
Pro-tip: If you like this training on AI implementation in business, consider checking out other free AI app development trainings we are offering here, here, here, here, here, here, here, here, here,here, and here.
🤍 A Single Store Collaboration 🤍
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## The Essential LangChain Training for Data Professionals [FREE]
URL: https://www.data-mania.com/blog/free-langchain-training/
Type: post
Modified: 2026-03-17
Based on my experience as a fractional CMO that advises data and AI startups, it’s my opinion that learning to use LangChain is an URGENT and essential step for anyone who’s serious about supporting the AI field from an implementation perspective. In today’s blog post I want to explain why I believe LangChain training is such an essential next step for all data professionals, and to share with you a free on-demand LangChain training you can join to quickly get educated for free.
Here’s why you need to master LangChain today
Knowing this framework will put you at the forefront of conversational AI technology.
You’ll develop expertise in a burgeoning niche domain: LangChain represents a niche yet rapidly growing domain in AI. Knowing this framework will put you at the forefront of conversational AI technology. It’s not every day that professionals get the chance to get ahead of the pack in terms of specialized knowledge like this.
Practical, hands-on skills trump theory: The real value of learning LangChain lies in your ability to practically apply it to solve everyday problems. Theoretical understanding may work fine for business leaders in the data and AI startup space, but if you’re an implementation worker, then theory is not likely to do you a lick of good. Conversely, if you have hands-on experience and skills to pave the way, you’re likely to be light years ahead of other data professionals – meaning, your opportunities will be ripe for the picking (and almost you alone).
This is survival of the fittest: In the data and AI space, staying updated with the latest technologies is not just a matter of professional development. It’s a necessity for survival and success. LangChain is poised to be a significant player in the future of AI. Early expertise in this area won’t just give you a competitive advantage, it will future-proof your relevance.
Enhanced time-to-value for your employers or clients: When you take time to learn LangChain now, that means less on-the-job time you’ll spend trying to figure it out on an as-needed ad-hoc basis. Mastering LangChain now can lead to you building more robust and effective AI solutions, and reduced time and resources spent on data integration issues.
In my opinion, the benefits you get when you upskill with LangChain now go far beyond just learning a new technology; they encompass career growth, enhanced problem-solving skills, and staying relevant in a field that is evolving at breakneck speed. For data professionals and startup founders, this is an investment that is likely to yield significant returns with respect to your knowledge, opportunities, and professional growth.
Get this awesome free LangChain training for beginners & learn how-to use to chat with multi-modal data!
Join us for this beginners LangChain training for beginners. In this session you’ll learn to chat with multi-modal data!
Sign Me Up >>
Whether you’re just considering it, experimenting, or already harnessing the power of multi-modal AI systems daily, there’s always more to learn and explore.
Our “Beginners Guide to LangChain: Chat with Your Multi-Modal Data” on-demand training on is designed to address these challenges.
Join us to explore the innovative LangChain framework, which is revolutionizing the way we approach AI and data integration.
In this free LangChain training, you’ll learn:
How LangChain revolutionizes conversational AI by enabling multi-modal data integration.
Techniques for combining various AI models for more dynamic and context-aware AI interactions.
Best practices in implementing LangChain in your AI projects (these BPs will be tailored for beginners).
Future trends in AI and how LangChain is shaping the next generation of conversational AI.
Plus, a hands-on demo showcasing the practical application of LangChain in overcoming common integration challenges.
Whether you’re a seasoned data professional who’s facing these issues daily, or just starting to explore the possibilities of AI and data integration, this session is designed to provide you with actionable solutions and new perspectives.
✨ Don’t miss this opportunity to take a LangChain training that’ll elevate your AI projects
Join us to transform your approach to multi-modal data integration and harness the full potential of AI in your daily workflows.
Sign Me Up >>
Reserve your seat now in this free LangChain training and take the first step towards mastering the art of data integration with AI.
Pro-tip: If you like this training on AI implementation in business, consider checking out other free AI app development trainings we are offering here, here, here, here, here, here, here, here, here,here, and here.
🤍 A Single Store Collaboration
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## 5 Ways AI Helps Streamline Data Collection
URL: https://www.data-mania.com/blog/streamline-data-collection/
Type: post
Modified: 2026-03-17
The world has become more data-driven than ever. Today, data serves as the building blocks that create an organization’s entire digital architecture. It essentially fuels technology’s ability to streamline business operations and achieve performance goals. In today’s post we’re looking at 5 amazing ways you can use AI to streamline data collection for your business.
Due to the increased use of digital platforms and the Internet of Things, we now generate a whopping 120 trillion gigabytes of data every day. Businesses that can access this treasure trove and use it effectively can take the lead in the corporate race and gain a strategic advantage.
Traditional data collection techniques are laborious and time-consuming, which is why many businesses now use AI tools to generate or collect big data.
You might be wondering; how AI collects data on such a massive level? Scroll down to explore the importance of data collection and how businesses use various AI techniques to collect data from countless sources.
How is AI Related to Big Data?
AI and big data are not mutually exclusive concepts but rather complementary tools that enhance each other’s capabilities. Just as we measure human intelligence based on a person’s ability to gather and apply information, we determine an AI system’s effectiveness in terms of how well it learns and adapts to big data. The more data an AI system can access, the better equipped it becomes to generate accurate and useful outputs.
Big data has been termed the “new oil” for businesses because it’s extensively used as a primary input for various AI-powered business suites like SAP, as well as dedicated analytical tools and predictive models.
SAP uses complex Gen AI to train its systems on specific industries and company data, which requires big data to fuel the learning algorithms and refine the precision of its insights.
We suggest you visit SAP website to learn more about SAP’s AI architecture and modern AI systems. You will discover how their sophisticated algorithms help in collecting valuable data from various internal and external sources and how enterprise AI is transforming industries worldwide.
Why is Data Collection Necessary?
Still confused about how AI collects data? We all recognize that businesses and organizations thrive and grow based on their ability to gauge customer expectations and meet or exceed their needs.
Their performance depends on how quickly they respond to changing market trends and consumer behavior. For this reason, businesses must gather minute-to-minute data related to on-ground market conditions, customer preferences, and fluctuating economic indicators.
Besides understanding their customers, data collection is also indispensable for businesses to monitor their competitors’ activities and compare their products or services.
It’s only when they streamline data collection that they can businesses can identify market opportunities and invest strategically in areas that haven’t been explored yet.
Although traditional ERP systems and supply chain automation help streamline business operations, they cannot provide a deeper insight into business performance or predict future trends.
This is why effective data collection sits at the core of AI-powered enterprise suites like SAP. This data helps businesses identify performance gaps and bottlenecks in day-to-day operations, and generally streamline data collection.
Moreover, businesses need to compare historical data with real-time data to figure out the latest trends and alter their overall business strategy. For this purpose, they need to maintain massive datasets of historical operational data and collect real-time data from various sources.
5 Ways To Use AI to Streamline Data Collection
We understand that big data fuels enterprise AI, but exactly how does AI collect data? Instead of relying on traditional manual procedures, businesses use AI-powered tools and algorithms to expedite the complex and time-consuming process of data collection.
Various AI tools and techniques can streamline data collection and ensure that the required data is always available at hand. Here are five ways AI helps streamline data collection from internal and external sources:
1. Chatbots and Voice Assistants
Chatbots and voice assistants have become the new face of customer service. These tools use natural language processing to interpret customer queries and generate appropriate responses without human intervention.
While many of us recognize chatbots as automated customer support models, have you ever wondered how AI collects data from chatbots to enhance its learning capabilities?
Both chatbots and voice assistants actively collect data from customers or website visitors by analyzing customer queries. More importantly, data collected through chatbots and voice assistants is simultaneously processed to assess customers’ reactions to the AI-generated response.
Moreover, chatbots can be used to conduct active surveys from customers and capture critical customer data, such as their demography, preferences, and feedback. Interactive conversations and quizzes can collect heaps of data that can be used to analyze customers’ buying behavior and product performance.
Chatbots and voice assistants can be easily scaled to handle large volumes of data. This makes them an ideal tool for businesses that need to collect a lot of data from a wide range of sources.
2. Crowdsourcing
Crowdsourcing platforms have completely revolutionized the way businesses and organizations collect a wide range of data. Crowdsourcing involves collecting data from a large number of organizations, intermediaries, and people through online platforms and mobile apps.
Businesses can use crowdsourcing platforms to collect data through polls, gamification, viral challenges, and surveys.
Crowdsourcing is one of the most cost-effective and fastest methods of data collection compared to traditional manual methods. The data is typically collected from a broader range of sources and is free from biases.
Moreover, businesses can collect data from sources that would otherwise be difficult to access through manual methods. This could be product reviews of their own product or their competitors’, market research data, scientific research data, social media trends, and more.
3. Web Scraping and Crawling
Wondering how AI collects data from public forums? Web scraping is the answer! Web scraping and crawling are probably the most common AI-based techniques to discover new content and gather data from various online platforms.
AI tools help businesses scrap heaps of useful content from websites and use this data for analysis. This content can range from social media posts, comments, news, online marketplace content, product reviews, product descriptions, blogs, and data from public forums.
Website crawlers and scrapers can also be used to analyze website content on competitor websites and monitor any changes. Social media content and product reviews can provide you with valuable feedback on customer sentiments.
Newer and more sophisticated web crawlers use natural language processing to categorize data and filter out valuable content.
4. Data Cleaning and Correction Tools
Web crawlers, crowdsourcing, chatbots, and other data mining techniques essentially gather all types of data that isn’t always useful. AI-powered tools help businesses sift data and discard chunks of data that may not serve any purpose.
Data such as incomplete phrases, outliners, etc., are cleaned out. AI tools also correct data errors, such as duplicate values, missing parameters, etc. This keeps businesses from storing heaps of useless data and reduces the cost of remote or local data storage. It also helps them improve the quality of data and make it more reliable and accurate for further analysis.
5. Computer Vision and Image Recognition
Collecting text data is simpler, but how does AI collect data from visual content? Visual data contains some critical information that can be highly beneficial for businesses. AI-powered tools not only extract meaningful information from text but also employ advanced techniques to scrape valuable data from images, videos, infographics, and other graphics.
This helps businesses gather valuable data from social media platforms, websites, customer interactions with voice assistants, and audio/video files. Some advanced AI tools can efficiently analyze facial expressions and detect emotions.
This data can then be used to gauge customer preferences, product performance, or customer satisfaction levels.
The Bottom Line
Using AI to streamline data collection is an amazing use case! AI is all geared up to shape the future of the modern world in general and the corporate sector in particular. While sophisticated technology helps businesses streamline their operations, the success and failure of technology depend heavily on the quality and quantity of relevant data.
The above tools not only streamline the data collection process but also ensure that the collected data is clean and free from errors. However, for any business to reap the most benefits of AI data collection tools, it’s essential to ensure responsible use and seamless integration of these tools with existing systems.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## 3 Showstopping Data Analytics Use Cases To Uplevel Your Startup Profit-Margins
URL: https://www.data-mania.com/blog/data-analytics-use-cases/
Type: post
Modified: 2026-03-17
In today’s data-driven landscape, data analytics use cases are the cornerstone of strategic business decisions. They’re indispensable stepping stones that lead to sound strategies that propel companies ahead in hyper-competitive markets.
In the narratives that follow, we’ll explore how some notable SaaS startups have harnessed the power of Equals, a novel spreadsheet SaaS, to transcend typical data challenges by delivering use cases that transformed their operations and solidified their industry standing.
In case you’re new to the Data-Mania blog…
Let me start by first introducing myself and how I’m qualified to speak on data analytics use cases.
I’m Lillian Pierson, a Fractional CMO for deep tech B2B businesses. Over the last decade that I’ve spent immersed in the B2B tech marketing space, I’ve really come to appreciate the critical role of data in shaping strategic marketing planning. My experience working with a spectrum of clients, from Fortune 100 giants like Amazon and Dell to innovative SaaS startups like Domino Data Lab and Cloudera, has many times over demonstrated the transformative power of data-driven insights.
Data tools, particularly advanced platforms like Equals, are essential in my arsenal. They offer granular insights that are vital for precise and effective long-term planning. Especially in the high-stakes, rapidly evolving landscape of analytics and AI, staying ahead of the curve isn’t just a goal, but an absolute necessity.
Working with Equals, I’ve observed its profound impact on operational efficiency and decision-making. Its seamless integration and real-time data syncing capabilities align perfectly with the needs of fast-paced tech environments. In the work that I do guiding companies through major launches and team development, the ability to access and analyze data in real time is crucial.
In essence, Equals empowers its users to transform data into a strategic asset, for marketing purposes or otherwise. It helps bridge the gap between data collection and actionable insights. It shores up strategic decision-making such that every plan is grounded in solid, data-driven rationale.
This not only elevates the strategies you develop but also reinforces the trust that clients place in your expertise.
As someone who thrives on delivering ‘knock-the-cover-off-the-ball’ greatness in marketing, I find that Equals is more than just a tool; it’s a catalyst for strategic solutions that drive real growth in record time.
If you’re curious about how Equals can dramatically improve your business trajectory, read on, because in today’s blog post, I’m sharing 3 real world data analytics use cases that underscore how the right data analytics tools can drive significant transformations in efficiency, accuracy, and strategic decision-making.
3 data analytics use cases to inspire your strategic vision…
We all need inspiration to work from… that’s why this week I’m excited to share 3 compelling data analytics use cases to inspire your next data strategy.
Take a look at the efficiency breakthrough…
Seeking to optimize their time, a notable productivity SaaS company embraced Equals for better streamlined data processes.
The client seized this opportunity to utilize Equals to overcome common hurdles in data handling. By doing so, they were able to leverage Equals’ precision and automation capabilities to enhanced their growth.
With the adoption of Equals, they also saw a massive overhaul in their accounting processes.
The results? Time savings, enhanced data accuracy, and the ability to rapidly identify growth opportunities. In other words, a complete reimagining of their approach to data.
I’d Like To Try Equals >>
Explore the new-found clarity and insight that this insurance marketplace cultivated
An insurance marketplace startup saw an opportunity to streamline their fragmented data systems by enhancing flow and efficiency. That’s when they tested out Equals as a solution for streamlining their data systems and transforming their operations.
Soon thereafter, executives reported back that Equals had made it almost effortless for them to manage extensive metrics from the wide variety of data sources they needed to report from.
This, in turn, enhanced the company culture such that it was more deeply rooted in data-driven decision-making.
Peek under-the-hood of the data-driven transformation of a popular healthcare startup
A cutting-edge healthcare startup embraced the challenge of integrating complex electronic healthcare systems. They found an effective solution with Equals.
Their adoption of Equals resulted in an immediate shift to a more data-driven model.
“The value we received was massive and immediate,” noted the startup’s co-founder.
This journey is a testament to how real-time data integration can streamline operations and positively impact revenue.
Now it’s your turn to lead a massive transformation
These narratives aren’t just based on real-life success stories; they’re blueprints for leveraging data analytics to revolutionize business processes. Whether it’s enhancing decision-making, improving operational efficiency, or driving strategic growth, the right data tool is a game-changer.
I’d Like To Try Equals >>
Right now you have the opportunity to explore these possibilities for your business. I invite you to start a 14-day free trial and experience how a strategic approach to data can elevate your business operations.
This is your chance to redefine your engagement with data. Click here to begin your trial, and join the ranks of successful businesses that are harnessing the power of data analytics for growth and efficiency.
Here’s to your data-driven success!
Lillian
P.S. This isn’t just a trial; it’s a gateway to unlocking your business’s full potential through data. Start your journey today!
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Llama Index Tutorial: Free On-Demand Video Training
URL: https://www.data-mania.com/blog/llama-index-tutorial/
Type: post
Modified: 2026-03-17
Looking for the ultimate Llama Index tutorial? Awesome! You’re in the right place. Read on.
First, let’s talk about the amount of pressure we are under to upskill right now… If I’m honest, it sometimes feels overwhelming. If you’ve been feeling that way lately too then, know that YOU ARE NOT ALONE.
Here’s the deal, though.
Even if you’re not going to be building generative AI applications, it’s worth its weight in gold to at least learn how they work, how to best use them, and what their limitations are.
For example, from the small bit of time I invested in taking this one 60-minute llama index tutorial class, I learned the ins and outs of LlamaIndex such that I could use it to build a generative AI application.
Explore the World of Multimodal AI with a Llama Index Tutorial
Imagine the future of work, where text-, image-, and video- generating models aren’t just vast knowledge stores but instead are nimble, integrated, and supremely adaptable components of every single digital system we use on a daily basis.
To some this may sound futuristic, but I assure you it is not. The revolution has already begun, and a big driver of that revolution is LlamaIndex.
LlamaIndex isn’t just another tool. Developed by the brilliant Jerry Liu, it’s the bridge between what LLMs are today and what they will be tomorrow. Let me explain…
Here’s the LlamaIndex advantage:
Integrative & Easily Customizable: Traditional LLMs hold vast knowledge. When fused with LlamaIndex, they can access and manage external, domain-specific data, transforming their application potential.
Enhanced Performance: LlamaIndex doesn’t just integrate data; it optimizes LLM performance, ensuring you get rapid and accurate results every time.
Endless Possibilities: From healthcare to finance, the right data strategy using LlamaIndex can revolutionize any domain, opening up avenues you’ve never imagined.
If your goal is to be at the forefront of data & AI strategy, understanding LlamaIndex isn’t just beneficial—it’s essential.
Learn to Build Multimodal AI with a live training on LlamaIndex!
Since we’re all quite curious about what the cutting-edge of generative AI looks like from the inside… I’m excited to invite you to tomorrow’s free live workshop!
We anticipated a lot of interest in this workshop, but the speed at which seats are filling up has taken even us by surprise. (really, that’s a testament to how groundbreaking LlamaIndex truly is!)
In this 1-hour llama index tutorial, you’ll discover the future of app development. Save your seat for this on-demand training now before we take it down.
Dive deep into the innovative realm of multimodal AI with this llama index tutorial – where text meets image data to create groundbreaking applications. Stay ahead in industries like healthcare, automotive, and customer service, where multimodal AI is not just a trend but a necessity.
Believe it or not, it’s projected that over 70% of businesses will use multimodal AI for customer support by 2025.
Believe it or not, it’s projected that over 70% of businesses will use multimodal AI for customer support by 2025.
Here’s why you can’t miss this session:
Cultivate in-demand skills: Believe it or not, it’s projected that over 70% of businesses will use multimodal AI for customer support by 2025. Be part of the elite group that’s prepared for this shift.
Get a live demo & code share: Witness firsthand how to build a chat application using multimodal data, and take home valuable insights and code examples in this llama index tutorial.
Partake in expert discussions: Get tips on integrating text and image data, utilizing LlamaIndex efficiently, and keeping up with the latest AI trends through a comprehensive llama index tutorial.
Don’t miss out on this opportunity to future-proof your skills and lead in the tech landscape. Register now for this on-demand training.
Elevate your development projects with the power of multimodal AI – sign up today!
Secure Your Spot Now >>
Looking forward to seeing you there,
Lillian
❤️ This is a SingleStore collab!
Pro-tip: If you like this training on AI implementation in business, consider checking out other free AI app development trainings we are offering here, here, here, here, here, here, here, here, here, here, here,here, and here.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Creating Datasets: A Reproducible 9-Step Process & Coding Demo
URL: https://www.data-mania.com/blog/creating-datasets/
Type: post
Modified: 2026-03-17
Creating datasets is a foundational step in online research. This process is essential for uncovering hidden patterns and trends, quantifying results, and supporting informed decision-making.
Well-documented datasets enhance research reproducibility and foster collaboration among researchers and organizations. Moreover, datasets adapt seamlessly to advanced technologies like machine learning.
In essence, creating datasets is the key to extracting valuable, quantifiable insights from the extensive field of online information, contributing to the credibility and advancement of research efforts. In this article, we’ll help you to master the art of crafting custom datasets efficiently. We’ll start first with a strategy for creating dataset, and then we’ll follow that with a simple Python coding demo that shows you how to do it!
8 Strategic Steps For Planning and Creating Datasets for Online Research
If you want to create custom datasets for online research, you should start with the following 8 steps:
Step 1. Define Your Research Objectives
Clearly outline your research objectives before diving into creating datasets. Identify the specific insights you aim to gain, setting the foundation for a targeted approach.
By articulating the research goals, you not only set the direction for data collection but also ensure relevance and purpose. This clarity guides the selection of data points, sources, and methodologies, streamlining the entire research process.
Step 2. Identify Necessary Data Points
Pinpoint the essential data points needed to achieve your research goals. Categorize data types (numerical, categorical, or textual) to streamline the collection process.
By categorizing, you streamline the collection process, ensuring that each data point serves a specific purpose in addressing your research objectives. This facilitates efficient data gathering and contributes to the overall structure of the dataset.
Step 3. Leverage Diverse Data Sources
To ensure a comprehensive dataset, utilize diverse sources. Combine manual collection, web scraping, and existing datasets, fostering a holistic perspective.
Web scraping Techniques
Web scraping techniques involve responsibly extracting relevant information from websites using tools like BeautifulSoup or Scrapy.
BeautifulSoup and Scrapy are Python libraries facilitating efficient web scraping, ensuring compliance with website terms of use. Ethical extraction involves respecting website policies, avoiding excessive requests, and prioritizing user privacy.
For example, in gathering customer opinions from product reviews, web scraping enables the extraction of fine-grained insights, contributing diverse perspectives to the dataset. It’s essential to balance the power of web scraping with ethical practices, ensuring accurate, legal, and respectful acquisition of data for comprehensive analysis.
Manual Data Collection
Implement surveys, interviews, or observations for data not readily available online. Develop structured questionnaires to gather accurate and meaningful insights.
Step 4. Data Cleaning and Validation
Maintain data quality through rigorous cleaning and validation processes. This involves identifying and rectifying errors, missing values, and outliers that can compromise the accuracy of the dataset. The use of tools like Pandas in Python streamlines this process, providing functionalities to identify inconsistencies and handle data anomalies effectively.
Cleaning ensures uniformity and reliability, preparing the dataset for accurate analysis. On the other hand, validation confirms that the data meets specific criteria, enhancing the overall integrity of the dataset.
Step 5. Ensure Data Privacy and Compliance
Adhere to data privacy regulations and ethical standards. Anonymize sensitive information and comply with legal requirements, such as GDPR, when dealing with personal or proprietary data.
Adhering to these regulations protects your privacy rights and fosters ethical data practices. Anonymization techniques, like encryption or aggregation, safeguard identities while allowing meaningful analysis. Compliance with legal requirements mitigates risks, ensuring organizations operate within the law.
Step 6. Optimal Dataset Size
Consider the size of your dataset based on your research objectives. Strike a balance between comprehensiveness and manageability. For instance, you can cover an extended timeframe when studying climate change impact.
Step 7. Adopt an Iterative Approach
View creating datasets as an iterative process. Refine your dataset as research progresses, addressing feedback and enhancing relevancy. Update information regularly for real-time insights.
Adopting an iterative approach in dataset creation involves continual refinement, addressing feedback, and enhancing relevancy. Embrace the dynamic nature of the process by actively seeking and incorporating feedback, addressing limitations, and aligning the dataset with the increasing research objectives.
Regular updates ensure real-time insights, while technology integration streamlines the iterative cycle. Transparent documentation facilitates collaboration and builds trust, balancing complexity for depth while maintaining usability. This continuous learning process not only refines the dataset but also fosters adaptability, making it vital to effective and evolving research practices.
Step 8. Document Your Process
Thoroughly document the dataset creation process, including sources, cleaning procedures, and any transformations applied. By detailing each step, you provide a roadmap for reproducibility, enabling the replication of the study by peers or future researchers. This transparency also aids in troubleshooting potential issues and ensures the credibility of the dataset.
Creating Datasets Coding Demo: How to Create a Dataset of Airbnb Reviews with Python and BeautifulSoup
Now, let’s practice your skills in creating datasets with a real-life example. This is to empower your data analysis skills by creating a custom dataset of Airbnb reviews using Python and BeautifulSoup. This guide offers a concise, step-by-step approach to gathering and organizing Airbnb reviews for insightful analysis.
Step 1: Install Required Libraries
Ensure Python is installed and install the necessary libraries.
pip install requests beautifulsoup4 pandas
Step 2: Import Libraries
In your Python script, import the required libraries.
import requests
from bs4 import BeautifulSoup
import pandas as PD
Step 3: Choose an Airbnb Listing
Select an Airbnb listing and copy its URL for review extraction.
Step 4: Send HTTP Request
Fetch the HTML content of the Airbnb listing using requests.
url = ‘paste-your-Airbnb-listing-URL-here’
response = requests.get(url)
html = response.text
Step 5: Parse HTML with BeautifulSoup
Parse the HTML content for easy navigation.
soup = BeautifulSoup(html, ‘HTML.parser’)
Step 6: Locate Review Elements
Identify HTML elements containing reviews by inspecting the page source. Typically, reviews are within tags with specific classes.
Step 7: Extract Review Details
Loop through review elements, extracting pertinent information like reviewer name, rating, date, and text.
reviews = []
for review in soup.find_all(‘div’, class_=’your-review-class’):
reviewer = review.find(‘span’, class_=’reviewer-class’).get_text(strip=True)
rating = review.find(‘span’, class_=’rating-class’).get_text(strip=True)
date = review.find(‘span’, class_=’date-class’).get_text(strip=True)
text = review.find(‘div’, class_=’text-class’).get_text(strip=True)
reviews.append({‘Reviewer’: reviewer, ‘Rating’: rating, ‘Date’: date, ‘Text’: text})
Step 8: Create a DataFrame with Pandas
Transform extracted data into a Pandas DataFrame for easy manipulation.
df = pd.DataFrame(reviews)
Step 9: Save the Dataset
Save your dataset to a CSV file for future analysis.
df.to_csv(‘airbnb_reviews_dataset.csv’, index=False)
Conclusion
This marks the end of our creating datasets tutorial. You’ve now successfully created a dataset of Airbnb reviews using Python and BeautifulSoup. This structured dataset is now ready for in-depth analysis providing valuable insights into customer sentiments. Expand your knowledge by applying these steps to different Airbnb listings, uncovering patterns within the extensive world of Airbnb reviews.
Pro-tip: If you liked this post, be sure to check out our 3 Showstopping Data Analytics Use Cases To Uplevel Your Startup Profit-Margins.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## The 16 Best Tools For AI Development In 2024
URL: https://www.data-mania.com/blog/tools-for-ai-development/
Type: post
Modified: 2026-03-17
If you’re deep into the AI game, you know having the right tools for AI development can make or break your projects. The landscape for tools for AI development has exploded. We’re talking a smorgasbord of options to supercharge your AI endeavors.
To help you get your year off to a brilliant start, today I want to you offer you recommendations for the top 16 AI development tools for 2024, and an invitation to a free on-demand event where you can go to gather the vital context you need to select the most appropriate tools for your specific needs.
So, let’s get down to business with the top 16 tools for AI development.
The 16 Best Tools for AI Development
For simplicities sake, I’ve broken the tools for AI development into 3 categories:
Model Training and Experimentation Tools
AI Deployment and Production Tools
Data Storage and Processing Tools
We’ll start with model training and experimentation tools…
Model Training and Experimentation Tools
In the toolkit of tools for AI development, the ones that focus on model training and experimentation are absolute gold. These are the catalysts for innovation and excellence in AI.
Hugging Face: A platform that provides state-of-the-art models for natural language processing and supports model training and experimentation, with a focus on open innovation and collaboration. (https://huggingface.co/)
Fast.ai: A library that provides a deep learning framework for Python, making it easier for developers to build and train machine learning models. This library has a strong focus on simplicity and accessibility. (https://www.fast.ai/)
PyTorch: An open-source machine learning library that enables developers to build and train deep learning models by offering a flexible and efficient platform for experimentation and research. (https://pytorch.org/)
C3 AI: A comprehensive enterprise AI platform that supports model training, experimentation, and deployment, with a strong product focus on scalability and reliability. (https://c3.ai/)
DataRobot: An automated machine learning platform that supports model training and experimentation, thereby enabling organizations to build and deploy highly accurate machine learning models. (https://www.datarobot.com/)
AI Deployment and Production Tools
Effective deployment is key, and tools for AI development focusing on deployment and production are your launchpads for success.
Algorithmia: A platform that provides a marketplace for deploying and managing machine learning models, making it easier for developers to deploy models at scale. This platform focuses on scalability and reliability. (https://algorithmia.com/)
Azure ML Studio: A platform that allows developers to create, train, and deploy machine learning models using a user-friendly interface and pre-built templates, streamlining the model development and deployment process. (https://azure.microsoft.com/en-us/services/machine-learning-studio/)
AWS Machine Learning: A service that enables developers to build, train, and deploy machine learning models using the AWS platform by offering a wide range of tools and features for model deployment and management. (https://aws.amazon.com/machine-learning/)
Google Cloud AI Platform: A platform that provides a range of AI tools and services, including machine learning models, natural language processing, and computer vision, with a focus on innovation and scalability. (https://cloud.google.com/ai-platform)
IBM Watson Studio: A platform that combines data, models, and applications in one place, allowing developers to create and deploy AI models more efficiently, with the main focus here being on end-to-end AI lifecycle management. (https://www.ibm.com/cloud/watson-studio)
Data Storage and Processing Tools
Rounding off our list of tools for AI development are those focused on data storage and processing. These are the workhorses behind successful AI projects.
Databricks: A cloud-based data analytics platform that provides a unified environment for data, analytics, and machine learning, streamlining the data storage and processing workflow, with a platform focus on scalability and reliability. (https://databricks.com/)
Google Cloud Data Fusion: A platform that combines data from multiple sources, allowing developers to create and manage data pipelines for real-time analytics and machine learning, with a strong focus on data integration and processing. (https://cloud.google.com/data-fusion)
Amazon S3: A cloud-based object storage service that allows developers to store and retrieve data from various applications, offering a scalable and cost-effective solution for data storage, with a focus on reliability and security. (https://aws.amazon.com/s3/)
Azure Blob Storage: A cloud-based object storage service that enables developers to store and manage large amounts of unstructured data, offering a scalable and secure solution for data storage, with a focus on simplicity and accessibility. (https://azure.microsoft.com/en-us/services/storage/blobs/)
Snowflake Data Cloud: A cloud-based data warehousing platform that supports various data workloads, including data warehousing, data lakes, and data science. (https://www.snowflake.com/)
SingleStore: A unified database for operational analytics, empowering developers to build and deploy modern applications that require real-time insights, with a focus on performance and scalability. (https://www.singlestore.com/) With its advanced features for data management, its high-speed data processing, and support for real-time analytics, SingleStore Pro Max’s is a perfect fit for most AI development needs.
Evaluating Which Tool is Right for Your Needs
As you explore the myriad of tools for AI development, it’s important to understand the practical application and integration of these technologies in real-world scenarios. This is just the type of context and education that you can obtain for free by joining this on-demand event by SingleStore.
Presenters at the event will not only demonstrate the powerful capabilities of SingleStore, but they’ll also provide invaluable insights into how these features can be leveraged for building smarter, faster generative AI applications — a topic that’s, of course, front-of-mind for leading data scientists and data engineers these days.
Discover the Future of AI Development Tools
Discover the Future of AI and Real-Time Analytics with SingleStore Pro Max – A Must-Attend Event for Innovators!
📆 Date/Time: On-Demand
This event is only for developers, data engineers, and tech visionaries that are ready to be at the forefront of AI and real-time analytics innovation.
The world of data platforms is evolving rapidly, and staying ahead isn’t optional here folks.
It’s time to leap into the future with SingleStore Pro Max: The Powerhouse Edition.
🔥 Why Attend This Pivotal Event?
Innovative features: Experience the monumental shift with features like 1,000x faster vector search and an on-demand compute service for GPUs/CPU.
Practical insights: Hear from leading brands and their journey in applying these breakthroughs in real-world scenarios.
Network with industry leaders: Engage with SingleStore’s top executives and product experts, including CEO Raj Verma and the visionary product team.
Hands-on demos: Dive into live demonstrations of generative AI, vector technology, and the brand-new SingleStore Kai™.
This event is a deep dive into the tools and AI knowledge essentials for application developers, solution architects, data engineers, and analysts. Be part of this transformative journey.
🔗 Join Now!
Don’t miss the symphony of innovation and expertise that this event will be. Register here to be part of the future of AI and real-time application development.
👉 Join us to help redefine the boundaries of technology!
🤍 This blog post is part of a SingleStore collaboration. 🤍
Pro-tip: If you like this training on AI implementation in business, consider checking out other free AI app development trainings we are offering here, here, here, here, here, here, here, here, here, here, here, here,here, and here.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## My 3 Favorite Vector Database Tutorials (w/ an easy intro primer)
URL: https://www.data-mania.com/blog/vector-database-tutorials/
Type: post
Modified: 2026-03-17
Ah, vector databases! Surely you’ve heard of them. Considering that the vector database market is projected to reach $1.7 Billion by 2027, data professionals REALLY need to make sure they’re staying ahead of the curve with respect to this tech. In this blog post, I’m sharing my favorite vector database tutorials.
But first, an easy intro to vector DBs
Vector databases are a fundamental component of generative AI infrastructure. With the massive generative AI explosion we’ve seen over the last year, the need for vector databases has also taken off at breakneck speed.
But, what is a vector database exactly?
Simply put, a vector database is a database management system that’s designed to efficiently store, manage, and retrieve multidimensional data, often used for similarity searches in machine learning and generative AI applications.
While traditional relational databases store, manage, and retrieve data from structured tables and defined columns, vector databases think a bit outside that box.
Their vectorized data format (aka; “vectors”) is especially handy when dealing with large-scale and multifaceted datasets.
I’ll borrow this handy-dandy diagram from Jatin Solanski to illustrate:
What’s this got to do with the price of tea in China?
With generative AI breaking the forefront of every single aspect of business and digital work, technologies that directly support generative AI are of course getting their day in the sun. Case in point, vector databases.
With respect to large language models (LLMs), vector databases are significant because they allow for efficient storage and retrieval of high-dimensional data. They enable quick similarity searches while also enhancing the model’s ability to reference vast amounts of information. In this way, vector databases improve the response accuracy and context understanding of LLMs.
Source: Pinecone
Here are some of the ways that vector databases are helpful when working with LLMs:
Encoding information: LLMs can produce feature vectors, which are compact representations of longer text inputs or other data types. Vector databases store these feature vectors.
Efficient similarity searches: When a new query comes in, an LLM generates a corresponding vector for it. The vector database can then quickly identify “nearest neighbors” or vectors that are most similar to the query vector. This is much more efficient than scanning through raw data.
Scalability: As the amount of data and knowledge grows, it becomes impractical to directly search raw data every time. Vector databases allow LLMs to scale by providing a means by which to efficiently search through vast amounts of data using compact vector representations.
Handling contextual information: Vector representations capture semantic meaning and context. When searching for relevant information, vector databases can identify vectors (and thus data points) that not only match the direct query but also align with the contextual or semantic intent behind it.
At the most basic level you could say that vector databases act as bridges that allow LLMs to tap into vast reservoirs of data in an optimized manner, thus ensuring rapid and contextually accurate responses.
Now that you understand what vector databases are and why they’ve become so important so fast, I wanted to let you in on 3 of my favorite vector database tutorials that you can use to get up-to-speed in record time.
Vector database tutorial #1: A Beginner’s Guide to Vector Databases
🔍 Discover the Future of Data Management: A Beginner’s Guide to Vector Databases.
📅 TRAINING TIME: On-demand
The world of data is evolving, and with it, so are the tools and technologies we use to harness its full potential. Because the vector database market is projected to reach a staggering USD $1.7 Billion by 2027, data professionals REALLY need to make sure they’re staying ahead of the curve with respect to this tech.
Join us for an eye-opening session where we explore the burgeoning world of vector databases. This training is a practical guide into why and how vector databases are becoming indispensable in our data-driven era, with a specific focus on how to use them to enhance AI capabilities and manage complex spatial data.
What you’ll gain from this 1-hour training:
Learn the fundamentals of vector databases and their pivotal role in modern data management.
See how 73% of AI and machine learning projects are being revolutionized by vector databases.
Overview the best practices for integrating vector databases into your existing data infrastructure.
Experience a live demonstration replay, including a hands-on code-sharing session, to see vector databases in action.
Whether you’re a seasoned data professional or just starting, this training is custom-tailored to provide you a comprehensive understanding of vector databases and their practical applications, all in 60 sweet minutes!
Don’t miss out on your chance to stay at the forefront of data management innovation. Click here to register now and secure your spot in this transformative journey into the world of vector databases.
Vector database tutorial #2: Vector Embedding Training
In this vector database tutorial, you’ll learn how to build an LLM app on inventory, product & reviews data!
Sign Up Here >>
Vector database tutorial #3: Real-time AI threat detection using Vector DBs & Kafka
Learn to achieve real-time AI threat detection using vector DBs & Kafka in this free vector database tutorial.
This Looks Awesome >>
I hope you find these three vector database tutorials as valuable as I have! A word of thanks to the training host, SingleStore, for making these trainings freely available and for sponsoring The Convergence.
Pro-tip: If you like this training on AI implementation in business, consider checking out other free AI app development trainings we are offering here, here, here, here, here, here, here, here, here, here, here, here, here,here, and here.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Image Detection AI: What Data Professionals Need To Know In 2024
URL: https://www.data-mania.com/blog/image-detection-ai/
Type: post
Modified: 2026-03-17
What do retail store cameras have to do with your data career trajectory? A LOT, it come to find out!
You know those cameras that retailers all across their stores? Well, the most innovative stores are using those cameras as a data source for object detection, and then integrating object / image detection AI technology into their inventory management systems!
Yes, I am serious. I mean, why wouldn’t they?
Integrating object / image detection AI technology into inventory management systems offers A LOT of benefits.
From a data management perspective, it facilitates the seamless flow of visual data into existing data systems and analytics tools. it enables a richer, more comprehensive analysis of inventory levels, trends, and patterns.
But from a business perspective, retailers that deploy this practice are known to significantly reduce the incidence of stockouts and overstock, while also minimizing waste and optimizing supply chain operations. Furthermore, the ability to analyze inventory data in real-time allows for more agile decision-making and provides businesses a competitive edge.
While the business benefits are manifold, for data professionals, the mastery of these types of technologies offers new career paths and significantly increases the value of your skillset in an increasingly data-centric business world.
In today’s blog post, I want to share an interesting success case of this type of technology in-action and then provide you access to a free live training where you can go to quickly get trained on inventory management using object /image detection AI.
An IRL example of using image recognition AI to deliver real-time in-store execution
In the retail analytics sector, companies like Infilect have leveraged image recognition AI to deliver real-time in-store execution insights to achieve more than 97% accuracy in real-time shelf metrics and stock level reporting for CPG manufacturers, distributors, and retailers. This technology streamlines stock tracking and planogram compliance, while also significantly improving self-checkout processes and assisting visually impaired customers in navigating retail environments more independently.
The implementation of this technology has demonstrated a tangible business impact for Infilect clients. Some wins they’ve achieved include:
A 2%-5% lift in same-store sales
Savings of over 25 million minutes in store audits, and
A substantial influence on trade payouts, amounting to $10 million.
By automating stock tracking and improving planogram compliance, retailers can now rely on more accurate data for their business processes, thereby reducing manual auditing errors, enhancing the checkout experience, detecting counterfeit products, and ultimately, boosting customer loyalty through reduced wait times.
But, this blog is not about Infilect and its product. This email is about you and how I can help you get ahead in your data career.
If you’re working on data projects in any type of executional capacity, then you know just how important it is for you to stay trained on the latest techniques. Sometimes it’s hard to find learning resources on really specific topics though (especially topics like Inventory Management Using Object / Image Detection AI).
That’s why I am really grateful that my partner SingleStore has taken the bull by the horns and is stepping forward to provide you with this free live training on this exact topic!
TRAINING INVITE: Inventory Management with Image Detection AI Technologies
In the fast-evolving landscape of data and AI technologies, staying ahead on innovative techniques is an important action you can take to future-proof your career and relevancy.
We invite you to a very unique, on-demand training that will change how you think about image detection AI technologies and retail inventory management.
Topic: Inventory Management Using Object / Image Detection AI
In this session, you’ll dive deep into the transformative power of AI in image detection technologies. These advancements are reshaping the very fabric of inventory management systems.
Why Attend?
Learn about the fundamentals of AI and machine learning and their application in inventory management.
Experience a live demonstration showcasing AI tools for real-time inventory tracking.
Develop know-how on implementing AI-driven systems to optimize efficiency and significantly reduce operational costs, with accuracy improvements of up to 98%.
Gain insights into demand forecasting using AI, thus enabling better supply chain decisions.
This training is an indispensable resource for professionals in data application development and analytics. Join us as we explore the latest trends, practical applications, and share codes to kickstart your journey.
Save My Seat >>
I hope to see you there!
This post is brought to you as part of a long-term partnership with SingleStore!
Pro-tip: If you like this training on AI implementation in business, consider checking out other free AI app development trainings we are offering here, here, here, here, here, here, here, here, here, here, here, here, here, here,here, and here.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Improve RAG Performance With These 3 Simple Best Practices
URL: https://www.data-mania.com/blog/improve-rag-performance/
Type: post
Modified: 2026-03-17
You know when you need support for a product and they send you to a stupid chatbot that wastes your time and only provides you outdated or irrelevant responses? So annoying, right?! Well, the sad part is that some human built that untenable “solution”… Today, I come as a bearer of good news. You don’t have to be “that builder”. There are several straightforward best practices you can put into place to improve RAG performance, so that the AI applications you build are actually helpful to other people in real life. And, I’m going to share those best practices with you in this short blog post!
If you’ve been following along with Convergence emails this year, then you already know what RAG is and how it’s helpful. But just in case, RAG stands for Retrieval-Augmented Generation which is a methodology that combines the retrieval of custom user-provided information from a large database with a generative large language model to produce informed and contextually relevant outputs.
If you’re a GPT-4 user then you’re aware of custom GPTs… The ability to upload pdfs and images into your GPT to act as a custom information source is the most accessible user-friendly instance of RAG I’ve ever seen. So, now that you’re clear on what RAG is, let’s talk about best practices you can put into place to improve RAG performance, and where you can go to learn more about building and evaluating RAG applications.
Three Best Practices to Improve RAG Performance, Almost Overnight 😉
Garbage in, garbage out.
You need a solid strategy for selecting reliable information sources for RAG because – well, the quality of your information sources directly impacts the accuracy and reliability of the content that you generate. It’s the principle of garbage in, garbage out. High quality input data leads to outputs that are informed by credible and authoritative information sources. Additionally, a well-defined strategy for selecting information sources helps mitigate the risk of propagating misinformation or biased content. Furthermore, selecting high-quality sources enhances the model’s ability to generate nuanced and contextually relevant responses, which consequently results in significant improvements to user experience and satisfaction.
So, without further ado… my three favorite best practices for selecting reliable information sources to improve RAG performance are detailed below.
Chunking and Indexing with Advanced Retrieval
This best practice involves the preprocessing of data through “chunking”. Chunking is the process of breaking down text into manageable segments for storage in embedding vectors. This method employs a variety of indexing methods, examples of which include constructing multiple indexes for different user questions and routing user queries via an LLM to the appropriate index.
Advanced retrieval methods, including the use of cosine similarity, BM25, custom retrievers, or knowledge graphs, improve the results of the retrieval process. Reranking the results from the retriever and employing query transformations can further refine the accuracy and relevance of the information sourced.
Employing Domain-Specific Pre-Training and Fine-Tuning
This best practice focuses on tailoring the AI’s training to specific domains by extending the original training data, fine-tuning the model, and integrating it with external sources of domain-specific knowledge.
Domain-specific pre-training involves building models that are pre-trained on a large data corpus that represents a wide range of use cases within a specific domain. Fine-tuning these models on a narrower dataset that’s tailored for more specific tasks within the domain tends to improve RAG performance while also reducing the limitations associated with parametric knowledge (eg; context inaccuracy and the potential for generating misleading information).
Improve RAG Performance by Integrating with Non-Parametric Knowledge
This best practice addresses the limitations of LLMs by grounding their parametric knowledge with external, non-parametric knowledge from an information retrieval system. By passing this knowledge as additional context within the prompt to the LLM, it can significantly limit hallucinations and enhance the accuracy and relevancy of responses. This approach allows for the easy update of the knowledge base without changing the LLM parameters and enables responses that cite sources for human verification.
While, taken collectively, these best practices promise to improve the accuracy, reliability, and context relevance of responses generated by RAG systems, there is more you can do to improve RAG performance. For that, I encourage you to attend this free on-demand training session, which is a beginner’s guide to building & evaluating RAG applications.
A Beginner’s Guide to Building & Evaluating RAG Applications
Join us for an exclusive on-demand training that’s designed to demystify RAG and its place in the world of LLMs and precise information retrieval.
You’ll see the mechanics of how RAG is revolutionizing text generation, while also learning how to leverage RAG in your own projects right away.
Whether you’re new to the genAI field or just looking to improve your skills, this session will provide incredible insights into building and evaluating effective RAG applications.
You’ll come away with:
Foundational knowledge of how RAG works and its transformative impact
Practical strategies to improve RAG performance by selecting information sources and designing retrieval systems
Hands-on experience with integrating and optimizing RAG components
A framework for assessing the performance of your RAG applications
A glimpse into the future of RAG technology and its expanding role across industries
Don’t miss the opportunity to elevate your expertise with a on-demand demo and code-sharing session!
This is your direct path to implementing and assessing RAG technologies in your projects. Register now to secure your spot in this forward-looking live training so that you can step into the future of AI with confidence.
Save Me A Seat >>
This post is brought to you as part of a long-term partnership with SingleStore!
Pro-tip: If you like this training on AI implementation in business, consider checking out other free AI app development trainings we are offering here, here, here, here, here, here, here, here, here, here, here, here, here, here, here,here, and here.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Choosing Between Discriminative vs Generative Models
URL: https://www.data-mania.com/blog/discriminative-vs-generative-models/
Type: post
Modified: 2026-03-17
The differences between discriminative vs generative models have significant downstream implications for your data and AI strategy. Today I want to provide you some analogies to quickly illustrate the differences between these types of models, then I’ll explain how these differences impact your strategic decision-making process, and how you should go about implementing your model choices.
Spoiler Alert: This content was meant to be included within the pages of my upcoming Wiley book on data and AI strategies for growth. I decided to cut it from my manuscript and share it directly with Convergence members here, but if you’d like to be notified when the book becomes available, be sure to jot your name down on this list.
Illustrating discriminative vs generative modelswith examples
I’ll start off with a simple example. You know how Gmail quickly separates spam from non-spam emails? What it’s doing is “classifying” or “segregating” the email data into two buckets. A discriminative model does exactly that. It identifies patterns that differentiate one type of data from another. When you’re training a discriminative model, you’re teaching it to understand or discover patterns that are distinct to each data type. So, a good model here is one that can detect spam mail from a mile away and throw it in the bin.
When trained well, your generative model can actually create a text that resembles a spam email. Woohoo! 🥳
Generative models are different. Instead of just identifying the differences between the data types, they’ll actually attempt to “understand” the inherent characteristics of spam and non-spam data. This is analogous to profiling. These models could be used to clue in on what goes into creating a spam or a non-spam email and – with each iteration – they’ll get more accurate and more precise. So, when trained well, your generative model can actually create a text that resembles a spam email. Woohoo! 🥳
Choosing between discriminative vs generative models
When choosing between these two types of models, you need to closely examine the specific objectives and requirements of the data strategy you’re building. For instance, if the strategy is focused on identifying trends in customer behaviors or segmenting markets, discriminative models might be more appropriate. Conversely, if the strategy involves innovating new products or simulating data for stress testing, then generative models would be more suitable.
Let’s say you have all your data resources in one place, and you want to build a forecasting or predictive feature atop them. If you wanted to predict whether a customer will churn, or forecast sales for the next quarter, then the discriminative model would be a better choice because it will help you predict, forecast, cluster, or classify based on your requirement.
If, however, you’re looking to add a new feature but you don’t have the data resources in place support it, then you’ll typically start by gathering data. In this data-gathering phase, you can start off by using a LLM + RAG to generate synthetic data that you can use for the pilot phase while you put your data-gathering mechanism or pipeline in place. Here, you’ll be using what we refer to as the generative model. In fact, there are third-party companies that solely work on helping you generate data through simple queries or prompts.
Tip: When you’re using LLMs to generate data, be sure to provide detailed prompts to help you generate close to real-world data.
Action steps for implementing your model choices
Now that you understand the fundamental differences between discriminative vs generative models and how they fit into differing strategic needs, the next step is to evaluate your current projects and data initiatives. To do that, start by asking yourself:
Which projects could benefit from more precise classification or prediction? Consider using discriminative models for these to improve accuracy and efficiency.
Where might you innovate or create with data? For projects needing innovation or simulation, look into employing generative models to foster creativity and extend your data capabilities.
Assess your data readiness: Do you have the necessary data to support these models? If not, it might be time to explore synthetic data generation or to bolster your data collection strategies.
Consult with experts: If you’re unsure about the best approach, consider reaching out to an advanced data science consultant who can provide you with the insights and guidance you need to tailor to your specific circumstances.
By actively applying these considerations, you can more effectively align your AI strategy with your business objectives to gain a clear competitive edge. Keep in mind, when choosing between discriminative vs generative models, the right model will be able to support your current needs and adapt to future challenges and opportunities.
I hope this post was on discriminative vs generative models helpful! And, if you’d like to be notified when book launch festivities begin, be sure to jot your name down on this list.
Warm regards,
Lillian Pierson
PS. If you’re looking for marketing strategy and leadership support with a proven track record of driving breakthrough growth for B2B tech startups and consultancies, you’re in the right place. Over the last decade, I’ve supported the growth of 30% of Fortune 10 companies, and more tech startups than you can shake a stick at. I stay very busy, but I’m currently able to accommodate a handful of select new clients. Visit this page to learn more about how I can help you and to book a time for us to speak directly.
PPS. If you liked this blog post, share it with a friend!
Discover untapped profits in your marketing efforts today!
Tired of guesswork and inefficiencies in your marketing strategy?
Make sure to download my B2B Marketing KPI Scorecard & Pipeline Tracker before I move it behind a paywall.
It’s packed with insights and processes specifically designed to help B2B tech companies to boost marketing ROI and speed up sales processes — no strings attached! Grab yours today and start seeing the benefits by tomorrow.
HAND IT OVER >>
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## RAG Agent 101: A Primer for Data Pros
URL: https://www.data-mania.com/blog/rag-agent/
Type: post
Modified: 2026-03-17
Ardent Convergence reader’s know what RAG is, but what you may not know is how much loot you can make with RAG agent development skills.
I’m talking like a cool $855k per year that’s being paid by Anthropic for ENTRY-LEVEL AI researchers.
And if that’s not enough to get your attention, then listen to this…
You can get started developing your AI engineering skills today, for free, with me.
In this installment, I’m talking about what RAG agents are, how they work, and how to get your hands a little dirty with an exciting free training session.
RAG Agent 101: Cliff Notes Version
RAG agents are a fusion of retrieval-based and generative AI models that are designed to improve the capability of machine learning systems in handling complex information tasks. At its core, a RAG agent employs a two-step process: first, it retrieves relevant information from a vast data set, and then it uses this information to generate responses or predictions.
This dual approach allows for more accurate and contextually relevant outputs, especially in scenarios that require a nuanced understanding.
Components of a RAG Agent
A RAG agent typically consists of several key components that work together to accomplish its tasks:
Retrieval module: This component is responsible for the initial step of the RAG process. It sifts through large datasets to find content that closely matches the query at hand. The retrieval module is often powered by a deep learning algorithm that assesses and ranks data relevance.
Transformer-based model: After retrieval, the selected information is passed to a transformer-based model, which is a type of deep learning model that’s renowned for its ability to handle sequences of data. This model uses the retrieved information to generate coherent and contextually appropriate responses. The transformer adjusts its output based on the nuances of the input it receives, which improves the overall adaptability of the RAG agent.
The retrieval module ensures that the transformer has access to the most relevant and accurate data, thereby enabling the generation of high-quality output. This harmonious interplay both improves the efficiency of data processing, while elevating the quality of decisions and responses that are generated by the AI systems.
Importance of RAG Agents in Modern Data Environments
There are several beneficial reasons that forward-thinking companies should look to integrate a RAG agent. Let’s explore those…
Improving Data Retrieval Outcomes
The first and foremost advantage of RAG agents is their ability to improve data retrieval processes. By integrating retrieval and generative components, these agents can pinpoint and extract the most relevant information from extensive databases. This capability is particularly vital in environments where the accuracy and speed of information retrieval directly influence business outcomes.
Application in Decision-Making
RAG agents are instrumental in automated decision-making systems. Their ability to quickly assimilate and process large volumes of data enables them to provide real-time recommendations and decisions.
For example, in customer service, RAG agents can analyze incoming queries and historical data to generate responses that are not only timely but also contextually appropriate, thus improving customer satisfaction and operational efficiency.
Benefits Over Traditional Models
Compared to traditional retrieval-only or generative-only models, RAG agents offer several distinct advantages. Those are:
Contextual relevance: The hybrid nature of RAG agents allows them to understand and respond to queries with a level of detail and specificity that is not achievable by standalone models.
Scalability: As databases, traditional models struggle to maintain the speed and accuracy of their responses. With their efficient data handling and processing capabilities, RAG agents scale more effectively with increasing data.
Flexibility: They adapt to a variety of tasks, from answering complex queries to providing data-driven insights, which makes them versatile tools for numerous industries.
The continued development and integration of RAG agents into data systems will undoubtedly play a pivotal role in shaping the future of AI applications in business moving forward.
Practical Applications of RAG Agents
One of the best ways to illustrate the effectiveness of RAG agents is through real-world applications.
For instance, a major online retailer implemented RAG agents to improve its customer service chatbots. By integrating RAG technology, the chatbots could retrieve product information, customer order histories, and frequently asked questions to provide more accurate and personalized responses. This led to a significant decrease in customer wait times and an increase in satisfaction rates.
Another example comes from the healthcare sector, where RAG agents were used to streamline medical research. Researchers used RAG agents to quickly sift through thousands of academic papers and clinical reports to find relevant studies related to specific medical conditions. This capability significantly reduced the time needed for literature reviews, allowing for faster progression in research and development.
Other notable RAG agent use cases:
Healthcare: In healthcare, RAG agents assist in diagnosing diseases by quickly analyzing symptoms and medical histories against vast databases of medical information. They also support personalized medicine by tailoring treatments based on individual patient data.
Finance: Financial institutions use RAG agents for real-time market analysis and fraud detection. By analyzing transaction data and comparing it against historical patterns, RAG agents help identify potential fraud swiftly and accurately.
Customer service: Many businesses employ RAG agents in their customer service operations to improve interaction quality. These agents pull relevant information based on customer inquiries, thus ensuring that responses are both accurate and contextually tailored.
E-Commerce: E-commerce platforms leverage RAG agents for personalized shopping experiences. By analyzing past purchases and browsing behaviors, RAG agents recommend products that are more likely to resonate with individual customers.
Getting Started with RAG Agents
For those looking to integrate RAG agents into their operations, several platforms and tools are key:
Hugging Face Transformers: This library offers a range of models that can be adapted to create RAG agents, with extensive documentation and community support.
Google Cloud AI and ML Platforms: These provide robust infrastructure and services to develop and deploy RAG agents.
IBM Watson: Known for its powerful cognitive capabilities, IBM Watson can be used to build sophisticated RAG agents that require deep contextual understanding.
Implementing RAG agents involves careful planning and execution. Here are some best practices you’ll want to keep in mind:
Data quality: Ensure that the data you use for training RAG agents is high-quality, diverse, and representative of real-world scenarios.
Continuous Learning: Regularly update the models with new data to keep the RAG agents accurate and relevant.
Ethical Considerations: Be mindful of privacy and ethical concerns. Don’t allow RAG agents to handle sensitive personal data.
By incorporating RAG agents, organizations can improve their operational efficiency, improve decision-making, and bolster customer satisfaction.
Free Training & Demo On How To Build Your Own RAG Agent
If you’re ready to revolutionize your data management and decision-making processes? I invite you to join us for an exclusive training that is dedicated to exploring the innovative world of RAG agents. This on-demand training session is designed for data professionals who are eager to harness the potential of RAG technology to elevate their operations and achieve new levels of efficiency and accuracy.
What You’ll Learn:
Insights from Industry Experts: Learn from leading data scientists and AI specialists who are pioneering the use of RAG agents in various sectors.
Live Demonstrations: Witness firsthand the capabilities of RAG agents through live demonstrations that showcase their application in real-world scenarios.
Tools and Technologies: Discover the key tools and platforms that facilitate the development and deployment of RAG agents in your organization.
Best Practices: Gain valuable insights on how to implement and optimize RAG agents effectively, thus ensuring you make the most out of this transformative technology.
This is an invaluable opportunity for data professionals, IT managers, and business leaders to understand and leverage the benefits of RAG agents. Whether you are looking to improve your customer interactions, improve data retrieval, or drive more informed decision-making, this event will provide the knowledge and tools needed to succeed.
Take The Training Now! (before they take it down)
** This blog is produced in proud partnership with SingleStore. Check out some of their other trainings here.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## No-Code MySQL Optimization: Strategies for Seamless Scaling
URL: https://www.data-mania.com/blog/mysql-optimization/
Type: post
Modified: 2026-03-17
Efficient database performance is crucial for seamless scaling and consistent growth. The problem is that MySQL often faces performance bottlenecks that slow down your applications and impact your bottom line. Fortunately, there are many no-code MySQL optimization strategies that are available to help you overcome these challenges without the need for extensive coding knowledge.
By leveraging these advanced techniques, tech leaders can ensure their MySQL databases run in the smooth manner that’s required for their businesses to thrive. In this post, we’ll explore some practical no-code strategies for optimizing MySQL performance that will enable your company to scale effortlessly and maintain a competitive edge.
Brought to you by:
SingleStore – Join their free training on achieving 100x better SQL performance at scale with no-code changes.
Solutions for MySQL Performance Bottlenecks
MySQL performance bottlenecks significantly hinder your application’s efficiency and scalability. Common issues include slow query execution, inefficient indexing, and suboptimal configuration settings. These bottlenecks degrade user experience and limit your ability to scale operations in a seamless manner.
For B2B tech startups and consultancies that need rapid growth, it’s crucial to identify and address these performance issues. No-code optimization tools provide an accessible and effective way to diagnose and resolve these bottlenecks so that you can achieve smoother and more reliable database performance.
MySQL Optimization Strategy 1: Index Optimization
Proper indexing is vital for MySQL optimization, as it allows the database to retrieve data more efficiently. However, creating the right indexes can be complex without a deep understanding of your data and queries. No-code tools simplify this process by automatically analyzing your database usage patterns and suggesting optimal indexes.
These tools identify which columns would benefit most from indexing and implement these changes without requiring manual coding. By utilizing automatic index optimization, you’ll significantly speed up query response times, while reducing load on the database, and improving the overall application performance.
MySQL Optimization Strategy 2: Query Optimization
As you no doubt know, poorly written queries lead to slow response times and increased server load. No-code query optimization tools analyze your existing queries, identify inefficiencies, and suggest improvements.
These tools provide insights into how your queries interact with the database. They bring to light issues like unnecessary complexity and missing indexes. By leveraging these tools, you can streamline your queries and ensure they run as efficiently as possible, which in turn improves performance and frees up resources so that your company can handle more transactions and can scale more smoothly.
MySQL Optimization Strategy 3: Configuration Tuning
MySQL optimization requires optimal configuration settings, but manually tuning these settings is often complex and time-consuming. No-code platforms simplify this process by automatically adjusting key configurations, such as memory allocation, cache sizes, and connection limits (all these adjustments are made based on your database’s workload, of course.)
These platforms continuously monitor performance and make real-time adjustments to ensure optimal efficiency. This means you’re able to maintain peak performance without the need for in-house database expertise, which of course enables more seamless and scalable operations.
MySQL Optimization Strategy 4: Monitoring and Diagnostics
No-code monitoring tools provide real-time insights into your database’s health by automatically detecting issues like slow queries, high resource usage, and potential bottlenecks. These tools generate detailed diagnostics and performance reports that support proactive management and a quick resolution of problems. Automated monitoring ensures that your MySQL databases run smoothly, and they support the seamless scaling and disruption prevention that you need to more readily drive business growth.
Obviously no-code MySQL optimization strategies are powerful, but how to get started?.?
Free Training Alert: Supercharge Your MySQL Apps 100x at Scale with No Code Changes
Join our on-demand training, “Supercharge Your MySQL Apps 100x at Scale with No Code Changes,” for deeper insights and practical optimization strategies.
What you’ll get:
Achieve rapid performance gains: Learn how to optimize your MySQL applications for a 100x performance improvement without writing a single line of code.
Scalable solutions for growth: See strategies that allow your applications to seamlessly handle increased traffic
Practical, hands-on learning: Get actionable tips and step-by-step guidance from industry experts that you can apply immediately to your MySQL environments.
Stay competitive: Equip yourself with the latest MySQL optimization techniques to keep your applications running smoothly and maintain a competitive edge in the market.
Register now!
If you enjoyed this post, you’ll probably also enjoy this training on GenAI recommenders too.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Startup Revenue Growth: 8 Must-Have Systems for 2024
URL: https://www.data-mania.com/blog/startup-revenue-growth-guest-post/
Type: post
Modified: 2026-03-17
Startups exist in a fiercely competitive ecosystem. Without adapting and growing – death is certain. That’s why you need to future proof your startup revenue growth by making sure you’re covered on the 8 essential business system types that we’re sharing in this blog post.
The facts are scary – in the global startup industry, that’s predicted to be worth over $3.6 trillion by 2025, an overwhelming 90% of startups will fail in the first 3 years of operations.
In 2024, the startup ecosystem is filled with fiercely competitive and adaptive entrepreneurs who are extremely agile, globally-connected, and highly reliant on truly innovative systems for their company’s survival and startup revenue growth.
Emerging systems and technologies such as artificial intelligence, blockchain, and advanced data analytics are now serving as lifelines for startup entrepreneurs, offering unprecedented opportunities to scale, connect with target audiences, streamline operations, and increase revenue.
In this article, we compiled a list of 8 essential system types for both seasoned startup entrepreneurs and first-time founders starting their journey that cater to various aspects of startup operations and help boost startup revenue growth in 2024.
Why founders need to embrace new and innovative systems to accelerate startup revenue growth
Startups seeking to stay ahead require innovation and the adoption of innovative systems.
As startup founders and entrepreneurs, you need powerful and innovative systems in place in order to:
Accelerate overall startup revenue growth,
Optimize startup operations,
Connect with new and existing customers,
Protect your assets and infrastructure, and
Navigate the complexities of the business landscape.
These functions are critical to accelerating your startup revenue growth in an efficient manner.
Startups must therefore strategically integrate digital resources like digital marketing tools, financial management platforms, e-commerce platforms, cybersecurity resources, remote work solutions, and others to drive revenue growth and build a foundation for long-term success in the ever-changing world of entrepreneurship.
8 essential system types of startup revenue growth
We’ve broken down these system types into 8 logical categories as follows…
1. Startup marketing tools
The crazy world of startups requires a strategic approach to marketing.
In 2024, the business landscape is very dynamic and as such, leveraging the right marketing systems and B2B e-commerce solutions is a startup game-changer.
To boost revenue growth, startups need tools with features that provide solutions to their specific marketing needs These tools include:
Social media marketing tools: Leading social media platforms such as Instagram, Facebook, Twitter, and LinkedIn provide startups with a direct avenue to connect with their target audience. Tools like Hootsuite, Buffer, and Sprout Social help startups build a strong online presence and engage their audience effectively by streamlining social media management, scheduling, and analytics.
Email marketing automation tools: From automated drip campaigns to personalized content to customers, platforms like MailChimp, HubSpot, and Klaviyo help streamline workflows, boost conversion rates, and nurture leads throughout the customer journey.
SEO and content marketing resources: Platforms like SEMrush, Ahrefs, and Google Analytics boost online visibility by offering clear insights into keyword performance, competitor analysis, and overall website health. Also, you can use tools like Grammarly to create compelling content that captivates your audience and drives organic traffic.
2. Financial management platforms
To maximize startup revenue growth, effective financial management is critical.
Financial planning and management tools are important for startups looking to optimize revenue growth. From forecasting, budgeting, and creating an investor agreement template, to real-time analytics, these platforms offer a comprehensive suite of features designed to enhance financial decision-making. These tools include:
Accounting software for startups: Platforms like QuickBooks, Xero, and FreshBooks offer easy-to-understand interfaces, automated bookkeeping, and real-time financial reporting for accurate and efficient accounting to streamline financial operations and ensure compliance with accounting standards.
Expense tracking apps: Apps like Expensify, Zoho Expense, and Receipts by Wave offer receipt scanning, automated categorization, and real-time expense reporting for startups to maintain financial health, make informed decisions, gain control over expenditures, optimize budgets, and improve their overall financial efficiency.
Payment and invoicing platforms: For startups, timely and accurate invoicing and efficient payment processing are critical for maintaining healthy cash flow. Payment and invoicing platforms like Stripe, Square, and FreshBooks Payments drive seamless online transactions, automate invoicing processes, and improve the overall payment experience for startups and their clients.
3. Project management solutions
For startups juggling multiple projects and deadlines, project management solutions are critical.
Efficient project management tools drive innovation, productivity, and revenue growth for startups seeking to execute projects and boost revenue growth. These tools include:
Task management apps: Platforms like Todoist, Wunderlist, and Microsoft To-Do provide task categorization, due date tracking, and collaboration functionalities to help startups organize, prioritize, track tasks, and enhance team productivity.
Collaboration platforms: In this era of remote work and global collaboration, startups need collaboration platforms like Slack, Microsoft Teams, and G-Suite to enable real-time communication but also offer file sharing, project-specific channels, and integration capabilities.
Workflow automation tools: Tools like Zapier, Automate.io, and Integromat optimize startup processes, reduce manual efforts, and focus on high-impact activities that boost revenue growth and successful project execution.
4. Customer relationship management (CRM) systems
Customer relationships lie at the heart of startup success.
Centralizing customer data, tracking interactions, and personalizing communication help startups maintain a loyal customer base and grow revenue. These tools include:
Customer data management tools: Platforms like Airtable, Insightly, and Pipedrive offer features such as contact segmentation, data analytics, and integration capabilities, providing startups with a comprehensive view of their customers and enabling personalized and targeted engagement.
Sales automation platforms: To optimize sales funnels, reduce manual tasks, and focus on building meaningful relationships with prospects, startups can use tools like Salesflare, Close.io, and Freshsales to automate lead management, sales outreach, and pipeline tracking.
Customer support software: Offering features such as ticketing systems, live chat, and knowledge bases, customer support software like Zendesk, Intercom, and Freshdesk helps startups provide timely and personalized support, address customer queries, and improve overall customer experience.
5. E-commerce platforms
Having a strong online presence on e-commerce platforms is a necessity for startups.
The global shift towards e-commerce has reshaped the business landscape, and startups now have to establish a strong online presence with platforms such as Shopify, WooCommerce, and BigCommerce. To continually retain their online presence, startups have to use tools such as:
Online store builders: For startups, online store builders like Wix, Squarespace, and Weebly offer intuitive drag-and-drop interfaces, responsive design templates, and integrated e-commerce functionalities to help startups design and manage their e-commerce websites with ease.
Payment gateways and checkout solutions: Seamless and secure payment gateways and checkout solutions like Stripe, PayPal, and Square support multiple currencies, and improve the overall checkout experience for customers shopping on startups’s online stores.
Inventory management tools: Inventory management tools such as TradeGecko, Zoho Inventory, and Orderhive enable startups to track stock levels, manage product variants, synchronize inventory across multiple sales channels, streamline order fulfillment, and enhance overall customer satisfaction.
6. Cybersecurity resources
Startups can’t afford to joke about the threat of cyber-attacks
The surge in digitalization has opened new opportunities for startups, but it has also given rise to cyber threats. There’s a critical need for startups to prioritize cybersecurity to protect sensitive data, safeguard customer trust, and secure the digital infrastructure of startups. These tools include:
Antivirus software: Leading antivirus programs such as Bitdefender, Norton, and McAfee offer real-time scanning, threat detection, and system optimization features as first-line defense programs against viruses, malware, and other cybersecurity threats.
Secure communication tools: Effective and secure communication tools like Signal, Wickr, and Telegram provide end-to-end encryption, secure file sharing, and self-destructing messages, ensuring that confidential communications remain protected and a secure business environment is created.
Data encryption solutions: As startups handle an increasing amount of sensitive data, data encryption solutions such as VeraCrypt, Symantec Endpoint Encryption, and BitLocker enable the encryption of files, folders, and entire disk drives.
7. Analytics and data insights solutions
Startups require analytics and data insights to make smart decisions and boost revenue.
To understand their customers, optimize operations, and drive revenue growth, startups must embrace the power of data-driven decision-making. Tools that can help with these include:
Web analytics platforms: Understanding user behavior across web-based platforms is crucial for startups looking to optimize their online presence. Platforms such as Google Analytics, Mixpanel, and Hotjar offer insights into website traffic, user engagement, and conversion rates.
Data visualization tools: Data, when presented visually, becomes a powerful tool for understanding complex trends and patterns. Tools like Tableau, Power BI, and Infogram help startups to transform raw data into interactive and easy-to-understand visual information to communicate insights effectively, make informed decisions, and foster a data-driven culture within their organizations.
Business intelligence solutions: Tools such as Looker, Domo, and Sisense, go beyond basic analytics to provide holistic insights into various aspects of business operations. These solutions integrate data from multiple sources and offer a unified view that facilitates strategic decision-making to analyze trends, forecast future performance, and optimize business processes for sustained revenue growth.
8. Remote work solutions
In today’s world of startups, remote work is a pivotal strategy for success.
The modern startup environment demands flexibility, efficiency, and seamless collaboration – qualities that remote work solutions provide. By embracing remote work solutions, startups will not only attract top talent but also optimize operations for sustained revenue growth. These tools include:
Video conferencing platforms: In the startup world where face-to-face interactions are not always possible, video conferencing platforms have become essential for effective communication. Platforms such as Zoom, Microsoft Teams, and Google Meet provide startups with the capability to host virtual meetings, conduct presentations, foster team collaboration in real-time across geographical gaps, improve team connectivity, and contribute to the overall success of remote work initiatives.
Project management tools with remote capabilities: The coordination of tasks and projects is vital for remote teams. Project management tools such as Asana, Trello, and Notion help with task assignment, progress tracking, and team collaboration, ensuring that startups can maintain operational efficiency regardless of their team members’ physical locations and drive revenue growth in the remote work landscape.
Communication and chat apps: Effective communication is the backbone of remote work success. Communication and chat apps like Slack, Microsoft Teams, and Telegram go beyond traditional email communication, offering real-time chat, file sharing, and collaboration features to create a virtual workspace where remote teams can stay connected, fostering a culture of collaboration essential for boosting startup revenue growth.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## [PODCAST] AI in B2B Marketing Success in 2024
URL: https://www.data-mania.com/blog/podcast-ai-in-b2b-marketing-success-in-2024/
Type: post
Modified: 2026-03-17
In this episode of Digital Transformation Success, we chat with Lillian Pierson, a fractional CMO with extensive experience in the B2B tech space. Lillian shares insights into digital transformation success, product-led growth, and the impact of data and AI in B2B marketing strategies.
From influencer marketing to product validation and building communities, Lillian’s expertise sheds light on the dynamic landscape of modern marketing. Join us for a deep dive into strategies for exponential growth and the importance of aligning product and marketing efforts.
Whether you’re a founder, leader, or marketing enthusiast, Lillian’s insights are sure to inspire and inform. Tune in and gain valuable insights from a seasoned marketing professional.
3 Knowledge Bombs on AI in B2B Marketing in 2024
Generative AI’s Transformative Impact: Generative AI technologies, such as those capable of producing text, images, and videos, are revolutionizing marketing and content creation by enabling rapid, high-quality output and personalized customer interactions. This automation not only increases efficiency but also drives deeper customer engagement and loyalty.
Market Validation and Product Market Fit: Thorough market validation, including market research, competitive analysis, and voice of customer surveys, is essential for developing products that truly meet customer needs. Skipping this step can lead to significant financial losses and product failure, highlighting the importance of validating ideas before heavy investment.
AI-Driven Personalization and Retention: AI-driven platforms like Humanic combine predictive analytics with generative AI to create personalized user experiences that enhance engagement and retention. By automating tailored responses to user behavior, these technologies support product-led growth and reduce the need for constant human intervention.
In This Episode, You’ll Learn
06:14 Built marketing automation for SaaS products’ growth via Humanic AI.
09:12 Strategy type impacts growth, not strategy itself.
12:36 Retain, build loyalty, and upgrade new users.
16:50 Proper research ensures successful business ventures.
17:48 Retrofit SaaS product, multi-criteria decision-making, market research.
23:29 About Lillian’s new book, The Data & AI Imperative: Designing Strategies for Exponential Growth
Links & Resources Mentioned In This Episode:
Humanic AI agent powered automated marketing platform for PLG companies.
The Data & AI Imperative: Designing Strategies for Exponential Growth (available for pre-order now)
Enjoyed The Show?
Head over to the Digital Transformation Success podcast and get yourself subscribed today!
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Wiley & Renowned Data-Driven Growth Strategist Announces the Upcoming Release of The Data & AI Imperative—A Blueprint for Leveraging AI Strategy for Extraordinary Business Growth
URL: https://www.data-mania.com/blog/ai-strategy-book/
Type: post
Modified: 2026-03-17
Wiley and renowned data-driven growth strategist, Lillian Pierson, are excited to announce the release of The Data & AI Imperative: Designing Strategies for Exponential Growth, set to launch on December 5, 2024. This highly anticipated book provides business leaders, technology professionals, and data professionals with a comprehensive guide to harnessing the transformative power of data and AI strategy to drive sustainable growth.
In The Data & AI Imperative, Pierson shares her proprietary STAR Framework™, an actionable roadmap designed to help organizations unlock the full potential of AI strategy and their data assets. The book equips readers with the strategies they need to implement AI-driven projects, optimize decision-making, and capitalize on cutting-edge technologies that are reshaping industries across the globe.
About the Book
The Data & AI Imperative is a masterclass in leveraging data and AI strategy to fuel business growth. Lillian Pierson, a celebrated leader in data and AI strategies for growth, distills nearly two decades of experience into a clear, actionable roadmap for companies of all sizes.
The book introduces Pierson’s proprietary STAR Framework™, a step-by-step guide that helps organizations assess their current data capabilities and build strategies that align AI initiatives with business objectives. From AI-driven marketing and product innovation to operational efficiency and ethical AI practices, The Data & AI Imperative covers the full spectrum of AI’s potential in the modern business world.
Readers will discover how to:
Design and execute strategies for AI projects and products that drive measurable growth.
Apply AI strategy to optimize product development, customer engagement, and decision-making.
Address key challenges such as AI ethics, data privacy, and regulatory compliance.
Execute AI strategy for real-time business innovation and competitive advantage.
With real-world case studies, deep industry insights, and practical tools for decision-making, The Data & AI Imperative is an essential resource for executives, product managers, and data professionals seeking to thrive in the AI era.
About the Author
Lillian Pierson, P.E., is a globally recognized data-driven growth strategist, educator, and founder of Data-Mania. With over 20 years of experience, Pierson has helped organizations across the globe—ranging from Fortune 500 companies to fast-growing tech startups—design and execute successful data and AI strategies. Her unique ability to translate complex AI and data concepts into actionable business strategies has made her one of the most sought-after experts in the field.
Pierson has educated over 2 million learners through her books, courses, and consulting work. She is known for empowering businesses to harness the full potential of data and AI strategy to achieve sustainable growth. Her contributions have driven the expansion of some of the most well-known global brands, making her an influential voice in the fields of growth, data science, and AI leadership.
The Data & AI Imperative is her latest work. It’s designed to give business leaders and professionals the tools they need to thrive in today’s AI-driven world.
Why The Data & AI Imperative is Timely
As businesses across the globe grapple with the rapid rise of artificial intelligence and data technologies, The Data & AI Imperative arrives at the quintessential moment. AI is no longer a future trend—it’s a present reality that’s reshaping industries from healthcare and finance to retail and manufacturing. To remain competitive, organizations must embrace AI-driven solutions that fuel innovation, enhance customer experiences, and streamline operations.
Lillian Pierson’s book offers a timely and practical guide for navigating this transformation. With AI playing a pivotal role in everything from product development to personalized marketing, business leaders need a clear AI strategy to integrate these technologies into their operations. The Data & AI Imperative empowers executives, data professionals, and product managers to take control of their AI journey and leverage cutting-edge tools like generative AI, predictive analytics, and machine learning to unlock new growth opportunities.
By addressing both the opportunities and challenges posed by AI—including ethics, regulatory compliance, and data privacy—this book offers a comprehensive framework for success in a rapidly evolving technological landscape.
Target Audience
The Data & AI Imperative is designed to serve a wide range of professionals who are looking to harness data and AI strategy to drive their businesses forward. The book speaks directly to:
C-suite Executives (CEOs, CMOs, CIOs): Those responsible for guiding the strategic direction of their organizations will find actionable insights for integrating AI strategy and data into business plans that generate measurable growth. This book provides the tools to leverage AI to optimize processes, increase operational efficiency, and create sustainable competitive advantages.
Product Managers and Business Leaders: Those tasked with developing and launching new products or initiatives will benefit from detailed case studies and real-world examples of how AI can be used for ideation, innovation, and product development. The strategies outlined in the book will help them maximize AI’s potential to scale products and services in competitive markets.
Data Professionals and Technology Teams: For data scientists, AI engineers, and IT professionals, the book offers an in-depth look at how to build data strategies and deploy AI technologies effectively. Readers will learn how to align data initiatives with larger business objectives, overcome common technical challenges, and ensure ethical, compliant AI deployments.
With insights that are tailored for diverse roles, The Data & AI Imperative is an indispensable guide for anyone involved in shaping the future of their organization through data and AI strategy.
Key Features of The Data & AI Imperative
Lillian Pierson’s The Data & AI Imperative stands out as a comprehensive guide that combines theory, practical applications, and strategic frameworks. Some of the key features of the book include:
The STAR Framework™: Pierson introduces her proprietary framework that is designed to help businesses assess their current data capabilities and build AI strategies that align with organizational goals. The framework provides step-by-step guidance for implementing data-driven initiatives that yield predictable, sustainable growth.
Real-World Case Studies: The book includes detailed examples of successful AI implementations across different industries, offering readers a clear understanding of how leading companies are using AI to innovate, improve decision-making, and drive growth. These case studies demonstrate the tangible benefits AI can deliver, from operational efficiency to enhanced customer experiences.
Ethics and Compliance: Addressing the growing concern around ethical AI practices, Pierson provides a thorough exploration of data privacy, AI transparency, and regulatory compliance. She helps readers navigate the legal and ethical challenges that come with deploying AI, ensuring businesses can avoid risks and maintain trust with customers.
Practical Tools and Resources: Throughout the book, readers are given access to practical resources like templates, worksheets, and checklists to help them implement the strategies outlined. These tools support the successful execution of data and AI projects by providing a clear, actionable roadmap from planning to deployment.
Future-Proofing Businesses: The book brings to light exactly how businesses can prepare for the future by integrating a robust AI strategy into their core business strategies today. Pierson covers emerging technologies such as generative AI and foundation models, providing readers with insights into how these innovations will continue to reshape industries in the years to come.
These features ensure that The Data & AI Imperative is not only an informative read but also a highly practical resource for business leaders and professionals looking to drive exponential growth through data and AI.
Conclusion
The Data & AI Imperative: Designing Strategies for Exponential Growth will be available on December 5, 2024, in digital, paperback, and audiobook formats. Pre-orders are now open on Amazon, Wiley, and other major retailers.
This must-read book is an essential resource for anyone looking to future-proof their business with data-driven strategies and cutting-edge AI technologies. Whether you’re a seasoned executive, product manager, or data professional, The Data & AI Imperative provides a clear roadmap for developing and executing AI strategy that drives measurable and sustainable growth.
Don’t miss out on what the AI strategy experts are raving about! Read what industry leaders are saying and secure your pre-order of The Data & AI Imperative today.
And be sure to join the exclusive launch list to secure access to launch events and virtual trainings with Lillian Pierson, where she will discuss the insights behind the book’s early success and provide actionable takeaways for your business. Register now for this limited-edition event series.
For media inquiries, interview requests, or review copies, please contact:
Lillian Pierson
Email: lillian@data-mania.com
LinkedIn: https://www.linkedin.com/in/lillianpierson
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## How AI Is Transforming Video Creation: A Deep Dive into Synthesia
URL: https://www.data-mania.com/blog/how-ai-is-transforming-video-creation-a-deep-dive-into-synthesia/
Type: post
Modified: 2026-03-17
Enter AI video creation, a new paradigm that shifts the focus from production logistics to storytelling. Among the emerging leaders in this space is Synthesia, a platform that enables anyone to create professional videos using AI avatars and automated voiceovers – with no filming required.
In this post, we’ll explore what Synthesia is, why it matters, and how teams can leverage AI video to scale content production in a data-driven environment.
What Is Synthesia?
Synthesia is an AI video generation platform that allows users to create videos by typing a script – without the need for cameras, actors, or traditional editing workflows. Instead of filming, you select a lifelike AI avatar, choose a language and voice, paste in your script, and the platform produces a polished video.
Synthesia supports 140+ languages and accents, and offers a library of customizable avatars, making it a powerful solution for global teams and scalable video workflows.
At its core, Synthesia turns text into impactful video content that is:
fast to produce,
consistent in quality, and
easy to localize across markets.
Why AI Video Matters
Video consumption continues to rise year after year, but many organizations struggle to meet demand due to production bottlenecks:
Scheduling talent and crews adds weeks to a timeline.
Editing and post-production require specialized skills.
Localization multiplies costs and time.
According to Synthesia’s own industry data, 73% of organizations now see video as essential for training and internal communication – yet many lack the resources to produce enough of it. AI video tools like Synthesia address this gap by removing typical barriers to scale.
In essence, it’s not just about making videos faster – it’s about aligning video production with modern data-driven business insights and workflows.
Use Cases That Move the Needle
AI video creation isn’t limited to social clips or marketing teasers. Because Synthesia can generate videos quickly and in multiple languages, it unlocks high-impact use cases:
Corporate Training
HR and L&D teams can produce consistent onboarding videos, safety briefings, and skill courses without chasing availability for studio time.
Product Tutorials and Support
Customer success teams can generate how-to videos that demonstrate features clearly and professionally – keeping documentation and customer education in sync.
Localized Marketing
Global campaigns often struggle with localization. Synthesia simplifies this by letting teams produce one version of a video and automatically adapt it for different regions and languages.
Internal Comms at Scale
From CEO announcements to change management messaging, leaders can communicate more regularly without expensive production cycles.
How Synthesia Fits Into Modern Workflows
From a process perspective, Synthesia transforms what used to be a traditional linear workflow:
Old Model: Concept → Script → Filming → Editing → Localization → Publish
New Model with AI Video: Script → AI Video Generation → Publish
This is a huge shift, especially for teams that already use data analytics to guide content strategies:
Turn insights into video quickly
Test different messaging with minimal cost
Track performance like a digital asset
Iterate rapidly based on viewer behavior
It frees teams to focus on what matters most – the message – rather than the mechanics of production.
Pros and Cons at a Glance
Pros
Cons
No filming equipment or crew needed
Not designed for cinematic production
Supports 140+ languages and voices
Limited advanced visual editing features
Fast turnaround for iterative workflows
Premium features require paid plans
Ideal for corporate, training, and internal use
Less suitable for high-end creative studios
The Bigger Picture: AI + Data = Smarter Content
Data-driven organizations already track engagement, performance, and user behavior to shape their content strategies. Adding AI video generation into the mix accelerates not just execution, but experimentation:
Generate multiple versions of videos to A/B test messaging
Quickly localize video to match regional user data
Use performance data to refine scripts and delivery styles
In a world where video consumption continues to grow, the fusion of AI and data opens doors for smarter, faster, and more scalable visual communication.
Conclusion
Synthesia represents a fundamental shift in how we think about video production. By reducing technical barriers and focusing on content and communication outcomes, it empowers teams to produce professional-grade videos that align with business goals – faster and more cost-effectively than ever before.
As AI continues to evolve, tools like Synthesia aren’t just optional – they’re becoming essential for organizations that want to keep pace with digital communication demands.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## How Advanced Business Education Is Reshaping Growth Strategy in Tech
URL: https://www.data-mania.com/blog/how-advanced-business-education-is-reshaping-growth-strategy-in-tech/
Type: post
Modified: 2026-03-17
Technology companies rarely fail because they lack ambition. More often, they struggle because growth outruns structure.
That tension has become more visible over the past decade. As scaling firms move into regulated markets, expand internationally and manage increasingly complex data systems, executive expectations have shifted. Within that context, interest in a doctorate in business administration online has grown among senior leaders who want deeper analytical grounding behind their strategic decisions. The appeal is not theoretical. It reflects a change in how growth is evaluated.
Early-stage companies can rely on velocity. Speed to market, rapid hiring, aggressive iteration. But once revenue climbs and institutional capital enters the picture, velocity alone is not enough. Investors want clarity. Boards want governance architecture. Risk committees want documentation, not instinct. Strategic leadership has become more forensic.
When Growth Becomes Structural
Digital adoption has spread across nearly every sector. The World Bank has documented steady global increases in firm-level digital integration, which means technological capability is no longer rare. It is expected.
At the same time, digital transformation spending continues to rise. Statista projects global investment in digital transformation will approach $3.9 trillion by 2027. That scale of spending signals something important: transformation is no longer a side initiative. It is core infrastructure.
For technology firms, this creates layered exposure. A new feature launch may carry regulatory implications in multiple jurisdictions. Data storage decisions affect compliance risk. Pricing models influence long-term revenue recognition and investor reporting.
In that environment, leadership cannot operate on intuition alone. Informal processes that worked in a 40-person startup often fracture at 400 employees. Decision-making must hold up under external scrutiny.
Doctoral business programs focus heavily on systems analysis. They require candidates to examine cause and effect, to model organizational behavior and to defend conclusions through structured methodology. That habit of disciplined reasoning maps directly onto the demands of scaling enterprises.
The question shifts from “Can we grow?” to “Can we grow without breaking internal coherence?”
Data Is Abundant, Judgment Is Not
Most executive teams are saturated with metrics. Revenue dashboards update hourly. Customer retention curves are broken down by region and segment. Forecasting tools simulate multiple scenarios.
The global big data analytics market is projected to exceed $650 billion by 2029, according to Statista. That number reflects how deeply analytics are embedded in enterprise decision-making.
Yet more information has not eliminated uncertainty. If anything, it has made interpretation more contested.
Metrics rarely speak for themselves. Correlations appear persuasive until market conditions change. A strong quarter can conceal underlying volatility. Leaders often face competing models that suggest different strategic paths.
Doctoral-level research training introduces a different discipline. Executives are expected to design studies, test assumptions and account for limitations before presenting conclusions. The process forces clarity around methodology.
Consider a software company debating whether to enter a heavily regulated international market. Surface-level growth projections may look attractive. A deeper research approach would evaluate regulatory friction, infrastructure costs, currency exposure and compliance reporting obligations together rather than in isolation.
That approach slows decision-making slightly. It also reduces blind exposure.
In capital-sensitive markets where investor confidence can shift quickly, that distinction matters.
The Signaling Effect of Structured Expertise
Higher education institutions have responded to demand for flexible advanced programs. Research from Encoura indicates that nearly 9 in 10 colleges and universities plan to expand online offerings. Senior professionals are pursuing advanced study without leaving active roles.
At the same time, the executive education market continues to expand. Industry projections estimate growth from $49.17 billion in 2025 to nearly $78 billion by 2030. Organizations are still investing in leadership capability, even amid broader budget discipline.
The difference between an MBA and a DBA often becomes clearer at scale. MBA programs tend to emphasize management breadth and leadership execution. Doctoral programs lean more heavily into applied research, institutional analysis and long-term strategic modeling.
In sectors such as fintech, enterprise infrastructure and health systems technology, governance literacy has become central to board-level discussion. Investors assess risk management architecture alongside revenue growth. They examine how decisions are evaluated internally, not just what decisions are made.
A doctoral credential does not replace operational experience. It signals exposure to structured evaluation frameworks. In institutional environments, that signal contributes to credibility. Credibility, in turn, influences access to capital and partnerships.
Online Doctoral Study and Executive Reality
One reason online doctoral formats have gained traction is practical. Senior leaders rarely have the option to relocate for multiyear study. Digital delivery allows coursework to fit around operational responsibilities.
More importantly, applied research components are often integrated into real organizational challenges. Topics may focus on innovation governance, performance measurement systems, organizational change, or strategic risk modeling. The research is not abstract. It intersects with daily leadership decisions.
Executives pursuing advanced study often test frameworks inside their own companies. They collect internal data, refine hypotheses and adjust conclusions when evidence contradicts expectations. Academic rigor becomes part of operational rhythm.
Technology markets are unlikely to simplify. Regulatory oversight continues to expand. Data volumes continue to grow. Investor scrutiny remains intense.
In that environment, growth depends less on acceleration alone and more on structural resilience. Governance clarity, analytical discipline and long-term coherence have become competitive variables.
Advanced business education has entered this world not as a trend or status marker, but as one response to that structural pressure. For leaders navigating scale in data-intensive industries, the ability to defend strategy through disciplined reasoning is increasingly part of the role itself.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Checklist for Multiple Linear Regression
URL: https://www.data-mania.com/blog/a-5-step-checklist-for-multiple-linear-regression/
Type: post
Modified: 2026-03-17
A 5-Step Checklist for Multiple Linear Regression
Multiple regression analysis is an extension of simple linear regression. It’s useful for describing and making predictions based on linear relationships between predictor variables (ie; independent variables) and a response variable (ie; a dependent variable). Although multiple regression analysis is simpler than many other types of statistical modeling methods, there are still some crucial steps that must be taken to ensure the validity of the results you obtain.
PRO-TIP: Check out my brand new article on the use of regression and predictive analytics for improving marketing performance.
When using the checklist for multiple linear regression analysis, it’s critical to check that model assumptions are not violated. This is to fix or minimize any such violations, and to validate the predictive accuracy of your model.
Since the internet provides so few plain-language explanations of this process, I decided to simplify things – to help walk you through the basic process.
Please keep in mind that this is a brief summary checklist of steps and considerations. An entire statistics book could probably be written for each of these steps alone. Use this as a basic roadmap, but please investigate the nuances of each step, to avoid making errors. Google is your friend.
Lastly, in all instances, use your common sense. If the results you see don’t make sense against what you know to be true, there is a problem that should not be ignored.
Before getting into any of the model investigations, inspect and prepare your data. Check it for errors, treat any missing values, and inspect outliers to determine their validity. After you’re comfortable that your data is correct, go ahead and proceed through the following fix step process.
STEP 1. SELECTING YOUR VARIABLES
To pick the right variables, you’ve got to have a basic understanding of your dataset, enough to know that your data is relevant, high quality, and of adequate volume. As part of your model building efforts, you’ll be working to select the best predictor variables for your model (ie; the variables that have the most direct relationships with your chosen response variable). When selecting predictor variables, a good rule of thumb is that you want to gather a maximum amount of information from a minimum number of variables, remembering that you’re working within the confines of a linear prediction equation.
The two following methods will be helpful to you in the variable selection process.
Try out an automatic search procedure and let R decide what variables are best. Stepwise regression analysis is a quick way to do this. (Make sure to check your output and see that it makes sense)
Use all-possible-regressions to test all possible subsets of potential predictor variables. With the all-possible-regressions method, you get to pick the numerical criteria by which you’d like to have the models ranked. Popular numerical criteria are as follows:
R2 – The set of variables with the highest R2 value are the best fit variables for the model.
note: R2 values are always between 0 and 1.0
Adjusted R2 – The sets of variables with larger adjusted R2 values are the better fit variables for the model.
Cp – The smaller the Cp value, the less total mean square error, and the less regression bias there is.
PRESSp – The smaller the predicted sum of squares (PRESSp) value, the better the predictive capabilities of the model.
STEP 2. REFINING YOUR MODEL
Check the utility of the model by examining the following criteria:
Global F test: Test the significance of your predictor variables (as a group) for predicting the response of your dependent variable.
Adjusted R2: Check the overall sample variation of the dependent variable that is explained by the model after the sample size and the number of parameters have been adjusted. Adjusted R2 values are indicative of how well your predictive equation is fit to your data. Larger adjusted R2 values indicate that variables are a better fit for the model.
Root mean square error (MSE): MSE provides an estimation for the standard deviation of the random error. An interval of ±2 standard deviations approximates the accuracy in predicting the response variable based on a specific subset of predictor variables.
Coefficient of variation (CV): If a model has a CV value that’s less than or equal to 10%, then the model is more likely to provide accurate predictions.
STEP 3. TESTING MODEL ASSUMPTIONS
Now it’s time to check that your data meets the seven assumptions of a linear regression model. If you want a valid result from multiple regression analysis, these assumptions must be satisfied.
You must have three or more variables that are of metric scale (integer or ratio variables) and that can be measured on a continuous scale.
Your data cannot have any major outliers, or data points that exhibit excessive influence on the rest of the dataset.
Variable relationships exhibit (1) linearity – your response variable has a linear relationship with each of the predictor variables, and (2) additivity – the expected value of your response variable is based on the additive effects of the different predictor variables.
Your data shows an independence of observations, or in other words, there is no autocorrelation between variables.
Your data demonstrates an absence of multicollinearity.
Your data is homoscedastic.
Your residuals must be normally distributed.
STEP 4. ADDRESSING POTENTIAL PROBLEMS WITH THE MODEL
Most of the time, at least one of the model assumptions will be violated. In these cases, if you’re careful, you may be able to either fix or minimize the problem(s) that are in conflict with the assumptions.
If your data is heteroscedastic, you can try transforming your response variable.
If your residuals are non-normal, you can either (1) check to see if your data could be broken into subsets that share more similar statistical distributions, and upon which you could build separate models OR (2) check to see if the problem is related to a few large outliers. If so, and if these are caused by a simple error or some sort of explainable, non-repeating event, then you may be able to remove these outliers to correct for the non-normality in residuals.
If you are seeing correlation between your predictor variables, try taking one of them out.
If your model is generating error due to the presence of missing values, try treating the missing values. You can also use dummy variables to cover for them.
STEP 5. VALIDATING YOUR MODEL
Now it’s time to find out whether the model you’ve chosen is valid. The following three methods will be helpful with that.
Check the predicted values by collecting new data and checking it against results that are predicted by your model.
Check the results predicted by your model against your own common sense. If they clash, you’ve got a problem.
Cross validate results by splitting your data into two randomly-selected samples. Use one half of the data to estimate model parameters. Use the other half for checking the predictive results of your model.
More resources to get ahead…
Get Income-Generating Ideas For Data Professionals
Are you tired of relying on one employer for your income? Are you dreaming of a side hustle that won’t put you at risk of getting fired or sued? Well, my friend, you’re in luck.
This 48-page listing is here to rescue you from the drudgery of corporate slavery and set you on the path to start earning more money from your existing data expertise. Spend just 1 hour with this pdf and I can guarantee you’ll be bursting at the seams with practical, proven & profitable ideas for new income-streams you can create from your existing expertise.
Learn more here!
Take The Data Superhero Quiz
You can take a much more direct path to the top once you understand how to leverage your skillsets, your talents, your personality and your passions in order to serve in a capacity where you’ll thrive. That’s why I’m encouraging you to take the data superhero quiz.
This free and super-fun 45-second quiz is all about you and how your personality type aligns with the very best career path for you. It’s fun, free and it will provide you personalized data career recommendations, complete with potential roles that fit your unique skills and passions, as well as salaries associated with those roles.
Take the Data Superhero Quiz today!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Hierarchical Moderated Multiple Regression in R (Step-by-Step Demo)
URL: https://www.data-mania.com/blog/hierarchical-moderated-multiple-regression-analysis-in-r/
Type: post
Modified: 2026-03-17
In this tutorial, you’ll learn how to perform Hierarchical Moderated Multiple Regression in R, a technique that helps uncover how one variable changes the relationship between others in a dataset.
Moderator models are often used to examine when an independent variable influences a dependent variable. More specifically, moderators are used to identify factors that change the relationship between independent (X) and dependent (Y) variables. In this article, I explain how moderation in regression works, and then demonstrate how to do a Hierarchical Moderated Multiple Regression in R.
Pro-tip: Check out our new article on how hierarchical modeling informs growth forecasting in marketing here.
Understanding Hierarchical Moderated Multiple Regression in R
Hierarchical, moderated, multiple regression analysis in R can get pretty complicated so let’s start at the very beginning. Hierarchical moderated multiple regression extends traditional regression by testing interaction effects across levels, allowing you to see how moderators influence variable relationships. Let us have a look at a generic linear regression model:
Y = β0 + β1X + ϵ
Y is the dependent variable whereas the variable X is independent i.e. the regression model tries to explain the causality between the two variables. The above equation has a single independent variable.
So, what is moderation analysis?
Moderator (Z) models are often used to examine when an independent variable influences a dependent variable. That is, moderated models are used to identify factors that change the relationship between independent (X) and dependent (Y) variables. A moderator variable (Z) will enhance a regression model if the relationship between the independent variable (X) and dependent variable (Y) varies as a function of Z.
How does a moderator affect a regression model?
Let’s look at it from two different perspectives. First, looking at it from an experimental research perspective:
The manipulation of X causes change in Y.
A moderator variable (Z) implies that the effect of the X on the Y is NOT consistent across the distribution of Z.
Second, looking at it from a correlational perspective:
Assume a correlation between variable X and variable Y.
A moderator variable (Z) implies that the correlation between X and Y is NOT consistent across the distribution of Z.
Now before doing a Hierarchical Moderated Multiple Regression in R, you must always be sure to check whether your data satisfies the model assumptions!
Checking the assumptions
There are a couple of assumptions that the data has to follow before the moderation analysis is done:
The dependent variable (Y) should be measured on a continuous scale (i.e., it should be an interval or ratio variable).
The data must have one independent variable (X), which is either continuous (i.e., an interval or ratio variable) or categorical (i.e., nominal or quantitative variable) and one moderator variable (M).
The residuals must not be autocorrelated. This can be checked using the Durbin-Watson test in R.
This goes without saying, there needs to be a linear relationship between the dependent variable (Y) and the independent variable (X). There are a number of ways to check for linear relationships, like creating a scatterplot.
The data needs to show homoscedasticity. This assumption means that the variance around the regression line is the somewhat same for all combinations of independent (X) and moderator (M) variables.
The data must not show multicollinearity within the independent variables (X). This usually occurs when two or more independent variables that are highly correlated with each other. This can be visually interpreted by plotting a heatmap.
The data ideally should not have any significant outliers, highly influential points or many NULL values. The highly influential points can be detected by using the studentized residuals.
The last assumption is to check if the the residual errors are approximately normally distributed.
Demonstrating hierarchical, moderated, multiple regression analysis in R
Now that we know what moderation is, let us start with a demonstration of how to do hierarchical, moderated, multiple regression analysis in R
> ## Reading in the csv file
> dat <- read.csv(file.choose(), h=T)
Since the data is loaded into the R environment. I’ll talk about the data a bit. The data is based on the idea of stereotype threat. A couple of students are set up for an IQ test. When the students come up to take the test, they are given threats. These are implicit and explicit threats, such as “women usually perform worse than men in this test”. This, in turn, tends to affect the performance of the women candidates.
Here, the independent variable (X) is the experimental manipulation (threat) and the dependent variable (Y) is the IQ test score. The variable working memory capacity (wm) is the moderator. We will investigate how the threat affects the IQ test scores with the idea that maybe working memory (wm) has an effect on this relation. (i.e. to see if any of the participants who have a strong working memory are not impacted by the stereotype threat). Therefore, the moderator might say that the stereotype threat may work on some people and not work on some others.
The three threat categories are:
Explicit threat
Implicit threat
No threat (control)
Each group consists of 50 students. Let’s look at the structure of the data. The data:
> str(dat)
'data.frame': 150 obs. of 7 variables:
$ subject : int 1 2 3 4 5 6 7 8 9 10 ...
$ condition : Factor w/ 3 levels "control","threat1",..: 1 1 1 1 1 1 1 1 1
$ iq : int 134 121 86 74 80 105 100 121 138 104 ...
$ wm : int 91 145 118 105 96 133 99 97 96 105 ...
$ WM.centered: num -8.08 45.92 18.92 5.92 -3.08 ...
$ d1 : int 0 0 0 0 0 0 0 0 0 0 ...
$ d2 : int 0 0 0 0 0 0 0 0 0 0 ..
Looking at the structure of the data frame… The condition variable is categorical with three levels as already discussed. Since there are three categorical variables, we have to create dummy variables of n-1. Where n is the number of categories. So d1 and d2 are the dummy encoded variables. When d1 and d2 is 0, the condition is control. When d1 is 1 the condition is threat1. When d2 is 1 the condition is threat2.
> head(dat)
subject condition iq wm WM.centered d1 d2
1 1 control 134 91 -8.08 0 0
2 2 control 121 145 45.92 0 0
3 3 control 86 118 18.92 0 0
4 4 control 74 105 5.92 0 0
5 5 control 80 96 -3.08 0 0
6 6 control 105 133 33.92 0 0
Now that we know how the data looks like, I’m going plot a boxplot with the IQ and the test condition.
> ggplot (dat, aes (condition, iq)) + geom_boxplot()
Looking at the three groups in your boxplot. It is quite noticeable that the IQ score decreases when there is a threat and that also the severity of the threat affects the IQ scores a little bit. i.e intrinsic vs extrinsic threat. So it seems like the presence and the severity of a threat affects the IQ scores in a negative way. The presence of threat decreases the IQ scores by a large margin.
Also by plotting a scatter plot:
> ggplot (dat, aes (wm, iq, color = condition)) + geom_point()
Looking at the scatter plot, there is a clear distinction between the control cluster and the two threat cluster. As seen from the box plot, the scatter plot also shows that people who took the exam in the control condition had a better score on the IQ test than the other two groups.
Since this has been established, getting some correlation values will help with this problem. The correlation values have to be computed for each threat group.
> # Make the subset for the group condition 'control'
> library(dplyr)
> mod_control <- dat %>% subset(dat$condition == 'control')
> # Make the subset for the group condition 'threat1'
> mod_threat1 <- dat %>% subset(dat$condition == 'threat1')
> # Make the subset for the group condition = 'threat2'
> mod_threat2 <- dat %>% subset(dat$condition == 'threat2')
> # Calculate the correlations
> cor(mod_control$iq, mod_control$wm, method = 'pearson')
[1] 0.1079827
> cor(mod_threat1$iq, mod_threat1$wm, method = 'pearson')
[1] 0.7231095
> cor(mod_threat2$iq, mod_threat2$wm, method = 'pearson')
[1] 0.6772917
There is a really strong correlation between IQ and WMC in the threat conditions but not in the control condition.Now to build a model without moderation and a model with moderation. Generally, when both the independent (X) and moderator(Z) are continuous.
Y = β0 + β1X + β2Z + β3(X * Z)+ϵ
With β3 we are testing for a non additive effect. So if β3 is significant there is a moderation effect. This model is not valid when variable X is categorical.
When the independent variable (X) is categorical and the moderator variable (Z) is continuous. The model changes a bit.
Y = β0 + β1(D1)+β2(D2)+β3Z + β4(D1 * Z)+β5(D2 * Z)+ϵ
With this specific data, the independent variable being the stereotypical threat with three levels. I have already explained about how dummy encoding is done. So D1 and D2 are used for three levels in the model. The product of the dummy codes and WMC is used to look for the moderation effect.
Let’s run the R code for the models.
> # Model without moderation
> model_1 <- lm(dat$iq ~ dat$wm + mod$d1 + mod$d2) > Getting the summary of model_1
> summary(model_1)
Call:
lm(formula = dat$iq ~ dat$wm + mod$d1 + mod$d2)
Residuals:
Min 1Q Median 3Q Max
-47.339 -7.294 0.744 7.608 42.424
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 59.78635 7.14360 8.369 4.30e-14 ***
dat$wm 0.37281 0.06688 5.575 1.16e-07 ***
mod$d1 -45.20552 2.94638 -15.343 < 2e-16 ***
mod$d2 -46.90735 2.99218 -15.677 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 14.72 on 146 degrees of freedom
Multiple R-squared: 0.7246, Adjusted R-squared: 0.719
F-statistic: 128.1 on 3 and 146 DF, p-value: < 2.2e-16 >
# Create new predictor variables for testing moderation (product of the working memory and the threat condition)
> wm_d1 <- dat$wm * dat$d1 > wm_d2 <- dat$wm * dat$d2 > # Model with moderation
> model_2 <- lm(dat$iq ~ dat$wm + dat$d1 + dat$d2 + wm_d1 + wm_d2) > Getting the summary of model_2
> summary(model_2)
Call:
lm(formula = dat$iq ~ dat$wm + dat$d1 + dat$d2 + wm_d1 + wm_d2)
Residuals:
Min 1Q Median 3Q Max
-50.414 -7.181 0.420 8.196 40.864
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 85.5851 11.3576 7.535 4.95e-12 ***
dat$wm 0.1203 0.1094 1.100 0.27303
dat$d1 -93.0952 16.8573 -5.523 1.52e-07 ***
dat$d2 -79.8970 15.4772 -5.162 7.96e-07 ***
wm_d1 0.4716 0.1638 2.880 0.00459 **
wm_d2 0.3288 0.1547 2.125 0.03529 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 14.38 on 144 degrees of freedom
Multiple R-squared: 0.7409, Adjusted R-squared: 0.7319
F-statistic: 82.35 on 5 and 144 DF, p-value: < 2.2e-16 All variables have a significant effect on the IQ scores, because all p-values are significantly small. The effect of stereotype threat is strongly negative. The effect of working memory capacity is slightly positive. From the model with moderation it can be seen that the moderator variables wm_d1 and wm_d2 are significant so there is indeed some moderation effect seen in the data. Since both the models are ready, we have to compare them. using ANOVA is good way to compare models.
> # Compare model_1 and model_2 with the help of the ANOVA function
> anova(model_1, model_2)
Analysis of Variance Table
Model 1: dat$iq ~ dat$wm + mod$d1 + mod$d2
Model 2: dat$iq ~ dat$wm + dat$d1 + dat$d2 + wm_d1 + wm_d2
Res.Df RSS Df Sum of Sq F Pr(>F)
1 146 31655
2 144 29784 2 1871.3 4.5238 0.01243 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The p-value indicates that the null hypothesis is rejected. This means that there is a significant difference between the two models, so the effect of the moderator is significant. This tells us that:
People with high WMC were not affected by the stereotypical threat.
Whereas the people with low WMC scores were affected by the stereotypical threat and scored low on the IQ test.
Plotting the scatter plot along with the regression line. The first plot is for the first order or primary effects of WMC on IQ
> # Illustration of the primary effects of WMC on IQ
> ggplot(dat, aes(wm, iq)) + geom_smooth(method = 'lm', color = 'brown') +
+ geom_point(aes(color = condition))
The second scatter plot illustrates the moderation effect of WMC on IQ:
> # Illustration of the moderation effect of WMC on IQ
> ggplot(dat, aes(wm, iq)) + geom_smooth(aes(group = condition), method = 'lm', se = T, color = 'brown') + geom_point(aes(color = condition))
We can clearly see a change in slopes, so this indicates moderation.
Mastering Hierarchical Moderated Multiple Regression in R equips data scientists with deeper insight into how moderating variables shape outcomes across models. In what ways might you consider applying this analytical method in your own work?
Author Bio:
This article was contributed by Perceptive Analytics. Rohit Mattah, Chaitanya Sagar, Prudhvi Potuganti and Saneesh Veetil contributed to this article. Perceptive Analytics provides data analytics, data visualization, business intelligence and reporting services to e-commerce, retail, healthcare and pharmaceutical industries. Our client roster includes Fortune 500 and NYSE listed companies in the USA and India.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## 4 Steps To Become A More Efficient Data Services Provider
URL: https://www.data-mania.com/blog/efficient-data-services-provider/
Type: post
Modified: 2026-03-17
If you’re a new data entrepreneur trying to start or scale your business, you’re most likely strapped for time. Whether you’re trying to build out your side hustle while working your corporate job, or you’re freelancing and are so busy with client data work you feel you have no time to work ON your business, the solution lies in becoming an efficient data services provider and maximizing the time you DO have (however limited it may be!).
I’ve now scaled Data-Mania into a full-scale training and advising company that supports data professionals in becoming better data leaders. We’ve trained over 1 million data professionals and counting. But I too started off as a one-woman show selling data services. I used these exact tips myself to become an efficient data services provider so I could scale my business, reach more people, and create a bigger impact. I still use these tips to this day to help make my data thought leadership services more efficient for both myself and my clients.
Read on to learn about what it takes to boost your productivity and become an efficient data services provider so you can earn MORE money while putting in FEWER hours.
Step 1: Become an efficient data services provider by using proven business strategies
In the case of becoming a successful, efficient data services provider, the advice to “take the road less traveled” does not apply. There is absolutely no need for you to waste your time trying to DIY your own business strategy or spend hours Googling and reading books on how to scale your business.
Well, perhaps that’s what an inefficient data services provider would do.
But that’s not you, is it? ????
Because I’m specifically telling you how you can maximize your time as you start and scale your data business, I want you to listen to this first step carefully.
Sure, you can spend 5-10 years learning by trial and error what goes into building a successful data business. OR, you can take the fast-track route by getting the exact steps you need to take to scale, so you can spend MORE time implementing, and actually, you know, building the business (rather than wasting hours trying to figure out what works!).
This is the route of an efficient data services provider. Everything that you want to learn how to do has been successfully done before by somebody else. Efficient entrepreneurs save time by studying proven strategies rather than wasting their time trying to chart their own path.
Want to save time and get ahead by accessing exclusive resources for your freelance data business?
Download the “Ultimate Toolkit for Data Entrepreneurs” – Direct, actionable recommendations from Lillian on the exact tools and processes you need to set your data business up for long-term success.
Step 2: Clarify the exact offers which you plan to sell through your business
Once you’ve taken it upon yourself to get access to proven, reliable business strategies, the next thing you’ll want to do is double down on your offers.
This is the foundation of your business, and you can’t move on to expanding and scaling until you take care of the basics.
Take the time to get clarity on your offers. Survey your offers and figure out what is the most appropriate offer, and what price point makes the most sense for said offer.
You’ll also want to get clear on your processes. If you don’t have clear offers that follow a standardized process, it’s going to be very difficult to 1) make good money, and 2) begin scaling your business.
Rather than offering a million different things or customizing each detail of your services for each client, try to focus on a few core offers that have repeatable processes. Offering a ton of services without clear objectives, deliverables and processes can quickly turn into an operational headache, eating up a bunch of your precious time and energy needed to build your data business.
Knowing your offers inside-out is also imperative for your marketing plan. Ask yourself the questions like, “Who is it for? What are the key benefits? What is the transformation?”. You should be able to recite the answers to these questions in your sleep.
When you only have a few key offers that you know through and through, marketing them and creating content around them becomes a whole lot easier. You won’t be spreading yourself too thin and you won’t be confusing your audience by throwing a new offer at them every week.
Remember: confused people don’t buy. Clarity is king! Get clear on your offers, your price-points, your objectives, and your processes.
Want more resources on building a successful data business? Download the “Ultimate Toolkit for Data Entrepreneurs” – Direct, actionable recommendations from Lillian on the exact tools and processes you need to set your data business up for long-term success.
Step 3: Begin creating SOPs and documenting business processes
Once you’ve gotten clear on exactly what you’re selling and how you serve your clients, it’s time to create and document standardized operating procedures (or SOPs for short).
A Standard Operating Procedure is a document or video that provides clear instructions for how to do a certain task. The whole point of SOPs is getting the knowledge of how to run your business out of your head and onto paper (or I should say a Google doc!).
You can record your SOPs in Google Docs, in your project management system (like Asana or Trello), or using a screen recorder such as Loom.
SOPs are the foundation you need to start creating and managing your team like a true Data CEO! When you have organized, repeatable processes that are clearly documented, bringing on team members to help outsource part of your workload will feel like a breeze.
Step 4: Start building your team and delegating to freelancers + contractors
The fourth and final step to becoming an efficient data services provider is to start building your team!
If you’re still side hustling and working 9-5 at your corporate job, you may be wondering if now is the correct time to start outsourcing your work.
While many people traditionally think they need to be full-time in their business before they start hiring, dipping your toes into outsourcing while you have the security of your full-time job is an ideal situation.
Why, you ask?
Because you have the safety-net of your employer and you can re-invest your corporate salary into growing your business.
This is exactly what my former client Adriana Martinez did.
With the help of my coaching, she was able to secure a 30% raise at her corporate job. Because of this, she made more resources to invest in growing her business. While she dreamed of the freedom entrepreneurship would bring, she knew she did not want to be responsible for implementing all of the work herself (especially with a small child at home!). She decided to hire her team while still at her corporate job so she could have a larger outsourcing budget while she focused on her role as a successful data CEO.
Many of my clients were in similar situations and found that hiring freelancers to do the work while they worked on the higher-level CEO tasks I taught in my coaching curriculum was the best option for them. Because many of them were making six-figures in their day job, they were able to invest in building out their team instead of spending all their time at both their corporate job and side hustling on the nights and weekends.
This strategy means that once they quit their job, they have a business built and ready to go that works like a well-oiled machine.
They get all the freedom and perks that come with being a business owner, but they don’t have to go through the split time where they’re building a side hustle, trying to do everything themselves while also working a full-time job.
I hope these tips have shown you that it IS possible to become an efficient data services provider and scale your business, no matter what else you have going on in your life. Whether you’re juggling the demands of family, a corporate job, or even completing your education on the side, you can learn how to maximize your time and create a profitable business without working yourself into oblivion!
If you’re looking for MORE resources on how to start your own successful data business, download the “Ultimate Toolkit for Data Entrepreneurs”. You’ll get direct, actionable recommendations from Lillian on the exact tools and processes you need to set your data business up for long-term success.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Charge Premium Prices For Data Services: 3 Secrets To Make More Money
URL: https://www.data-mania.com/blog/charge-premium-prices-for-data-services/
Type: post
Modified: 2026-03-17
Whether you’re a data entrepreneur looking to scale as quickly and painlessly as possible or a data freelancer who’d be over-the-moon if you could just (finally) start to charge premium prices and book those high-paying, ideal clients everybody goes on about, here’s the truth:
Getting to the “next level” in your business isn’t always easy.
What’s the “next level” you ask? Well, that’s the stage in your business where:
High-profile leads roll in as consistently as the tides
Your revenues crawl higher up that chart month-after-month &
Your killer services continuously sell out, no problem.
After partnering with tons of data entrepreneurs over the years, helping them transform their big business visions into tangible realities, sign 5-star clients & deliver top-notch products and services, I’ve come to realize that there are a few key principles that are key to success.
In this post, I’ll share with you three insider secrets that you need to understand if you want to charge premium prices for your data services. Are you ready?!
FYI – We’ve produced an updated and greatly expanded version of this blog post. For the best version of this content, we recommend you read this updated, improved version instead post instead.
SECRET #1: If you want to charge premium prices and be paid like an expert, you need to show up like one.
Many data professionals who make the jump from corporate to owning their own data business start off by freelancing. It’s a great way to get your feet wet, work with different clients, and see what sort of projects you excel at. Some new freelancers tend to set their rate slightly lower as they rack up experience, and they’ll be pretty keen to take on any project to build up their portfolio, testimonials, and get their name out there as a freelance data professional.
While this is all fine and dandy in the beginning, you don’t want to stay in this stage too long if you want to charge premium prices for your data services.
In order to take your business to the next level, you’ll have to ask yourself questions like:
What do I want to specialize in?
What sort of clients do I want to work with?
What kind of unique processes can I create to produce the best results for my clients?
Pretty soon after you start your business, you’ll want to specialize in a certain area of data science so that you can become seen as the go-to expert for that service and command those higher rates.
When clients hire experts, they expect to pay a higher price, but they also expect that you’ll have your own processes and solutions for solving their problem (as opposed to them than having to spend ample time hand-holding or training someone less experienced!).
Make sure you present yourself as an expert in your niche, and that your marketing materials and client processes reflect your experienced nature!
SECRET #2: Believe it or not, you *can’t* (and shouldn’t!) do it all on your own.
As data pro’s, we’re often used to being the do-ers. We’re used to coding away all day, building different models, using data to create epic results for companies. We get shit done, and we do a pretty good job at it! However, I often see this attitude holding new data entrepreneurs back.
The truth is: just because you’re ABLE to do something in your business, doesn’t necessarily mean you should.
Yes, you’re smart and totally capable of doing certain tasks within your business and I’m sure you’d do a fantastic job at them…but you have to ask yourself, as a CEO, is this really the best use of my time?
A lot of the time, the answer is no.
While sometimes the answer is delegating, other times the answer is automating by improving your tech and processes. In my free guide, the “Ultimate Toolkit for Data Entrepreneurs” I’ll show you the exact tools I’ve used to help scale and run my multi-6-figure data business.
I’ve seen the importance of delegation firsthand in my own ventures. Once upon a time, I wanted to hire a chatbot specialist to help me with a lofty-yet-attainable goal: book clients through FB messenger even when I’m not online. I knew exactly who I needed to hire to make it worth my while – a specialist with more knowledge than me and their own proven processes in place.
Sure, I could’ve easily whipped up a chatbot in 4-5 days (with a mocha latte by my side) or even written it from scratch in a few weeks.
But I recognized the value in hiring someone who’d done this a million times over, rather than have me trying to DIY it.
And my decision paid off: This chatbot specialist got the job done quickly. He was in and out for $100 and two days’ time.
As tech-savvy data pros, I understand it’s tempting to work on the tasks that are easy and fun for us – like coding a quick chatbot – but the truth is, if you’re writing code (and cute chatbot responses), you’re NOT working on CEO-level tasks that will truly give your business the necessary boost to grow & scale.
When we try to do it all on our own we prevent ourselves from scaling as fast as we could.
SECRET #3: You are only as good as the quality of your team.
Perhaps by this point, you’re realizing that in order to get to that next level you need to start hiring some support.
The mistake I see so many new data entrepreneurs make, however, is that they often try to go for the cheapest option when bringing on support. They want to keep their profit margins high, and so they think hiring someone who offers the most ‘bang for their buck’ is the smartest business decision.
The reality?
“Cheap” service providers often end up costing you more in the long run than if you’d just found someone quality in the first place.
With the example of my chatbot, I could have outsourced to a cheap freelancer or rallied an intern or college student willing to put my bot together for pennies (or free).
But what would that have cost in my time? What would that have cost if they made a mistake (and I had to do it over again?) What would it have cost in the mental energy wondering if they were doing things properly?
In the end, the “expensive” option is usually actually less costly.
When building your dream team, you should actually look to hire team members who know more than you do in their respective fields, true experts in their own realm that are going to elevate your business, rather than relying on interns or cheap freelancers.
If you’re just starting out your freelance data career, I recommend you focus all your efforts on mastering the first secret I shared – showing up as an expert. By refusing to compete on price and focusing your energy on creating high-level processes, you’ll attract high-end clients and you can begin to charge premium prices.
If you have a couple of freelance clients under your belt and you’re looking to get to that next level by building out your team, remember the second and third points I shared – focus on getting support and make sure that support is high-quality. When you are well supported, you’ll be able to get out of the weeds of implementation and step into your CEO shoes, where you can strategize on how to increase business revenues.
No matter WHAT level you’re at – whether you’re more advanced or just starting out – it’s important to set yourself up with a solid foundation by creating systems in your business.
Don’t wait until your overloaded with clients to get your tech and systems set up to allow you to scale! Make sure your business is strong and scale-able from the beginning by creating proper workflows.
I walk you through exactly how to do this in my latest Free Guide – The Ultimate Toolkit for Data Entrepreneurs. You’ll learn about my favorite FREE and low-cost tech that has enabled me to scale my business into the multi-6-figure range – think marketing systems, operations, team management, project management tools and so much more. Essentially, everything you need to set your data business up for success!
Download the Toolkit below
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Octane AI: Is It All Hype or Does It Help?
URL: https://www.data-mania.com/blog/octane-ai-is-it-all-hype-or-does-it-help/
Type: post
Modified: 2026-03-17
Ever heard of Octane AI? In this article, we’re going to dig deeper on what it is and break down the good and the not-so-good features of this new e-commerce technology.
Due to data breaches and increasing reports of representatives mishandling customers’ information or not being straightforward about why they need it, people have become more reluctant to provide companies with their details.
It’s also more difficult for companies to use the same tracking mechanisms they once did. A 2021 Apple software update required all apps to request permission before tracking and selling details about a person’s online activities. Previously, Facebook and other apps regularly used pixels to monitor people’s online activities. It then charged companies to show relevant ads to individuals in a practice called retargeting.
It worked well because people don’t often purchase products on their first visits to websites. They may research features and compare prices during those initial website interactions and return several weeks later to buy. However, they might never come back.
Retargeting ensures people keep seeing a website’s ads elsewhere online, raising their chances of eventually purchasing. Apple’s user-controlled approach to tracking doesn’t make retargeting entirely out of the question.
However, many people prefer it if apps don’t track them. That means marketers and others who work with data must pursue different approaches. People at a company called Octane AI believe zero-party data is the way forward.
What Is Octane AI?
Octane AI is a platform that allows merchants that use Shopify and Klaviyo to create personalized interactions that encourage site visitors to give details about themselves. Individuals willingly and proactively provide this information, known as zero-party data.
An Introduction to the Zero-Party Concept
For a while, first-party data was a valuable type of data to collect because it was information marketers could gather from their own online and offline sources, such as social media or newsletter signups. That’s in contrast to third-party data, which comes from entities that don’t have direct relationships with the data sources. Marketers must pay for third-party data.
Zero-party data is information that customers directly provide to the company. Zero-party marketing is all about companies using the details people give to build relationships with them and personalize the content they see through various channels, such as a company website, email or text message.
As a Forrester report explained, “While first-party data is rich with behavioral data and implied interest, zero-party provides explicit interest and preferences — and you must use it to improve the value you provide to consumers.”
Octane AI: Help or Hype?
People understandably wonder if Octane AI could genuinely improve marketing or if its offerings will cause a short-term buzz without staying power. Here are three positive and potentially negative factors to consider.
The Good
Octane AI Could Reduce Cart Abandonment Rates
Cart abandonment is a major issue in e-commerce. A far higher number of customers than you might think put things in their shopping carts and leave without completing the transactions. Data from 2020 showed that the rate surpasses 96% in the automotive industry. Even insurance, the best-performing sector represented, still had a rate of more than 67%.
Those statistics show there’s lots of room for improvement. Octane AI claims that it can convert more than 10% of abandoned carts into sales with personalized messaging.
It Connects With People in a Format They Like
Octane AI specializes in an emerging option called conversational commerce. It allows people to engage in real-time chats with live agents or bots rather than using methods that require waiting times, such as phone calls or email forms.
A 2020 survey of American adults revealed that 72% are more likely to purchase from brands that offer chat messaging. The same research showed that 69% prefer to communicate with companies that way over phone calls. If people like the communication format, the chances of them becoming loyal customers could go up.
It Gives Brands Relevant Data
The earlier discussion of changes in the marketing industry explained why brand representatives can no longer rely on customer tracking in the background. Octane AI shows transparency with its data collection methods. One example is the shoppable quiz.
“The shoppable quiz is the idea of bringing the in-store conversation to the front page of an e-commerce website. It allows them to ask questions once you walk through that virtual front door,” explained Matt Schlicht, Octane AI’s CEO and co-founder. For example, a shampoo brand might have a shoppable quiz to ask about someone’s hair type, helping them purchase the most appropriate product.
The Possible Downsides
People May Find the Personalized Content Slows Their Shopping Process
Octane AI shows people customized content through quizzes and pop-up windows as they shop. The goal is to get information from them to improve the shopping experience. However, some people may get frustrated by what they perceive as obstacles that prevent them from quickly purchasing something on a site.
Consumers May Not Want Text-Based Shopping Cart Prompts
Many companies already send people emails encouraging them to purchase items left in their carts. Octane AI takes that approach further by contacting people via text message and Facebook Messenger to urge them to complete their purchases. Some people may find that intrusive, especially if the messages arrive when they’re busy.
Many People Dislike Pop-Up Windows
A large part of Octane AI’s strategy relies on windows appearing as people browse sites. However, many consumers are so conditioned against such content that they download pop-up blockers or quickly close the windows before reading them.
Screenshots on the Octane AI site show that the windows have Xs, allowing people to get rid of content. If too many people treat the pop-ups as they often do, brands won’t have the expected data-collection opportunities.
Will Octane AI Be a Marketing Game-Changer?
Octane AI’s approach combines conversational commerce with zero-party data collection. It’s too early to tell whether customers will embrace or shy away from this method, but it’s clear the old ways of data gathering and marketing no longer suffice.
If you’re digging this real talk on Octane AI and whether it’ll be helpful to your marketing efforts, then be sure to check out this post about Omnichannel Analytics and Chanel Scoring to learn how these can help in increasing your sales and improve your business’s ROI.
A Guest Post By…
This blog post was generously contributed to Data-Mania by Shannon Flynn. Shannon Flynn is a freelance blogger who covers business, cybersecurity and IoT topics.
You can follow Shannon on Muck Rack or Medium to read more of her articles.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## From $8/hr to Data CEO with Lillian Pierson
URL: https://www.data-mania.com/blog/from-8-hr-to-data-ceo-with-lillian-pierson/
Type: post
Modified: 2026-03-17
In this episode of Data Career Podcast with Avery Smith, previously live on LinkedIn, Lillian Pierson shares about her journey from being an $8/hour employee to building her multi-six figure data business: Data-Mania.
She talked about the lessons she learned and shared tips and insights on how you can avoid them, as well as her strategies in scaling her business and keeping up in this ever-changing world of data.
Enjoy the show!
If you want to know more or get in touch with Lillian, follow the links below:
Data Science For Dummies, 3rd Edition hits the streets in September, 2021 – but not without a proper launch party to celebrate. You’re invited! RSVP here: https://businessgrowth.ai/
Weekly Free Trainings: We currently publish 1 free training per week on YouTube! https://www.youtube.com/channel/UCK4MGP0A6lBjnQWAmcWBcKQ
Becoming World-Class Data Leaders and Data Entrepreneurs Facebook Group: https://www.facebook.com/groups/data.leaders.and.entrepreneurs
LinkedIn: https://www.linkedin.com/in/lillianpierson/
The Data Entrepreneur’s Toolkit: A recommendation set for 32 free (or low-cost) tools & processes that’ll actually grow your data business (even if you still haven’t put up that website yet!). https://www.data-mania.com/data-entrepreneur-toolkit/
Listen on Apple Here: https://apple.co/37N5O9m
Listen on Spotify Here: https://spoti.fi/3AK6QiX
Discover your inner Data Superhero!
Most of the time, custom advice is all you need to achieve both your dream salary AND the satisfaction that you crave from your data career.
In our free, fun, 45-second data career path quiz, you’ll uncover your inner Data Superhero type and get personalized data career recommendations that directly align with your unique combination of data skills, personality and passions.
Take the Data Superhero’s Quiz today!
Get the Data Entrepreneur’s Toolkit
There’s always that data professional who starts an online business and hits 6-figures in less than a year. Now? It’s your turn and we’re ready to help get you there with our Data Entrepreneur’s Toolkit (designed to help you get results for your data business fast).
It’s our favorite 32 tools & processes (that we use), which includes:
Marketing & Sales Automation Tools, so you can generate leads and sales – even in your sleeping hours.
Business Process Automation Tools, so you have more time to chill offline, and relax.
Essential Data Startup Processes, so you feel confident knowing you’re doing the right things to build a data business that’s both profitable and scalable.
Download the Data Entrepreneur’s Toolkit for $0 here.
Execute Upon the Data Strategy Action Plan
This is our crowd-favorite data strategy product. No long video trainings, no books to read, no needless theory. Just clear, concise guidance on what your next data strategy steps should be, starting today.
It’s a step-by-step checklist & collaborative Trello Board planner for data professionals who want to get unstuck & up-leveled into their next promotion by delivering a fail-proof data strategy plan for their data projects.
There are also 2 bonus guides, if you need help improving communications with your senior executives and stakeholders
And, it comes with a bonus, members-only community, if you’d like a private sounding board for getting valuable input from other data strategists.
Start executing upon our Data Strategy Action Plan today.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## 13 of the Smartest Artificial Intelligence Companies According to Forbes
URL: https://www.data-mania.com/blog/artificial-intelligence-companies/
Type: post
Modified: 2026-03-17
Artificial Intelligence Companies are baking some mind-blowing goodness this year and I’m about to tell you all about them here!
By the end of this article, you’ll have an industry-insider perspective on the 13 smartest artificial intelligence companies according to Forbes in 2020.
Even if you’re a seasoned data professional, these startups are going to blow your mind. Believe me – they’re awesome – and I’ve been in the industry full-time since 2012. I help data professionals transform into world-class data leaders and entrepreneurs…
Data professionals like Kam Lee, who took his AI startup to $350k with a 67% profit margin within his first year of working with me.
As for the background on the list that I’m about to share with you, you may know that back in 2016, MIT produced a list of their recommended smartest artificial intelligence companies. They followed it up with a 2017 listing and then they discontinued the series.
Back in 2016, the forerunners for smartest AI companies were IBM, Line, Facebook, NVIDIA and Tesla. These are huge name brands, so of course, they would be in the smartest artificial intelligence companies list and lots has changed in this year’s listing.
If you prefer to read instead of watch, then read on…
In 2020, Forbes produced a list of America’s most promising artificial intelligence companies. Within that listing, they categorized the companies into 13 different categories. So in this article, I’ve taken my favorite pick from among these 13 categories representing each category with a company.
These are the criteria I used to pick these companies:
LASTING IMPACT – how well-positioned is this AI company to solve emerging global problems in the long term
MARKET OPPORTUNITY – how prime is this market in terms of opportunity for data professionals to create your own businesses in this market
TANGIBLE IMPACT – the visceral impact these technologies have on the lives of real-people today – you could think of it like “convenience innovation”
LASTING IMPACT AI Companies
#1 Environmental Engineering
AMP Robotics – makes robots that identify and sort waste.
They perform recycling and reuse on behalf of humans. The technology they created is an absolute game-changer. WHY? Because it impacts the long-term sustainability of the planet for our grandchildren onwards.
Let me give you a little statistics to try to paint a picture of how huge this problem actually is:
Every year, people are producing over > 800,000 tons of waste. Imagine an Olympic-sized swimming pool filled with garbage. That is actually the volume of garbage that is being produced by the human population every single year. Now, 46% of that waste is disposed of unsustainably where it could have been recycled or reused.
USA – waste generation is 3x the global average, but only 35% is being reused
Germany – reused rate is almost up to 70%
Now, AMP Robotics combines the power of deep learning, computer vision and robotics to automate waste sorting, thereby reducing waste sorting costs by >70%. That truly is a game-changer for the waste management industry.
#2 Defense
Anduril Industries – builds surveillance systems for national intelligence.
Look, I get it – no one likes personal surveillance but you also don’t like to have to worry about bombs going off in the mall when you go shopping on a Friday night, right? Well, that actually happened to me – a bomb went off in a mall right below me and I live in a place where there’s no surveillance whatsoever.
Companies like Anduril Industries are vital for keeping people safe, so you can enjoy a great quality of life, right?
The United States has 7,000 miles of border to defend, of course most of that distance is inert. But still, you need some sort of surveillance in place to monitor when there are some “outlier” events.
Now, what Anduril really does is it provides intel automation. It combines sensor data collection, computer vision, renewable sources, and drone technology to produce security devices that have a low-carbon footprint yet produce tremendously accurate security intel within minutes.
#3 Cyber Security
Blue Hexagon Cybersecurity – detects network and cloud cyber security attacks.
Back in mid-December 2020, the US government suffered a huge cyber attack. But apparently, the intruders were inside the system for 9 entire months before they were exposed. They were in places like the government’s most critical agencies – including the departments of State, Homeland Security, Treasury and Commerce, and more.
Senator Romney said that, “They had the capacity to show that our defense is extraordinarily inadequate; that our cyber warfare readiness is extraordinarily weak.”
But, had the government been using tech like Blue Hexagon, it’s very likely that the intruders would have been detected faster than 9 months, hopefully within just a few milliseconds.
#4 Software Development
DataRobot Software Development – makes software for companies to develop AI models.
It essentially lowers the staffing requirements for companies in order to implement AI. One of the biggest barriers for modern companies in keeping up with technology and staying tried and true on a competitive landscape is access and capability to hire data professionals, specifically data scientists. Data Robot Software helps solve this problem because it:
Combines software development with machine learning automation to decrease in-house staffing needs for data scientists.
Helps lower the cost of AI projects for companies
Could help even the playing field (in the long term) between the haves and the have nots in terms of being able to implement AI in order to gain and maintain competitive advantage
I actually interviewed Owen Zhang, who was the Chief Product Officer / Advisor at DataRobot for the longest time. If you wanna hear from him on how he won the Kaggle competition, you can watch the interview here.
Market Opportunity AI Companies
#5 Productivity Software
Drift – builds chatbots to automate customer interactions.
Personally, I see a huge area of opportunity for data professionals in this market because it’s got a low barrier of entry + low cost of customer acquisition.
In fact, a lot of the drift chatbots are not even AI-enabled, they are rules-based chatbots. To easily and quickly get people through the door, Drift offers a FREEMIUM product. It helps them automate lead generation and increases word of mouth marketing. Their paid services include:
PREMIUM – rules-based bots and automation pipelines
ENTERPRISE – has access to AI features (called “Drift Automation”)
#6 Database Software
SafeGraph – creates data sets by tracking commercial activity.
The reason I find it really cool and a prime opportunity for data professionals is because it has a low barrier of entry for anyone that ever worked with GIS technologies. A lot of data that SafeGraph is using to produce its products comes from governments and public data sets. They form data partnerships where they get anonymous mobile data that gives commercial activities and where they’re happening. They combined that with government and public data sets and they resell it.
I took the liberty to check out their terms and conditions and their data privacy policy because it was pretty interesting. They said, “We obtain a variety of information collectively from trusted third-party partners, such as mobile application developers.”
So basically, they are partnering and buying data, recombining it and selling it to retail companies. In terms of the AI that their technology uses, this is going to come down to pretty simple stuff like clustering and nearest neighbor classifications…most of which can be done on a click and point basis inside of any sort of GIS.
On a side note, this is a classic example of a data monetization business model. They get the data from a data partner, convert it to the format needed by retail consumption, and sell it to them.
Regardless of what you think about the ethics of that, the company made it on Forbes top 50 list so the status quo says, that’s okay.
#7 Workflow Software
UiPath – creates bots that carry out repetitive processes.
Why does this represent a market opportunity for data professionals? Because this is just really a Robotics Process Automation (aka; business process outsourcing). This area has a low barrier of entry, but it requires a setup and configuration time which makes it lucrative for data entrepreneurs and it offers a huge ROI for their clients.
UiPath distinguishes itself pretty well by using AI to build more efficient robots – quite a bit more sophisticated than website chatbots. They are either rules-based or as sophisticated as implementing computer vision.
#8 Customer Service
Cresta – assists customer service agents in real time.
This is a huge market opportunity for data professionals because it actually has a very low barrier of entry. It’s really not that hard to develop a SaaS application that uses ML and rules to deliver these types of insights.
I did a video on how Humana uses chatbots in order to assist their customer service agents in real-time in order to increase their customer service quality and retain their customers. You can watch it here.
Tangible Impact AI Companies
#9 Healthcare
Biofourmis – monitors patients’ health using wearables.
There is quite a “cool factor” on this one. The Biofourmis AI based ecosystem compares data on 20 or more variables that they collect from signals off of their wearable devices. Not only that, but they are collecting data on these wearables from millions of people in REAL TIME. The result of all this is a powerful pattern recognition. This is designed to help physicians predict and prevent any health deterioration before it happens.
#10 Financial Services
Lemonade – uses bots and bots alone to sell insurance on the internet.
This means you don’t have to call in or fill up a long form or go to an office to see anyone. You just need to do the following:
Go to their website
Put in your information
They will do a risk analysis using AI
They categorize you according to your information and risk category
They quote you a policy
You pay
You are insured
How incredible is that?!
#11 Communication Software
Krisp Technologies – removes background noise from calls.
They use AI to learn the user’s voice then suppresses noise that isn’t that voice (i.e., knocking or slamming the door, kids running, etc.) It’s so easy to set up and you can try it for free, then it’s only $5 a month!
#12 Hardware
Cerebras Systems – builds computer chips for AI use.
They developed a Wafer-Scale Engine (WSE) that is larger than any chip on the market:
Delivers more compute, more memory, more communication bandwidth
Enables AI and machine learning at previously impossible scale and speed
How does it compare to NVIDIA’s largest GPU?
Cerebras’ WSE has 1.2 trillion transistors while NVIDIA’s A10 has 54.2 billion transistors.
#13 Automotive
Ghost – develops self-driving technology for installation into conventional cars.
How could that not be cool?!? You can install self-driving into your Toyota Prius or the Mercedes Benz you already own and it would drive and operate like a Tesla!
In 2019, Ghost raised 63.7 million to develop and deploy this technology to be released in 2021. It has eight high-definition cameras that installs in your car and deploys computer vision and it’s only $3,495!
I know we just discussed a crap-ton of awesome new tools and innovation made by these artificial intelligence companies, but if you’re a data geek like me, then you’re probably down to see some more…
If that’s you…I’m giving out a FREE Toolkit for New & Aspiring Data Entrepreneurs – It’s a collection of the top 32 Tools & Process That’ll Actually Grow Your Data Business.
Download the Data Entrepreneur Toolkit here.
Share It On Twitter by Clicking This Link -> https://ctt.ac/fWb4x
Watch It On YouTube Here: https://youtu.be/ovE9xVmpYuM
NOTE: This description contains affiliate links that allow you to find the items mentioned in this video and support the channel at no cost to you. While this channel may earn minimal sums when the viewer uses the links, the viewer is in NO WAY obligated to use these links. Thank you for your support!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## 4 Sure-fire Ways To Be Successful Selling Data Courses Online
URL: https://www.data-mania.com/blog/selling-data-courses-online/
Type: post
Modified: 2026-03-17
If you’re a new or aspiring data entrepreneur, you may have thought about selling data courses online in order to boost your income and create a new revenue stream. Trust me when I say this…there is a RIGHT and a WRONG way to go about this.
If you embark on your course creation journey without the proper steps and strategy, your so-called “good idea” could quickly turn into a disastrous revenue hit for your business. In today’s post, I’m going to show you exactly what you need to do to avoid bombing out big time with your efforts to sell online courses and books! So let’s get started, shall we? ????
Oops but before that… Quick heads-up! We’ve enhanced and expanded this blog post in our more recent article on 10+ Tech Startup Ideas: Products, Services & Tech Business Models For Early-Stage Startup Founders! We recommend you read the updated version instead! 😉
Anyway, going back… Part of the reason I’m passionate about sharing this information is that I myself have experience launching courses, books, and digital products – and while some of those products were uber-successful (and continue to bring in recurring revenue for my business to this day!), others were a total flop.
A little more on why I’m qualified to teach on selling data courses online
To date, I’ve written five books. The first book I wrote was a self-published travel book in 2012. It basically failed due to lack of market research before product creation! The other four books were published by Wiley and are on the topics of data science and big data. And so far, they’ve been bought and read by over 250,000 readers across the world.
In terms of courses, I launched my first course back in 2014 on R. I created it myself without much of a real strategy behind it. I didn’t take any program or get coaching on how to properly create and launch a successful course. In the end, I wasn’t too proud of it. It didn’t make much money, and I ended up taking it down within six months.
I now have five courses with LinkedIn Learning all on data science, which I earn $3,000 a month in residual income from, and we’ve scaled to 1 million learners. I’ve also pre-sold a program for $90,000 in less than eight months.
So as you can see, I have a fair bit of experience selling digital products the right way – and the wrong way too! Let me help you skip the trial and error so you can get straight to being successful selling data courses online. Sound good?!
Step 1: Decide who it’s for and the transformation it provides.
The #1 mistake I see so many people make who want to get into course creation is that they start with what they know first. They think, “what am I knowledgeable about that I could sell a course on?”
While it may be natural and intuitive to think this way, this is actually the complete WRONG way to go about creating and selling data courses online.
The worst thing you can do is spend six months creating this long course, and then only AFTER figure out who wants to buy it and how you’re going to sell it and at what price points.
Instead, your very first step should be thinking about WHO is going to buy your course. Another is what sort of transformation it provides.
If you want to sell a high-ticket course, you have to go BEYOND a skill and task-based course. Yes, simply teaching someone a coding language (without any REAL emphasis on how learning this skill will change their life) might work for a cheap $10 Udemy course, it is NOT enough to sell a pricier program.
Go further than “I will teach you about Machine learning” or “I will help you master Python”. How will this course really benefit them and create change in the participant’s life?
You absolutely MUST bake a full transformation into your course and marketing message – otherwise, people might as well go pay $10 over to Udemy HERE (who happens to be selling data courses online for almost a decade – and pretty much killing it the entire time.).
Looking for more advice on what it takes to start a successful freelance data business! I’ve got you ???? Download the Ultimate Toolkit for Data Entrepreneurs and learn the exact processes I use in my business everyday that have helped me scale to the multi-6-figure range ????
Step 2: Warm your audience up.
The second step to being successful selling data courses online is to start creating content around your course topic. Also, becoming a thought leader in said topic BEFORE the course launches.
If you don’t begin showing up as an expert in your chosen niche within data, and are simply talking about a whole bunch of random topics, it’s going to be that much harder to establish the credibility and authority needed to sell your course.
If you build an audience interested in your course topic and nurture them with valuable free content that showcases you as the go-to expert, when you finally DO launch your course, they will be warmed up and ready to invest in you.
Step 3: Have a pre-sale period.
The third step to successfully selling data courses online is to have a pre-sale period. This means that rather than building out the entire program, you can create about a third of it. And then do some pre-sales to figure out how well the course is selling. This is also the time to see who’s buying it and resonating with the content. When you have good intel on these things, you can tweak your marketing and messaging so it’s attracting the right audience.
Having a pre-sale period means that you won’t put up too much of an investment up-front. This means investment in both time, money, and energy before validating your course idea. If you launch a pre-sale and you’re getting NO traction, you can cancel and give refunds to the few people who bought it. Then go back to the drawing board to see where you went wrong.
You might have the opposite problem. You may pre-sell the program and you get too many sales and you’re overwhelmed with clients! This is a good kind of problem to have! It means there’s clearly interest and demand in your course. You’ll have to bring on a team to balance your workload, but with the added income from your course, you’ll have the revenues to do so!
Step 4: Decide how you will publish your course
There are a couple of different ways you can choose to publish and sell your course or book. There are pros and cons to each option as well. I’ll walk you through the benefits and drawbacks of both self-hosting your online data course (or book!) as well as partnering with a brand.
Self-Publishing
Online Courses:
When you self-publish your online course, you retain the rights to it and own your content. This means your potential for profit is higher, as you’re not splitting revenues with a partner or publisher.
This also means, however, that you’ll need to do all of the marketing for your course yourself. This can be somewhat difficult in the beginning if you have a small audience. But if you are willing to put in the time and effort to grow your communities, self-hosting your online course has the potential for a high reward. You will also need to pay for the software to host your online courses, using something like Teachable (which I use and strongly recommend).
Books:
Self-publishing can be a great strategy if you’re looking to put out a book. You’ll want to make sure you’re publishing one for the right reasons. However, because books are very low-cost and have a high marketing requirement, they won’t be bringing in a whole lot of cash.
What I recommend for those looking to self-publish is to see their book as more of a lead generator for their higher-ticket programs. Amazon has the option to set your book price at .99 cents. This can attract leads and drive traffic to your business.
Are you just getting started setting up your data business? Download the Ultimate Toolkit for Data Entrepreneurs! Learn about my BEST recommendations for the top tools and processes to set your business up for success!
Publishing With a Partner
Online courses:
When you publish your course with a partner, you don’t actually retain the rights to the content. This is the case with my LinkedIn Learning courses. LinkedIn owns the course content, and promotes it to its users, and I earn royalties on that. While I make a much lower profit percentage than if I self-hosted these courses, these courses require little to no marketing on my part. Yet, they still bring in decent recurring revenue each month.
Another pro is the potential to scale. Depending on the brand you partner with, you have an opportunity to reach a massive audience. I’ve now taught 1 million learners via my partnership with LinkedIn Learning, which is an impact that would be difficult to have on my own. Working with a prestigious brand can also give your business credibility, especially if you’re just starting out.
If you’re curious to see what my LinkedIn Learning data courses look like on the front end, you can check them out HERE.
If having a big impact on as many people as possible is one of your goals, working with a partner could be beneficial to you. Remember at the end of the day however, you don’t own the content and have less control over it.
Books:
The biggest advantage to getting published is the credibility it can bring to your business. When your book is labeled by publishers, it shows a clear, tangible sign to potential customers that you’ve been recognized as an expert. And if you have a good publisher, you shouldn’t have to do too much marketing yourself!
What’s important is the ability to see your book as a credibility-builder, and as something that can lead to future (profitable) opportunities, not as a way to make money in and of itself. Very few people get rich selling books. They can however lead to speaking gigs, consulting opportunities, and as entry-points to working with you at a higher level.
???? If you’re curious to see my most popular data title with Wiley, you can check out Data Science For Dummies HERE.
Online courses are a hot topic right now and can be a fantastic opportunity to scale your business. By creating a digital product, you can have a more “passive” (creating and marketing these products is still work, however!) income stream alongside selling data services. Make sure you follow these steps carefully. Don’t skip out on the important foundations if you want to be successful in selling data courses online!
Want to learn about which platform I use to host my online courses? (As well as all the other incredible tech I use to manage my multi six-figure data business?) The Ultimate Toolkit for Data Entrepreneurs features my best recommendations for free and low-cost tools and tech to set up your data business up for success!
Download the Toolkit below!
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## The 4 Best Books for Tech Entrepreneurs & Data Founders
URL: https://www.data-mania.com/blog/best-books-for-tech-entrepreneurs/
Type: post
Modified: 2026-03-17
In my 8 years of running my own data business, I’ve taken countless courses and coaching programs, read hundreds of books, and consumed a whole lot of resources. Today I wanted to share my four best books for tech entrepreneurs and data founders. This is so you can take your entrepreneurial career to the next level.
My Recommendations on The Best Books for Tech Entrepreneurs + Data Founders
I’ve selected a few different books. Some of which are suited to more advanced entrepreneurs. Others will be perfect for those just starting out their journey in becoming a tech entrepreneur!
Note: Some of the links below are affiliate links, meaning I may receive a small commission if you purchase through them. Thank you for supporting small business ❤️
Book 1 – The $100 Start-Up
Who it’s for: Beginner entrepreneurs looking to get advice and inspiration to start their businesses
This one is an oldie but a goodie! The $100 Start-up, by Chris Guillebeau teaches you all about the principles that you need to start a successful business. The author then illustrates those business principles through real-life examples of entrepreneurs who started a business with only $100 (or less!).
The businesses he uses as examples make for a fun read. Rather than focusing on super technical or serious businesses, he chose intriguing, eclectic businesses as case-studies – think ex-lawyers turned yoga teachers or two ex-corporate designers who started selling custom-made maps out of their apartment (who now have a 6-figure business!).
This book is perfect for the corporate side hustler looking for that extra push needed to finally start your data business! Guillebeau shows that you don’t need all the bells and whistles or to put a massive up-front investment to start a successful business. All you need is passion, grit, hard work, and the right mindset and strategies.
Buy the $100 Start-Up Here
Book 2 – Platform
Who it’s for? Those new to the online business world. Thos who want to know more about creating a personal brand and developing thought leadership
Another one of the best books for tech entrepreneurs is the book Platform: Get Noticed in a Noisy World by Michael Hyatt.
While not specifically geared towards those in the tech/data space, this is a must-read for anyone who wants to use social media and online marketing to grow a personal brand. It’s perfect for anyone, whether that’s as a consultant, premium service provider, or company founder.
If you are working away at your data career, what you need to know is this:
Your data career is your business. You can either sell your data career to an employer and go the route of being a successful employee. Or you can build a brand around your data career and turn it into your own business.
If the latter sounds intriguing to you, but you’re having a hard time trying to wrap your head around the idea of how do I actually stand out from the crowd? And how do I use that influence to then monetize it and build a business?, you HAVE to read Platform by Michael Hyatt. It teaches you everything you need to do in order to start building thought leadership around what people refer to nowadays as “the personal brand”.
Platform will help you build your thought leadership. You can then use it to attract either new jobs if you’re looking to be an employee, OR new clients if you are looking to be a data entrepreneur.
Buy Platform Here
Want MORE resources and tools to start (and grow) a successful data business? Download The Ultimate Toolkit For Data Entrepreneurs and get 32 free (or low-cost!) tools to help you save you hundreds of hours and get epic results!
Book 3 – Clockwork
Who it’s for? Entrepreneurs who want to streamline operations and get out of implementation and task-level work. Those who create a kick-ass team (who doesn’t need their presence to get shit done!)
For entrepreneurs who’ve been in business for a little while, number three on the list of best books for tech entrepreneurs is called Clockwork by Mike Michalowicz. This book is for the overwhelmed solopreneur. This can also be for the entrepreneur who’s begun to build a small team or agency that is still heavily dependent on their presence as the founder in order to run smoothly.
If you find yourself constantly putting out fires, spending all your time delegating to and managing team members, I’d like you to try and picture this.
What if you could get your data business to run like a well-oiled machine…even if you weren’t in the picture?
While removing themselves from daily operations may sound like a pipe dream for many entrepreneurs, for many, it’s a reality. This is all because they’ve implemented the strategies in Clockwork.
If systems, processes, efficiency, and team management are things you’d like to focus on, you’ll definitely want to pick yourself up a copy.
Purchase Clockwork here.
Book 4 — Rocket Fuel
Who it’s for? Entrepreneurs who have a team but still spend too much time managing and implementing their visions. Those who want to hire an integrator to bring their ideas to life
If you’re someone with big ideas and a big vision, but you find yourself getting caught up in the details, you absolutely need to read the book Rocket Fuel by Gino Wickman and Mark C. Winter. Rocket Fuel teaches you all about how to take your VA or operations manager and turn them into an Integrator.
Essentially, an Integrator makes sure that all the moving parts of your business are working together. They keep things moving along so that the business can meet its KPIs.
The authors describe how the integrator and the visionary are the perfect pair. They also detail how their work together is critical for the success of any business. When these two unite, it’s like rocket fuel. The business is able to achieve things it would never have been able to had they not come together.
The CEO gets the freedom and mental space to focus on their vision and their big goals. Meanwhile, the integrator is able to work out the details and the intricacies of project and team management. If you’ve started to scale your data business by bringing on team members, but find yourself in a constant cycle of delegation and management, this book might be exactly what you need to get out of the weeds of implementation and step into your shoes as a true data CEO.
This is actually exactly what I’m working on in my business right now. My current executive assistant is in training to become the Integrator of Data-Mania. What’s incredible is there is also an online Rocket Fuel University for visionaries and integrators, which I’ve purchased to get my EA up to speed. She’s been loving learning more about what it takes to become a kickass integrator so she can take on an even bigger role within the company!
Purchase the book Rocket Fuel here.
So that’s my little round-up of the best books for tech entrepreneurs! If you go ahead and read any of them, I’d love to hear what you think. Let me know your thoughts in the comments!
And if you’re looking for more resources to support you through data entrepreneurship, you’ve GOT to check out The Ultimate Toolkit for Data Entrepreneurs. Packed with 32 free (or low-cost) tools, I show you the exact tech and processes I’ve used to start and scale my data business to the multiple 6-figure mark.
Get everything you need to set your data business up for success ?
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## How She Built a Data Empire Working Abroad! (Lillian Pierson) – KNN Ep. 41
URL: https://www.data-mania.com/blog/how-she-built-a-data-empire-working-abroad-lillian-pierson-knn-ep-41/
Type: post
Modified: 2026-03-17
How She Built a Data Empire Working Abroad! (Lillian Pierson) – KNN Ep. 41 // Today I had the pleasure of interviewing Lillian Pierson. Lillian helps data professionals transform into world-class data leaders and entrepreneurs. To date she’s educated over 1 Million data professionals on AI, and over 10% of the data entrepreneurs have landed 6-figure contracts within the first 7-months of working with her. In this episode, Lillian shares the story of her early affinity towards data. As a child it was fun and easy for her to begin working with spreadsheets. We also touch on her hands on work using data analytics to help design reaction protocols for substituting Hydrogen with Deuterium atoms from within nucleic acids. Toward the end, we explore how she made the transition into data entrepreneurship and we both discuss our experiences growing as content creators and educators.
Watch It On YouTube Here: https://youtu.be/mDaLmmWT2KI
Discover your inner Data Superhero!
Most of the time, custom advice is all you need to achieve both your dream salary AND the satisfaction that you crave from your data career.
In our free, fun, 45-second data career path quiz, you’ll uncover your inner Data Superhero type and get personalized data career recommendations that directly align with your unique combination of data skills, personality and passions.
Take the Data Superhero’s Quiz today!
Get the Data Entrepreneur’s Toolkit
There’s always that data professional who starts an online business and hits 6-figures in less than a year. Now? It’s your turn and we’re ready to help get you there with our Data Entrepreneur’s Toolkit (designed to help you get results for your data business fast).
It’s our favorite 32 tools & processes (that we use), which includes:
Marketing & Sales Automation Tools, so you can generate leads and sales – even in your sleeping hours.
Business Process Automation Tools, so you have more time to chill offline, and relax.
Essential Data Startup Processes, so you feel confident knowing you’re doing the right things to build a data business that’s both profitable and scalable.
Download the Data Entrepreneur’s Toolkit for $0 here.
Execute Upon the Data Strategy Action Plan
This is our crowd-favorite data strategy product. No long video trainings, no books to read, no needless theory. Just clear, concise guidance on what your next data strategy steps should be, starting today.
It’s a step-by-step checklist & collaborative Trello Board planner for data professionals who want to get unstuck & up-leveled into their next promotion by delivering a fail-proof data strategy plan for their data projects.
There are also 2 bonus guides, if you need help improving communications with your senior executives and stakeholders
And, it comes with a bonus, members-only community, if you’d like a private sounding board for getting valuable input from other data strategists.
Start executing upon our Data Strategy Action Plan today.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Data Entrepreneur: How to Sign High-Paying Clients
URL: https://www.data-mania.com/blog/data-entrepreneur-how-to-sign-high-paying-clients/
Type: post
Modified: 2026-03-17
Wondering how to sign high-paying clients in your data business? Newer data entrepreneurs are always looking for better ways to get high-paying clients sign up for their businesses, so if you’re one of them, then this article is for you!
YouTube URL: https://youtu.be/BTFnkghunps
If you prefer to read instead of watch then, read on…
We’ve produced an updated and greatly expanded version of this blog post. For the best version of this content, we recommend you read this updated, improved version instead post instead.
In case you’re wondering why I’m qualified to talk about this topic… I’ve been a data entrepreneur since 2012 and after serving 10% of Fortune 100 companies from within my own data business, I started coaching other new data entrepreneurs on how to hit 6-figures FAST.
⭐10% of my mentorship clients have land 6-figure CONTRACTS w/in the first 7 months of signing up with me
My name is Lillian Pierson and I support data professionals to become world-class data leaders and entrepreneurs.
We are going to talk about how to sign high-paying clients for your data business, yes, BUT FIRST, let’s discuss…
“What qualifies as “high-pay” in the small data business sector?”
Data Implementation Services – $300 per hour
Data Leadership Services – $500 per hour
Data Products** – $1000 per hour of your time invested
** Products can usually be scaled so they don’t actually require 1 customer to pay a lot of money, but you’re still able to earn a pretty decent return on investment for the time you spent on those products…I will cover this in more detail later on in this article.
What is required of you (for someone to be willing to pay a lot of money)?
Necessity
Uniqueness
Trust
So to answer the question on how you can sign more high-paying clients into your data business, we need to address what actions you can take in order to build up necessity, uniqueness and trust.
How to Establish Necessity
You need to make sure that your customers need whatever it is that you sell – either products or services. It can’t be just one of these things that’s nice to have – it really needs to be something that solves a problem for them and that it is an urgent necessity that will require them to actually make that purchase.
How do you make sure that what you sell is needed?
You need to do market research to identify the huge gaping whole in the market that you have the expertise to fill. Look on job boards, freelance marketplaces, look inside FBGs, Quora or anywhere else where people are reaching out for help on your area of expertise. By looking at how people are asking for help in terms of job postings or questions, you can get a pretty good idea at their level of urgency and you can also see the degree of saturation in terms of other data professionals filling those requirements.
I’d love to hear from you in the comments! Please share with the community about what you currently sell in your data business and who you serve.
How to Cultivate Uniqueness
There must be some topics, sub-disciplines, specialties or offers in the data space that you are SUPER SICK of hearing everyone else talk about. So what I want you to do is stop and DO NOT DO what they are doing.
For example, “data storytelling” is super popular right now. It would not be a good time to start a course and teach data storytelling to other data professionals – just because everyone else is already doing it – it’s definitely not unique.
What you could do however, is take those “data storytelling” skills and turn them into a unique client avatar – a very specific class of professionals who really need that type of help! For example: Healthcare Leaders, Marketing Leaders, and so many more.
And where can you identify who needs that type of help: MARKET RESEARCH
How to Build Trust
There are quite a few ways that you can build trust with prospective customers. Some of them are:
Offer low price, quick win products or services
Something that they can dip their toe in the water to see what kind of results you can generate for them and then they know they want to keep working with you
Word-of-mouth referrals
If you have people that you have worked with in the past, even if it’s in an employment environment, and they recommend you for the transformation you made in their business, then it’s gonna be a lot more likely that their colleagues will trust you as well and make that purchase from you.
Credibility
Another way to build trust is the external show of credibility factors. Yes, of course degrees and certificates are great, but in all honesty in the business world, no one cares that much about your degree unless you went to MIT, Harvard, Stanford or something like that. The big thing really is testimonials – if you can get results for people and show those results as proof and evidence of your ability to get results for more people – that’s going to be one of the things that really helps potential clients trust you.
Long-term relationship-building
This usually requires you to have a website, email list, content strategy, etc. – this is the long game. This is you showing up day after day, week after week, year after year, and continually giving value and contributing and helping your community. These are the types of things that build trust in the long term with your community members and prospective clients.
If you’re like most data entrepreneurs, then you’re selling your time for less than $300 per hour – which is a huge mistake. That’s why I did this video on how to do Data Consulting at >$300/hour. Be sure to check it out!
What to sell to maximize your chance of signing high-paying clients?
I put together a list of products and services you can offer to increase your chances of signing high-paying clients. These services include:
Starter Audit
Data Strategy Services
Data Cleaning Services
Machine Learning Model Building Services
Data Visualization Services
Data Pipeline Services
Custom Chatbot Services
Basically any data skill you provide to an employer can be repurposed into a high-ticket package and sold on the open market to start-ups or to anyone who is looking to hire freelancers.
In terms of what products you can sell:
Books
Course
Plug-n-Play Dashboard
SaaS Trial Version
There is an almost an infinite combination of ways to sell your data expertise as products and services that high-paying customers want to buy. The trick to actually signing high-paying customers is to make sure that what you’re trying to sell them is actually what they WANT and NEED. To do this, try surveying and interviewing people that belong to your customer demographic – so you can hear from them about what they really need and in what format they want that need met.
Let me illustrate with that data storytelling example…
If you speak to most healthcare leaders who want to spend money to get their data storytelling needs met…most of them that have a decent budget will NOT want to buy a course or a book on the topic. This is because this is something that will require their time to read and put themselves together. They will just want to pay someone a fair rate to come in and serve up a data storytelling solution on a silver platter for them. This will make it easy for them to take the credit for the quick win. So in this case, you’d sell a data visualization service package – not a book nor a course.
For Data Entrepreneurs…
If you’re digging this convo on how data entrepreneurs can sign high-paying clients for their business, then I know you’re going to love my FREE Data Entrepreneur’s Toolkit.
It’s an ensemble of all of the very best, most efficient tools in the market. I’ve discovered these tools after 9 years of research and development. A side note on this, many of them are free, or at least free to get started. And they have such powerful results in terms of growing your business. These are actually the tools we use in my own business to hit the multiple 6-figure annual revenue mark.
Download the Toolkit for $0 here.
You may also love it inside our Data Leader and Entrepreneur Community on Facebook. It’s chalked full of some of the internet’s most up-and-coming data leaders and entrepreneurs who’ve come together to inspire and uplift one another.
Join our community here.
Hey! If you liked this post, I’d really appreciate it if you’d share the love with your peers! Share it on your favorite social network by clicking on one of the share buttons below!
NOTE: This description contains affiliate links that allow you to find the items mentioned in this article and support the channel at no cost to you. While this blog may earn minimal sums when the reader uses the links, the reader is in NO WAY obligated to use these links. Thank you for your support!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## The Secret To Building AI Software That Actually Sells
URL: https://www.data-mania.com/blog/the-secret-to-building-ai-software-that-actually-sells/
Type: post
Modified: 2026-03-17
Wanna know the secrets to building AI software that sells? If you’ve got an awesome idea for an AI Software, and you want to make sure it’s actually going to sell before you start building, this post is for you! I’m going to show you 5 easy ways to make sure that your AI software will be a success BEFORE you ever write one line of code.
At the end of this post, I’m going to share the exact process I used to bring in $68k in revenue within the first 2 weeks of launching my last product. That was a pre-sold product, sold in December of 2020 and released in Jan 2021 – so I know that this approach works in 2021.
YouTube URL: https://youtu.be/JM_ALyIs6G0
If you prefer to read instead of watch then, read on…
For the best data leadership and business-building advice on block, subscribe to my newsletter below and I’ll make sure you get notified when a new blog installment gets released (each week). 👇
Why am I qualified to say anything about building AI software that sells? Well, I’ve been selling data science products since 2014…to the tune of 6-figures in profits per year, just from data products alone.
My name is Lillian Pierson and I support data professionals to become world-class data leaders and entrepreneurs.
If you’re reading this, you probably have an awesome idea for an AI startup or an AI software – and what we need to do is to make sure that you’re actually starting with consumer needs and not technology. To validate your focus, that’s going to require market research…
Phase 1: Market Research
I’ve been updating my book Data Science for Dummies lately and when I was looking at tools that I recommended in 2016, I found that most of them are now extinct. In other words, they’re not supported anymore, the websites are down and the businesses are no longer operating.
I’m sure that the people who created these tools were smart people but they had the wrong focus. So what I want for you is to focus on monetizing and then building and it all starts with MARKET RESEARCH.
After you have researched your AI software idea, do your market research and make sure that:
There is a need for it in the market
That people are willing to pay to have that problem solved
There’s not too much competition
After you’ve nailed down the exact niche of who you’re going to help and how you’re going to help them, AND made sure that there’s a demand and not too much saturation, then you will be ready to start delivering.
Phase 2: Sell & Deliver the Service
Selling services is the fastest way to monetize your data business. Another added benefit is you can get paid and earn money while building your software. You can also build your business and your tribe of your future software users while you’re actually building them the product.
Here’s what you need to do:
Deliver the AI SaaS as an ML service
Sell it to customers
Optimize the processes
Tweak it to better fit on-the-ground needs of your ideal client
Phase 3: Build your Business while you Build your SaaS
This would take anywhere from 12 to 18 months, but because you’ve monetized your services, you don’t have to worry about hitting the market fast because you have some leeway – you have some budget in order to bring in a team that can help you develop your product faster and make your business more scalable.
So you’re basically growing your customer base while making money from your machine learning services.
Now, it’s time to do the following:
Reinvest some of the money you earn
Hire a team
Save money for operations & maintenance
Have an extra budget
Build up your social media and email list
Hire launch help!
Phase # 4: Pre-sell Your AI SaaS
How do you make sure that your product release is profitable?
This is why you will need an email list of your prospective clients, as well as, a bank of clients you’re already working with – because these are the people who will likely pre-purchase your product before it’s actually ready for release. This is also why it’s important to build trust by contributing to your community, posting blogs and getting to know your customers via email marketing, so when this time comes, they will be ready to work with you and try out your product.
What you’ll need to do:
Pre-launch to your list
Offer them an extra bonus for signing up early – probably a custom configuration set-up or support that you can give them for free because they trusted you
Set a release date
Create your launch content
This pre-launch will probably last about 2 weeks – launch privately inside your email list and then you can start working to get these pre-sales about 5 weeks before the official release of your product.
Speaking of selling data science services If you want to learn how to sell yours for $300/hour or more, then check out this video I did on Consulting Rates for Online Data Analytics Services in 2021.
Now peeking back up, so far you have done market research, started delivering AI SaaS ideas as a machine learning service, started building your SaaS product as you deliver your service and you have pre-sold your product.
Now it’s time for you to launch your product!
On this 5th step, I will show you how to do a profitable launch so when your product is released, you are rolling in the moolah…
Phase # 5: Product Launch
Remember we discussed setting your product release date? Great! Take that product release date and back calculate 8 weeks prior, because that’s when you need to start working on your launch.
Let’s look at the timelines:
T – 8 weeks: Create your launch storyboard:
Map out the content you’re going to share each day of the launch and because you’ll be launching a new product, you probably want to release for 3 weeks. You want to think of content ideas for anywhere between 16-21 days depending on how frequently you want to publish your content during your launch.
T – 7 weeks: Create your email copy:
You need to put together about 14-16 emails for your customers – they follow your storyboard and they help sell your product. It’s probably going to be about 40-60 pages of email copies written out.
T – 6 weeks: Create your launch event copy:
Anytime you’re launching a product, you want to make sure that you are showing up and doing live events that will help build trust. Offer free mini-trainings that will get mini-transformations for your target customers and ideal clients which will give a little taste of what it’s like using your product. Make sure you have your copy in place for all the live events – may it be a webinar, 5-day challenge or a live Q&A event.
T – 5 weeks: Create your social posts:
Take the email copies you created and repurpose it to social media posts.
T – 4 weeks: Schedule your content:
Get all your copies pre-scheduled as much as possible so you don’t have to worry about publishing stuff. I also want to advise you to leave a gap here just because crap happens and you need to leave a little wiggle room on your launch calendar, always.
T – 3 weeks: Warm up content:
Start publishing some of the content you created – these warm up content should demonstrate your expertise as a solution-provider in your niche and start building people’s trust and kind of give them an idea that something’s coming.
T – 2 weeks: Launch event promo:
Start promoting that launch event part and get as many people as possible to sign up.
T – 10 days: Launch event:
Hold the launch event and unveil your new AI SaaS product available for purchase at the very end. You want to give people some sort of early bird bonus for signing up early, especially since your product isn’t officially released until the end of the launch, which is gonna be 10 days later.
T – 7 days: Early bird ends:
End your early-bird pricing.
T 7 – 4 days: FAQs:
Do FAQ type of content to help people make the decision of whether or not they want to get the special deal you’re offering.
T 3 – 0 days: Last call:
Do a real push and let people know that they might be missing an opportunity to be an early adopter of this new product. This is probably where you’re gonna get 70% of your sign ups.
Product release date:
Make sure that you have your team ready and in-place to help support you. This includes all of the customer service inquiries that will be coming in as people start using your products. Be prepared for that by hiring support before you actually start launching. That way, when you need admin people in place, they are prepared, trained and ready to go. And you can then have a seamless customer experience for all of your clients.
If you’re digging all these tips and tricks on building AI software that sells and how to make money for your artificial intelligence expertise, then I’m sure you’re going to love my FREE Data Entrepreneur’s Toolkit.
It’s an ensemble of all of the very best, most efficient tools in the market! I’ve discovered these tools after 9 years of research and development. A side note on this, many of them are free, or at least free to get started. And they have such powerful results in terms of growing your business. These are actually the tools we use in my own business to hit the multiple 6-figure annual revenue mark.
Download the Toolkit for $0 here.
You may also love it inside our Data Leader and Entrepreneur Community on Facebook. It’s chalked full of some of the internet’s most up-and-coming data leaders and entrepreneurs who’ve come together to inspire and uplift one another.
Join our community here.
Hey! If you liked this post, I’d really appreciate it if you’d share the love with your peers. Share it on your favorite social network by clicking on one of the share buttons below!
NOTE: This description contains affiliate links that allow you to find the items mentioned in this article and support the channel at no cost to you. While this blog may earn minimal sums when the reader uses the links, the reader is in NO WAY obligated to use these links. Thank you for your support!
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Consulting Rates For Online Data Analytics Services in 2021 in Western Economies – CHARGE $300/HOUR+
URL: https://www.data-mania.com/blog/consulting-rates-for-online-data-analytics-services-in-2021-in-western-economies-charge-300-hour/
Type: post
Modified: 2026-03-17
Wondering what the data analytics consulting rates are in western economies? Let me guess – you’ve got some data analytics skills, and you’re vibing on the whole “how do I turn my data skills into $300/hour” thing, right? Sweet! Keep reading because in today’s article, you’re going to learn exactly how to do that, even if you’re a brand new data freelancer!
YouTube URL: https://youtu.be/6IsOSX2dY68
If you prefer to read instead of watch then, read on…
Even if you’re a seasoned data analytics freelance consultant, I know this post is going to be SUPER valuable to you in picking up some of the missing pieces that are probably holding you back from being as profitable as you can be.
How do I know? Well, because I first started out as a freelance data scientist back in 2012…And after supporting 10% of Fortune 100 companies from within my own data business, I coach other data professionals how to hit 6-figures in their own businesses FAST.
Hi, I’m Lillian Pierson and I help data professionals transform into world-class data leaders and entrepreneurs…
In today’s post, we will talk about:
What is Data Analytics Consulting
Entry Level Rates
High End Rates
How To Land Contracts At High-End Rates
What is “Data Analytics Consulting” Anyway?
Well, it’s basically offering your data analytics expertise on a contract basis outside of an employment environment. So, this could be services or advisory support on anything related to Digital Analytics and Business Analytics to Marketing Analytics, and even ad-hoc data analysis would qualify.
Of course your data analytics consulting rates are going to depend on what you’re actually selling, but you have tons of options in terms of the types of services you render as an analytics freelance consultant. Some of these are:
Google Analytics, Tag Manager & Data Studio Services
Tableau Data Visualization Services
PowerBI Data Visualization Services
Creating Business Calculators, Dashboards & KPIs
A/B Testing
Conversion Rate Optimization
Surveys
But you’re not here to talk about all this, you want to get to the good stuff about money, so let’s talk about the rates…
Entry-Level Data Analytics Consulting Rates
There are two ways you can price your services as a new data freelancer: (1) Competitive Analysis, (2) Employment Rate Equivalency. Let’s dig deeper into each of these ways…
Competitive Analysis
I took the liberty of going to Upwork and checking out what people are charging for their services over there. I used these to filter my search: “data analytics, U.S. only, no earnings yet”.
Their rates seem to range between $19 – $350 per hour, but honestly, I’m guessing the $350 per hour guy doesn’t really care about landing jobs on the platform, because even the most experienced analytics professionals aren’t charging that much. And just by eye-balling the numbers, it looks like the median might be somewhere around $65-ish per hour for these types of services.
But, if you are a data analytics newbie and a freelancing newbie, you may want to charge less than the average, and tell all potential clients that you are new to the field and it’s reflected in your very low rates.
Taking freelance jobs is one good way to get experience in data science and analytics but make sure that you set the client’s expectations properly.
Employment Rate Equivalency
You really just want to take 2x what someone is making on an hourly basis as an employee doing the same type of work on an employment basis. You can do this by using this formula:
(Annual Salary / Hours Worked Per Year) x 2
But, we don’t necessarily need to go into that level of detail here right, because what we’re really looking at is how to go about landing $300+/hour contracts.
I’m assuming that you’re reading this because you’re already a data analytics professional, so I’d love to hear from you in the comments about what your specialty is within data analytics.
Moving on to what more experienced data analytics freelancers are charging…
Higher-end Data Analytics Consulting Rates
You would think that they’ll be charging more if they’re experienced as freelancers, right? But sadly, it’s not the case. Most of these freelancers are not charging anywhere near how much the work is actually worth to the business.
Competitive Analysis
I went over to Upwork and searched “data analytics, U.S. only, >$10k earnings.” These people are both experienced with data analytics services and freelancing in general. Their rates seem to range between $42 – $250 per hour. And just by looking at the numbers, the average seems like it might be somewhere around $130-ish per hour for these types of services.
If you’ve reached this point of the post and you’re thinking, “You know what, I’m not really into freelancing and I just prefer to be an entrepreneur and a visionary in my own business…” then that’s awesome! I’ve created a video on how to go from Data Science Freelancer to Data Entrepreneur Almost Overnight. Check it out here.
This part of this article is the fun part for me – because I LOVE helping data professionals THRIVE! So check this out…
Achieving $300/Hour Rates
#1 Trick: Sell deliverables, Not Time
I’ll be honest, my minimum consulting rate is $1k per hour, so that sort of prevents me from offering much in terms of actual data implementation services. But I have been able to make $1k per hour delivering data analytics services in the past…Let me tell you how I managed to pull that off.
The client booked the call and told me what they needed, so I asked if it was ok for me to use an analytics tool to get the result, or if they wanted me to code the solution from scratch. They said using the application is fine. So, I gave them an estimate of 2.5 hours work and quoted them $2,500 – they were so thrilled because it was way less than what they expected to pay. I was able to produce the results in a very short amount of time – the client was so happy because they got everything they needed quickly, on time and under budget and I still made $1k/hour. Now, If I had told them I will charge them $1k/hour, they would probably have said no, but instead they purchased the deliverable from me and they were happy with the deal.
That goes to show that you want to be selling by deliverable instead of time.
#2 Trick: Sell Your Offers in Packages
This builds on top of trick #1, but basically, you want to sell your offers in packages. This makes it even easier for you to increase the value proposition of your offer, thus increasing the likelihood of making that sale.
Things you could add to increase the value of your deliverables are reusable assets such as:
Documentation
Training Videos
User Guides
Dashboards
Specifications
Warning: You want to make sure that these value add-ons are reusable between many clients, or easily delegated to someone else, instead of creating something from scratch for each and every client. You want to think about what your offers are and what reusable assets you can give to multiple clients to increase the value of your packages without taking more of your time.
Standard Offer
For this, you need to create a standard offer and this could be your placeholder offer. You need to document everything that comes with this offer – deliverables, bonuses and price points. If a customer comes to you and they need more than what’s included in your standard offer and they have a budget, then it may be worth your while to create a custom offer for them.
But, you don’t want to break your golden rule of charging less than your worth.
Where to sell
You want to avoid selling these packages in freelance marketplaces like Upwork, because it’s going to be very difficult for you to sell high-end services at rates that are fair to you. In this sort of environment, people are offering services in (somewhat like) widgets and it makes it very easy for buyers to shop around, so it quickly becomes a race to the bottom. Therefore, I would advise you to avoid selling your services at any sort of job marketplace.
If you liked this article, go ahead and show the love by sharing it and leaving a comment telling me a little about what type of data analytics services you’re offering today.
Share It On Twitter by Clicking This Link -> https://ctt.ac/Hl621
Watch It On YouTube Here: https://youtu.be/6IsOSX2dY68
Free resources that’ll help…
Get The Ultimate Toolkit For Data Entrepreneurs
There’s always that data professional who starts an online business and hits 6-figures in less than a year. Now? It’s your turn and we’re ready to help get you there with our Data Entrepreneur’s Toolkit (designed to help you get results for your data business fast).
These are our favorite 32 tools & processes (that we use), which include:
Marketing & Sales Automation Tools, so you can generate leads and sales – even in your sleeping hours
Business Process Automation Tools, so you have more time to chill offline, and relax.
Essential Data Startup Processes, so you feel confident knowing you’re doing the right things to build a data business that’s both profitable and scalable.
Get the it for free here!
Take The Data Superhero Quiz
You can take a much more direct path to the top once you understand how to leverage your skillsets, your talents, your personality and your passions in order to serve in a capacity where you’ll thrive. That’s why I’m encouraging you to take the data superhero quiz.
This free and super-fun 45-second quiz is all about you and how your personality type aligns with the very best career path for you. It’s fun, free and it will provide you personalized data career recommendations, complete with potential roles that fit your unique skills and passions, as well as salaries associated with those roles.
Take the Data Superhero Quiz today!
NOTE: This blog post contains affiliate links that allow you to find the items mentioned in this article. While this blog may earn minimal sums when the reader uses the links, the reader is in NO WAY obligated to use these links. Thank you for your support!
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## What Is a Conversion Funnel and the Best Funnel Software For Data Entrepreneurs
URL: https://www.data-mania.com/blog/what-is-a-conversion-funnel/
Type: post
Modified: 2026-03-17
If you’ve ever been curious about starting your own data business, you’ve probably heard the term ‘funnel’ thrown around, but there’s often quite a bit of ambiguity on what it really means. Let’s answer the question, ‘what is a conversion funnel’ and discuss the best options for new data entrepreneurs, so you can start putting the pieces together for a profitable data business.
So what is a conversion funnel, anyway?
People often refer to a funnel as a series of web pages that lead someone to take a certain action – perhaps that action is signing up for your email list, or making a purchase.
But that’s actually only a half-truth.
In fact, a funnel is really a series of web pages that feed into one another that includes a transaction system.
And if it’s a true funnel, it should feature an upsell. An upsell occurs when your transaction has been initiated and you are then redirected to an option where you can get a separate, related item, one that is higher in price. You can opt-in or out of that transaction, and sometimes if you opt-out you are redirected to a down-sell.
So when people wonder, what is a conversion funnel, in its essence, the concept of a funnel is actually fairly simple. But often, it’s the transaction systems and tech used to build funnels can make funnels complex. Oddly enough, despite the boom of online small businesses and e-commerce shops, few vendors have found a way to simplify these transaction systems required to build funnels.
ClickFunnels – The Apple of the Funnel World
Now that we’ve answered the question what is a conversion funnel? let’s discuss the different funnel software available. Due to the complexity of much of the available funnel software, most of which do not permit for simplification, many users turn to the popular program Clickfunnels.
I like to call Clickfunnels the ‘Apple’ of the funnel world. It’s sexy, features a sleek design, and is more intuitive than the other products available. But it also boasts a hefty price tag, and it doesn’t ‘play nice’ with other tech tools. If you are on their basic $100/month plan, it’s fairly limited in terms of its capabilities. And if you want to increase its functionality, you’ll usually need to purchase THEIR integrations and tools each month rather than connecting it with ofter (often more affordable) software. Your monthly bill can often run you $300, $500, or even $1000/month (Eek! ????).
WooFunnels – An Alternative Funnel Option for Data Entrepreneurs
Since I’m a data person, I decided to ditch Clickfunnels and try one of the more ‘complicated’ funnel solutions (because if you’re a fellow tech nerd, I know you also get joy out of figuring things out!).
Here at Data-Mania, we now have our funnels on Woocommerce on our WordPress content management system using WooFunnels. We use something called Upstroke (a one-click upsell add-on available for WooCommerce) and Google Data Studio for our conversion tracking.
In a way, it’s kind of like the Android approach to funnel-building. And boy am I ever glad we ditched the fancy, limiting software and switched to this approach!
Want MORE of my top recommendations for the best tools and softwares for new data entrepreneurs? Download the Ultimate Toolkit For Data Entrepreneurs here!
Pro’s and Con’s of WooFunnels for New Entrepreneurs
If you’re a new data entrepreneur, I think WooFunnels is a fantastic option to build your first funnel. Let’s break down its pros and cons.
Pros
✅ Reduce Your Monthly Costs. I save SO much money on software each month by taking a ‘build-your-own’ approach rather than footing a big monthly Clickfunnels bill.
✅ Added flexibility. I can get all sorts of extensions, add-ons, and analytics without a headache on incurring tons of extra costs.
✅ Integrates with your current WordPress site. If you already have a WordPress site, it can be handy to have your funnels, blog, and normal site “under one roof”.
Cons
❌ Tougher learning curve. Woofunnels is not quite as simple and intuitive as Clickfunnels, it can take a bit longer to figure out. But once you’ve got the hang of it, it’s really not too hard. (Plus, as a data person, I know you’ve done things that are a whole lot more complicated! ???? )
We’re saving on software costs each month, and we have a LOT more flexibility. I’m able to get all sorts of extensions and add-ons without being boxed into one provider.
Ultimately, as a new data entrepreneur, you’re going to want to get a little scrappy. Sometimes that means using your time and tech skills to build-out your own funnel, on software a little less well-known (but JUST as good if not better in my opinion!) to save thousands of dollars a year.
So many new entrepreneurs get Clickfunnels or other fancy software just because they “feel like they should” or because “that’s what everyone else is doing”. But part of being a successful data entrepreneur is making sure that each investment makes sense for YOU and is worth it in terms of ROI.
The thousands of dollars spent on expensive software could be going towards a new team member, business coaching, or a course that is going to give you the knowledge you need to see BIG results in your new data business.
Because knowing which tools are really worth it isn’t always easy, my team and I have put together an AWESOME resource for you. The Ultimate Toolkit for Data Entrepreneurs features direct, actionable recommendations on the exact tools and processes you need to set your data business up for long-term success. These tools are tried and tested, and many of them are free or affordable, making them perfect for new or aspiring data entrepreneurs.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## The Best Revenue Models for Startups in the Tech and Data Space
URL: https://www.data-mania.com/blog/revenue-models-for-startups/
Type: post
Modified: 2026-03-17
Are you ready to get out of the feast and famine freelancer cycle and start implementing some SERIOUSLY profitable revenue models for startups? Keep reading.
As new entrepreneurs, we often start our businesses filled with enthusiasm and an “I’ll-do-anything-it-takes” mentality. We’re ready to get down and dirty and do whatever is needed to strike out and build something of our own.
When we get our first client, we’re over the moon.
It may not be for much money – but hey, someone is paying us to do what we love, right?
We land another small project here, a gig there, until all of a sudden…our calendar actually starts to look pretty full with all these “small projects”.
Pretty soon, the clients you were SO happy you signed may start to become demanding. They’re asking for changes, blowing up your phone, requesting meetings and tasks left, right and center.
Not exactly the “freedom entrepreneur lifestyle” you imagined, huh?
While I now run my startup Data-Mania, a multi-6-figure data training and advising company, I too used to know this struggle all too well.
Here’s my story…
I was a solo data freelancer working to quit my 9-5. I started my freelance career taking requests and small projects in the evening after my day job – often only for a couple of hundred bucks.
And while eventually I got enough of these small freelance jobs to quit my 9-5, I still had no predictable way to bring in revenue into my business. I didn’t have a specific buyer avatar – nor did I specialize in a certain service. I’d never thought to consider the ideal revenue models for startups – I’d simply taken whatever work came my way.
Pretty soon I had 10 different freelance clients from 10 different industries, all underpaying me to do 10 different types of services.
My business was not scalable, not at ALL profitable, and definitely not fun.
One of my mentorship clients, Kam Lee, actually coined a name for this phenomenon. He coined it the low-budget hamster wheel.
He joined my mentorship program so he could escape that hamster wheel. And wouldn’t you know – in just 1 year – not only did he get off the hamster wheel, but he managed to transition from burnt out data services provider to scaling data CEO making $350k/year through his data business and SaaS company, Finetooth Analytics.
Ask myself or ask Kam Lee, the reality is that being a solo freelancer often simply isn’t sustainable – or scalable, in the long run. To really hit that next income level, as well as create a business that isn’t 100% dependent on your TIME, you have to start looking into other revenue models for startups.
Below are some of the BEST revenue models that have worked both for myself and for other founders in data-intensive industries. So if you LOVE tech, are data-driven, and want to know the best types of models to build a business that’s bigger than yourself, keep reading!
If you prefer to read instead of watch then, read on…
For the best data leadership and business-building advice on the digital block, subscribe to my newsletter below and I’ll make sure you get notified when a new blog installment gets released (each week). 👇
First Things First: Business Models vs. Revenue Models
Before discussing any of the revenue models for startups, it’s SUPER important to understand the difference between a revenue model and a business model.
Simply put, a revenue model is just a portion of the business model that’s responsible for bringing revenues into the business.
A business model, on the other hand, is a conceptual model that explains how a business delivers value in exchange for money.
So technically, a business could have multiple revenue models. You could combine some of these on the list to amp up your revenue! This is especially true for AI products, which often layer usage, seat, credit, or outcome-based pricing.
Revenue Models for Startups #1 – Unit Sales
Let’s get into the very first of the revenue models for startups – that being unit sales.
Essentially, this is just the direct sale of ownership of a product.
For an online business, this could be the sales of merchandise or the sale of a license to use a digital product, like a course or an ebook.
Pros to this revenue model
One of the biggest pros is that this revenue model is super scalable. If implemented successfully, you can use it to hit 7 figures in your business.
Another bonus is that you can automate or delegate most of the client delivery work. So it’s not actually taking any of your personal time to deliver the products.
Cons to this revenue model
The biggest negative to this revenue model is that it can be almost impossible to implement if you don’t have an audience. You can create an incredible product and try to sell it, but if you don’t have a large enough audience to sell it to, it’s going to be extremely difficult.
Another risk to this type of revenue model for startups is the product development time.
You always (and I mean ALWAYS!) want to make sure that you validate your offer BEFORE spending hours and hours building it and taking it to market.
How to implement this revenue model
If you already have an audience, the best way to go about implementing the unit sales method is to FIRST sell 1-1 coaching or advising and build out the offer. By offering your solution in a 1-1 capacity first, you can validate your transformation, fill in the gaps as you go, and then productize it.
On the flip side, if you’re not an expert, or have a small audience or no audience at all, then you’re going to want to make a small digital product first – something less than $99. You can sell that product to your audience as you work to grow that audience size!
Revenue Model #2 – Licensing
Let’s move on to the licensing revenue model. Essentially, this is where you sell a license to someone in exchange for their right to use your intellectual property.
By implementing the licensing model, you sell your customer a license in exchange for the right to use your intellectual property as if it was their own.
Often, these types of products go by the name of:
White label products
PLR products (i.e. private license rights products)
Done-for-you content
Pros of this revenue model
The pro to this type of content is that it is VERY easy to develop. Plus, you can actually use it to make sales as you grow your audience.
A great example of this would be PLR content, in other words, private license right content.
Most of this content for sale on the internet is super affordable – it’s usually always less than $100. Because it’s low-priced, it’s a great way to create something (relatively easily!), sell it to a bunch of clients so they can use it as their own, and grow your audience along the way!
Cons to this revenue model
The biggest negative to licensing is that selling a product like this is not a high-ticket sale. It’s a low-ticket earner.
You want to be absolutely sure that you distinguish yourself from every other PLR provider out there. Because if I’m being honest, there’s a lot of white label or PLR content that is absolute garbage. You DON’T want your brand to be associated with all of the other spammy brands that sell PLR products.
How to implement this revenue model
I would suggest starting by Googling phrases “PLR” and “white label data products” to see what kind of other stuff is already out there.
You could even buy a few low-cost products just to get your feet wet and see what is being offered by other providers.
Next, you’ll want to start thinking about your specific data expertise to see what you could sell through licensing.
Finally, you’ll want to look for quality markers within any sample products you buy to see where and how you could make upgrades and distinguish yourself in terms of quality branding.
Another good way to find inspiration for what to create is to do market research. See what people in your community are struggling with so your product can help them solve their problem.
Just remember – whatever you put into the world, people will associate with your brand. Don’t produce anything that would ultimately harm your brand’s reputation!
Revenue Model #3 – Subscriptions
Thirdly, let’s take a look at the subscription model.
Essentially, this is the right to access your product, service or community on a subscription and pay-as-you-go basis. Many product-led startups choose between freemium and free trial models to drive subscriptions.
In some cases, this could look like a service retainer package.
Pros of this revenue model
The big pro on the subscription model is that they’re generally easy to sell. It’s also a GREAT way to get paid while you build out your product suite and test the service with users. Another plus? It’s got a fairly small time to market (unless you’re trying to sell SAAS subscriptions, of course!).
With the subscription model, you can continue to bring revenue into your business month after month as you continue to grow your audience through social media marketing.
Cons to this revenue model
On the flip side, the CON to this type of revenue model for startups is that it can be extremely high-maintenance.
If your membership gets too big, you’re going to have to bring in team members to help you run the administrative side. You’ll have to deal with people joining and cancelling, asking questions and requiring customer support.
If you DO choose to go with the membership model, you’ve got to be super clear in your contract about what your membership includes, and what it doesn’t.
While I can totally see why some people dig this model, the idea of having to admin it and all of the headaches that would come with people joining and cancelling are why I’ve never decided to pursue a membership.
However, I do think that if you keep it small and manageable, it can be a great way to add a new revenue stream to a data business.
How to implement this revenue model
A SUPER quick way to implement this would be to create a Facebook support group, agree to give a monthly curriculum drop and then do a once per month Q&A group call. Say you offered that package for $77/month, and sold that package to 13 people, you’d have your $1,000 per month in monthly recurring revenue! Not bad, right?
Revenue Model #4 – Fee for service
The fee for service model is where you sell your services according to a fixed price like a lump sum – OR an hourly basis.
But I want to stop you right here.
Never, ever, sell your time for money when doing data implementation work.
That is a fabulous way to go from having one boss at your 9-5 job to having 15 “micro-bosses” all trying to manage your time and tasks.
Pros of this revenue model
Want to start a data business FAST? The biggest pro to the fee-for-service model is that it’s super simple and easy to get started.
Even if you’re a brand new data entrepreneur, you can still begin selling data implementation services for at least $150/hour. But the key here is to sell your services by the DELIVERABLE – never the hour.
Say, for example, you build custom chat bots. You’d charge a package fee to build the bot, rather than charge your clients for hours worked on their bot.
Cons to this revenue model
With the fee for service model, you’ll quickly find out that it’s not very scalable. You only have so many hours in a day. At a certain point, if you start off with a fee for service revenue model, you’ll have to switch into something like an agency or a software as a service business model.
How to implement this revenue model
The answer to this is simple! All you need to do to get started straight away is log onto UpWork and start browsing freelance jobs in your chosen specialty.
As you get more experienced, however, you may want to look into more long-term strategies to attract high-end clients, such as building your brand on social media.
At the end of the day, there is SO much you can do with a data-driven business. Don’t limit yourself to freelancing or being a services provider if you think your heart may be elsewhere! Get creative, assess your strengths and personality, and dive deep into these revenue models for startups to see which one would work FOR YOU. The world is your oyster!
Want more resources to start, grow and scale a data business? Download my FREE Data Entrepreneur’s Toolkit, a collection of 32 free and low-cost tech tools & processes that I used to scale my business, Data-Mania, into the multiple 6-figure range.
Share It On Twitter by Clicking This Link -> https://ctt.ac/fRx6p
Watch It On YouTube Here: https://youtu.be/ZtZGTN3bz0I
NOTE: This description contains affiliate links that allow you to find the items mentioned in this video and support the channel at no cost to you. While this channel may earn minimal sums when the viewer uses the links, the viewer is in NO WAY obligated to use these links. Thank you for your support!
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## The Journey to Becoming a Successful Data Entrepreneur
URL: https://www.data-mania.com/blog/the-journey-to-becoming-a-successful-data-entrepreneur/
Type: post
Modified: 2026-03-17
In this episode, Cassidy speaks with Lillian Pierson, a world-class data leader, entrepreneur, and educator, and the CEO of Data Mania. They discuss different approaches data professionals can take to become data entrepreneurs and how data science has evolved over the years.
Lillian has trained over 1 million data professionals through her YouTube videos, online courses, and books. She’s particularly passionate about helping data professionals and new data entrepreneurs start their own business. As an independent consultant, she has supported over 10% of Fortune 100 companies and she’s been featured in Forbes, Fortune, National Geographic, Washington Post, Christian Science Monitor, and The Guardian, among dozens of other publications.
They discuss:
Lillian’s journey building an online business around data education
Avenues for becoming a data entrepreneur
Defining your audience in the data & analytics space
Data science career trajectories
Lillian’s 2 methods to start a side hustle
Approaching data as a product versus a supply chainThe case for creating data products
If you want to know more or get in touch with Lillian, follow the links below:
Weekly Free Trainings: We currently publish 1 free training per week on YouTube! https://www.youtube.com/channel/UCK4MGP0A6lBjnQWAmcWBcKQ
Becoming World-Class Data Leaders and Data Entrepreneurs Facebook Group: https://www.facebook.com/groups/data.leaders.and.entrepreneurs
LinkedIn: https://www.linkedin.com/in/lillianpierson/
The Data Entrepreneur’s Toolkit: A recommendation set for 32 free (or low-cost) tools & processes that’ll actually grow your data business (even if you still haven’t put up that website yet!). https://www.data-mania.com/data-entrepreneur-toolkit/
Listen on Narrative Science website: https://narrativescience.com/resource/podcast/data-entrepreneur-lillian-pierson
Listen on Apple Here: https://podcasts.apple.com/us/podcast/the-journey-to-becoming-a-successful-data-entrepreneur/id1535701027?i=1000527222765
Discover your inner Data Superhero!
Most of the time, custom advice is all you need to achieve both your dream salary AND the satisfaction that you crave from your data career.
In our free, fun, 45-second data career path quiz, you’ll uncover your inner Data Superhero type and get personalized data career recommendations that directly align with your unique combination of data skills, personality and passions.
Take the Data Superhero’s Quiz today!
Get the Data Entrepreneur’s Toolkit
There’s always that data professional who starts an online business and hits 6-figures in less than a year. Now? It’s your turn and we’re ready to help get you there with our Data Entrepreneur’s Toolkit (designed to help you get results for your data business fast).
It’s our favorite 32 tools & processes (that we use), which includes:
Marketing & Sales Automation Tools, so you can generate leads and sales – even in your sleeping hours.
Business Process Automation Tools, so you have more time to chill offline, and relax.
Essential Data Startup Processes, so you feel confident knowing you’re doing the right things to build a data business that’s both profitable and scalable.
Download the Data Entrepreneur’s Toolkit for $0 here.
Execute Upon the Data Strategy Action Plan
This is our crowd-favorite data strategy product. No long video trainings, no books to read, no needless theory. Just clear, concise guidance on what your next data strategy steps should be, starting today.
It’s a step-by-step checklist & collaborative Trello Board planner for data professionals who want to get unstuck & up-leveled into their next promotion by delivering a fail-proof data strategy plan for their data projects.
There are also 2 bonus guides, if you need help improving communications with your senior executives and stakeholders
And, it comes with a bonus, members-only community, if you’d like a private sounding board for getting valuable input from other data strategists.
Start executing upon our Data Strategy Action Plan today.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## How To Create A Data Product That Generates At Least $450000 Per Month
URL: https://www.data-mania.com/blog/how-to-create-a-data-product-that-generates-at-least-450000-per-month/
Type: post
Modified: 2026-03-17
Who’s curious about how to create a data product and learn how profitable they can really be?
If that’s you, then keep reading because today I’m sharing the real-life story of how one company is making almost half a million in monthly revenue by – for good or for bad – reselling personal data that is generated on people’s mobile phones.
But I won’t stop with just a story – at the end I’ll break it all down for you in a simple, incremental way that you can use to guide your own thinking and decision-making if you want to build your own data product.
YouTube URL: https://youtu.be/FS3nmSV1ebo
If you prefer to read instead of watch then, read on…
For the best data leadership and business-building advice on block, subscribe to my newsletter below. I’ll make sure you get notified when a new blog installment gets released (each week). 👇
Get to Know Me…
How did I come across this juicy info? And why am I qualified to reverse engineer a data product like this, anyway?
I came across this information as part of my recent research on data privacy and data partnerships, which I was doing as part of a book rewrite. This is for the 3rd edition of Data Science for Dummies that I originally wrote in 2014.
And how I’m qualified to reverse engineer a data product? Well, I have been building them for other companies since 2012, and selling them in my own business since 2014.
After serving 10% of Fortune 100 companies from within my own data business, I started coaching other data professionals on how to hit 6-figures in their own businesses FAST.
Newer data entrepreneurs like Dr. Chantel here – who is still just in the process of starting her data business, but already used what I taught her to land a $3k contract for just 15 hours of work!
Before I dish out the deets on how to create a data product that’s capable of generating half a mill a month in revenue, let’s do a REALITY CHECK: There’s no such thing as overnight success…
There’s No Such Thing as Overnight Success
Once you see how this data company did it, you’ll probably start getting lots of ideas for ways you can make money with data expertise and data resources, but just remember: success doesn’t happen overnight and it definitely doesn’t happen unless you stay focused.
If you’re anything like I was back in 2012 – when I first started my data biz (then side hustle – Data-Mania), then you love working with data but don’t love having to:
Answer to someone else
Spend your life building someone else’s business in exchange for a paycheck
Looking over your shoulder about when the next shoe will drop in terms of layoff and job changes
Heck I didn’t like those things then and I know I wouldn’t like them today – if I had to deal with them…
The thing is, starting a business and working for yourself isn’t as easy as making a decision.
The follow through is where the money is at.
I remember having no idea what I would actually sell, and that was after I quit my day job.
Personally, it took me several years (and tens of thousands of dollars in working with business mentors), before I developed the firm grasp I have today on how the data industry works from inside out. And of course lots has changed in my perspective over the years which is why I wanted to make sure I got to share this information with you – to try and help you start thinking like a data entrepreneur instead of a data employee.
The first thing you need to know about this data product is that it’s built on top of data that its owner acquired through a data partnership.
Data Partnerships
From a business perspective, a data partnership is a paid agreement between companies wherein companies sell their business data and their customers’ personal data to other businesses. With respect to data privacy and misuse of personal data, there is a lot NOT to love about data partnerships, but I’ll let you decide for yourself after you learn about how this specific data product works.
Hey, I’d love to hear from you! Do you have any ideas for cool data products? Tell me about them in the comments and I promise to reply back and try to help you as much as I am able there!
Monetizing A Data Partnership
It’s time for me to name names – the company that is generating $450k per month selling personal data that they acquired through data partnerships is called SafeGraph.
Safegraph is backed by angel investor Peter Theil and reportedly raised $45 Million off of one pitch deck in their Series B funding round. That must have been a pretty compelling pitch deck, right?! To get a good view on what these investors actually bought into, we pry into how this “Data-As-A-Service (DaaS)” platform takes data resources from data partners and directly converts it into tens of millions of dollars per year in revenue.
How It Works
Safegraph buys people’s location-based data from mobile app developers who’ve collected that data on its users as part of running the application services they use on their phones. If you read the terms and conditions for your smartphone apps, they hopefully include a term in there that gives them the right to resell any data they collect on you, as part of the services run by each of your phone apps. You would be agreeing to this by the act of installing an application on your phone.
So, SafeGraph comes in.
Instead of wasting time and money reinventing the wheel, they just form partnerships with application developers to get your location data. They then take that data and really narrow it down into location-based activities. These are focused around commercial spaces like shopping malls, coffee shops, grocery stores, and the like. Once they’ve cleaned that data, they put it on their web-based mapping application. They then present it as a product available for purchase by retail companies.
These retail companies then use that information for improved decision-making. SafeGraph’s data tells them where you go, and how frequently, how long you stay there, and so much more.
They’re collecting data from tens of millions, if not hundreds of millions, of American people’s smartphones. Every single cell tower ping goes straight into SafeGraph’s ”file” on you, for lack of a better word. Tens to hundreds of trillion pings per month, if you can imagine. All of this location data has been openly aggregated and sold by SafeGraph since 2016, to the tune of hundreds of millions of dollars in revenue for this small Colorado-based data company.
If you’re enjoying this article on how to create a data product, you’d probably really dig the video I did on “The best business models for data entrepreneurs.” Check it out here.
The Breakdown
So, if you were going to map out an idea for your own data product, I thought it’d be helpful to break it down – from a business perspective – what’s really going on with the SafeGraph data product.
Business A – Mobile App Developer
They are the original data “owner” because they’ve collected your location data from your mobile phone. And because of the terms and conditions, they own that data. They then sell it to Business B in exchange for money.
Business B – SafeGraph
They are the intermediary data business and what they do is turn that raw data into a market research product. In fact, I wouldn’t say that SafeGraph is necessarily an AI SaaS company. It’s because I don’t know about what types of predictive modelling they have going inside of the application. My best guess is that it’s a market research product. This product is thenavailable through web applications and is sold on a subscription basis to Business C. And that is how to create a data product.
Business C – Retail Companies
They can be McDonald’s, Starbucks, etc. And they are the ones who collect information about people’s activities around these retail locations in exchange for money. And that is the sale of the data product.
In order for SafeGraph or any intermediary business to produce that data product, they actually have to render data services in-house. So, if you’re a data scientist or any sort of data professional those would be in your territory. Those services are needed in order to convert raw data into the product that can then be installed to the customer’s database. The data partnership aspect is the agreement between the mobile app developers and business B.
If you’re digging this real talk on how to create a data product and data businesses, then I know you’re going to love my FREE Data Entrepreneur’s Toolkit.
It’s an ensemble of all of the very best, and most efficient tools in the market! I’ve discovered these tools after 9 years of research and development. A side note on this – many of them are free, or at least free to get started, and they have such powerful results in terms of growing your business. These are actually the tools we use in my own business to hit the multiple 6-figure annual revenue mark.
Download the Toolkit for $0 here.
You may also love it inside our Data Leader and Entrepreneur Community on Facebook. It’s chalked full of some of the internet’s most up-and-coming data leaders and entrepreneurs who’ve come together to inspire and uplift one another.
Join our community here.
Hey! If you liked this post about how to create a data product, I’d really appreciate it if you’d share the love with your peers. Share it on your favorite social network by clicking on one of the share buttons below!
NOTE: This description contains affiliate links that allow you to find the items mentioned in this article and support the channel at no cost to you. While this blog may earn minimal sums when the reader uses the links, the reader is in NO WAY obligated to use these links. Thank you for your support!
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## AI Startups – 4 Hiring Mistakes You Need to Avoid
URL: https://www.data-mania.com/blog/ai-startups-4-hiring-mistakes-you-need-to-avoid/
Type: post
Modified: 2026-03-17
Wondering what hiring mistakes to avoid in your startup business? If you’re a newer data freelancer or business owner and you’re thinking about hiring some help to manage the work – consider this a heads up – there are some SUPER costly mistakes that it’s so easy to make. Learn what those mistakes are in this article – as well as what they can end up costing you.
YouTube URL: https://youtu.be/bM07iwwgo1U
If you prefer to read instead of watch then, read on…
In case you’re new to our community, my name is Lillian Pierson and I support data professionals to become world-class data leaders and entrepreneurs.
Why am I talking about the hiring mistakes you need to avoid now? Well, for starters – I’m hiring. I’ve been way too busy with the rewrite of the 3rd edition of my book Data Science for Dummies and more data projects coming down the pipeline…So, it’s gotten to the point that I can’t remain the only data pro working inside my biz.
For now – I’m going to teach you a thing or two about hiring mistakes to avoid in your data business from the collection of the major lessons I’ve learned in the last 8 years of hiring in my own business…
Backstory Testimonial
If you’re building your data business the right way, then you’re gonna see fast client growth – growth like my student here, Jordan Goldmeier, who started our program in January and used it to land $50k contracts in the last 6 months – 6 of which were at $500/hour. Now, he’s looking to make another $100k this year from the methods he is still learning inside DCC, so – in order for this to happen, he’s going to need to hire some help.
So, in this article, I’m going to pretend that this is for him and share what not to do when hiring people for your business. I will use just a few of my hiring missteps to illustrate why I suggest NOT doing these things…
Mistake #1: Do NOT Hire Based On Half-Baked Project Plans
Half baked job descriptions scare off good applicants and invite people who don’t know what they’re doing. Don’t throw a half baked project idea on Upwork and then try to hire someone to finish the plan and implement it for you, because that’s really unrealistic.
That might work if you’re paying an experienced consultant for $300/hour, then you can probably find someone who can do strategy-level project planning and then implement it for you. But, if you are hiring someone for $20/hour, don’t expect that the person can deliver a good technical strategy and implement it, because people who can do that type of work are not going to sell their services for $20/hour, even if they are in the Philippines.
How do I know?
Because I made that mistake.
What it cost me:
Created a CRM that I never used because the tech was junk so that’s wasted money
Dirtied up my data which cost me time to fix
Paid $1.5k for someone to copy/paste onto web page which is a rip off
Spent $5k on service charges
Spent $1k worth of my own time cleaning up after
The total Cost of this mistake for me is $6k.
This is a great time to hear from you – I would love it if you would tell me in the comments, what sorts of mistakes have you made when hiring people in the past?
Mistake #2 Do NOT Hire For < $8 / Hour
You can hire people for $4/hr or even a lot less in Upwork, but the thing is – time is money. You’ll spend time in hiring and onboarding them – teaching them your processes and getting them ready to do the job. If you hire for <$8/hour you can expect that you’ll be spending your time:
Checking their work
Trying to get them to fix their work
You can also expect that you might incur reputational risk which can be very costly to your business. This sort of thing happens if you hire someone who’s interacting with your clients and they don’t know how to speak in your brand language or maybe they’re not native English speakers, so it comes across as low-quality, which reflects poorly on your business.
In terms of the mistake I made in this department, back in 2017, I hired a 20 year old CS student in Pakistan and she actually did okay, but I couldn’t keep working with her, because I couldn’t scale my business and depend on her at the same time. She just wasn’t reliable enough and I spent so much time doing these things:
Constantly checking her work
Asking her to fix things
Being the PM
Intervening in every step of the process
What I have found is, if you wanna hire someone who is a “one and done” – who you can hand things over to and expect them to get done on time, you need to at least pay $8/hour.
Mistake #3 Do NOT hire >5 People At Once
The thing about hiring is when you hire someone, you have to spend a lot of time in:
Hiring
Onboarding
Quality Control
Revisions Request
If you make good hiring decisions, hopefully it won’t take too much time to do those latter points, unless you have a project manager who is going to manage that individual after you hire them. You don’t want to hire more than 1 or 2 people at a time, because you’ll be so busy managing and overseeing these people that you won’t be able to get your own work done.
I’ve made this mistake a few times – every time I get offered a big contract, I bite off more than I can chew in terms of work. Then I hire a bunch of people at one time to help me deliver the projects. Inevitably, it always ended up a huge mess and way more frustration than I needed to deal with.
So, unless you have a project manager and a process in place for onboarding people where it doesn’t require your time, don’t try to bring in more than 1 person. But the good news is, if you make quality hires, then you can work with your team members long-term and you’ll have resources available to you when you need them (on-call basis), or you can also have full-time or part-time people in your business. I actually do all three to support Data-Mania today.
That’s really what it should look like – you should have a network of people helping you support your business who you have long-term relationships with and who know your standards so you can work together for years.
In the end, you’ll have to think of hiring like you’re starting a new relationship and not think of it like a one-night stand.
If you are digging all these real-talk on hiring mistakes to avoid in your business, then I know you’re gonna love my video on How To Create A Data Product That Generates At Least $450000 Per Month. Check it out here.
Mistake #4: Just Say NO to Randos
Don’t just hire random people that put together a good application and charge a decent rate.
Why?
Good application could mean that they have good marketing skills
Decent rate could mean that they’re just smart enough to know what their skills should be worth in Western economies
In fact, I find that the best workers are the ones who undercharge and the least reliable workers are the ones who overcharge. It’s really a nuance thing, so you need to spend time getting to know the person and looking at what they’ve done in the past. And just because they write an amazing application that tells you everything you want, don’t expect that they know what they’re talking about and that they’re worth the fees they’re charging.
I did this when I hired an admin assistant for $20/hour because his application was good and he sounded like he knew what he was talking about, so I gave him a shot. He was sort of a “self-starter” which is generally pretty good, but he ended up making important business decisions on my behalf without my permission. For example:
He set up Intercom for my business which is not at all what I needed – it’s good for other business models like SaaS or where people need to submit tickets. I had to source the correct technology we needed, which was HelpScout.
He set up Google For Business without asking me and he ended up setting up the wrong name.
He had no mind for the budget. He came from a much larger company that sold software and just assumed that the requirements were the same.
I got a trillion notifications.
Then, in the end, he ghosted me.
In terms of what it cost me: I paid him $1.5k in fees and $10k of my own time and lots of stress and headaches.
4 BIG HIRING RISKS
I’m going to break down most of the risks associated with making bad hires. It kind of all boils down to financial risks, but there are different ways that you can incur financial risks within your business so it’s worth it to break it down and look at what those are, so you can avoid them.
COMPLIANCE RISK:
Copyright infringement
Licensing issues
Data privacy risk
Steal your data
REPUTATIONAL RISK:
Potential improper treatment w/ customer
Misalignment with brand language
Steal your email list and go nuts spamming your customers saying bad things about you
Steal your branded social media account
FINANCIAL RISK (where Time = Money):
Hiring and onboarding
Trying to get them to fix their work
Fixing their mistakes (if possible)
Reverting their changes – big one for tech workers
OPERATIONAL RISK
Wasted money on unusable tech
Using the wrong tech – switching is cluster (Intercom >> HelpScout)
Irrevocable “errors”
What it really comes down to is, if you hire the wrong people or too many people, you’re gonna end up thinking to yourself, “It would have taken me less time and less money just to do it myself and I would’ve been a lot less frustrated…” and I don’t want that for you.
If you like this article on hiring mistakes to avoid in your AI startup, then you’re gonna want to watch part 2 where I shared the 3 Hiring Steps You Ever Need in your data business. Check it out here.
You may also love it inside our Data Leader and Entrepreneur Community on Facebook. It’s chalked full of some of the internet’s most up-and-coming data leaders and entrepreneurs who’ve come together to inspire and uplift one another. I left a sample job description in there that you can use to pattern after in making your next hire.
Join our community here.
If you’re digging this real talk on hiring mistakes you need to avoid, then you probably want to avoid technology mistakes as well. That’s why I want to invite you to download our FREE Data Entrepreneur’s Toolkit.
It’s an ensemble of all of the very best, most efficient tools in the market I’ve discovered after 9 years of research and development. A side note on this, many of them are free, or at least free to get started, and they have such powerful results in terms of growing your business. These are actually the tools we use in my own business to hit the multiple 6-figure annual revenue mark.
Download the Toolkit for $0 here.
Hey, and if you liked this post on hiring mistakes to avoid in your data biz, I’d really appreciate it if you’d share the love with your peers by sharing it on your favorite social network by clicking on one of the share buttons below!
NOTE: This description contains affiliate links that allow you to find the items mentioned in this article and support the channel at no cost to you. While this blog may earn minimal sums when the reader uses the links, the reader is in NO WAY obligated to use these links. Thank you for your support!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## AI Startups – The Only 3 Hiring Steps You Ever Need
URL: https://www.data-mania.com/blog/ai-startups-the-only-3-hiring-steps-you-ever-need/
Type: post
Modified: 2026-03-17
What are the hiring steps you need to take when looking for someone to help you in your data business? If you’re thinking about hiring someone, then don’t make another move until you watch this video where I’m about to give you the only 3 steps you’ll ever need to make to ensure your hiring investment produces an ROI for your company.
I did an earlier video on hiring mistakes, and this is the sequel video where I show you how to avoid them – I really recommend you go watch that prequel after watching this one.
YouTube URL: https://youtu.be/i6QT2dZ–zU
If you prefer to read instead of watch then, read on…
For the best data leadership and business-building advice on block, subscribe to my newsletter below and I’ll make sure you get notified when a new blog installment gets released (each week). 👇
In case you’re new to our community, my name is Lillian Pierson and I support data professionals to become world-class data leaders and entrepreneurs.
In terms of why I’m sharing this content now, well, in fact – I’m hiring. So I thought I’d share my journey and my approach to hiring because I sure wish I had this information 8 years ago when I started hiring for my own business, Data-Mania. I’ve been way too busy with the rewrite of the 3rd edition of my book Data Science for Dummies and more data projects coming down the pipeline…So, I need another data pro to help me inside the biz.
But for now – I’m going to teach you a thing or two about hiring data professionals to support you in your data business…
Backstory Testimonial
I know what it’s like to get so many clients in your business that you don’t have enough support to help them all. That’s the situation for my student here, Jordan Goldmeier. He started our program in January 2021 and used it to land $50k contracts in the last 6 months – 6 of which were at $500/hour. Now, he’s looking to make another $100k this year based on the methods I taught him in the course, so – in order for this to happen, he’s going to need to hire some help.
So, in this article, I’m going to pretend that this is for him. Here is what NOT to do – Hiring Mistakes You Need to Avoid, especially about why you don’t want to just throw a job on Upwork real fast and hire that way. Watch it on the blog here.
Now let’s talk about what TO DO. I’ve broken it down into 3 simple steps…
STEP 1: Casting The Net Wide
There are 3 things you need to do as part of this step.
1: Write a quality job description
Business mission, vision, and values
Clear, specific expectations for the candidate
Always include a red herring (a little trick to show that proves the applicant read the job description)
2: Prepare a complete application form
Make it long and detailed to help you assess the candidates better and weed out people that don’t care about your business
Include a mock-up scenario to see what they would do on something like that in real life
Include open-ended questions to get a better idea of their writing style and who they are
Make references back to the job description and ask them questions about it – you don’t want someone to apply if they don’t even take the time to read the job description
3: Share your job posting
Share it on all your social media platforms – relevant Facebook groups, LinkedIn, Twitter, Instagram
Tip: When sharing to social media, make sure to add a little extra red herring step. In this post I did, I asked them to drop a comment and send me a DM so they can get the full job description and most people failed to do that. So when you share it social, don’t just give it to them, make sure that you make them follow a process and follow instructions in order to get the job posting.
Share it in your email list
Post it to job boards
Post it to Upwork
The job description limit is only up to 5000 characters, so you may want to upload a PDF of the full thing
The application questions are limited to 5 so you can just combine the questions, where possible
If you’re reading this, then it’s a pretty good bet that you’re thinking of hiring someone. So, I would love to hear from you.
Tell me in the comments below, who are you looking to hire or what role are you hiring for?
Maybe someone from the community is a good fit!
STEP 2: Narrowing In On The Best Applicant
There are 2 parts to this step…
PART A: The Vibe Check
Go through the applications and pick the top 2 candidates
Schedule a 20-min vibe check – not an interview, just a personal vibe check to see what it feels like to talk to them
Use calendly so you don’t have to bother about the scheduling
Ask about red herring
PART B: Send small test
If they pass the vibe check, then send them a small test.
Send follow-up email with a similar test
Ask them to rank their data expertise on programming languages, etc. (assign a 1 for “no experience,” assign a 10 for “expert level”)
Look at results and schedule interview(s)
You can watch the demo of the small test I did here.
STEP 3: Make an offer
PART A: Hold an interview with the candidate
If the position is lower and non-technical, perhaps you can get by with a 20 or 30-minute interview
If you’re looking to hire someone part-time, full time or for something that’s really technical, then you probably want to invest at least 60 minutes
Ask them about:
Their responses thus far and ask them to explain why they responded the way they did
Their education
Any similar projects
Their add-on capabilities
Some references
PART B: Make an offer
Tell the person about why they were hired
Provide complete onboarding package
Reiterate role expectations, hours, pay, vacation, benefits etc.
Provide clear sets they need to take by a deadline in order to accept the offer
Here’s a bonus for you!
I’ve made a sample job description that you can use to pattern after when creating your own! You can get a copy of it inside of our Facebook group – Becoming World-Class Data Leaders and Entrepreneurs.
Join our community here.
You can also watch a demo of it here.
If you’re digging this real talk about the hiring steps you need to take, then I know you’re gonna love our FREE Data Entrepreneur’s Toolkit.
It’s an ensemble of all of the very best, most efficient tools in the market I’ve discovered after 9 years of research and development. A side note on this, many of them are free, or at least free to get started, and they have such powerful results in terms of growing your business. These are actually the tools we use in my own business to hit the multiple 6-figure annual revenue mark.
Download the Toolkit for $0 here.
Hey, and if you liked this post about the hiring steps you need to take, I’d really appreciate it if you’d share the love with your peers by sharing it on your favorite social network by clicking on one of the share buttons below!
NOTE: This description contains affiliate links that allow you to find the items mentioned in this article and support the channel at no cost to you. While this blog may earn minimal sums when the reader uses the links, the reader is in NO WAY obligated to use these links. Thank you for your support!
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## How to become a Data Entrepreneur, Validating Ideas, and Stakeholder Management with Lillian Pierson (MindSpeaking Podcast Ep. 9)
URL: https://www.data-mania.com/blog/how-to-become-a-data-entrepreneur-mindspeaking-podcast/
Type: post
Modified: 2026-03-17
The 9th episode of the MindSpeaking Podcast hosted by Gilbert Eijkelenboom, the Founder of MindSpeaking, features Lillian Pierson, the CEO and Head of Product at Data-Mania. Known widely across the data community, she supports data professionals in transforming into world-class leaders and entrepreneurs. She has trained well over 1,7000,000 individuals on the topics of AI and data science.
In this episode, you will learn important key points from Lillian – things she’s been learning from her experience being a data entrepreneur. The following timestamps, as provided in the original video here 👉 https://youtu.be/NcveBVz0i4E, will help you access your most favorite part and the topic you are most interested in right away!
How to Become a Data Entrepreneur Interview highlights:
00:00 Introduction
00:24 Introduction of the guest
01:53 Who is Lillian Pierson?
03:31 Product management and growing data-intensive businesses
04:30 What makes you so passionate about products?
05:33 Being an entrepreneur or building their own business
07:48 What can you learn from product managers?
12:42 Stakeholder feedback
18:29 What made you move to Thailand and how did those career choices develop?
21:25 Dream about traveling the world, living abroad, and being an entrepreneur
25:02 How to slow down?
28:48 What have you learned about teaching online?
31:33 Moment of reflection
33:18 How do you think Data scientists need to be taught differently?
36:10 About Data-Mania
39:51 Where to follow Lillian Pierson?
40:53 Conclusion
About MindSpeaking Podcast
The MindSpeaking Podcast, hosted by Gilbert Eijkelenboom, is about the Human Side of Data. Gilbert invites thought leaders to spread their insights about data, communication, and personal development.
You can connect with him and get his free resources using the links below:
👨🏻💻 Gilbert’s Linkedin: https://linkedin.com/in/eijkelenboom
📙 People Skills for Analytical Thinkers: https://mindspeaking.com/buy-book
📈 Discover your maturity level: https://mindspeaking.com/maturity-model
🗣 Free email course for Analytical Thinkers: https://mindspeaking.com/conversation
Watch / listen to the full episode using the links below:
Apple: https://apple.co/3EjhceS
Spotify: https://spoti.fi/3EgERMG
YouTube (with Timestamps): https://youtu.be/NcveBVz0i4E
If you want to know more or get in touch with Lillian, follow the links below:
Free Training on YouTube: Lillian Pierson
Becoming World-Class Data Leaders and Data Entrepreneurs Facebook Group
Lillian Pierson on LinkedIn
The Data Entrepreneur’s Toolkit: A recommendation set for 32 free (or low-cost) tools & processes that’ll actually grow your data business (even if you still haven’t put up that website yet!).
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## AI in E-Commerce: How Recommendation Systems Can Solve Business Needs?
URL: https://www.data-mania.com/blog/ai-in-e-commerce-how-recommendation-systems-can-solve-business-needs/
Type: post
Modified: 2026-03-17
Our world today uses Artificial Intelligence (AI) and recognizes its benefits in industries such as healthcare, insurance, telecommunications, and many more. But if you’re looking specifically at how AI in E-Commerce can help solve business needs, then this post is for you.
Artificial Intelligence has become an inescapable part of the eCommerce industry in recent years. From automated customer service to product recommendations, AI technology transforms how businesses interact with customers and make purchase decisions. You will need to understand how AI is changing the eCommerce space, the advantages and disadvantages of its use, and the implications for the future of the industry. You will need a good understanding of artificial intelligence.
AI in E-Commerce
Artificial Intelligence or AI in eCommerce refers to the use of artificial intelligence technology to automate and optimize operations in the eCommerce industry. It helps develop more accurate customer profiles, provide personalized product recommendations, automate the checkout process, and detect fraud. Artificial Intelligence (AI) can also improve the customer experience by providing real-time feedback and support. AI in eCommerce is becoming increasingly important as companies strive to keep up with the changing demands of customers and technology.
Models for AI in e-commerce include recommendation systems, pricing algorithms, predictable customer segmentation, personalized product image searches, and AI chatbots. These models can apply as single solutions or in combination to solve different business needs. E-commerce companies are successfully using them for marketing and business objectives.
You will learn about recommendation systems implementations in the eCommerce industry in this article.
Recommendation Systems in eCommerce
Recommendation systems are an essential tool in the eCommerce industry. These allow businesses to leverage customer data to personalize their offerings and increase customer satisfaction. By understanding customer behavior and preferences, recommendation systems provide tailored recommendations to individual customers. This leads to more effective marketing, higher sales, and improved customer loyalty.
E-commerce recommendation systems have become increasingly popular in recent years due to data science and artificial intelligence advancements. These systems are designed to analyze customer data and suggest products or services that may be of interest to the customer. By utilizing data science and artificial intelligence, eCommerce businesses can gain data-driven insights into consumer behavior. This then allows them to meet their customers’ requirements better and increase sales.
Recommendation systems use data points such as previous purchases, browsing history, and reviews to suggest products that customers may be interested in. These systems can also recommend related items or services, such as accessories or services related to the items purchased. This can help increase customer satisfaction and build customer loyalty.
In addition, recommendation systems can help customize the online shopping experience by providing personalized recommendations based on individual preferences. This can help customers find the items they are looking for quickly and easily. Furthermore, recommendation systems can also help identify cross-sell and upsell opportunities.
Recommendation systems is a powerful tool for eCommerce businesses, allowing them to leverage data science and artificial intelligence to improve customer satisfaction and increase sales. By utilizing these systems, businesses can gain valuable insights into customer behavior and use that information to offer better products and services.
Recommender engines analyze users’ prior buying behavior.
Recommender engines are a powerful tool. Many businesses use it to help increase sales and improve customer loyalty. By analyzing users’ prior buying behavior, recommender engines can make personalized recommendations tailored to a user’s individual preferences and interests. A variety of online retailers and streaming services, such as Amazon and Netflix, use this technology to deliver targeted product and content recommendations to users.
Recommender engines can deliver these targeted recommendations because they can analyze a user’s buying behavior. By analyzing a user’s past purchases, the system can make predictions of what a user is likely to buy in the future. This information can then be used to present the user with product and content recommendations that are likely to be of interest to them.
In addition to analyzing users’ buying behavior, recommender engines also use other information about the user, such as their geographical location, browsing history, and demographic information. By combining this data with their buying behavior, the system is able to create more accurate and personalized recommendations.
Recommender engines have become essential for businesses that want to increase sales and improve customer loyalty. By leveraging the data they have collected on users’ buying behavior and other information, businesses can present users with more relevant product and content recommendations, which leads to higher sales and increased customer loyalty.
Recommender engines predict new probable purchases of the users.
Recommender engines are powerful tools that can help businesses predict the future buying behavior of their customers. By leveraging large amounts of user data, these engines can identify patterns and trends in customer behavior and provide predictions about what customers may purchase in the future. This can help businesses maximize their revenue by targeting their marketing efforts to those customers who are likely to buy specific items.
Recommender engines use a variety of algorithms to analyze customer data, such as collaborative filtering and content-based filtering. Collaborative filtering uses past user data to make predictions about what a customer might purchase. For example, if a customer has previously purchased a certain type of product, the engine might recommend similar products. Content-based filtering uses the characteristics of a product to make recommendations. For example, if a customer has purchased a specific type of item, the engine might suggest similar items with similar features.
Recommender engines can be used to create personalized experiences for customers. By providing tailored recommendations, businesses can increase customer satisfaction and loyalty. This can result in higher conversion rates and increased revenue. Additionally, recommender engines can be used to identify new potential customers who may be interested in purchasing a particular product.
Recommender engines are a powerful tool for businesses to increase their sales and improve customer satisfaction. By leveraging customer data and algorithms, these engines can provide predictions about future customer purchases, allowing businesses to target their marketing efforts better and increase their revenue.
Recommender engines select or show the products or services users are most likely to purchase again.
Recommender engines are a powerful tool for predicting users’ future purchasing behavior. By analyzing a user’s past purchases and browsing history, recommender engines are able to select or show products or services that the user is most likely to purchase again.
Recommender engines are becoming increasingly popular as they are able to identify items that a user may not have even considered buying, thus helping to increase customer loyalty. They can also be used to personalize content to target customers with specific interests or needs.
Recommender engines use various algorithms to determine which products or services a user will most likely purchase again. This can include analyzing past purchases, browsing history, user ratings, and other forms of data. The algorithms are designed to identify patterns in the data and present suggestions that the user will likely find relevant and useful.
By using recommender engines, businesses can increase customer satisfaction and loyalty. Recommender engines can also be used to create tailored offers and discounts, which can be an excellent way to attract new customers and encourage repeat business.
Interested In Learning To Build Your Own Recommendation Systems For eCommerce?
If all this talk about recommendation systems has you eager to try your hand in building your own, then we have good news. The founder of Data-Mania, Lillian Pierson, has a course on LinkedIn Learning on Building Recommendation Systems with Python. It’s free for anyone who’s got a LinkedIn Premium Membership, and if you don’t – they only cost $29.99 per month.
Wrapping Up
In conclusion, AI-based recommendation systems have become integral to e-commerce, providing businesses with valuable insights into their customer’s preferences and behavior. They are extremely effective at helping businesses identify the products and services their customers are most likely to purchase, and the personalized recommendations they provide enable customers to find the items they want to buy quickly and easily. By leveraging the power of AI in e-commerce, businesses can increase their profits and improve customer satisfaction. You can also apply these technologies to real-world practices through artificial intelligence applications.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## 5 Essential Customer Acquisition Pillars For Scoring High-Ticket Sales In Your Tech Startup (with offer ideas & how-to tips)
URL: https://www.data-mania.com/blog/5-essential-customer-acquisition-pillars-for-scoring-high-ticket-sales-in-your-tech-startup/
Type: post
Modified: 2026-03-17
Believe it or not, it’s pretty easy to start clearing some majorly high-ticket sales for your tech startup, even if you’re brand new as a tech founder. You definitely don’t have to be a customer acquisition expert to start raking in this type of sales either – but it does help to have a few simple basics in place.
That’s why today I’m going to show you the 5 essential customer acquisition pillars you need to start scoring high-ticket sales for your tech startup. Brace yourself, because I’m even going to provide you with ample ideas for offers you can quickly take to market, how-to tips on building out the customer acquisition pillars, and even a take-home homework assignment (if you’re up for it).
Follow me on:
Before I begin, let me clarify one thing…
You don’t need to be a “hustler”, a “smooth talker”, or anything like that. You just need to take a pragmatic approach to reverse-engineering your way into high-ticket sales, and I’m about to show you how to do just that.
Every person CAN make high-ticket sales for their tech startup by doubling down on these 5 customer acquisition pillars. You don’t even need to be a “hustler”, a “smooth talker”, or anything like that. You just need to take a pragmatic approach to reverse-engineering your way into high-ticket sales, and I’m about to show you how to do just that.
Oh yeah, and this post is going to be loooonnnggg – so if you want to jump around, here is a menu you can use to do that:
Looking for something specific? Scroll straight to what you’re looking for by clicking a link below:
Defining “High-Ticket” In High-Ticket Sales
High-Ticket Services
High-Ticket Products
5 Essential Customer Acquisition Pillars You Need To Start Scoring High-Ticket Sales
1. An Appropriate Offer
2. Appropriate Pricing
3. A Clear Unique Value Proposition
4. Clear Marketplace Needs
5. Know, Like & Trust
1st Things 1st: Defining “High-Ticket” In High-Ticket Sales
Pricing is heavily dependent on what’s actually being sold, right? Therefore, it follows that you cannot define “high-ticket” pricing tiers or a feasible customer acquisition strategy, unless you first clarify the offer you’re looking to sell.
Let’s look at some typical “high-ticket” pricing for products versus services.
High-Ticket Services
Technical services come in all shapes and sizes. Let’s look a bit at what bigger companies are offering and then take a peek at how bootstrapped tech founders are doing things.
Examples of services commonly offered by large tech companies
Examples of high-ticket sales in technical services include:
Custom software development services: Services like this can range from $5k to $10k for a small application, all the way up to $2 MM+ for a large enterprise system.
Network and infrastructure consulting projects: This type of consulting project can include designing and implementing a new network for a business and can cost over >$20k for starters.
Cybersecurity consulting projects: Consulting projects like these can include penetration testing, vulnerability assessments, and incident response planning and can cost hundreds of thousands of dollars.
Cloud migration projects: This type of project can include moving a business’s data and applications to a cloud computing platform and can cost hundreds of thousands of dollars.
Oh, and if the cloud migration project idea piques your interest, you’d probably get a lot out of this free training I did on how to build an evergreen data migration strategy.
It should be noted that these prices are just rough estimates and will vary widely depending on the specific project and the company providing the service.
It’s also important to note that these services might be provided on an hourly basis or a retainer basis (meaning that the client pays a fixed fee on a regular schedule [e.g. monthly] instead of paying according to project milestones or an agreed-upon one-time, lump-sum fee).
A rule of thumb for bootstrapped tech service providers
If you have the expertise, then you can really offer all the same services that are described above, even as a new tech startup founder.
You’ll probably be charging less per project than those companies though, simply because of the project scale that’s typical for small and medium sized businesses.
👍 A good rule of thumb for high-ticket sales pricing for small service-based tech startups is just this: If you’re earning over $300/hour for tech implementation services, or over $500/hour for tech leadership / consulting services, then you can safely say that your customer acquisition and pricing is within the “high-ticket” range for your services.
At a bare minimum, if you’re just starting out as a tech startup founder, a basic rule of thumb I always preach to my clients is:
👍 If you’re earning over $300/hour for tech implementation services, or over $500/hour for tech leadership / consulting services, then you can safely say that your customer acquisition and pricing is within the “high-ticket” range for your services.
You can watch a bit about this topic more below:
High-Ticket Products
We’re all techies here, right? So, when we talk about products in tech, most people assume we’re talking about SaaS products.
While that’s a natural conclusion to draw, there are actually quite a few different product types that tech startup founders can monetize.
For example:
– SaaS (Software as a Service) Products
– Engineered Equipment Products
– Digital Information Products
I’ll quickly give some examples of each below.
Examples of high-ticket SaaS (Software as a Service) products
Examples of high-ticket SaaS (Software as a Service) products include:
Enterprise Resource Planning (ERP) systems: These can include financial management, supply chain management, and human resources management functionality. These types of systems can cost several thousand dollars per month or more depending on the size and complexity of the installation.
Customer Relationship Management (CRM) systems: These can include sales force automation, marketing automation, and customer service functionality. You can easily sell access to these systems for well over $1k/month, depending on the size and complexity of the installation.
Supply Chain Management systems: These systems can include inventory management, logistics management and demand planning functionality. You can easily sell access to these systems for well over $1k/month, depending on the size and complexity of the installation.
Marketing Automation Platforms: These platforms can include features like email marketing, lead generation, and analytics. You can easily sell access to these systems for well over $200/month, depending again on the size and complexity of the installation.
Business Intelligence Platforms: These platforms can include features like data visualization, data warehousing, and big data analytics. You can easily sell access to these systems for well over $1k/month, depending on the size and complexity of the installation.
It’s important to note that these prices are just rough estimates and can vary widely depending on the specific product and the company selling it. And also, it’s important to remember that these products might be sold on a usage-based pricing model or as a customized package that fits the client’s needs.
Examples of high-ticket engineered equipment products
Examples of high-ticket engineered equipment products include:
Industrial equipment: This can include manufacturing equipment, medical equipment, and scientific instruments. These can cost well over $300k, depending on the specific product and application.
Custom hardware: This can include specialized computer systems, electronic devices, and scientific instruments. These can cost well over $300k depending on the complexity and functionality of the product.
Military equipment: This can include weapons systems, aircrafts, and vehicles. These can cost well over $10 MM depending on the type and complexity of the equipment.
It should be noted that these prices are just rough estimates and can vary widely depending on the specific product and the company selling it. And it’s important to remember that these products might be sold on a subscription basis or as a financed product within a lease or a loan structured.
Examples of high-ticket digital information products
This category is ideal for all the bootstrappers out there. You can pre-sell these products and monetize them quickly (with high-profit margins) in a new tech startup, even if you don’t have or want investors.
The basic rule of thumb I follow here is, if you can earn at least $1000/hour for the time you invest in building, marketing and delivering a digital information product, then that product falls into the “high-ticket” category. That’s after you subtract out product development costs that also went into building the product though, of course.
This type of product can usually be scaled so each product doesn’t actually require 1 customer to pay a lot of money, but you’re still able to earn a pretty decent return on investment for the time you spent on product development and marketing…
Ok, now that we’re clear on what qualifies as “high-ticket” in high-ticket sales, let’s dig into the meat and potatoes.
5 Essential Customer Acquisition Pillars You Need To Start Scoring High-Ticket Sales, Almost Overnight
When most people hear the words “tech startup”, they automatically envision a VC-backed SaaS startup that is working hard to justify its next funding round.
What they don’t usually envision is a highly profitable tech startup that’s heavily focused on customer acquisition and high-ticket sales. Why? Well, because most tech startup founders don’t have strong backgrounds in business, marketing, and customer acquisition to be honest. If that’s you, fret not – the skills can be learned or delegated.
“Not only is VC funding NOT a necessity – for new and aspiring tech founders, investor funding is likely to harm your chances of business success & profitability.”
But I’m here to tell you today, not only is VC funding NOT a necessity – for new and aspiring tech founders, investor funding is likely to harm your chances of business success & profitability.
Why? Well, because most VC-backed tech startup founders focus way too heavily on the tech that they’re building without ever looking at the market, its customers or which customer acquisition strategies are most appropriate.
It really doesn’t matter how brilliant a developer or CTO you are, if you build something no one needs or wants, customer acquisition is highly unlikely, and your project is destined to fail.
On the other hand, when you’re a self-funded startup founder, you don’t have the luxury of making that mistake. You have to figure out how to sell products or services people need or else you won’t make any money and your fledgling tech startup will fail.
If it’s not profitable it’s not a business, it’s a hobby.
So, what are the 5 essential customer acquisition pillars you need to start scoring high-ticket sales? The come down to the following:
Appropriate Offer – You need an appropriate offer to sell.
Appropriate Pricing – That offer needs to be priced appropriately (ie’ “high-ticket” pricing).
Unique Value Proposition (UVP) – It doesn’t matter whether you’re selling a high-ticket service or product, the offer needs to have very clear UVPs.
Marketplace Needs – There must be a market for your offer, and people in that market must actually need what you have to sell (ie; it can’t be a “nice to have”)
Know, Like & Trust – If a person does not know you, like you and trust you – it’s highly unlikely they’ll ever buy a high-ticket product or service from you.
Now that you know what the 5 pillars of customer acquisition are, let’s look at the quickest, easiest ways for you to build them out in your own tech startup.
1. An Appropriate Offer
Let’s start by looking at the basic characteristics of an offer that you can use to generate high-ticket sales.
High-ticket offers are characterized by a high price point and a correspondingly high perceived value. Some specific characteristics of high-ticket offers include:
High perceived value: High-ticket offers are typically associated with products or services that are perceived to be of high quality, exclusive, or unique in some way. They are often seen as providing significant value or delivering a significant return on investment.
Highly customized: High-ticket offers are often tailored to the specific needs of the customer or small group of customers. They may involve custom solutions or services that are not available to the general public.
High level of expertise: High-ticket offers often require a high level of expertise or specialized knowledge. They may involve complex products or services that are not easily understood by the general public.
High-level of support and service: High-ticket offers often include a high level of customer service and support. This may include ongoing maintenance, training, and technical support.
High-profit margins: High-ticket offers are usually associated with high-profit margins, which means that the company selling the product or service makes a large profit per sale. More on pricing next…
High-commitment: High-ticket offers often require a high level of commitment from the customer. They may involve long-term contracts or require significant up-front investments.
Overall, high-ticket offers are often considered to be luxury products or services that are only affordable for a select group of customers.
These customers are often willing to pay a premium price for the perceived value, exclusivity, and high level of service and support that high-ticket offers provide.
I’m definitely not one to throw down a bunch of theories and leave you to figure things out for yourself, so let me give you a hand with some ideas for high-ticket offers.
Getting Started With High-Ticket Services
Believe it or not, according to the World Bank, almost 47% of workers worldwide are freelance service providers.
And within the US, tech workers like programmers, data analysts & mobile developers earn average annual salaries of up to $120,000 via freelancing alone (according to Upwork)!
Source: Upwork Freelance Marketplace
Of course, most freelance service providers on Upwork are not making high-ticket sales on their service packages (to the contrary, Upwork tends to encourage a race to the bottom).
Pros vs Cons of the Service-Based Tech Startup Model
🛑 I feel it prudent to stop right here and define the pros and cons of starting / running a service-based business. Even when you’re generating high-ticket sales off the back of your service offer, it’s not always gravy. It’s best you understand these before deciding to pursue this route.
Pros of the services business model include:
Low barrier to entry: All you need are skills and expertise to start selling services on the open market, sometimes not even that many of them either.
Low time & cost to market: Compared to products-based businesses, services take almost no time or cost to take to market. All you need are the skills, an offer, a market / customer, and a way to get paid. Consequently, services are the fastest way for new tech startup founders to monetize their business and start building a customer base.
Recurring revenue: When done well, services are typically provided on a recurring basis, which can generate a steady stream of income.
High profit margins: Services can often be sold at a premium price, resulting in high profit margins.
Low overhead costs: Services typically require fewer resources than products, resulting in lower overhead costs.
Cons of the services business model include:
Intangible value-adds: Services are often intangible, which can make it difficult for customers to understand the value they are getting, hence the need for a clearly articulated offer.
Difficult to scale: Services are often labor-intensive and can be difficult to scale. Unless you want to build a entire services agency, you’ll eventually need to switch business models in order to keep growing your business.
Time-intensive: Services take man-hours and are mainly delivered in a 1-on-1 format. Spending your time selling, administering, and delivering services will dramatically erode the amount of time you have available to work on growing and improving your own business.
12 High-Ticket Sales – Service Offer Ideas
To help you get started in picking the right offer for use in generating high-ticket sales, I put together a list of ideas for services you can offer to increase your chances of signing high-paying clients.
These services include:
Systems Starter Audits
Technical Strategy Services
Data Cleaning Services
Software Development Services
Machine Learning Model Building Services
Data Visualization Services
CRM Management Services
Data Pipeline Construction & Maintenance Services
Custom Chatbot Services
Cloud Data Migration Services
Tech Career Coaching Services
Tech Training Services
Basically, any technical skill you provide to an employer can be repurposed into a high-ticket service package and sold on the open market to startups or to anyone who is looking to hire external freelancers or consultants.
I actually did a video on this exact topic a few years back for the AI startup founders I was working with. If you’d like to watch that now, I’ll leave that for you below.
YouTube URL: https://youtu.be/BTFnkghunps
6 great service ideas of new tech startups.
Quick Example: Impromptu selling of tech expertise through a high-ticket service offer
If you speak to healthcare leaders today, you’ll find that a good percentage of them have access to a hiring budget to get their internal data storytelling needs met…
People with access to a decent budget will NOT want to buy a course or a book on the topic in order to take the DIY route. Obviously, who has time to waste struggling to get DIY results if you have the option of hiring an expert to get fab results served up without any time or effort whatsoever?
No, most of these healthcare leaders would greatly prefer to pay someone a fair rate to come in and serve them up a data storytelling solution on a silver platter. They’re likely interested in the quick win and the credit they’ll get for scoring it.
So, if you were to find yourself in an impromptu conversation with a person like this inside of your LinkedIn DMs, and you had data storytelling expertise, that conversation would be a good opportunity for you to offer and sell them a high-ticket data visualization service package.
On the other hand, if you make the mistake of offering this person a book or a training service so they can DIY, they’re not likely to be impressed with that idea.
What’s worse, once you’ve extended the person an irrelevant offer, it’s likely that you’ve lost most of the trust and credibility they’d been extending you. Consequently, it’s unlikely they’ll want to buy anything from you later – even if you try to raise the possibility of you doing a done-for-you service.
Story: How I used service offers to quickly get my tech startup off the ground
Like many new entrepreneurs, I offered services to get my business up off the ground and generating a profit back in 2012. I’ve since incorporated a variety of business models into Data-Mania, but here is a quick rundown of how I got started as a tech startup founder.
I started blogging about data science back in 2012. To be honest, I am not sure how “awesome” my blog was back then, but it quickly reaped me the following benefits:
Paid freelance jobs in 2012 – Multiple solicitations found their way to my inbox from editors asking to pay me to write blogs on data for their big-name websites.
Sponsored content jobs in 2012 – Big businesses (like IBM, for one) began emailing me telling me that they will pay me to write and publish a sponsored post on the data topic of their choosing.
Consulting leads started rolling in by 2013 – United Nations staff emailed me asking for my technical consulting services because they had seen my blog post related to humanitarian deployment and spatial intel.
A blog can be just the boost you need to start making sales in a new business, even high-ticket sales if you know how to pull that off.
Back in 2012, I didn’t have an article like this to tell me how to do customer acquisition or high-ticket sales. I had no idea about anything related to marketing, startups, etc… to be honest. So, I winged it and got subpar results.
The reason I am publishing this article now is so that you don’t have to waste your valuable time figuring all this out from scratch and can instead get started right away with acquiring high-value customers.
Products Are Also A Possibility For New Tech Startups, BUT…
If you’re like most tech startup founders, then you’re obsessed with the idea of building and monetizing via a scalable product.
I can’t lie, I love product too – but there are significant financial risks associated with building a product and bringing it to market. You need to know how to mitigate those risks before attempting to do it, and I don’t have space in this blog for that whole topic, so…
“If you’re a brand new founder and you don’t have an audience / market… if you don’t have access to a community of people who trust you, who’ve clearly stated that they have a problem that you know your product can solve… you’re best off not building any product at all.”
Just know, if you’re a brand new founder and you don’t have an audience / market… if you don’t have access to a community of people who trust you (more on that later), who’ve clearly stated that they have a problem that your product can solve… you’re best off not building any product at all.
One of the biggest mistakes new tech founders make is that they build a product for no one, then when it’s done, they have no one to sell it to and that product doesn’t actually solve a need in the marketplace.
In all honesty, I don’t understand why VCs continue enabling this common failure by giving money to “founders” who don’t have a market or a customer base, but hey…. If you know the answer, I’d love it if you’d tell me about it in the comments below.
If you’re reading this, I’m assuming you’re a pretty new founder or startup leader… and so, to reduce the risk of you making this ^^ overwhelmingly prevalent mistake, I’ve decided to leave product ideas out of this post.
2. Appropriate Pricing
We’ve already discussed pricing quite a bit earlier in this post, but I’ll add a bit more on to that by discussing why you need to price your high-ticket service appropriately.
Pretty soon after your customer acquisition rates ramp up, you’ll probably want to specialize so that you can become seen as the go-to expert for that service – and so that you can start commanding higher rates for your tech services.
When clients hire experts, they expect to pay higher prices. If you set prices that are too low, potential customers will quickly assume that either:
A. You don’t know the value of your own expertise or
B. Your offer isn’t all that great
Neither of the above scenarios is good for you or your fledgling tech startup, especially since it’s pretty simple to price out high-ticket services and adjust them later, after you’ve delivered the package a few times.
Inside my Data Creatives & Co. Course, I provide new tech startup founders with a plug ‘n chug Set-Your-Rate Calculator. Here is the example I provide them within that:
As you can see, brand equity plays a big part in how much you can charge for your tech services. The variables that comprise brand equity can be boiled down to:
Your experience level
Your audience size (related to that area of expertise, of course)
The number of testimonials you have
The number of times you’ve achieved this service-based transformation for customers in the past
The highest price you’ve sold your time for in the past (related to that area of expertise, of course)
Other important factors to consider are:
Location where your ideal client lives
Budget that your ideal client has for the work
Delivery costs you’ll incur
Contractor costs you’ll incur
The number of hours it will take you to deliver the service
Hopefully the above will give you a head start on pricing your service package after you’ve picked it out.
3. A Clear Unique Value Proposition
A unique value proposition (UVP) is a statement that clearly and concisely communicates the unique benefit or value that a product or service provides to its customers.
It’s a key element of a company’s marketing strategy and should be used to differentiate the company’s offerings from those of its competitors.
A UVP should be specific, measurable, and differentiable from other similar products or services.
6 Steps To Defining A UVP For Your Offer
There are 6 steps to defining a unique value proposition:
Understand your target customer: Start by identifying your target customer and their needs, wants, and pain points. This will help you to understand how your product or service can provide value to them.
Identify your competition: Research your competition to understand their offerings and how they position themselves in the market. This will help you to differentiate your product or service from theirs.
Identify your unique selling points: Determine what makes your product or service unique and what value it provides to customers. This can include features, benefits, or other aspects that set it apart from your competition.
Craft your UVP statement: Use the information you have gathered to craft a clear and concise statement that communicates the unique value of your product or service.
Test and refine: Once you have a UVP statement, test it with potential customers to see if it effectively communicates the value of your product or service. Adjust as needed.
Communicate your UVP: Use your UVP in your marketing and sales efforts to communicate the unique value of your product or service to potential customers.
Now that you know how to define a UVP for your offer, let’s look again at our data storytelling customer acquisition example.
Example, Continued: Back to our data storytelling services example
There must be some topics, sub-disciplines, specialties or offers in the tech space that you’re SUPER SICK of hearing everyone else talk about.
For example, “data storytelling” is super popular right now among people who specialize in data. It is not a good time to start a course and teach people data storytelling, and here’s why:
🚫 Competition: You’ll have way too many competitors trying sell the same exact transformation “ie; learn how to do data storytelling” to a small market of buyers. In other words, the market is (over?) saturated with suppliers. Because “everyone else” is already doing it – it’s definitely not a unique offer, so positioning it as unique won’t be that easy.
🚫 Your unique selling points: Unless you’re a published author on the topic or have built up some other significant credentials that demonstrate why you’re the go-to expert on data storytelling, you probably don’t have much of an unfair advantage in trying to sell a data storytelling information product. In other words, compared to top competitors in the market, you would need some very compelling selling points in this case.
That doesn’t mean you should give up your dreams of creating an impact through data storytelling though. What it means is that you need to do more work on your offer… to make it more compelling.
One thing you could do is take those “data storytelling” skills of yours and dial them down to support the very specific needs of one unique client avatar. In other words, you could tailor your offer to your “target customer”, a very specific class of professionals who really need the type of data storytelling help you have to offer!
One alternative could be to generate high-ticket sales from a services offer that support the needs of healthcare leaders, like we discussed in our example earlier.
4. Clear Marketplace Needs
Marketplace needs refer to the wants, desires, and pain points of customers in a specific market or industry. Understanding marketplace needs is crucial for developing products or services that will meet customer demands and be successful in the marketplace.
You need to make sure that your customers need whatever it is that you sell – either products or services. It can’t be just one of these things that’s nice to have – it really needs to be something that solves a problem for them and that it is an urgent necessity that will require them to actually make that purchase.
It’s important to note that marketplace needs can vary depending on the industry and target market, so it’s important to conduct research to understand the specific needs of the marketplace you’re targeting.
When conducting that market research, look for the huge gaping holes in the market that you have the expertise to fill. Look on job boards, freelance marketplaces, look inside Facebook Groups, Quora, Reddit or anywhere else where people are reaching out for help on your area of expertise. These are the pockets of demand where customer acquisition is likely to be easiest, provided that there are not too many suppliers already in the market,
By looking at how people are asking for help in terms of job postings or questions, you can get a pretty good idea of their level of urgency, and you can also see the degree of market saturation in terms of other service providers filling those requirements.
5. Last But Not Least: The Know, Like & Trust Factor
The “Know, Like, and Trust” factor (KLT factor) is a concept in marketing and sales that refers to the process by which potential customers become familiar with, develop a positive perception of, and ultimately trust a company or brand.
Know: The first step in the KLT factor is for customers to become familiar with your company or brand. This can be achieved through various marketing and advertising efforts such as content marketing, social media, and public relations.
Like: Once customers are familiar with your company or brand, the next step is for them to develop a positive perception of it. This can be achieved by consistently providing value, meeting customer needs, and building relationships with customers.
Trust: The final step in the KLT factor is for customers to trust your company or brand. This can be achieved by consistently delivering on promises, providing excellent customer service, and building a reputation for being reliable and trustworthy.
The KLT Factor is critical for tech startups because when a customer trusts a brand, they are more likely to become a loyal customer and recommend that brand to others.
Building trust takes time and effort, but it’s an important step in the customer journey, and it can be the difference between a customer choosing your business over your competitors.
There are quite a few ways that you can quickly build up KLT with prospective customers. I’ll give you a few ideas below:
Offer a low-price, yet highly valuable product or service – Something where they can dip their toe in the water to see what kind of results you can generate for them and then they know they want to keep working with you.
Generate word-of-mouth referrals – Once you’re generating word-of-mouth referrals, customer acquisition becomes a lot easier. If you have people that you have worked with in the past, even if it’s in an employment environment, and they recommend you for the transformation you made in their business, then it’s going to be a lot more likely that their colleagues will trust you as well and make that purchase from you. To facilitate this, you can deploy automated referral prompts via post-purchase nurture email sequence or even incentivize word-of-mouth referral by way of an affiliate program.
Credibility – Another way to build trust is the external show of credibility factors. Yes, of course degrees and certificates are great, but in all honesty in the business world, no one cares that much about your degree (unless you went to MIT, Harvard, Stanford or something like that). The big thing you need is great testimonials! If you can get results for people and show those results as proof and evidence of your ability to get results for more people – that’s going to be one of the things that really helps potential clients trust you.
Wrapping Things Up
Now that you’re abundantly clear on the 5 customer acquisition pillars you need in place to start scoring repeatable high-ticket sales for your tech startup, what’s left but for you to take action?
I’ll end this post with an action item assignment and an invitation.
The action item assignment: The challenge I leave you with here today is to go ahead and start building out your customer acquisition pillars by using the resources I provided above to define an appropriate offer for your startup (or future startup).
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Cybersecurity for Startups — the Importance of Zero-Trust Application Control
URL: https://www.data-mania.com/blog/cybersecurity-for-startups-the-importance-of-zero-trust-application-control/
Type: post
Modified: 2026-03-17
Cybersecurity for startups is more crucial than ever because of how much data is available and how easy it is to access. Leaders must protect new companies looking to disrupt niche markets against cyberattacks. That way, users remain safe while interacting with the product or service.
Zero-trust application control achieves this goal by providing security at all points in the cloud deployment life cycle — from development to management. Using this kind of security and compliance solution lets startups focus on what matters — growing the business.
Why Startups Need Zero-Trust Application Control
The application security world is constantly changing, meaning companies must adapt their strategies to stay on top of cybersecurity threats. The need for zero-trust application security is becoming increasingly important as machine learning and artificial intelligence develop more applications.
Product demand for zero trust is already rising, as research shows the market will go from $27.4 billion in 2022 to $60.7 million in 2027.
Zero trust is a model that recognizes every device’s trustworthiness level. Therefore, it’s impossible to put everything on the same level regarding the amount of access. Zero trust allows startups to limit each device’s entry based on capabilities and provenance.
This model works especially well when applied to the cloud because it allows startups to create an environment where any device can connect, but not all have equal access. Even if there are vulnerabilities in an application, bad actors can’t exploit them since they don’t have enough permissions to do so.
What Does Zero-Trust Implementation Look Like in Practice?
In the context of application control, zero-trust means startups enforce their security policies, regardless of whether a user is inside or outside the organization. This approach makes it much more difficult for attackers to compromise applications and gain access to the company’s network.
Startups with a zero-trust model in place must be able to monitor all incoming traffic and use multiple layers of defense to protect against attacks. Attackers that attempt to access the network would meet layers of firewalls, intrusion detection and prevention systems and other tools that prevent them from getting past the perimeter.
Startups can implement zero-trust application security across all levels of their infrastructure — physical and virtual networks, cloud services, apps and containers. This requires a shift from traditional approaches to security toward a model where every device has its own identity controlled by the management system.
How to Implement Zero-Trust Application Control
There are five key steps to take when starting a cybersecurity zero-trust application:
Deploy secure access service edge (SASE)
Use microsegmentation
Use multifactor authentication (MFA)
Implement the principle of least privilege (PoLP)
Validate all endpoints
1. Deploy SASE
Secure access service edge integrates software-defined wide area networking (SD-WAN) and points security solutions into a centralized cloud-native service. Deploying SASE as part of a zero-trust strategy will secure access to critical resources, mitigate risk and provide more consistent application performance.
Here are some aspects to consider with a SASE solution:
Integration: An SASE solution should integrate seamlessly with a network infrastructure. Organizations operating critical infrastructure on-premises should choose a SASE solution with built-in zero-trust capabilities. This allows them to connect their cloud and legacy systems securely without compromising security or adding complexity.
Features: The SASE solution should be able to stop potential threats, limiting damage and mitigating a breach’s impact. This can be done through microsegmentation and sandboxing.
Containment: There is no way to guarantee zero chances of a security breach. An ideal solution would contain threats once they enter the network, reducing their overall impact and avoiding panic.
2. Use Microsegmentation
Microsegmentation divides a network into multiple, smaller sections. This helps control access to various system parts and define which users or applications can reach each zone.
3. Utilize Multifactor Authentication
Multifactor authentication requires users to implement two or more identification methods. These include:
Knowledge factor: This should be information only the user would know, such as a PIN or password.
Possession factor: This is objects or information the user owns, like a smart card or mobile phone.
Inherence factor: This uses biometric characteristics to scan, such as a face, retina or fingerprint.
MFA will only allow the system to authenticate if these factors are valid.
4. Implement the Principle of Least Privilege
PoLP involves limiting the permissions granted to users so they have access only to those resources and files necessary for completing their work.
The principle of least privilege can control access rights for non-human resources such as systems, applications, devices and processes. This is done by granting only permissions needed for these resources to perform their authorized actions.
5. Validate All Endpoints
Someone should only trust a device or application if they know where it comes from and who made it. Zero-trust security allows validating endpoints, extending identity controls down to the endpoint level and controlling access based on identity rather than location.
Typically, this involves enrolling each device before accessing resources. This makes them easier to identify and verify.
Implementing endpoint verification establishes what devices can access resources and whether they meet security requirements.
Implementing Cybersecurity for Startups With Zero-Trust Application Control
Zero-trust application control is a powerful tool security teams use to protect their organizations against cyberthreats. While it allows for greater visibility into the applications in use, it also enables detecting potential issues and blocking them before they damage or compromise sensitive data.
The good news is that organizations can take many steps to execute cybersecurity for startups through zero-trust application control.
A Guest Post By…
This blog post was generously contributed to Data-Mania by Shannon Flynn. Shannon Flynn is a freelance blogger who covers business, cybersecurity and IoT topics.
You can follow Shannon on Muck Rack or Medium to read more of her articles.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Hiring for Your Tech Startup: Freelancers vs. Employees
URL: https://www.data-mania.com/blog/hiring-for-your-tech-startup-freelancers-vs-employees/
Type: post
Modified: 2026-03-17
Are you a tech startup owner who is looking for someone to hire as a team member? Are you looking for tips on who’s better between freelancers vs. employees to work on a full-time or part-time basis? If it’s a yes to all, then this post is for you! Discover some tips on how to choose depending on your need. Additionally, learn about the pros and cons of hiring either of the two.
Most startups employ both employees and freelancers:
Employees are individuals who are hired by a company or organization to work on a full-time or part-time basis. They are a permanent part of the company and typically receive a salary or hourly wage. The same goes with the benefits such as health insurance, retirement plans, and paid time off. They may also be eligible for bonuses or other forms of compensation.
Freelancers are self-employed individuals who work on a project-by-project basis for multiple clients. They are not permanent employees of any one company and typically work on a contract or consulting basis. Freelancers pay for their own taxes and benefits. Moreover, they do not have the same types of compensation and benefits as the employees.
Both employees and freelancers can bring their own set of skills and experiences to a company. But they can be quite different in the way they work, their compensation, and their rights and obligations.
Employees and freelancers management is the process of overseeing and coordinating the work employees and freelancers within an organization. This can include tasks such as hiring, training, scheduling, performance evaluations, and compensation.
Hiring Freelancers vs. Employees for Your Startup: Pros and Cons
Let’s look at the pros and cons of hiring freelancers vs. employees.
Pros and Cons of Hiring a Full-Time Employee
Full-time employees are usually more invested in the company and its goals. Therefore, they may be more motivated to work efficiently and effectively. They are usually more committed to the company in the long-term, which can lead to increased job stability and continuity.
Employees can receive training and development opportunities that help them grow and advance in their careers. There’s a usual employer-employee relationship, where the employer can have more control over the employee’s working hours, schedule, and tasks.
However, hiring full-time employees can be more expensive for a company, as they are typically entitled to a salary or hourly wage and benefits. It can be a time-consuming and costly process, including recruiting, interviewing, and onboarding. Companies may need to lay off or downsize full-time employees during slow business periods, but terminating a full-time employee can be more complicated and expensive than ending a contract with a freelancer.
Pros and Cons of Hiring a Freelancer
Hiring freelancers can be more cost-effective for a company, as they only receive payments for projects and do not receive benefits. Even if the per-hour cost of an independent contractor is higher, it might be less expensive than employing someone full-time. Freelancers often have specialized skills and expertise that can be valuable for specific projects or tasks.
Another important advantage is greater flexibility in terms of scheduling and workload. Hiring freelancers can be a quicker process than hiring full-time employees, as it does not require the same level of commitment or onboarding. Companies don’t need to worry about layoffs or downsizing as they can just end the contract with the freelancer after the completion of the project.
However, freelancers may not be as committed to the success of the company as full-time employees, and may not be available for long-term projects or commitments. Employers have less control over freelancers’ working hours, schedule, and tasks. Likewise, freelancers may not receive the same level of training and development as full-time employees.
Another potential issue with freelancers is security. Because the workers are often unknown and not invested in the company, there is higher risk of them acting against the company’s interests.
Freelancers vs. Employees: How to Choose
When deciding between hiring a freelancer or an employee, consider the following factors:
Duration of the project or task: Freelancers are typically better for short-term or project-based work, while employees are better for long-term or ongoing work.
Specific skills or expertise needed: Freelancers are often experts in a specific field or skill and can be hired for their specialized knowledge. Employees may have a more general set of skills and can be trained for the specific needs of the company.
Flexibility: Freelancers have more flexibility in terms of their work schedule and location, while employees are more tied to the company’s schedule and location.
Costs: Hiring a freelancer may be more cost-effective in the short-term, as there are no benefits or long-term commitment costs. However, hiring an employee can be more cost-effective in the long-term, as they can be trained and developed to become more valuable to the company over time.
Legal and compliance: Hiring an employee makes the company responsible for legal compliance and regulation such as work permit, taxes, insurance, etc. Freelancers are typically responsible for their own compliance and regulation.
Ultimately, the decision between hiring a freelancer or an employee will depend on the specific needs of your company and project.
Conclusion
In conclusion, hiring the right personnel is an important decision that can have a significant impact on the success of the company. Both freelancers and full-time employees have their own set of advantages and disadvantages, and the choice between the two will depend on the specific needs and goals of the startup.
Freelancers can be a cost-effective solution for short-term projects or specialized skills, while full-time employees can provide consistency, stability and a higher level of commitment to the company. Ultimately, it is important for tech startups to carefully evaluate their options and make an informed decision that will best support the growth and development of their business.
Hey! If you liked this post, I’d really appreciate it if you’d share the love by clicking one of the share buttons below!
A Guest Post By…
This blog post was generously contributed to Data-Mania by Gilad David Maayan. Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Samsung NEXT, NetApp and Imperva, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership.
You can follow Gilad on LinkedIn.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## 50+ Data Side Hustle Ideas To Dramatically Increase Your Income in 2023
URL: https://www.data-mania.com/blog/50-data-side-hustle-ideas-to-dramatically-increase-your-income-in-2023/
Type: post
Modified: 2026-03-17
Attention data professionals! Are you tired of relying on one employer for your sole source of income? Are you looking to break free from corporate slavery and start a data side hustle? If so, we have some exciting news for you! 🎉
Introducing Income-Generating Ideas For Data Professionals, a 48-page listing of income-generating product and service ideas for data professionals who want to earn additional money from their data expertise, without relying on an employer to make it happen! 🤑
Why data side hustle & why now?
Why wait until the next newsletter to tell you this? We couldn’t contain our excitement about this game-changing product for data professionals, so we had to share it with you right away! 🙌
We know that job security in the tech industry is a big concern right now, and that’s why we’ve put together over 50 genius ideas for data products and services that you can quickly package together and start selling on the side.
These ideas will help you generate extra income streams and reduce your reliance on just one employer. 💪
And the best part?
These income-generating ideas are not just theoretical mumbo-jumbo. They’re practical, profitable, and proven. In fact, some of our students have made over $1,000 per hour using these ideas. 💰 You could be next!
But we don’t just give you a list of income-generating ideas and leave you to figure it out on your own.
Inside the product, we also share expert tips on how to make your offers irresistible, how to save time and effort, and how to upsell and cross-sell like a pro. We even throw in some digital and data SaaS products that you can sell within your data side hustle without breaking a sweat.
And the best part? This brand-new product is available to you right now for just $17! That’s less than half a tank of gas, and we promise it’ll take you a whole lot further. 🚗
⏰ So, why waste your time looking for other ways to generate extra income? Get your hands on Income-Generating Ideas For Data Professionals and start building your new data side hustle today!
Prices for this product will go up next week, so don’t delay.
And don’t worry, we’ve got your back with access to monthly support calls AND an entire collection of quick start guides to help you get started.
Plus, with an iron-clad money-back guarantee, there’s no reason not to check it out and start building your income-generating offers today. 💯
So, what are you waiting for? It’s time to start earning some extra cash with this highly profitable data product and service ideas. Check out Income-Generating Ideas For Data Professionals at https://www.data-mania.com/product/income-generating-ideas-for-data-professionals/ today and start your data side hustle journey now!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## 10+ Tech Startup Ideas: Products, Services & Tech Business Models For Early-Stage Startup Founders!
URL: https://www.data-mania.com/blog/tech-startup-ideas-tech-business-models/
Type: post
Modified: 2026-03-17
Are you feeling stuck when it comes to generating tech startup ideas for your product or service? With so many different routes you can take and theories to consider, it’s no surprise!
But, you’re not alone! Many tech startup founders face the same challenge. That’s why I created this blog post specifically to help you generate innovative tech startup ideas.
I’ll guide you through the steps to clarify your ideal customer profile, identify your unique value proposition, and choose the right tech business model for you. Plus, I’ll share relatable examples to inspire you and make the process less daunting.
Whether you’re just dreaming up a tech business idea or have already begun your journey, this blog will help you gain clarity, confidence, and momentum.
Looking for something specific? Scroll straight to what you’re looking for by clicking a link below:
Challenges Facing Early-Stage Tech Startups
Avoiding GTM Theory Land
A Fresh Take on Building A Tech Startup
Check Out These Tech Startup Ideas & Business Models
So let’s get started and create some awesome tech startup ideas together!
Challenges Facing Early-Stage Tech Startups
Building a tech startup is tough! A lot tougher than people think. So before we get into the actual tech startup ideas… I want you to be well equipped for some of the challenges you might face!
It’s rare that a business is so in tune with its niche that it can float along with little to no effort.
And unfortunately, many fail within their first year of business.
20% of startups fail within their first year
Sadly, the statistics are even worse for tech startups. With fundraising and VC investments in the picture, tech startups can burn through millions of dollars without ever getting a paying customer. Which, of course, is the main goal of any business. So, you could argue that these startups were never really businesses at all, but rather very expensive, wasteful hobbies!
Reasons for failure include:
Building the wrong product
Being in the wrong market
A lack of market research
Bad partnerships
Ineffective marketing
The founder not having expertise in the industry where they want to offer solutions
Getting Stuck on GTM Theory
As a tech startup founder, you don’t want your business to end up as another statistic. But in an attempt to avoid failure due to customer acquisition and GTM (Go-to-Market) problems, it’s easy to get sucked into the needless world of “GTM theory land.”
Many founders burn themselves out trying to study overly complicated theories on GTM topics. This often happens to people who’ve been exposed to the “Overwhelm to Sell Method,” which is a tactic used by online GTM experts who create content that covers lots of theory on GTM topics.
Instead of simplifying the topic, they make things more complicated than they need to be so that people feel like the GTM concepts they’re teaching are super hard and complicated. Then they push people to buy their overpriced programs to get help in simplifying the topic and achieving results.
None of this is rocket science! It just takes some time to learn. But these people benefit by obfuscating the subject to create an appearance that the client needs to purchase the expensive program to achieve results that are actually pretty simple to attain. (We won’t name any names!)
Reading GTM theory books such as “Go To Market Strategy: Advanced Techniques and Tools for Selling More Products to More Customers More Profitably” or “Go to Market: The Marketing and Scaling Blueprint for Startups” can also be a waste of time.
Books like this may teach you principles. But they’re not helpful when you get into the real world and start applying them to your own tech startup ideas.
Your first priority should be to generate revenue and build loyalty with your customers.
By avoiding the pitfalls of GTM theory overkill and expensive mentorship services, you can set yourself up for success and build a strong foundation for long-term startup growth.
We’ll get into the practical tips and examples on how to do that later.
Take action by choosing a tech startup idea now and getting started today!
A Fresh Take on Building A Tech Startup
Coming up with the next big product or idea – that’s A LOT of pressure for tech startups! However, one proven approach is to start by finding a market and monetizing it first. By doing this, you can ensure long-term profits and develop a successful business from day one.
Unfortunately, many GTM coaches out there want to take you through all the theories of how to DIY your tech startup growth and then get you to buy their overpriced mentorship services. In this blog, we’re taking a different approach!
I’ve helped over 100 tech founders start up fast, all while growing my own tech startup on the side.
In this single blog post, I’ll give you the training and tech startup ideas you need to decide on a tech business model and a good offer to start making sales.
And the best part? No purchase is required 😉 I’m sharing EVERYTHING you need to know for FREE!
via GIPHY
Start With The Market
By focusing on building a market and monetizing it first, you can avoid getting bogged down with GTM theory overkill and expensive mentorship services.
It is better to concentrate on growing a loyal customer base, generating revenue, and establishing a solid foundation. That’s exactly what my client Kam Lee did…
You can listen to him tell his story in the video below, or keep reading if you prefer that method instead…
Follow me on:
Let me tell you a little bit about how starting with the market helped Kam’s business, Finetooth Analytics. He has really been on a roll since teaming up with Data Mania!
Despite barely having an online presence, he was able to generate $350K in recurring revenue in his first year!
And get this: his profit margin is a jaw-dropping 67.7%.
But it wasn’t always smooth sailing for Kam. When we first met him at the start of 2020, he was juggling a bunch of small, custom projects with low budgets under his old brand, K Lee Studios.
He quotes: “It was like being a hamster on a wheel… I was having to rely on outbound measures to bring in new business and it was difficult to prove the value of my services.”
Seeing disconnects, he felt less confident producing quality content.
Through his work in my course, Kam understood that he had to start by finding the right market and making a connection with people in that market.
Following my market research methods, he discovered a far more viable offer than attribution tracking; Namely, marketing mix modelling (MMM). After discovering this market need, he quickly and successfully transformed his MMM service concept to a service offer.
This just goes to show, with the right strategies, you can succeed even in a crowded market.
Five Strategies That Will Help You Identify a Market for You Tech Startup Ideas
One of the biggest challenges for founders is finding their ideal customer and market for their amazing tech startup ideas.
I mean, how can you build a loyal customer base and generate revenue if you don’t know who you’re targeting?
Fortunately, there are several strategies that can help you identify your ideal customer and market:
Strategy 1: Define your ideal customer
Before you start looking for your market, you’ve got to know who you’re targeting. Define your ideal customer avatar (ICA) – this includes stuff like their age, interests, habits, and what bugs them.
Strategy 2: Ask them what they’re looking for
Find people that look like your ICA. Conduct surveys and interviews to get the lowdown on their pain points, motivations, and behaviors. By understanding their needs, you can create a product or service that meets them.
Strategy 3: Suss out the competition
Look at what your competitors are doing and how they’re positioning themselves. Look at their strengths and weaknesses? Can you fill in any gaps?
Strategy 4: Join the social media party
Social media and online communities are great places to connect with potential customers and get feedback on your tech business ideas. Engage in relevant groups, participate in conversations, and share your ideas with your target audience to get feedback.
Strategy 5: Use market research tools
Take advantage of tools like Google Analytics, SEMRush, and Ahrefs to help you gather insights into your customers’ behavior and preferences.
Trust me, the time you put into identifying and understanding your target market with pay off tenfold! Not only will you understand who your target audience is, you’ll know what they actually want. Meaning, you can create a product or service that really speaks to them.
The more you learn the more you can adapt your approach. Listen to feedback and stay ahead of the game by making improvements regularly!
So, now you know how to find your market, let’s take a look at some…
Effective Tech Startup Ideas
You know that feeling when you’re at a crossroads, trying to decide which path to take for your business? It’s tough to know which direction to take! Especially in an industry that’s always changing and evolving.
But fear not, because in this section, I’m going to break down some of the most popular and successful tech business ideas out there.
If you want, you can watch this section instead of read it in the YouTube video below:
So, grab a cup of coffee, get comfortable, and let’s dive into some of the most exciting tech startup ideas out there…
Service Business / Agency Tech Startup Ideas
What it is: Freelancing / freelancing with a team
If you’re a freelancer, you probably already have a service-based business. And chances are, you do all of the work solo. Am I right?
Another way to do service-based business would be something like freelancing with a team. Bringing in a partner who can offer the same level of data services you do might be a good idea. Or perhaps hire a team to help you with all of the admin, marketing, sales, etc. – all that stuff can be supported by team members.
You don’t have to do it alone if you don’t want to! There are a few different ways to bring in help with a service-based business.
Service-Based Tech Business Models PROs and CONs:
PROs:
It’s the quickest way to get your business off the ground
You can sell your time for 2x what you would make as an employee (and that’s as a minimum)
Selling your time is the most valuable thing you own, which is one reason it’s easier to use as an offer when you’re trying to make high-ticket sales
CONs:
As far as liability goes, you’ve got to think about who will bear responsibility in the event something goes wrong (in most cases, it’s probably going to be you)
It’s not scalable long-term. Services are great for starting a business and getting profitable, but you’ve got to start thinking about how to shift this
Quick tip: For scalability, you can transform your service-based business into agencies or SaaS (software-as-a-service).
Service-Based Freelancing Tech Startup Ideas and Example
Freelancing your services is a great way to get your tech startup ideas off the ground FAST!
The bottom line is that no matter what your skill set, there’s an opportunity for you in service-based tech startups. Examples of freelance services you could offer include:
Data analytics consulting: Help businesses analyze their data and make informed decisions using statistical analysis, data visualization, and other data analytics techniques.Mobile app development services: Design and develop custom mobile apps for businesses or individuals, including iOS and Android apps.IT consulting services: Offer IT consulting services to businesses, including network setup, security, cloud computing, and software development.Data analysis and reporting services: Provide data analysis and reporting services to help businesses make better decisions.Cybersecurity consulting services: Help businesses protect their data and systems from cyber threats by delivering cybersecurity consulting services.Cloud computing services: Offer cloud computing services to businesses to help them reduce costs and improve flexibility and scalability.UX/UI Design Services: Offer design services to help businesses create visually appealing and easy-to-use digital products.
Freelancing vs Consulting: Figuring Out The Right Option for You
There are two ways you can provide services as a tech professional: freelancing or consulting.
We cover consulting extensively in the next section, but what you need to know now is that…
Freelancing typically involves delivering implementation level work, while consulting usually involves delivering strategy and advisory services.
Think about what works best for you and your tech startup.
If you’ve got more experience with delivering implementation type work (aka; coding, software development, model-building, etc.), then freelancing is probably the better option for you. Consultancy may be your thing if you’re experienced with and enjoy delivering strategic planning and advisory type support.
If consulting sounds like you’re thing, I’ve got something that might interest you… the Data Strategy Action Plan! With it, you’ll be able to create a killer data strategy that’s just as good as what big consulting firms charge a whopping $250,000 for.
An Example of What You Can Do with A Service-Based Tech Startup
I mentioned the success of Kam Lee earlier, and he’s As a great example of how successful a service-based business can be!
Staying afloat during the pandemic required businesses to adapt and pivot. Kamarin Lee, the Chief Marketing Data Scientist at Finetooth Analytics, did just that and had an explosive year.
Within his first year in business, Kam landed $350k of annual recurring revenue (ARR) for his company, with a 67.7% profit.
This was a huge jump from his previous business, K Lee Studios, which was focused on small, customized projects with low budgets.
Wanna hear more about Kam’s success story? Listen below.
Agency Model Tech Startup Ideas & Examples
Agency models are also service-based businesses. The difference between agencies and freelancing or consulting is… With agencies, you’re not doing all of the heavy-lifting with respect to client delivery. You have a team to help you.
It’s often the natural progression for freelancers looking to scale their business.
Types of tech agencies include:
App development agencySoftware development agencyDigital marketing agencyAnalytics and reporting agencyMarketing automation agencyPay-per-click advertising agencyData mining agency
An Example Of What You Can Do with a Tech Services Agency
It’s exciting to launch a tech startup. But it’s also challenging. Especially for the founders, who shoulder the responsibility of ensuring the business’s success.
Lori, the co-founder and CEO of Future Sight AR, was another student of mine. She owns an AR/VR services agency.
Through her work in an earlier version of our Data Creatives & Co. course, Lori was able to land a $100k contract for her agency (all off of a LinkedIn post that she didn’t even write – she just used the automation method I taught her to source and publish the post!)
Lori’s experience serves as an excellent example of how scaling your business into an agency model can skyrocket your results when going solo is no longer feasible.
Listen to her full story below.
Looking for more cool tech startup ideas? I’ve put together over 50 income-generating ideas!
Within an hour of reading it, you’ll have dozens of ideas for new ways to make money with your data expertise.
In fact, some of our students have made over $1,000 an hour using them. You could be next!
Coaching / Advising Tech Startup Ideas
What it is: Group coaching / mentoring, 1-on-1 advising
Coaching and advising are great ways to connect with your customers on a more personal level. Group coaching provides a sense of community and support, while 1-on-1 advising offers a more intimate and individualized experience.
If you’re looking for a quick and easy setup, coaching and advising can bring in decent hourly rates, especially if you offer group access at a lower price point. However, if you’re aiming for higher-end clients, it’s essential to charge a premium rate for your 1-on-1 advising services.
PROs and CONs of coaching / advising tech business models:
PROs:
Coaching and advising offers are quick and easy to set up.
Coaching and advising offers rates are decent (per hourly investment of your time).
CONs:
Coaching and advising offers can amount to lower ticket sales, compared to what you can make if you sell something like an enterprise grade SaaS.
Delivering upon coaching and advisory services takes time away from your business.
What to Charge for Your Time
As a tech consultant or advisor, figuring out what to charge for your time can be tricky.
Your rates should be competitive and reflective of the value you provide. But you also want to avoid pricing yourself out of the market or undervaluing yourself.
As I price my coaching services, I start by thinking about the type of service I’m offering…
For example, 1-on-1 advising is a premium service that typically commands a higher price point than group coaching or mentoring. Seasoned advisors charge as much as $1200 per hour for 1-on-1 consultations – often much more.
Don’t forget that not everyone can afford (or needs) this level of service. It is likely that only a small percentage of your audience will actually convert with this model. If you really want to build trust and establish relationships, create a lower-priced offer to get people into your sales funnel.
When it comes to group coaching or mentoring programs, pricing can vary massively. You need to account for both the duration of the program and the scope.
A 4-month program priced at $3-4K may be considered low-priced compared to enterprise-grade packages or access to SaaS for larger corporations.
The right price point for your services depends on your target market, the value you provide, and the competition.
Example Coaching / Advising Packages:
If you’re offering coaching services, there are many creative ways you can structure your packages to provide maximum value to your clients.
One approach is to tier your packages based on the level of service you provide. Clients can then choose the package that best fits their needs and budget.
For instance, your lowest tier package may simply include a 60-minute coaching call. This can be ideal for those who have specific questions or challenges they need help with.
However, for clients who require more comprehensive support, you can offer higher-tier packages that come with added perks and benefits.
One of the perks you can offer is access to additional resources, such as worksheets, templates, or toolkits that complement your coaching sessions. This gives your clients tangible resources they can refer to long after your coaching sessions have ended.
Short-term advising examples:
Here is an example of what people are charging and delivering within executive coaching and productivity advising packages.
Image source: https://paperbell.com/pb/real-coaching-rates/
Another example of a short-term advising package is what I offered in my Data Strategy VIP Day program when I used to offer that.
As part of my VIP day offer, I also gave customer free access to my data strategy product suite, which includes:
Data Strategy Action Plan
Data Strategy Starter Kit and
Data Use Case Evaluation Workbox
Longer-term coaching examples
For clients who want to make significant changes in their lives and need accountability and ongoing support, long-term coaching works well.
Essentially, it allows for a more intense coaching experience, which can lead to better results and stronger relationships between coach and client.
The key to designing long-term coaching programs is keeping the client motivated and engaged.
This may include regular check-ins, progress tracking, and goal setting sessions. You can also offer additional resources or support, such as access to a private community or exclusive content.
Feel free to check out my own 3-month private business coaching to see what I offered as part of that program.
Overall, long-term coaching programs are a great way to provide ongoing support and guidance to your clients, while also building a deeper connection and relationship with them.
SaaS Tech Startup Ideas
What it is: Software as a service (SaaS) that you deliver in a cloud environment
A SaaS tech business model involves providing customers with access to software and services through a web-based application.
Essentially, instead of selling a product that customers purchase and keep, a SaaS business charges a recurring subscription fee to use their software or service.
Because of its ability to generate steady, predictable revenue streams and its scalability, this model is becoming increasingly popular in the tech industry.
Some well-known examples of successful SaaS businesses include; Salesforce, Zoom, and Dropbox.
PROs and CONs of SaaS business models:
PROs:
You can scale your SaaS product quickly and easily.
There’s a lot of potential to make high profits with SaaS-based businesses.
CONs:
It takes a long time to develop and launch a SaaS product.
You need to have technical skills to build and maintain the product.
It can be expensive and requires significant capital investment to build a SaaS product.
SaaS business models have some great upsides with the scalability of products and massive revenue-generating potential.
But, let’s not forget the downsides; The biggest one being the time it takes to market. SaaS solutions require a lot of technical know-how, time and money! You may even need to hire additional developers to help with development and maintenance, adding to your overall capital cost.
You’ll need skill and patience to really make it succeed.
Advice From a SaaS Growth Consultant…
I’ve worked with a lot of SaaS startup ups. I’ve also taken on SaaS consulting contracts. So, it’s safe to say… I know a thing or two about the industry!
So here’s my advice to ALL aspiring SaaS startups…
Start with the market! Don’t jump into building a product without validating its need.
Do your research. Is there a market for what you’re building? Trust me, I see this mistake ALL THE TIME!
Who’s your target audience? What are their pain points? How will a SaaS solution benefit them?
These are all questions you need to know the answer to before spending time and money building a product.
A successful SaaS product isn’t just about technology! It’s about solving a problem for your customers. Do your research and make sure that you’re building something that people need and want.
Information Product Tech Startup Ideas
What it is: Books, online courses, digital products, etc.
An information products business means you’re selling digital products like ebooks, courses, or webinars filled with valuable information that customers need to get a particular outcome.
It’s a good fit for new tech founders because it’s low-cost and scalable. You create something once, and sell to many customers without the need for physical products.
You could create info products like coding courses, ebooks, templates, or webinars, drawing from your tech expertise to provide valuable content that helps others grow their skills.
But don’t be fooled, creating and selling info products takes a lot of work.
It involves researching, developing high-quality content, building an audience, and promoting products effectively – not to mention maintaining the products and keeping customers happy.
But with the right approach and hard work, info product tech business models can be a profitable and rewarding venture.
PROs and CONs of Information Product Tech Business Models:
PROs:
Info products can be easy to sell.
Info products are very scalable (like, really, really scalable).
CONs:
You need to have an audience to sell to in order for the info products model to work for you.
Info product sales are generally lower-ticket.
Wait a minute! I get it, selling information products sounds cool and all, but if you haven’t built your personal brand or gained a following, it’s going to be a challenge to make a profit right away.
In my experience, it’s better to start with services and put a lot of effort into marketing to build the audience you need to eventually sell those awesome information products.
Wondering how the heck to even a sell a digital product? I’ve gotcha covered!
4 Sure-fire Ways To Be Successful Selling Digital Products
(From someone who’s actually been there and done it!)
Step 1: Focus on the audience and transformation
If you’re planning to create an information product, it’s important to start by focusing on your audience and the transformation you want to provide them.
In many cases, people create products based on what they know. Don’t fall into that trap! Identifying your audience isn’t enough. A product’s value comes from how it can help them reach their goals.
Let’s say you want to sell a high-ticket online course. It is not enough to teach a specific skill. You need to focus on providing a full transformation for your customers. (e.g. rather than simply teaching someone how to code, focus on how this skill will change their life).
Make sure to bake this transformation into your product and marketing message. If you don’t, people might as well go to Udemy and purchase a $10 course instead.
Put your focus on providing a value proposition that helps your audience achieve their goals.
If you’re looking for more advice on starting a successful freelance data business, download the Ultimate Toolkit for Data Entrepreneurs. You’ll learn the exact processes used by successful entrepreneurs to scale their businesses to the multi-6-figure range.
Step 2: Warm up your audience
The second crucial step in successfully selling your information products is to warm up your audience by creating valuable content around the topic of your product and establishing yourself as a thought leader in that space before launching.
It’s not enough to just create a product and wait for people to buy it. You need to build trust with your audience and showcase your expertise in the data niche. This means creating content that is relevant to your course topic and sharing it consistently with your audience.
By providing value through your content, you can nurture your audience and establish yourself as a go-to expert in the data space.
When you finally launch your product, your audience will already be warmed up and more likely to invest.
Step 3: Have a pre-sale period
To successfully sell your data product online, you need to have a pre-sale period.
Using the online course example… rather than building out the entire program, create about a third of it and do some pre-sales to figure out how well the course is selling and who’s resonating with the content.
This also gives you the chance to tweak your marketing and messaging so it attracts the right audience.
A pre-sale period means you won’t invest too much time, money, and energy before validating your course idea. If your pre-sale doesn’t get traction, you can cancel it and refund people who bought it, then go back to the drawing board to figure out what went wrong.
On the other hand, you may pre-sell the program and get overwhelmed with clients. But this is a good problem to have because it means there’s interest and demand for your course.
You can bring on a team to balance your workload and use the added income from your course to do so. So, make sure to have a pre-sale period to test the waters and ensure the success of your data course.
Step 4: Decide how you will publish your information product
Books and courses can be published and sold in many different ways. Self-publishing and partnering with a brand are two common approaches that have their own unique advantages and drawbacks.
Here are the pros and cons to both options:
Self-Publishing Pros & Cons
Pros:
You get to retain rights to your content.
There is a potential for higher profit margins.
You have more control over marketing and pricing.
Cons:
You’ll need to do all marketing yourself.
Self-publishing requires an investment in software for hosting.
It may take you longer to establish the credibility of the product.
Publishing with a Partner Pros & Cons
Pros:
You’ll have the potential to reach a larger audience.
You’ll get the opportunity for credibility-building through association with a brand.
This approach requires less marketing effort.
Cons:
You have less control over content and pricing.
You’ll have lower profit margins.
It may take a long time to establish a partnership with a reputable brand.
Examples of Real-Life Technical Information Product Success
An online course success case:
Let me tell you about one of my previous students, Jordan. He’s a total rockstar in the data industry! After using my Data Creatives & Co. Course to develop his go-to-market capabilities, he created his own amazing online course called “Excel: Dashboards for Beginners“.
It’s a great course for anyone wanting to learn how to create visually appealing and informative dashboards in Excel. It’s been a huge success. I’m so proud of him for putting in the hard work to create such a valuable resource for others in the industry.
As for earning potential… last I heard, Jordan was earning at least $1500 per month for just this one 45-minute course!
As for me, I’ve done 16 courses and trained almost 2 million knowledge workers on data science. While the earnings don’t scale linearly, you can get an idea of how much my courses have been making me over the years.
A template success case:
Back to Lori-Lee for a moment… She’s an excellent example of someone who has successfully created and sold a digital template product.
She recognized a need in the market for a well-designed startup pitch deck and created a template that solves this problem for entrepreneurs.
It’s been well-received and continues to bring in steady revenue. Lori-Lee’s success demonstrates the power of identifying a niche and creating a product that meets the needs of your target audience.
As I said at the start of this post… coming up with tech startup ideas is just the beginning. Turning that idea into a successful business is a whole different story.
It takes hard work, dedication, and strategic planning. But you can make it happen with the right mindset and approach.
Remember to focus on solving real-world problems, validate your idea with potential customers, and continuously iterate and improve your product or service.
If you’re take your tech startup ideas to the next level, be sure to check out my FREE MASTERCLASS 4 Steps to Monetizing Data & Tech Expertise.
Inside I’m sharing my top strategies and insights on how to turn your skills and knowledge into a profitable business.
I’ve recently joined the build in public community over on Twitter. I’m building a membership for the startup community.
Every day I’m sharing updates on progress made and key takeaways. I’ll also be doing a weekly #BuildInPublic newsletter. If you’d like to see how I go about building a membership, I’d love to have you inside of our newsletter community. You can join the conversation here.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Cloud Migration Strategies for Startups
URL: https://www.data-mania.com/blog/cloud-migration-strategies-for-startups/
Type: post
Modified: 2026-03-17
A few cloud migration strategies stand out as great options for startups, including rehosting, replacing, or refactoring existing apps and data. Cloud migration can be a challenging step in the digital transformation process. However, it ultimately comes down to determining how a business wants to handle its information.
Some startups have the technical expertise to completely rewrite existing apps, while others want the migration to be as simple as possible. There are great migration strategies for both approaches.
Rehost: The Easiest Cloud Migration Strategy
Rehosting is one of the best cloud migration strategies for startups. Rehosting involves using an infrastructure-as-a-service (IaaS) provider to move existing data and apps into a cloud server. This strategy is what we call, “lift-and-shift” since it transitions the computing environment into a cloud server as-is.
The rehost migration strategy is ideal for startups because it is quick, easy and cost-effective. It especially suits teams that lack extensive cloud expertise since there’s no need to rewrite any code.
IaaS providers can help facilitate the process, making it as easy as possible for businesses. An IaaS provider will offer companies the cloud infrastructure they need without the cost of additional software. This flexibility and easy price customization make using the cloud a great choice for startups. The rehosting strategy allows startups to minimize their migration costs.
The downside to this strategy is that it may not fully utilize the benefits of the cloud. Since apps are redeployed in a cloud environment as-is, they only use it on the most basic level. This may be fine depending on a startup’s unique needs, but it’s important to keep in mind for teams that want to maximize cloud environment features.
Replace: Switch to New Apps with SaaS
The replace or repurchase cloud migration strategy involves switching to completely new apps with the help of a software-as-a-service (SaaS) provider. This is generally the easiest way to gain the full benefits of a cloud environment.
The replace/repurchase migration strategy is the opposite of a replace migration, retaining very little of the original computing environment. It only migrates a business’s data. It does not include apps in the migration.
The most time-consuming part of the process is transferring all that data into the new cloud-based apps and getting set up in the new computing environment. However, startups can access full cloud features and functionality once that process is complete.
Data testing is an important part of a replace/repurchase cloud migration. This strategy relies on successfully migrating only the information, so startups must ensure it will migrate successfully.
Data testing involves analyzing the cloud environment to ensure it will be compatible with what’s being transferred. This allows startups to avoid issues like data loss, excessive downtime or security risks before migrating.
This migration strategy is a good option for startups that want to completely transition their computing to the cloud but aren’t concerned about retaining existing apps. Companies can use a SaaS or platform-as-a-service (PaaS) cloud provider for a replace-style migration.
Revise and Re-Architect: Rewrite Apps for the Cloud
Sometimes startups want to keep their existing apps and data in the cloud migration but don’t want to miss out on full optimization. A rehosting migration is simple and cost-effective, but it may impede functionality since the design of the apps is to run in a cloud environment.
In this case, one of the best cloud migration strategies is revising, also known as refactoring or re-architecting. This is by far the most complicated of the top options for startups. It requires extensive knowledge of code and the cloud. In a refactoring migration, the app’s code is rewritten or revised to optimize for a cloud environment. The PaaS provider usually helps in this aspect.
There are numerous approaches to refactoring apps for the cloud. Some focus on changing only the minimum amount of code necessary, while others revise 50% or more. The idea is to deconstruct and rebuild an app to run like it’s cloud-native. This allows startups to use the full range of tools and features the cloud offers, maximizing the ROI of a migration.
Technically, it is possible to refactor apps after migrating to the cloud. However, the most streamlined approach is to front-load code revisions in the migration process. This reduces costs and complications down the road, such as scaling difficulties. The downside of a refactoring migration is the higher upfront cost, but it’s balanced out by long-term simplicity.
Top Cloud Migration Strategies for Startups
Migrating to the cloud gives startups improved security, connectivity and flexibility. There are many approaches, but a few suits really well particularly for startups.
One of the easiest cloud migration strategies for startups is the simple rehost strategy, which is easy and affordable. Startups can also choose to switch to all-new cloud-native apps in a replace/repurchase migration. The revise or refactor cloud migration strategy involves completely rewriting existing apps to optimize them for a cloud environment. Companies must consider their needs, budgets and abilities when determining the best approach.
A Guest Post By…
This blog post was generously contributed to Data-Mania by Shannon Flynn. Shannon Flynn is a freelance blogger who covers business, cybersecurity and IoT topics.
You can follow Shannon on Muck Rack or Medium to read more of her articles.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## How to Grow a 6-Figure Tech Startup and Become a Technology and Product Marketing Influencer with Lillian Pierson
URL: https://www.data-mania.com/blog/growing-a-6-figure-tech-startup-an-interview-by-larisa-varlamova/
Type: post
Modified: 2026-03-17
The 17th episode of the B2B Marketing Now podcast hosted and founded by Larisa Varlamova, the founder of B2B Marketing Now, features Lillian Pierson. Lillian is the CEO and Head of Product at Data-Mania. Being known widely across the tech community, Lillian has trained well over 1,7000,000 individuals on the topics of AI and data science. Now, she is helping tech startup leaders and founders to make more money through improved product, growth, and marketing strategies.
In this episode, you will learn important key points from Lillian about growing a 6-figure tech startup while becoming a tech influencer and even a Chief Marketing Officer in a SaaS startup! Her business, Data-Mania, has been thriving for the past 10 years! Guess what? She made all this happen from the comfort of her own home in 🏝️ Koh Samui, Thailand.
Learn more of Lillian’s secrets by watching it here: https://youtu.be/lFPHoly-bTE
About B2B Marketing Now by Larisa Varlamova
B2B Marketing Now, hosted by Larisa Varlamova, is a media production hub where creative minds are having a conversation about what it takes to turn your brand into a media one. Join the conversation if you are in marketing, content creation, podcasting, community, web3, personal branding, acting, or public speaking.
Visit their website and socials below:
Larisa’s LinkedIn: https://www.linkedin.com/in/laravarlamova/
B2B Marketing Now: https://www.linkedin.com/company/varlaramedia/
B2B Marketing Now website: https://varlaramedia.com/
If you want to know more or get in touch with Lillian, follow the links below:
[Free Masterclass] 10x Your Startup Revenue: Get Lillian’s 4-step method for successfully repackaging your data & technology expertise, then selling it through a business you own.
Free Trainings on YouTube: Lillian Pierson
Lillian Pierson on LinkedIn
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## The New Tech Freelancers’ Guide To Earning Over $150/Hour For Freelance Coding Jobs & Technical Writing Freelance Projects (with sourcing, pricing & portfolio examples)
URL: https://www.data-mania.com/blog/freelance-coding-jobs-2/
Type: post
Modified: 2026-03-17
Welcome to the wild, wonderful world of tech freelancing! If you’ve got the skills and the drive, you can earn over $150 an hour for delivering freelance coding jobs or technical writing freelance projects.
Sound too good to be true? Buckle up, because I’m about to give you a whirlwind tour of everything you’ll need to know to make money from your tech skills.
In this guide, I’ll debunk the myths, reveal the secrets, and arm you with the knowledge needed to conquer the tech freelancing landscape. So sit back, relax, and get ready to discover the keys to unlocking your freelancing success!
Looking for something specific? Scroll straight to what you’re looking for by clicking a link below:
Separating Fact from Fiction in Freelance Coding Jobs
The Growing Trend of Freelance Work in the Tech Industry
Unlocking Your Earning Potential: Tips for Pricing Your Tech Services
Where To Find The Best Freelance Coding Jobs & Technical Writing Freelance Projects
Showcasing Your Skills: Tips for Building an Impressive Tech Freelance Portfolio
How to Complement Your Portfolio with a Strong Tech Freelance Resume
Preparing A Package For Your MVP Freelance Service
What to Look For In Potential Clients
Separating Fact from Fiction in Freelance Coding Jobs
When it comes to freelance coding jobs or technical writing gigs, there are plenty of myths floating around about earning potentials and the lifestyle it offers.
So let’s set the record straight and give you a realistic perspective on what it takes to succeed as a freelance tech worker.
Debunking the Myths: What You Need to Know About Freelance Coding Jobs
Ah, the life of a tech freelancer. You’ve heard the stories: work from a hammock on a beach in Bali, sip margaritas, and watch the money roll in. In this digital utopia, clients flock to you like moths to a flame, and all you need is a killer LinkedIn profile and a flashy portfolio.
When something sounds too good to be true – it usually is!
So here’s…
The Truth About Freelance Coding Jobs
The tech freelancing life can be amazing! But it certainly isn’t a walk in the park (and not everyone is cut out for it).
Keeping pace with the ever-changing trends and technologies requires hard work, dedication, and ongoing education. You can’t expect overnight success.
I’m not trying to put you off. I’m just keeping it REAL! And with the right tools and strategies, you can totally achieve your dream of earning $150/hour or more as a freelance coder or technical writer, like many of my clients have done, including Yves Mulkers here:
Ready? Set. GO >>
The Growing Trend of Freelance Work in the Tech Industry
More and more people are ditching full-time employment in favour of freelance work. And who can blame them? I mean, who wouldn’t want more flexibility to spend time with their kids, or travel the world?
Around 36% of Americans now consider themselves freelance workers, and the number is growing in Europe too, with over 3 million knowledge workers in France, Germany, and Spain alone.
Individuals aren’t the only ones jumping on the freelance bandwagon. The number of businesses using freelancers over employees is on the rise. Almost half of US business owners planned to hire freelancers in 2020 to fill talent gaps (I imagine this has increased post COVID). In the MENA region, 7 out of 10 employers are doing the same.
Statistics show that freelance work is becoming increasingly important in the technology sector. It’s the perfect time to move into a booming market and establish a successful career.However, with more competition comes the need for a strong marketing strategy, effective pricing, and an impressive portfolio (all of which we’ll cover in this blog).
Unlocking Your Earning Potential: Tips for Pricing Your Tech Services
When it comes to setting rates, every freelancer struggles. You want to earn a decent income. However, you do not want to overprice yourself and scare off clients.
While technical skills can boost earning potential (see chart below), marketing is crucial to commanding the rates you deserve as well.
Image source: https://www.freelancermap.com/market-study
Simply having technical skills won’t necessarily translate into higher earnings. You need to clearly communicate your unique value proposition and present yourself as an expert in your field.
Essential Video #1: Consulting Rates in 2023 – 2X YOUR RATES OVERNIGHT
In this video, I’m sharing expert tips on how to set your rates, including research on what most freelancers are charging and goal rates you should be aiming for to make bank.
Essential Video #2: How to Become a Freelance Data Scientist (Or Developer 😉)
In this video, you’ll learn not only how to become a freelance data scientist (or developer) but also what kind of salary you can expect. I’ll walk you through how to make $100,000 as a freelance data scientist, even if you don’t have any prior experience in the field.
Pricing Tips For Freelance Coding Jobs
When it comes to pricing your freelance coding services, it’s essential to set your rates appropriately to avoid a “race to the bottom.”
Here are some of my top tips to help you price your services right:
Research the market: Check out what similar services are priced at on freelance websites and ask other freelancers for their rates.
Consider your skill level: Be honest with yourself about your experience and expertise. Your rates can increase gradually as you gain more skills.
Factor in expenses: Remember to factor in expenses like taxes, health insurance, and software subscriptions when setting your rates.
Value-based pricing: Consider the value you bring to the client. If your work solves a significant problem or boosts their revenue, you can charge more for your expertise.
I’ve only scratched the surface of how to price your services as a tech freelancer with these tips. For a more in-depth look at pricing check out this Guide To Pricing Your Services at $100/Hour.
Pricing Tips For Technical Writing Freelance Projects
When it comes to pricing your technical writing freelance projects, there are a few things to keep in mind to make sure you’re getting paid what you’re worth.
First off, the type of content you’re writing can impact your rates. Crafting a long-form white paper will likely take more time and effort than a set of blog posts, so you’ll want to ask for more money.
Another thing to consider is how long and complicated the content is. Super technical content is likely going to have a longer word count and take longer to write, so make sure to factor that into your rates.
Lastly, you need to decide whether to charge per project or per hour. Charging per project makes sense if you know how long a project will take. If you’re unsure, charging per hour can be helpful if you’re working on projects with varying levels of complexity or if the scope of the project changes often.
The key to setting fair rates for your technical writing work is to think about all of these factors and charge a rate that reflects the value you bring to the project.
Where To Find The Best Freelance Coding Jobs & Technical Writing Freelance Projects
To find the best freelance coding jobs or writing gigs, you’ll need to know where to look. Here are some top platforms and strategies to help you land your dream projects:
1. Freelance marketplaces
Platforms like Upwork, Freelancer, and Toptal are great places to start looking for freelance work. These platforms connect freelancers with clients who need their skills and expertise.
There’s a ton of different jobs on there from short-term projects to long-term contracts. Just create a profile and start applying for jobs that match your skills.
2. Job boards
Job boards like WeWorkRemotely, Remote.co, and GitHub Jobs are also good resources for finding freelance tech jobs. These job boards post job openings for remote tech workers, which can be a great way to find new projects.
Top tip: Set up job alerts to stay up-to-date with the latest job postings.
3. Networking
Networking is a powerful way to find freelance work. Attend industry events, join online communities, and leverage your existing contacts to find potential clients.
Some great online communities for data workers include:
Dataleaders.net
Tableau Community
Reddit.com (just search for r/datascience)
4. Social media
Social media can be a goldmine for finding freelance opportunities. Some platforms like LinkedIn even have their own job boards.
Keep your profile up to date, and don’t be be afraid to share your work and accomplishments.
Showcasing Your Skills: Tips for Building an Impressive Tech Freelance Portfolio
Your portfolio is one of your most valuable assets. It showcases your skills, demonstrates your experience, and helps you stand out from the competition.
But how do you build a portfolio that really wows potential clients? Let’s dive in…
Coding Portfolios vs Freelancing Coding Portfolios
Is there a difference between a coding portfolio for a full-time job and one for freelance work? In short, YES!
If you’re a coder looking to land high-paying clients, your freelance portfolio needs to showcase your specific services and emphasize your flexibility to work with diverse clients.
Here’s what you most need to know about building badass freelance coding portfolios that’ll land you the good jobs right off the bat…
Data Science (and Coding) Freelancing Portfolios – Include These Projects
Check out the video below where I share five must-have data projects for your portfolio that will set you apart from the competition, complete with examples.
Since you took the time to watch the video ^^, you already know what I mean when I say that a coding portfolio for a full-time job is A LOT different from the type you’d use for freelancing.
While both should showcase your skills and projects, a freelance portfolio should focus on the specific services you offer and emphasize your flexibility and ability to work with diverse clients.
Creating A Killer Portfolio For Freelance Coding Jobs
A killer portfolio can make all the difference in landing freelance coding jobs. To make your coding portfolio stand out from the rest, follow these tips:
Show off your best work: Your portfolio should include a variety of coding projects to demonstrate your versatility and skill. Choose projects that showcase your strengths and that you’re proud of.
Include case studies: When presenting your projects, include case studies that demonstrate the challenges you encountered, the solutions you implemented, and the results you achieved. It will help you demonstrate your ability to solve complex problems.
Use visuals: Visuals are a powerful tool for showcasing your work. Include screenshots, GIFs, or even videos to make your portfolio engaging and easy to digest. This will help potential clients understand your work more quickly and easily.
Include testimonials: A testimonial from a client can boost your credibility and reassure potential clients that you’re the real deal. Highlight your strengths and skills by including quotes from previous clients.
Keep it updated: Regularly update your portfolio with new projects, skills, and achievements to stay relevant and showcase your growth.
Want to see some real-life coding portfolio examples? Jump over to this blog where I’m sharing some of my favorites (along with way MORE portfolio tips).
You can also check out a couple of my own coding demos inside these related posts:
Crafting a Winning Technical Writing Portfolio
If you’re a technical writer looking to land high-paying clients and grow your business, a winning portfolio is essential. It shows potential clients what you’re capable of, and helps you stand out from the competition.
Here are a few tips to ensure your technical writing portfolio looks great and gets you noticed:
Include different types of content: Your portfolio should show off your skills with different types of writing, like blog posts, white papers, and user guides. This way, potential clients can see that you’re versatile and can handle a range of projects.
Highlight your areas of expertise: If you have experience writing about specific industries or technologies, make sure to highlight that in your portfolio. You want potential clients to see that you have relevant knowledge and skills.
Showcase your writing style: Technical writing doesn’t have to be boring! Try to show off your personality and style in your portfolio. Making it engaging and easy to read.
Include some metrics: If you have any data that shows the impact of your writing, like increased user engagement or improved customer satisfaction, make sure to include that in your portfolio. This will help clients see that your writing has a real impact on their business.
Link to your published work: Finally, make sure to include links to any technical writing you’ve had published on websites or blogs. This will help clients see that you have experience and credibility in your field.
By keeping these things in mind, you can create a technical writing portfolio that shows off your skills and experience and helps you land your next freelance gig!
Technical Writing Examples
One of my previous students, Meor Amor is a technical writer and content creator based in Malaysia. He uses his portfolio to sell his product and services. By sharing his work online, he attracted a remote position as a Dev Advocate at Cohere (a $125MM Series B Data Company). Check out his work here!
In my own technical writing portfolio, I’ve written and published 9 books on data science over 7 years. These books have helped me gain credibility in the industry as an expert and thought leader. You can check them out here.
How to Complement Your Portfolio with a Strong Tech Freelance Resume
A good resume is just as important as a great portfolio.
Your portfolio showcases your work, but your resume offers more detail and context that can help clients understand why you’re the right candidate.
When you have a well-crafted resume, your skills, experience, and professionalism will stand out among other freelancers.
Here’s my top tips on creating a resume that complements your portfolio and impresses potential clients:
Adapt your resume to each job: Don’t use the same resume for every job application. Take the time to understand the client’s needs and highlight your most relevant experience and skills.
Use numbers to show off your achievements: When you’re writing about your past projects and experience, try to use numbers to show what you accomplished. For example, you could talk about how much you increased efficiency or how many people used your work.
Make sure to talk about your soft skills too: Being a great freelancer isn’t just about being technically skilled. Good communication skills, meeting deadlines, and working well with clients are all essential.
Include any relevant certifications or education: Include any degrees, courses or certifications you have that relate to the job you’re applying for on your resume. This can help show the client that you’re committed to your craft and are always learning new things..
Preparing A Package For Your MVP Freelance Service
To make yourself stand out from the competition and attract high-paying clients, consider offering a Minimum Viable Package (MVP) for your freelance services.
Here are some things to include in your MVP package:
Make it clear what you offer: Summarize the precise services you provide, such as UX design or web development, so customers understand exactly what they will get.
Price it out: Provide clear pricing details for each service, whether it’s hourly or project-based. This will help clients understand the value you provide and avoid any confusion.
Set realistic deadlines: Make sure you can meet deadlines and keep clients happy by setting realistic turnaround times for your services.
Communicate well: Establish the best way to communicate with clients, whether it’s email or project management tools. This will make sure everyone is on the same page.
Revision policy: Define your revision policy, including how many free revisions you offer and any additional costs for extra revisions. This will help avoid misunderstandings.
What to Look For In Potential Clients
When you’re first starting out it’s easy to say “yes” to any client. But you need to be selective. They need to fit both you and your business. By being selective, you can ensure that you’re working with people who appreciate your skills, communicate well, and offer ongoing opportunities.
You can avoid headaches and stress down the line by considering these factors:
Budget: You deserve to be paid what you’re worth, so look for clients who value your skills and are willing to pay your rates. Don’t settle for low-paying projects that will undervalue your expertise and skills.
Communication: Working with clients who are responsive, clear, and respectful is essential to a successful project. This will make sure that everyone is in agreement and stop any potential miscommunication in the long run.
Reputation: Research potential clients to see if they have a positive track record of working with freelancers. Look for testimonials, reviews, or ask fellow freelancers for insights. This will help you avoid any red flags or headaches down the road.
Scope clarity: Try to work with clients who have a well-defined project scope and expectations. This will help avoid scope creep or misunderstandings that can lead to delays or disagreements later on.
Long-term potential: Building long-term relationships with clients can lead to steady work and referrals. Look for clients who have ongoing needs, a growing business, or a history of rehiring freelancers. This can help ensure a steady stream of work and income over time.
Freelance coding jobs and technical writing projects can be a great way to make a good living. Just remember it’s not all rainbows and butterflies! You need the right tools and strategies in place if you really want to succeed!
If you need some help, my 4-step method for successfully repackaging your data & technology expertise, then selling it through a business you own is a great place to start!
Remember that freelancing is a journey, and you need to keep learning and adapting to stay competitive. Make sure to stay current with new technologies and trends, network with other professionals, and never stop honing your skills.
Whatever stage of your freelance career you’re at, I hope this guide has provided you with some valuable insights and tips to help you succeed. Good luck and happy freelancing!
Back in March, I started building in public. A few times a week, I share progress and learning from my journey towards building a membership for startup founders! If you’d like to join in on that conversation over on Twitter, please connect with me here. And, if you’d like to be notified when our forthcoming membership becomes available, please sign up for our newsletter here.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## How to get consulting jobs at top-tier IT consultant rates: An independent information technology consultant’s guide
URL: https://www.data-mania.com/blog/it-consultant-rates/
Type: post
Modified: 2026-03-17
Picture this: you’ve just launched your tech startup, and things are going great. You’re getting noticed by potential clients and people are keen to start working with you. But when the time comes to discuss your IT consultant rates, you find yourself struggling to set a fair price that also reflects the value of your services.
Sound familiar? If so, you’re not alone.
Understanding the ins and outs of IT consultant rates is crucial to ensure you’re not selling yourself short. That’s why I’m writing this blog post. We’ll delve into the intricacies of determining competitive consulting fees and provide you with the strategies you need to command top-tier pricing and grow your consulting business effectively.
We’ll start by exploring the broad scope of consulting before identifying the common mistakes that could be holding you back from earning what you’re worth. Of course, negotiating your rates is a critical part of the process. And, I’ll provide you with the tools you need to strike a balance between fair compensation and maintaining positive client relationships.
It doesn’t matter if you’re a seasoned IT consultant or just getting started, this blog post provides you with the insights and strategies you need to succeed.
Let’s get started!…
Looking for something specific? Scroll straight to what you’re looking for by clicking a link below:
The “Consulting” Umbrella
Common Consulting Mistakes That Lead to Low Rates
IT Consulting Rates 2023
Selling IT Consulting Services at Top-Tier Rates
Offering Discounts for Long-Term Projects
Negotiating Your Rates Wisely
Frequently Asked Questions It Consultant Rates
Looking to build and grow a profitable startup from scratch? Don’t miss out on our free 75-minute masterclass that shows you how to 10x your revenue in the tech startup industry.
You’ll gain valuable insights on repackaging your data and technology expertise to take your IT consulting business to the next level. Claim your spot now, there are only 100 seats available!
The “Consulting” Umbrella
As a tech specialist, you might be wondering what advisory services you can offer clients.
Well, the IT consulting industry in the USA was worth a staggering $623 billion in 2022.
It’s a rapidly growing field that offers immense opportunities for professionals like you.
Consulting covers a wide range of areas and usually falls into one of five main categories:
Strategy consulting,
Operations consulting,
Financial consulting,
Information technology (IT) consulting and
Human resources (HR) consulting.
As an experienced professional, you have the chance to tap into this thriving industry and carve out your niche.
By understanding the specific demands and trends within the IT consulting sector, you can position yourself to stand out from the competition. There’s plenty of room for growth no matter what your specialty is.
It’s okay to explore different consulting areas and find what works best for you and your clients.
Segmentation by Industry
Every industry in the IT consulting world has its own technology needs and challenges. Providing the best possible solutions to your clients requires recognition and understanding of their unique needs.
For example, healthcare companies find dealing with health a real challenge (one you can fix). Whereas a retail business might benefit from an e-commerce solution.
That’s why specializing in a specific industry vertical, like finance or manufacturing, can be a smart move. By focusing on a particular field, you can offer clients customized solutions that address their specific pain points and needs. This is what makes you stand out as an expert in your niche.
Other segmentation examples include:
Cloud Computing: As more businesses migrate their infrastructure to the cloud, consultants with expertise in cloud computing platforms like AWS or Azure are highly sought after.
Data Analytics: Companies need help making sense of their data; enter data analytics consultants who specialize in turning raw numbers into actionable insights.
Cybersecurity: With cyber threats on the rise, organizations require skilled professionals to safeguard their digital assets from potential breaches.
Consultant vs Freelancer – What’s the Difference?
Consultants and freelancers both play important roles in the business world. Freelancers are typically hired to complete specific tasks, such as building a website or developing a software application, while consultants are brought on for strategic guidance.
Successful consultants possess a wide range of skills and expertise. The ability to think critically and provide customized business solutions is key. Strong communication and relationship-building skills are also essential.
That’s why strategic consultants are often able to charge more than implementation workers. The work they deliver has the potential to drive higher ROI for the client, making it a valuable investment.
Editor’s Note: Strategic consultants are generally able to charge at least 2x more than implementation workers because the type of work they deliver often leads to much higher ROI than task-level work.
Common Consulting Mistakes That Lead to Low IT Consultant Rates
We all make mistakes. But, there are a few common ones I see all the time that can seriously impact your financial success!…
Underpricing Your Services
As a newbie in the IT consulting world, it can be tempting to lowball your rates to attract more clients. Tread with caution when considering this approach as it may have negative consequences that could harm your business in the future.
To avoid undervaluing your worth, start by doing some research on what similar IT consultants charge for their services. By knowing the market standards, you can set your prices accordingly.
But don’t stop at the numbers! Think about the value you provide to your clients. Businesses can thrive with your unique IT skills and expertise, and people are willing to pay a premium for that kind of knowledge!
Failing to Account for Personal Expenses
As an independent information technology consultant, it’s easy to overlook the personal expenses that come with being your own boss. But overlooking these costs can lead to major shortfalls in your income.
When setting your IT consultant rates, you need to take into account things like taxes, health insurance premiums, and retirement savings (the things you don’t think about because they’re usually taken straight out of your salary in corporate).
If you take a holistic view of your finances, you can set rates that cover your expenses while allowing you to maintain a comfortable lifestyle and build a thriving consulting business.
Lack of Clear Value Propositions
Making sure clients understand the value you bring is one of your biggest challenges as an information technology consultant. If you rely on generic value propositions like “great customer service,” you may have trouble standing out in a crowded field.
The key is to identify what makes you truly unique and focus on that. Do you have specialized skills in cloud computing or expertise in software development?
Whatever your secret sauce is, make sure you’re communicating those strengths clearly to potential clients. By highlighting your specific areas of expertise, you can differentiate yourself from the competition and justify charging higher rates for your consulting services.
Not Offering Flexible Pricing Options
If you want to attract those big fish clients, you need to be open to different payment structures. Being flexible with your pricing options can give you an edge and appeal to a wider range of clients with varying needs and preferences.
Not being flexible with your pricing can limit your ability to attract clients who have different budget constraints and project requirements.
Some clients may want a predictable monthly fee arrangement that gives them budget predictability, while others may prefer hourly or per-project rates that provide more flexibility.
Having more pricing options allows you to cater to clients with a wide range of needs.
Weak Marketing Efforts
Finding new clients can be a real challenge. Without adequate marketing efforts, potential clients may not even know about your services or may not perceive them as valuable.
It may seem like a lot of work, but trust me… investing some time and effort into building an online presence and increasing your visibility pay’s off tenfold!
Social media platforms like LinkedIn and industry-specific forums can be excellent channels for reaching new clients and demonstrating your expertise. Attending networking events is also a good way to establish yourself as a leader and connect with potential clients.
Avoiding these common mistakes will set you on the path toward earning top-tier IT consultant rates while ensuring long-term success for your business endeavors. Expenses can quickly mount when consulting is done incorrectly, so it’s important to be aware of the elements that could lead to a lower rate and make sure your project succeeds.
As we continue into 2023, IT consultant rates are likely to change as technology advances and market conditions shift; thus, it is critical for tech startup founders and leaders to stay informed of current trends in the industry.
Editor’s Note: As an experienced IT consultant, it’s important to price your services accurately and offer flexible payment options. Additionally, marketing yourself effectively can be key in securing lucrative contracts that bring in top-tier rates. To hit the ground running and maximize profits, avoid common pitfalls such as underpricing or failing to account for personal expenses.
IT Consultant Rates 2023
You’re a tech specialist, so I don’t need to tell you how fast-paced the industry is! With new technologies and advancements emerging every day, it can be tricky to keep up with the constantly evolving IT consulting rates.
And that’s not all! Things like your experience level, the industry you serve, and even where you live can affect the rates you charge.
So, to give you a little head start with the latest trends, I’ve put together some baseline figures for IT consultant rates in 2023.
Baseline IT Consultant Rates
Information Technology Consultants in the US earn an average of $75 per hour. That’s not too shabby!
If you have some specialist skills under your belt, you can charge up to $350 per hour or even more.
Entry-level IT consultant rates: $50-$75 per hour
Moderately experienced IT consultant rates: $100-$150 per hour
Highly experienced/specialized IT consultant rates: $200-$350+ per hour
Real Life IT Consultant Rate Examples
To help give you an idea of what IT consultant rates can look like in real life, I’ve compiled some rate examples across various IT consulting specialties. These examples will help you understand the range of rates in the industry and assist you in determining your own pricing strategy.
PricewaterhouseCoopers Management Consulting Rate Card. The rate card offers insights into the pricing structure of PwC’s consulting services, including hourly rates for various job roles and experience levels.
SFIA Rate Card
@EdConwaySky sharing some insight on Deloittes day rate table.
The above examples are just a reference point. They should be considered alongside other factors such as your expertise, experience, location, and what you are offering.
Also keep in mind that IT consulting rates can evolve over time, so regularly reassessing and adjusting your rates based on market trends and your professional growth is crucial.
Baseline IT Consultant Rates by Industry
Different industries may have different expectations when it comes to consulting fees. Here are some ballpark figures for various sectors within tech:
Data Analytics & Business Intelligence: $100-$250/hour (source: CIO Magazine)
Cybersecurity: $120-$300/hour (source: Indeed)
Cloud Computing: $100-$250/hour (source: Forbes)
Software Development: $75-$200/hour (source: Codementor)
These figures are just a starting point. Rates vary massively depending on experience and the complexity of the project. And if you’re just starting out in the consulting game, you may need to charge lower fees initially to build your reputation and get some projects under your belt.
In time, you can easily raise your rates as you gain experience and build a portfolio, attracting larger clients. Rather than getting hung up on these ballpark figures, focus on delivering high-quality work and building strong relationships with your clients. The rest will fall into place.
“Set your #ITConsulting rates competitively in 2023 with our baseline figures. Expertise, complexity & local market conditions all affect the rate you can charge – get the full breakdown here.” #GrowthMarketing #TechStartups
Click to Tweet
An Information Technology Consultant’s Guide to Selling IT Consulting Services at Top-Tier Rates
As an independent tech consultant, you have a ton of knowledge to share. But you need to be smart about how you sell your services if you want to earn the rates you deserve.
That’s where the 4 P’s marketing mix approach comes in.
This strategy includes;
People (aka your customers),
Product (the services you offer and how you package them),
Placement (where and how you promote your services), and
Pricing (how much you charge for your services)
Here’s how (I’ve also thrown in a bonus P to the mix)…
People (Customers)
The quickest way to get booked out is by selling a solution to people’s problems. To do that, you need to know EXACTLY who you’re ideal client is. And I mean every little detail!
Let’s say you’re an expert software developer for example. You might want to focus on helping tech startup founders or business leaders who are looking to develop customized software solutions to grow their businesses.
Think about their needs, pain points, expectations, and how your solution solves those problems.
By understanding their needs and expectations, you can tailor your offerings accordingly.
Product (in this case, “Service Packaging”)
Your services are your product (products aren’t always physical objects). And when it comes to service packaging is all about how you package and present your consulting services to clients. Rather than just listing off what you offer, it’s about howcasing the value and benefits you bring to the table.
Here’s an example…
Let’s say you’re an expert in Google Analytics consulting. Your service package would highlight how your skills can help clients gain a deep understanding of their website traffic, supercharge their marketing campaigns, and ultimately boost their revenue. It’s about showing clients the real impact you can make.
When you package your services in a clear and compelling way, you can stand out from the crowd and show that your expertise is worth the investment. Your job is to convince potential clients that you provide value and why they should choose you over your competitors.
So, when you create your service descriptions, make sure to focus on the specific benefits you bring and how you can solve your clients’ problems. This will help you justify charging higher fees for your consulting services and position yourself as a trusted and valuable partner in their business growth.
Here’s an example of one how one my previous students, Marybeth Maskovas has broken out her GA consulting packages…
Image source: Lime Insight Analytics
See what type of results the Founder of Lime Insight Analytics, Marybeth Maskovas, achieved through her work in our course:
Case studies are another powerful way to showcase the tangible results you’ve achieved for previous clients. By highlighting successful projects and the outcomes you helped clients achieve, you can build credibility and demonstrate your ability to deliver value. For instance, you might showcase how you helped a client increase their website traffic by 50% or how you improved their conversion rates through data-driven insights.
Finally, maintaining a strong online presence is crucial to attracting potential clients. By sharing thought leadership content that demonstrates your expertise and relevance to potential clients, you can build a following and establish yourself as an authority in your field. This might include publishing blog posts or sharing insights on social media that offer valuable insights into the latest trends and best practices in your industry.
Placement
When it comes to finding opportunities for high-paying consulting gigs, placement is crucial. If you’re just starting out, you need to be strategic and deliberate about your networking.
Consider joining local meetups or industry events related to technology startups to build your client base. Online forums and LinkedIn groups can also help you connect with potential clients.
However, poor placement can lead to missed opportunities, stagnant growth, and even failure. As a result, you could end up wasting time and resources on clients who aren’t a good fit.
Take a look at Ryan C’s Upwork profile below. With his experience, there’s no wonder he’s in the top 1% on Upwork charging only $150. He could easily be charging 10x that rate if he positioned himself better (i.e., not relying solely on freelancing sites).
Image source: Upwork
To maximize your chances of securing top-tier rates and breaki free from relying solely on freelancer job platforms, it’s crucial to have a deep understanding of your target audience.
Take the time to identify where they spend their time both online and offline. This could include industry-specific forums, social media groups, networking events, and even local meetups.
By immersing yourself in these communities and engaging with potential clients, you can build valuable relationships and establish yourself as an expert in your niche. When clients see you as a trusted authority in your field, they’ll be more likely to hire you and pay premium rates for your services.
As a starting point, I have divided my networking recommendations into categories based on the stage you’re at in your consulting journey…
Entry level:
If you’re just starting out in consulting, it’s a good idea to join local meetups or industry events focused on technology startups.
These gatherings are awesome for newer consultants to meet potential clients, get leads, and even receive referrals. Building your client base is key at this stage, and these meetups are a great starting point.
Experienced consultant:
For experienced consultants who have been in the game for a while, you might want to consider partnering with specialized platforms like Consultport.
These platforms connect seasoned professionals with top-notch projects that require specific expertise. It’s an excellent way to find high-quality projects and expand your professional network.
All consultants:
No matter where you are in your consulting journey, it’s important to actively participate in online forums, LinkedIn groups, and other communities where tech startup founders and leaders hang out.
Engage in discussions, share your insights, and offer valuable advice on relevant topics. By showcasing your expertise and being a helpful presence in these communities, you can establish yourself as a trusted consultant and attract potential clients.
Pricing
Figuring out how to price your services is NEVER easy! So, here are a few different strategies you can try depending on your experience…
Entry-level consultants method:
As a new data freelancer, there are two main ways to price your services: competitive analysis and employment rate equivalency.
First off, you can scope out what other data analytics consultants are charging for their services on platforms like Upwork. It’s important to filter your search by location and experience level to get a better idea of what your competition is charging.
However, keep in mind that IT consultant rates on these sites vary drastically, and there are many people undervaluing their services.
Another method is using the employment rate equivalency, which involves taking twice the hourly rate of what someone would make as an employee in a similar position and using that as a baseline for your consulting rate.
In the beginning, I’d recommend charging a little bit less than the average rate and letting potential clients know that your rates reflect your level of experience. As your experience grows, your rates do too!
Want to know how you can quickly double your IT consultant rate? Watch this video…
Experienced Consultant’s Method; Pricing At >$350/hour:
There is a common misconception that experienced IT consultants charge more for their services, but that is not always the case. The truth is that many freelancers undervalue their worth, resulting in missed opportunities for higher earnings.
So, how can you avoid this and start earning what you deserve? Here are three tips to help you increase your IT consultant rates and bring in more revenue:
Sell Deliverables, Not Time: Instead of charging by the hour, try selling deliverables. This means providing clients with a specific outcome or result for a set price, rather than billing them for the time it takes to complete a project. Besides allowing you to charge more for your expertise, it also helps clients understand the value you provide.
Sell Your Offers in Packages: Another way to increase your earnings is by selling your services in packages. Combining your services increases the value proposition of your offer and makes it more attractive to clients.
Create a Standard Offer: A standard offer should include all the deliverables, bonuses, and price points you typically offer clients. It will serve as a starting point for negotiations and provide a baseline for your services.
Check the video below where I’m sharing even MORE insights on how you can start charging $300+ per hour!
BONUS P: Promotion
Promoting your IT consulting services can be challenging, but it’s essential for attracting the right clients and earning top-tier rates. You need to showcase your skills, knowledge, and expertise to potential clients, and there are plenty of channels to do it…
Social media platforms like LinkedIn or Twitter are excellent places to engage with other professionals and share valuable content that showcases your expertise. Guest blogging on reputable websites in the tech startup space can also help establish you as an expert in your field.
Webinars are another great way to build your personal brand and establish credibility as an IT consultant. Share your expertise and knowledge in a public forum to demonstrate your value to potential clients.
Remember, personal branding is essential for establishing credibility and attracting high-paying clients. If you establish yourself as a thought leader in your niche, you can command top rates for your services and stand out from the competition.
Editor’s Note: As an experienced IT consultant, you need to position yourself as a top-tier professional in order to command premium rates. To do this effectively, consider the 4 P’s marketing mix: People (Customers), Product (Service Packaging), Placement, and Pricing; while also promoting yourself through personal branding on social media and guest blogging opportunities.
Offer Discounts for Long-Term Projects
Building long-term relationships with clients and securing long-term projects are key for successful consultants. Offering discounts for long-term commitments can be a great way to do this. Not only will you maintain a steady income stream, but you’ll also encourage clients to stick around and keep doing business with you.
For example, you could offer a discount to clients who sign up for a 6-month contract. You get the time you need to make a real impact in their business, and they have the incentive to work with you long-term.
A Win-Win Situation
Offering a discount can sometimes feel like you’re giving away hard-earned cash. But believe it or not, it can actually be a win-win for both you and your client.
Let me break it down for you:
Firstly, a discount means more value for your client’s investment, and who doesn’t love that? By offering a lower rate, you’re providing them with top-notch tech consulting services at a reduced cost. This not only strengthens your client relationships but also helps them achieve their goals while keeping their budgets in check.
Now, let’s talk about the wins for you as the consultant.
Offering a discount can secure you a steady stream of work and income. Imagine locking in a client for an extended period of time with a discounted rate for ongoing support services. This creates a win-win situation where you have a guaranteed income flow without constantly chasing new projects.
But it doesn’t stop there. By providing a discounted rate, you’re building a solid foundation for long-term relationships with your clients. They appreciate the cost savings you’re offering and are more likely to stick with you for future projects.
Plus, satisfied clients are often eager to refer your services to others in need of tech consulting expertise. These referrals can be a game-changer, bringing you new opportunities and higher-paying projects down the line.
So, don’t shy away from offering a discount. It’s a strategic move that benefits your clients by giving them more value, while also setting you up for financial stability, long-term relationships, and potential referrals.
Tailoring Your Discount Strategy
Providing discounts to potential clients requires a strategic approach. You the discount to be big enough to entice long-term clients, but not too big that it comprises your profitability.
Here are some things to consider when tailoring your discount strategy:
Potential Clients: Identify potential clients who have ongoing or recurring needs in areas such as software development, cloud computing, or other IT services where your expertise lies.
Budget Constraints: Understand the budget constraints of these potential clients and tailor your discount offers accordingly so that they find value in committing to long-term engagements with you.
Type of Project: Consider offering higher discounts on larger projects or those requiring specialized skills which can justify higher hourly rates even after applying the discount.
Milestone-Based Discounts: Create milestone-based incentives wherein additional discounts are offered upon reaching certain project milestones; this ensures that the client remains engaged and committed to the project’s success.
Communicating Your Discount Offers Effectively
Let’s get real here. Offering discounts to potential clients is not about throwing numbers around and hoping they’ll bite. It’s about ensuring that they fully grasp the incredible value they’re receiving and how it can transform their business.
Listen up: it’s not just about the money they’ll save (although that’s definitely a nice bonus!). It’s about the deeper impact of long-term collaboration.
When you commit to working together for an extended period, you create an opportunity to truly understand their business, become their trusted advisor, and drive their success.
So, when you communicate your discount offers, go beyond the dollars and cents. Shine a light on the broader benefits of long-term collaboration – the seamless teamwork, the open lines of communication, and the deep understanding of their unique business needs. Paint a vivid picture of how your unwavering support will make their lives easier and their goals more attainable.
Editor’s Note: By offering discounts on long-term projects, IT consultants can benefit from increased cash flow and build lasting relationships with clients. This win-win situation requires tailoring the discount strategy based on potential client needs and budget constraints, as well as communicating it effectively in order to sweeten the deal.
Negotiate Your Rates Wisely
Negotiation is a real skill. You don’t want to scare off potential clients, but you don’t want to leave money on the table either.
Here are 5 tips on how to negotiate your IT consulting rates wisely:
1. Understand Your Client’s Budget Constraints
Knowing your client’s budget constraints is essential before you start negotiating. There’s no point wasting your time on calls or creating proposals if you’re not even in the same ballpark!
If there’s some wiggle room on budget, you can tweak your proposal to match. You can add or remove additional services/products to fit. It’s all about finding that sweet spot where both parties benefit and can achieve their goals.
2. Demonstrate Your Value Proposition
If you want to charge higher consulting fees, you need to show your clients why you’re worth the extra bucks. That means highlighting your expertise in cloud computing, software development, or whatever other relevant fields you specialize in. You can do this by…
Showcasing certifications and specialized training related to their project needs.
Providing testimonials from satisfied clients who have benefited from working with you.
Offering insights into potential cost savings or revenue generation opportunities they may not have considered without your guidance.
3. Benchmark Against Industry Averages
When it comes to setting your IT consulting rates, it’s always a good idea to do your homework and research industry averages. This means checking out what other IT consultants with similar experience and expertise are charging for their services.
I’ve given some information on IT consultant rates in this blog, but websites like Glassdoor, Payscale, and BLS can also be excellent resources for getting a sense of the current market. Taking the time to compare your rates with those of other consultants in your field helps you remain competitive while charging a fair price.
4. Create Flexible Pricing Options
To appeal to a wider range of clients and increase your chances of securing new projects, consider adopting a more flexible pricing strategy. One client might prefer to pay hourly rates, while another may prefer to pay a fixed fee per project or a monthly retainer.
Giving clients more options can make them feel more comfortable and confident in choosing your services. This approach also allows you to tailor your pricing to different budgets and requirements while still getting paid fairly for your skills and knowledge.
Try out different pricing models and see what works best for you.
5. Don’t Be Afraid To Walk Away
Image Source: Cleveroad
Your field and your skills are valuable. Even though it’s tempting to take on a project at a lower rate, you should not undervalue your skills.
If a potential client can’t meet your minimum rate, walk away. Initially, this might seem daunting, but in the long run, it will help you establish a reputation as an experienced and respected consultant.
When it comes to negotiating your IT consulting rates, it’s important to be confident in your worth and know your value. Negotiation is a two-way street, so be open to compromise while still standing firm on what you believe is fair
You can use the strategies we’ve discussed in this article, such as researching industry rates and leveraging your unique expertise and experience, to negotiate for top-tier compensation.
Editor’s Note: As an advanced IT consultant, it’s important to understand your worth and negotiate accordingly. To ensure you’re fairly compensated for your expertise, demonstrate the value of your services by highlighting past successes and industry-standard rates. Fear not to depart from discussions if a stalemate is reached – it will be beneficial in the end.
Frequently Asked Questions IT Consultant Rates
What are standard IT consulting rates?
Standard IT consultant rates vary based on factors such as experience, location, and project complexity. On average, they range from $50 to $200 per hour for entry-level consultants and can go up to $300 or more for highly experienced professionals. It’s essential to research market trends and adjust your rates accordingly.
What is the US hourly rate for an IT consultant?
In the United States, hourly rates for IT consultants typically range between $75 to over $250 per hour depending on their experience level and specialization area. For example, cybersecurity experts may charge higher fees than generalists due to increased demand in that field. Keep in mind that regional differences also affect pricing.
What is the market size for IT consultants?
The global market size of IT consultancy services has been valued at approximately $65.36 billion in 2023 with steady growth projected through 2026 driven by increasing digital transformation initiatives worldwide. The United States represents one of the largest markets within this sector due to its strong technology ecosystem.
Final Thoughts
And there you have it, everything you need to know to set competitive IT consultant rates and establish yourself as a successful IT consultant. From understanding your market and target audience to creating value-added service packages, each step plays a crucial role in your success.
Remember, as an IT consultant, your knowledge and expertise are your most valuable assets, and it’s up to you to showcase your value proposition and negotiate effectively to secure top-tier compensation. Stay informed about industry trends and continually adapt to market conditions to maximize your earning potential.
If you’re ready to take your IT consulting business to the next level, sign up for my FREE masterclass today. In the masterclass, you’ll learn how to successfully repackage your data and technology expertise and skyrocket your startup revenues. Don’t wait too long, seats for the masterclass are limited, so claim your spot now!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## The Top 13 SaaS Revenue Metrics That Every Founder Needs To Care About In 2023
URL: https://www.data-mania.com/blog/the-top-13-saas-revenue-metrics-that-every-founder-needs-to-care-about-in-2023/
Type: post
Modified: 2026-03-17
One of the best things about founding a Software-as-a-Service (SaaS) company, in addition to the 85%+ margins, is how simple the SaaS revenue metrics are to track. These tech startups are so efficient and “lean”. That’s why there are only a handful of metrics that you need to diligently track to gauge the health of your business and how it’s growing.
In this post, we’ll outline some of these key performance indicators (KPIs). We will also review why you need to start tracking these metrics if you aren’t doing so today. These metrics are crucial for securing successful funding rounds as your SaaS company grows and approaches its exit. Venture capitalists (VCs) and committed founders highly value these metrics as you await crucial funding rounds.
Let’s dive in!
What Are The Top 13 SaaS Revenue Metrics Founders Need To Care About?
Annual Recurring Revenue (ARR)
Annual Recurring Revenue (ARR) is the lifeblood metric of any serious SaaS company. SaaS companies usually offer B2B contracts billed annually, ranging from five to eight figures in value, depending on the client’s business size.
Typically, VCs like seeing at least $1m USD of ARR to invest in a startup’s Series A funding round. It’s one of the most straightforward metrics to track, but one of the most important, too. It shows how much revenue is coming in and is an anchor for describing the company’s overall size.
Monthly Recurring Revenue (MRR)
One of the more boring SaaS revenue metrics on this list, monthly recurring revenue (MRR), is calculated by taking the ARR and simply dividing by 12. Business-to-consumer (B2C) companies that charge smaller, monthly, subscriptions often used MRR. Jasper, an AI writing assistant, is an example of a B2C SaaS company that charges monthly subscriptions. They report their recurring revenue monthly, aligning with the frequency of incoming payments.
Reporting MRR has the advantage of allowing you to examine more detailed aspects of your incoming revenue. Specifically, you can concentrate on reporting monthly recurring SaaS revenue metrics such as:
Churned MRR – MRR lost from customers that have churned.
New MRR – MRR added from new sales/customers.
Retained MRR – MRR retained from current customers.
Downsell MRR – MRR lost to customer downsells.
Expansion MRR – MRR gained from upsells and cross-sells to existing customers.
Customer LTV (Customer Lifetime Value)
Any SaaS company that wants to avoid getting swallowed into the abyss of failed Silicon Valley startups must focus on customer lifetime value. To calculate LTV, you first need to calculate your average customer value by multiplying your average sale value by the number of transactions. To calculate the LTV, you simply multiply customer value by your average lifespan.
Retaining customers for extended periods is crucial because it increases your company’s average customer lifespan, a positively correlated metric with Customer LTV. Since customer acquisition is expensive, it’s essential to focus on retaining them to ensure the continued growth of customer lifetime value. To look at this from a different angle, if your churn rate is increasing, it’s highly likely that your customer lifetime value is decreasing.
(Clevertap).
Customer Acquisition Cost (CAC)
Customer Acquisition Cost (CAC), pronounced as “cack”, is one of my favorite SaaS revenue metrics. Despite its funny name, CAC holds significant importance to a SaaS company’s overall margin, and improved through skillful optimization and knowledge of sales and marketing spend.
Customer Acquisition Cost intends to help the business understand how much it costs to acquire a customer. To lower the CAC of your SaaS business, it’s crucial to discover and implement effective and innovative sales and marketing strategies.
Customer Acquisition Cost is calculated by dividing a SaaS company’s sales and marketing (S&M) spend in the preceding period (typically month or quarter) by the number of new customers acquired in the current period.
(Hubspot).
CAC Payback
CAC Payback is one of the more important efficiency SaaS metrics to track. This metric reflects the number of months it will take a customer to fully pay back its CAC. To determine the CAC Payback, divide your SaaS company’s sales & marketing spend by MRR * Gross Margin.
CAC Payback = (S&M Spend)/(MRR * Gross Margin)
You can do this calculation with ARR as well. It will determine the number of years it will take a customer to fully pay back the CAC.
Runway
“Runway” refers to the length of time your business can keep operating with the cash you have in relation to your monthly spending. It’s called runway because if you run out of cash, your business is kaput (the plane crashes off the track).
The higher your runway, the better. It’s essential to keep this number manageable, as you may need to cut back on expenses to prevent your company from facing financial trouble.
Many SaaS companies have to lay off employees. They simply let their runway get too low. Tech sales employees who are not performing are usually some of the first to be let go.
LTV : CAC Ratio
LTV and CAC are important, and combining the two metrics into a ratio gives you a glimpse into a SaaS company’s potential growth.
If a company has an LTV : CAC Ratio of 1:1, that means the company isn’t growing. If a company has a negative LTV : CAC Ratio of, say, 0.5:1, then it’s spending more money to acquire a customer than it will ever generate from that same customer. But if a company has a ratio of 5:1, it’s thriving as it’s generating 5x the revenue it would cost to acquire a customer.
A “good” LTV : CAC ratio benchmark is typically 3:1. Many VCs consider companies with an LTV : CAC ratio of 3:1 – 10:1 to be “solid” investments.
Burn Multiple
Burn Multiple is a fascinating SaaS efficiency metric that gives insight into how much revenue your company generates per dollar burned. This metric considers all expenses made in your company, including those from the development team, sales and marketing, hosting costs, and even those cookies you bought for clients. It’s a great companion to the aforementioned LTV : CAC Ratio, which only considers sales and marketing metrics.
New Sales ARR vs. Sales & Marketing Expense
This SaaS metric is straightforward, yet it provides valuable insights into the company’s growth. It’s a quick way to show how efficient a company is at customer acquisition.
Simply take how much new ARR was generated by the sales team and compare it to the sales and marketing spend in a given period.
Ideally, the new ARR is higher than the sales and marketing spend.
Churn Rate (CR)
Churn rate refers to the percentage of customers that cancel their subscriptions or stop buying from your company. Customer churn is an important metric to track. It impacts customer lifetime value. You need to get your customers to stick around to extract annual or monthly recurring revenue from them. If you won’t, all that sales and marketing spend to acquire your customers will have gone to waste.
A typical healthy churn rate for a SaaS business is 5-15%, so less than 5% is considered elite. This shows investors that you have happy customers.
(Recurly).
Gross Margin
A classic business metric, Gross Margin is an important metric to keep track of in your SaaS business. To calculate this, simply by subtract the Cost of Goods Sold (COGS) from revenue. COGS refers to the sum of all costs for making a product, including labor, costs of hosting the software, and software development. An important note to remember with COGS is that it does not include sales & marketing spend.
A “good” margin for a SaaS company is at least 75%; however, it’s common to see early-stage SaaS companies have a lower margin than this. The reason is that they invest heavily in their COGS to develop their product quickly.
Net Revenue Retention (NRR)
Net Revenue Retention (NRR) is one of this list’s more complicated SaaS metrics. Retention metrics begin by grouping customers into “cohorts”. It’s based on when the customers were onboarded to the business. The purpose of this is to see how they trend over time. You’ll want to group these cohorts weekly, monthly, quarterly, and annually. A typical NRR cohort chart is below:
(Weld).
To calculate NRR, subtract the total revenue that has churned from the amount of new revenue and expansion revenue gained. A typical “strong” benchmark for NRR is 130%. NRR less than 100% is problematic. This means the company’s growth is not compounding over time.
(ChurnZero).
Customer Concentration
This SaaS metric is the only qualitative metric on this list. It’s essential to look at all the logos you’ve onboarded and ensure your company isn’t comprised of only a few “whale” logos.
If one or two logos in your SaaS company comprise 80% of your ARR, it’s a red flag. That means, one of those customers churning will have a catastrophic impact on your business.
If your company’s largest customer is <15% of your total ARR, that’s typically an excellent place to be.
Wrap Up
As a SaaS founder, staying on top of these SaaS revenue metrics is essential to making informed business decisions that drive growth. If you haven’t started tracking these metrics, I hope you’ll begin in the latter part of 2023. Good luck and happy growing!
Hey! If you liked this post, I’d really appreciate it if you’d share the love. Click one of the share buttons below!
A Guest Post By…
This blog post was generously contributed to Data-Mania by Adam Purvis.
Adam Purvis is an Enterprise SaaS Sales Account Manager. You can find him blogging about sales outreach, business software, and SEO at adamjohnpurvis.com. You can connect with Adam on LinkedIn.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Calculate Customer Acquisition Cost: A Startup Guide
URL: https://www.data-mania.com/blog/calculate-customer-acquisition-cost-a-startup-guide/
Type: post
Modified: 2026-03-17
Wrapping your mind around CAC can feel like a steep climb, and frankly, many founders only manage to calculate customer acquisition cost after they’ve surrendered some startup equity just to learn the basics of go-to-market strategies.
But here’s the good news – at Data-Mania, we’re all about empowering startup founders. We believe in sharing, not sparing, the wealth of go-to-market expertise we’ve gathered over the years. And the best part? We’re giving it all away – no equity required!
In this blog post, we’re going to unwrap the mystery of CAC calculation, ensuring you’re well-equipped with a thorough understanding of this essential business metric.
We will explore various aspects of calculating CAC such as:
determining which expenses to include in your calculations,
whether or not to factor in overhead costs,
allocating shared costs effectively, and
accounting for one-time expenditures.
We’ll also shed light on to monitor CAC performance and advise you on how frequently you should give your figures a check-up.
Once you’ve got a grip on these concepts, you’ll unlock a treasure trove of insights into your tech startup’s performance. This knowledge is your secret weapon to make savvy decisions that not only improve your customer’s experience but also boost your bottom line. It’s all about turning data into decisions that deliver!
Looking for something specific? Scroll straight to what you’re
looking for by clicking a link below:
What is Customer Acquisition Cost (CAC)?
How to Calculate Customer Acquisition Cost
Tools To Help You Calculate Customer Acquisition Cost
What Expenses Should I Include in My Customer Acquisition Cost Calculation?
Why Do I Need to Include Overhead Costs in My CAC Calculation?
Why Do I Need to Include One-Time Costs in My CAC Calculation?
How Should I Allocate Shared Costs When Calculating CAC?
How Do I Account for Different Marketing Channels When Calculating CAC?
How Often Should I Calculate My CAC?
Reviewing The Performance of Your Customer Acquisition Cost
FAQs in Relation to Calculate Customer Acquisition Cost
Final Thoughts
What is Customer Acquisition Cost (CAC)?
Think of customer acquisition cost (CAC) as the price tag for bringing in new customers. This crucial figure is the key to understanding how much your tech startup has to shell out not only to reel in fresh customers but also to keep them hooked.
CAC serves as your compass, guiding you to assess the return on investment (ROI) from your marketing campaigns and sales drives. It acts as a spotlight, highlighting the areas where your strategies might need a little tune-up.
Case in point, let’s talk about my high-flying student, Kam Lee…
Kam is a true maverick who managed to pre-sell his SaaS, all the while pulling in an impressive six-figure income from his services.
How did he manage to keep his profit margins soaring to a whopping 67%? The answer – a solid grip on his customer acquisition costs. This golden nugget of knowledge allowed Kam to refine his spending, captivate the right audience, and send his profits stratospheric.
Bottom line – understanding your CAC is like unlocking a cheat code for smart customer acquisition.
Editor’s Note: In simple terms, CAC helps you assess the returns on your promotional campaigns and sales endeavors. It’s your business’ guiding light, highlighting the path to potentially fruitful tweaks in your marketing strategies. It’s a clear, no-nonsense snapshot of your business’s financial health.
How to Calculate Customer Acquisition Cost
Here’s a simple step-by-step guide to help you calculate customer acquisition cost:
Step 1: Tally All Direct Costs Related to Customer Acquisition
The first step to calculate customer acquisition cost is to sum up all the direct costs associated with acquiring new customers. These costs could include advertising and marketing expenses, sales commissions, lead generation costs, and any promotional costs.
Example: If you spent $1000 on Google Ads, $500 on a LinkedIn ad campaign, and paid $200 in sales commissions, your total direct costs would be $1700.
Step 2: Include Overhead Costs Related to Customer Acquisition
Overhead costs that contribute directly or indirectly to customer acquisition should also be included. This could be subscription fees for marketing tools, costs related to contractors or agency fees, or even a portion of your office rent.
Example: If you have a $50/month subscription to an email marketing tool, and you pay $100/month for a customer relationship management (CRM) system, you would add these to your total cost.
Formula: Total Direct Costs + Overhead Costs = Total Costs
Example: $1700 (from step 1) + $150 (from step 2) = $1850 Total Costs
Step 3: Allocate Shared Costs Among Different Sales Channels
If you use multiple channels for customer acquisition, you should allocate shared costs among these channels based on their contribution to sales. For instance, if an email campaign and a PPC campaign both contribute to a sale, you would split the cost of those campaigns equally for that sale’s CAC calculation.
Example: If your total advertising cost is $1500 and your email campaign contributed to 60% of the sales while the PPC campaign contributed to 40% of sales, then allocate $900 to the email campaign and $600 to the PPC campaign.
Step 4: Account for One-Time Costs
One-time costs such as setup fees or signup bonuses should also be included in your CAC calculation. While these don’t typically figure into monthly calculations, they still affect the overall ROI from customer acquisition over time.
Example: If you offer a one-time signup bonus of $100 for a new customer, include this in your CAC calculation.
Step 5: Divide the Total Costs by the Number of Customers Acquired
Finally, to calculate the CAC, divide the total costs by the number of customers acquired in the period the money was spent.
Formula: Total Costs / Number of Customers Acquired = CAC
Example: If the total costs are $2000 and you acquired 10 customers, your CAC would be $200.
Step 6: Regularly Recalculate Your CAC
Revisiting your CAC regularly is critical for evaluating the effectiveness of your marketing and sales efforts. Depending on the pace of your startup, it could be monthly, quarterly, or biannually.
Regular recalculation can help you identify trends, and opportunities for optimization, and give you a sense of whether your spending is in line with your long-term revenue and profit goals.
Still a bit overwhelmed? Check out this video from Eric Andrews ⬇️
Editor’s Note: Getting a precise calculation of your Customer Acquisition Cost (CAC) isn’t a ‘once in a blue moon’ task – it’s a quarterly routine. It’s about taking into account every penny you’ve invested in winning over your customers, from overheads to one-time setup fees.
Moreover, it’s about playing fair and attributing each expense to its rightful marketing channel. This isn’t just about bookkeeping – it’s about having a clear, unambiguous picture of the ROI from each of your marketing avenues. That’s how you turn data into power.
Tools To Help You Calculate Customer Acquisition Cost
Data is a lifesaver in the fast-paced world of startups. That’s why tools like Segmetrics can be game-changers when it comes to managing your marketing efforts and understanding your cost of customer acquisition.
I can speak from experience.
Prior to using Segmetrics (that is an affiliate link so I may receive a small comission if you purchase a plan after clicking that link), my marketing strategy felt a bit like a guessing game. I had a vague sense of where my customers were coming from, but no real clarity on the performance of different channels. It felt like I was running on a treadmill, expending energy and resources but not making the progress I wanted.
Everything shifted when I plugged my marketing channels into Segmetrics. Suddenly, I had a clear, data-driven view of my customer acquisition process. Now, even though I’m currently focused on organic marketing channels – which means I’m not calculating CAC against ad spend – Segmetrics is still an invaluable asset.
Through this tool, I am able to track each organic channel’s lead generation and sales in real-time. I’m able to cross-reference the performance of these channels against the time and money I invest in them.
The outcome?
Optimized growth, reduced overall CAC, and the confidence that my resources are being invested in the right areas.
Understanding your CAC isn’t just a good practice – it’s essential for driving growth and profitability. By calculating and optimizing your CAC, you can ensure you’re investing your resources wisely, just as I did.
For more on how I use Segmetrics to measure and quantify the ROI of my omnichannel marketing efforts, visit this blog post here or watch it below:
What Expenses Should I Include in My Customer Acquisition Cost Calculation?
When you go to calculate customer acquisition cost (CAC), remember that every cost matters – both the direct and the indirect.
Direct costs are your straightforward expenses – they’re tied directly to winning over new customers, like your marketing budget or sales commissions.
On the other hand, indirect costs are a bit more elusive but just as essential. They’re the overhead expenses quietly supporting your customer acquisition efforts, from administrative payroll to tech infrastructure investments.
Let’s take a deeper dive…
Marketing Spend:
The most obvious expense when calculating customer acquisition cost is the money spent on marketing activities designed to acquire new customers. This can span a wide spectrum of techniques, from online ads and SEO tactics to social media pushes and email campaigns.
The real trick is not just to spend, but to evaluate – scrutinize each marketing channel to see where your dollars bring in the best bang for the buck. It’s all about nudging your ROI into the sweet spot.
Sales Commissions:
Your CAC calculation should also include sales commissions as they represent costs associated with acquiring new customers. Depending on your business model, your commission expenses could include:
Commission for your sales team who successfully seal the deals.
Kickbacks from your referral programs, perhaps aimed at your loyal existing customers who spread the word and bring in fresh leads or new customers to your service or product.
Affiliate partnerships where you pay a commission to bloggers, influencers, or other businesses for promoting your products or services and driving new customers your way.
Incentives you offer to freelance sales agents who sell your product on a commission basis.
Overhead Costs:
Your CAC calculation should also include sales commissions as they represent costs associated with acquiring new customers. Depending on your business model, your commission expenses could include:
Commission for your sales team who successfully seal the deals.
Kickbacks from your referral programs, perhaps aimed at your loyal existing customers who spread the word and bring in fresh leads or new customers to your service or product.
Affiliate partnerships where you pay a commission to bloggers, influencers, or other businesses for promoting your products or services and driving new customers your way.
Incentives you offer to freelance sales agents who sell your product on a commission basis.
One-Time Costs:
When you calculate customer acquisition costs, don’t overlook those one-time costs, the ones that pop up occasionally yet make a significant dent in your budget.
Maybe it’s those travel expenses for the trade shows you attend, legal fees during your negotiation over a hefty contract, or even special promotions tied to exclusive partnerships.
Sure, these costs won’t show up on your monthly radar, but overlooking them could lead to distorted figures over the long haul.
Editor’s Note: Crunching the numbers on CAC means accounting for every penny spent directly or indirectly on winning over new customers. Your marketing budget, commissions for your salespeople, overhead costs like staff salaries, and tech investments all take center stage. Let’s not forget those one-off costs that pop up during certain periods.
Why Do I Need to Include Overhead Costs in My CAC Calculation?
As tech startup founders and leaders, the question often pops up – should you or should you not factor in overhead costs when calculating customer acquisition costs?
I personally lean towards including them, but it’s important to note there isn’t a definitive answer. Both paths come with their distinct advantages and challenges.
Advantages:
You get a comprehensive cost view. When overhead costs are included in your CAC calculation, you get a more complete picture of the true expenditure linked with bringing in a new customer.
Challenges:
Allocating overhead costs can be complex. Distributing shared expenses across different marketing campaigns can get tricky and complicated. The waters get particularly muddied when some of these costs don’t directly tie into attracting new customers or boosting revenue.
Let’s say your tech startup pays out $5K every month for office rent. This cost doesn’t tie directly to any specific campaign or channel, yet it takes a significant bite out of your overall profitability.
If your CAC calculation only includes direct advertising spend, this substantial cost won’t factor into the equation. However, if you decide to bring overhead costs into the mix, the results could be skewed depending on the budget allotted to each channel during that period.
Ultimately, gaining an in-depth understanding of your total spending to acquire customers is key. As you move forward, it’s crucial to comprehend how shared costs should be allocated to calculate your CAC accurately. (We’ll cover that later)
Editor’s Note: No definite answer exists as to whether overhead costs should be included in the CAC computation. Including these costs certainly paints a more detailed picture of the CAC, but it also introduces the complexity of accurately distributing shared expenses across multiple marketing channels.
Ultimately, businesses need to weigh up the pros and cons of including overhead costs in their CAC computations and make a decision that aligns best with their specific circumstances and requirements.
Why Do I Need to Include One-Time Costs in My CAC Calculation?
When calculating customer acquisition costs, it’s important to remember one-time costs. These are expenses that occur only once, such as setup fees or permit charges, and are not recurring.
By including these costs in your CAC calculation, you can better understand the total costs involved in gaining new customers.
However, there are situations where including these costs may not be necessary or beneficial:
If your growth strategy involves acquiring a large number of customers, each associated with similar one-time costs, their inclusion could artificially inflate your CAC, misrepresenting your regular acquisition costs.
Non-recurring, unique costs: There could be unique costs tied to a specific situation or time period that are not representative of your ongoing customer acquisition process.
Infrequent, large-scale promotions: One-time costs related to large promotions or marketing blitzes that significantly deviate from your regular spending can distort your standard CAC.
Early-stage startup costs: These costs, while important to your initial operations, might not directly contribute to the acquisition of each new customer.
Anticipated efficiency improvements: If you foresee significant reductions in certain one-time costs due to operational improvements, it may be best to exclude these from your ongoing CAC calculations.
Also, remember that some costs might seem like one-time expenses but could turn into regular costs. For example, you might make a large initial payment for a service and then have to pay smaller fees each year to keep using it. In this case, what seemed like a one-time cost has become an ongoing expense.
So, it’s important to consider all the initial costs when you calculate your CAC. This will give you a better idea of your total marketing spend. And remember, you should regularly update your CAC calculation to keep it accurate and helpful.
Editor’s Note: When calculating CAC, it’s essential to determine if one-off costs should be taken into account. These may include setup fees, installation fees, and license fees; however, in some cases, they can become recurring expenses if not managed properly.
Ultimately the decision of what to include or exclude from CAC calculation depends on your business strategy and goals.
How Should I Allocate Shared Costs When You’re Working To Calculate Customer Acquisition Cost?
For a tech startup, shared costs are essential when you are working to calculate customer acquisition cost.
Here’s how I do it…
1. Identify shared costs:
Your first step is to identify all the shared costs in your organization. In a tech startup, these could include expenses such as contractors, software subscriptions, cloud storage fees, co-working space rent, or other operational costs that are not directly linked to a particular department or campaign but are essential for your operation.
Example: Let’s say you have a $2,000 per month subscription to a suite of software tools that are used by your sales, marketing, and product teams.
2. Determine allocation basis:
Next, decide on the method you’ll use to distribute these costs. This can be based on the usage of each department or campaign, or the revenue they generate.
Example: You decide to allocate costs based on usage. After analyzing the usage logs, you determine that the sales and marketing teams use these tools 70% of the time for customer acquisition efforts.
3. Allocate costs:
Now, you can distribute these costs among your different customer acquisition efforts.
Example: Using the usage logs as the basis for allocation, you would allocate 70% of $2,000, which is $1,400, to sales and marketing.
4. Calculate CAC:
Once you’ve allocated these costs, include them in your CAC calculation. Add these allocated costs to your direct costs like ad spend.
Here’s a simple formula for calculating CAC with shared costs:
CAC = (Direct Costs + Allocated Shared Costs) / Number of Customers Acquired
Example: If you spent $600 on advertising (direct costs), and you’ve allocated $1,400 of shared costs to customer acquisition, and you acquired 50 customers:
CAC = ($600 + $1,400) / 50 = $40 per customer
Remember, allocating shared costs is more of an art than an exact science. The key is to be consistent with your method and adjust as necessary for accurate trend analysis over time.
Regular reviews of your allocation basis will help ensure that your CAC calculations remain accurate and reflective of your current business operations.
Editor’s Note: When calculating customer acquisition cost, it is essential to identify and allocate shared expenses among different channels or campaigns in order to understand the true cost of acquiring a new customer. It is essential to account for both direct costs linked with certain marketing activities, as well as overhead outlays which should be assigned on a customer-by-customer basis.
How Do I Account for Different Marketing Channels When You Calculate Customer Acquisition Cost?
Different channels have different costs, and this can affect your CAC calculation. Each channel must be accounted for individually in order to calculate your CAC accurately.
Here’s how to do it:
1. Identify your channels:
Start by pinpointing all the marketing channels you’re using. Let’s say, for instance, you use Facebook advertising, email marketing, and Google Ads as your primary channels.
2. Track spend for each channel:
After identifying your channels, calculate the total cost associated with each one.
For example:
Facebook advertising: $3000
Email marketing: $2000 (including costs of email automation software, content creation, and time spent on campaign management)
Google Ads: $3500
3. Measure results:
The third step is to determine how many new customers each channel brings in.
For instance, let’s assume you have acquired:
Facebook advertising: 45 new customers
Email marketing: 25 new customers
Google Ads: 35 new customers
4. Calculate CAC for each channel:
Now, divide the total cost for each channel by the number of new customers it has helped you acquire.
That would look like this:
CAC (Facebook advertising) = Total cost / Number of customers = $3000 / 45 = $66.67 per customer
CAC (Email marketing) = Total cost / Number of customers = $2000 / 25 = $80 per customer
CAC (Google Ads) = Total cost / Number of customers = $3500 / 35 = $100 per customer
Editor’s Note: Calculating customer acquisition cost (CAC) involves analyzing the different marketing channels used, assigning dollar amounts to each conversion based on their costs and ROI metrics, and weighing long-term versus short-term gains in order to make sound investments within budget constraints.
How Often Should I Calculate My CAC?
There isn’t a one-size-fits-all answer because the frequency depends on your business size, sales process complexity, and growth rate.
Startups: If you’re just starting out and still working on establishing a steady customer base, it’s crucial to calculate your CAC as frequently as possible. This constant measurement provides a clear picture of your marketing efforts’ ROI and helps identify optimization opportunities to reduce costs and increase profits.
Rapid growth phase: For businesses experiencing rapid growth, it’s advisable to calculate CAC at least quarterly. Regular tracking ensures your marketing efforts remain profitable over time. Plus, it allows you to spot and address potential issues early, preventing costly mistakes in the long run.
Mature businesses: Once your company has matured, continue tracking your CAC monthly or quarterly, depending on the complexity of your operations. This practice helps you react quickly to unexpected occurrences like increased competition or sudden market changes.
Major changes: Whenever your company undergoes significant changes, such as launching a new product or shifting target markets, you should recalculate your CAC promptly. This immediate adjustment prevents these changes from negatively affecting your profitability down the line.
Overall, CAC is not a static figure; it changes with time. Regular monitoring and adjustment of your CAC can aid in understanding the impact of various marketing channels, enabling better decision-making and maximizing ROI.
Editor’s Note: Tracking and evaluating CAC regularly is necessary to guarantee a good return on investment. Doing so allows you to identify any areas for improvement quickly, before they become too costly or difficult to reverse – due diligence pays off here.
For best results, it is recommended that CAC be recalculated whenever major changes occur within the organization.
Reviewing The Performance of Your Customer Acquisition Cost
Understanding and monitoring your customer acquisition cost is a crucial step, but it doesn’t end there. Periodically reviewing the performance of your CAC can give you invaluable insights into your marketing efforts and their effectiveness.
According to Price Inteligently, even a 1% improvement in customer acquisition can result in a 3.32% increase in bottom-line revenue.
The above example illustrates how refined customer acquisition strategies (no matter how small) can make a significant difference to your profitability. A regular review and optimization of your CAC should therefore be an essential part of your business growth plan.
Step 1: Benchmarking Your CAC
Start by establishing a benchmark or a baseline for your CAC. This will vary depending on your industry, business model, and customer lifetime value (LTV). Your goal should be to maintain a CAC that is significantly lower than your LTV.
Suppose you’re a SaaS startup, and industry standards suggest that your customer lifetime value (LTV) to CAC ratio should be about 3:1. This means that for every dollar spent on customer acquisition, you should be getting three dollars back over the customer’s lifetime.
Step 2: Monitor CAC Over Time
Once your benchmark is set, it’s crucial to monitor how your CAC evolves over time. If your CAC begins to increase significantly, it could indicate that your marketing efforts are becoming less efficient.
Conversely, a declining CAC might signify improving efficiency, or it could potentially mean that you’re not investing enough in customer acquisition to support growth.
Step 3: Assess Channel Performance
Different marketing channels may have different CACs. For instance, customers from a paid search campaign might have a higher CAC than those acquired through organic social media. By breaking down your CAC by channel, you can assess which methods are the most cost-effective for acquiring new customers.
Step 4: Analyze the Impact of Changes
Whenever there are significant changes in your marketing strategy, product offerings, or target market, closely monitor how these changes impact your CAC. This can help you quickly identify any shifts that are causing your CAC to increase and adjust your strategies accordingly.
Let’s say you decide to launch a major upgrade to your service. You calculate your CAC before and after the product upgrade. Increasing CAC after the launch may indicate that the messaging around the product upgrade isn’t effectively conveying its value, leading to higher acquisition costs.
Step 5: Regular Reporting
Regular reporting on your CAC and its performance should be a key part of your overall business strategy. This practice not only keeps you and your team informed about the cost-effectiveness of your marketing efforts but also helps in decision-making and strategy planning.
FAQs in Relation to Calculate Customer Acquisition Cost
What is a good CAC?
There’s no one-size-fits-all answer here. However, a good rule of thumb for tech startups is to aim for your CAC to be about 1-3x less than your customer’s lifetime value (LTV). But remember, this ratio can vary depending on your industry and current market conditions.
So, it’s crucial to evaluate your business scenario independently to set a CAC goal that aligns with your unique startup environment. After all, mastering how to calculate customer acquisition cost effectively is all about understanding your specific situation.
How do I know if my CAC is too high?
To determine if your CAC is too high, you need to compare it to the lifetime value of your customers (LTV). If your CAC exceeds your LTV, then your CAC is likely too high. It means you’re spending more to acquire customers than you’re making from them over their lifetime. This is a sign that you need to either find ways to reduce your CAC or increase your LTV.
How can I reduce my CAC?
A startup can reduce its CAC by improving its marketing efficiency, enhancing its sales process, optimizing its conversion rates, and nurturing existing customer relationships to encourage referrals.
Why does my CAC fluctuate over time?
CAC can fluctuate over time due to several factors. These can include changes in advertising costs, variations in your conversion rates, shifts in your marketing strategies, changes in the competitive landscape, and seasonal trends. It’s important to track these fluctuations to understand their causes and make necessary adjustments to your acquisition strategies.
Can high CAC be justified for certain startups?
Yes, a high CAC can sometimes be justified, especially for startups where customers have a high lifetime value (LTV). For example, in industries like SaaS where customers pay a recurring fee and tend to stay for long periods, a high initial CAC can be offset by the revenue generated over the customer’s lifetime. However, startups must be careful to ensure that the LTV significantly exceeds the CAC for sustainability.
How do upsells and cross-sells affect the CAC
When you upsell or cross-sell, you are effectively increasing the value of each customer without incurring additional customer acquisition costs. This means that you can spread your CAC over a higher revenue, which in turn decreases the CAC when expressed as a ratio of cost to revenue per customer.
Let’s say you spent $1000 on marketing to acquire 10 customers, making your CAC $100. But, if you successfully upsell or cross-sell to those customers, increasing the total revenue they bring in from $1000 to $2000, your CAC is now effectively $50 when expressed as a ratio of cost to revenue per customer.
How do free trials or freemium models affect my CAC?
Free trials or freemium models can increase your CAC as you may spend resources acquiring users who never convert to paying customers. However, they can also potentially lower your CAC if they lead to high conversion rates.
Free trials and freemium models can effectively lower the barrier to entry, enabling you to draw in more users. If a significant number of these users convert to paid customers, your CAC may decrease as the costs of acquisition are spread over a larger number of customers.
What is the difference between CAC and CPA (Cost Per Acquisition)?
While CAC refers to the cost of acquiring a new customer, CPA refers to the cost of acquiring a new lead, conversion, user action, or sale, depending on the company’s definition. CPA is often used in online advertising and refers to a narrower, more immediate outcome compared to CAC.
Final Thoughts
Calculating customer acquisition cost is a vital exercise to gauge the efficacy of your marketing and sales strategies. By tracking, analyzing, and calculating, you’ll gain insights that allow you to allocate resources wisely for the highest ROI.
Remember, CAC calculation isn’t just about tallying up expenses; it’s about examining every cost linked to the journey of winning a new customer and discerning the effectiveness of different marketing channels. This level of detail helps you spot potential areas of improvement and drive more value from your investment.
Regularly revisiting your CAC not only keeps you informed about current trends but also empowers you to make future investment decisions that propel growth and optimize profits.
Ready to skyrocket your tech startup growth?
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## How to Build a Tech Company: Essential Steps for Budding Entrepreneurs
URL: https://www.data-mania.com/blog/how-to-build-a-tech-company-essential-steps-for-budding-entrepreneurs/
Type: post
Modified: 2026-03-17
Are you intrigued to learn how to build a tech company from scratch? Do you have an innovative concept and want to test its feasibility before taking the entrepreneurial leap? You’ve landed on the right page! This guide will give you essential steps to cement the foundation for your tech company’s triumph even before you assemble your team.
By finishing this article, you’ll uncover the exact tactics that helped secure $500k in seed funding within the first month of the company’s establishment. This case study about how to build a tech company that was originally a vague idea jotted down on a napkin in late 2022 came to life in early 2023. This success story is a testament to our strategy’s power in today’s tech entrepreneurship climate.
So, get ready, budding visionaries! We’re about to explore the indispensable steps to build a thriving tech company. Buckle up! It’s time to launch your dream into the tech entrepreneurship stratosphere!
Conceptualization and Market Research
You are overflowing with innovative concepts and wondering which could become your tech industry jackpot. Welcome to the conceptualization and market research stage, where separating the viable from the unfeasible is crucial. Here, you’ll learn to sort through the flood of ideas and pinpoint the ones with real potential, but you’ll also comprehend the importance of constant tech learning in this process.
The learning curve is ever-present in this digital age, particularly in the tech sector. Knowledge is the driving force that maintains your edge in innovation. Hence, learning resources, priceless for IT professionals, are indispensable. They keep you updated, refine your existing abilities, and assist you in gaining new ones. This unceasing learning process could be the secret to identifying your potentially successful concept amid the noise of numerous ideas.
So, let’s embark on this journey of creativity, where you’ll excel in differentiating valuable ideas and recognize why continuous IT learning can choreograph your tech company’s success.
A. The process of ideation
Efficient ideation combines creativity with practicality. It requires brainstorming, of course, but it also necessitates structured thinking.
Begin by identifying a problem that needs a solution or an enhancement that could be game-changing. Search for gaps in the current market that your product or service could fill. Ensure that your idea aligns with your passion and expertise – the entrepreneurship journey is tough, and your enthusiasm will fuel your drive.
B. Conducting market research
Once you’ve identified your bright idea, it’s time to validate it with thorough market research. This step is crucial – it provides insights into your target audience, their needs, preferences, and your competition.
Understanding your target audience is essential to tailoring your product to their needs. Demographics, psychographics, and buying behaviors – these are vital data points that will help shape your product.
Your goal is not just to enter the market but to make a mark. So, don’t just follow the trends; aim to set new ones. With your viable idea and market knowledge, you’re now set to build your tech startup empire.
How to Build a Tech Company By First Establishing a Solid Business Model
You have your groundbreaking idea and have validated it with rigorous market research. The next key step? Constructing a robust business model to steer your tech startup toward success.
The essential components of a business model for a tech startup include:
Value Proposition: This defines what unique value your product or service brings to the market and differentiates it from competitors.
Customer Segments: Identifying your target audience is crucial. Knowing who they are, their needs and preferences, and how you’ll reach them can make or break your startup.
Channels: These are the mediums through which you deliver your product or service to your customers. It could be an online platform, physical stores, or a combination.
Revenue Streams: This highlights how your startup will generate income. It could be through sales, subscription fees, licensing, or other means relevant to your product or service.
Cost Structure: It’s essential to understand and plan for the costs associated with your startup. This could include development costs, marketing expenses, operational costs, etc.
Key Partnerships: Identifying strategic partnerships can help you access resources, reduce costs, or improve your offering.
Key Activities: These are the critical operations that need to happen for your value proposition to be created and delivered.
Remember, your business model aims to illustrate how your startup will function and be profitable. It’s a blueprint that will guide your entrepreneurial journey.
Funding Your Tech Startup
It’s time to dive into the often challenging but utterly crucial aspect of startup creation: securing funding. The financial backing you secure will serve as the lifeblood of your early operations, allowing your startup to develop its product, scale operations, and make an impact.
Overview of Common Funding Sources
Bootstrapping: This is when you fund your startup using your own savings or revenue from the business. While it may limit your initial resources, bootstrapping allows you to retain full control over your startup.
Angel Investors: These are high-net-worth individuals who typically provide funding in exchange for startup equity. They bring capital and often offer valuable mentorship and networking opportunities.
Venture Capital: Venture capitalists (VCs) invest in startups and small companies with solid growth potential. VC funding can provide substantial capital, but it often involves giving up a percentage of ownership and control.
Pitching to Investors
Understanding Investors: Different investors have different expectations. Research your prospective investors to understand their focus areas, investment criteria, and what they look for in a startup.
Crafting Your Pitch: Your pitch should communicate your value proposition, business model, market opportunity, and the strengths of your team. Ensure also to include a clear plan for how the investment will drive growth.
Financial Projections: Investors want to see a return on their investment. Prepare realistic financial projections that show potential profitability and a strategy for achieving it.
Prepare for Due Diligence: If investors are interested, they’ll thoroughly analyze your startup. Be prepared to provide detailed information about your business, including financial records, business plans, and market research.
With secured funding, you can now focus on developing your product. We’ll discuss this next, so don’t stop now – the journey to pioneering a successful tech startup continues!
Developing Your Product and Scaling Your Startup
At the heart of your journey on how to build a tech company, you’ve assembled your team and secured the necessary funding. The platform is primed to breathe life into your product and set strategies for your startup’s expansion. This phase is an exhilarating mix of creativity and foresight. It’s where the magic of conceiving a unique solution meets the strategic planning essential for growth, signaling your readiness to leave a lasting imprint on the tech world.
Importance of Minimum Viable Product (MVP)
In the landscape of tech startups, a Minimum Viable Product (MVP) is your first significant milestone. An MVP is the simplest version of your product, equipped with just enough features to attract early users and gather invaluable feedback for future product development. The MVP strategy allows you to validate your product idea, minimize expenditure, and learn about your users’ preferences before investing more heavily.
Building a Strong Brand and Marketing Strategy
Once your MVP is ready, it’s time to think about how you’re going to put it in front of your target audience. Building a strong brand and marketing strategy are crucial to ensure your startup’s visibility and customer engagement.
Branding: Your brand should effectively communicate what your startup stands for, its values, and what sets it apart from competitors. It is not just your logo or tagline; it’s the experience that people have with your startup at every touchpoint.
Marketing Strategy: A well-crafted marketing strategy is vital to getting your product in front of your target customers. This strategy could include SEO, content marketing, social media marketing, paid ads, PR, and more. The best strategy will depend on your business model, target audience, and the resources available.
Understanding When and How to Scale Your Tech Startup
With a viable product and a solid marketing strategy in place, it’s time to think about scaling your startup. Scaling is about capably growing your startup while maintaining or improving operational efficiency.
When to Scale: Understanding the right time to scale is critical. This decision often depends on factors like consistent and growing demand, solid unit economics, or the readiness of the operations and team to handle growth.
How to Scale: Scaling can be achieved by expanding your product line, entering new markets, or building strategic partnerships. Regardless of the method, ensure you have the operational robustness and financial resources to support this growth.
Conclusion
Understanding how to build a tech company is intricate, exhilarating, and challenging. Making your tech startup is more than creating a profitable business. It’s about nurturing your innovative ideas, translating them into reality, impacting the industry, and, perhaps, changing the world.
This path encapsulates the essence of how to build a tech company effectively. So, hold onto your entrepreneurial spirit, embrace the journey, and remember – the road to success is always under construction. Now, equipped with the knowledge of how to build a tech company, go forth and pioneer your successful tech startup!
Hey! If you liked this post, I’d really appreciate it if you’d share the love. Click one of the share buttons below!
A Guest Post By…
This blog post was generously contributed to Data-Mania by Rajeev Bera.
Rajeev Bera is a tech entrepreneur with a decade of experience in the software industry. As the founder of aCompiler, he uses his expertise to empower IT professionals and inspire innovation.
If you’d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Marketing with a Tech Twist: How an AI Marketing Strategy Can Boost Your Brand
URL: https://www.data-mania.com/blog/marketing-with-a-tech-twist-how-an-ai-marketing-strategy-can-boost-your-brand/
Type: post
Modified: 2026-03-17
Are you curious on how an AI marketing strategy can boost your brand? Read this article as it discusses how to create and implement your marketing strategy with ease!
In the early stage of a startup, marketing is just one of the many essentials that needs attention. But founders don’t always have the time or budget to allocate to this endeavor. Moreover, getting the word out isn’t always easy.
Does this sound familiar? Are you looking for ways to streamline your marketing without blowing your budget or spending valuable hours trying to reach your target audience?
AI may be the answer!
With the advancement of AI, there are new ways to improve your marketing campaigns right from the start.
In this article, we’re going to tell early-stage startup founders how to create an AI marketing strategy that will automate, simplify, and streamline their campaigns. Let’s begin!
What is AI Automation in Marketing?
Using AI automation in marketing may seem like something out of a science fiction film, but it’s actually common and used daily. We see it on Facebook Messenger, website chats and other chat apps where companies have deployed a chatbot. We also use Siri, Alexa, and Hey Google all the time.
It’s really just about finding ways to use artificial intelligence and machine learning within your marketing strategy. The best part is that it’s become very affordable, so even startups with limited budgets can make use of AI automation.
How to Create a Marketing Strategy with AI
Something that’s important to note about AI automation is that you need to deploy it wisely. If you’re using a chatbot, for example, it needs to be trained well on appropriate keywords. Remember, your chatbot is essentially a customer service representative.
Of course, chatbots aren’t the only way to make use of AI automation in marketing. Let’s take a look at the best practices for creating a marketing strategy with AI:
1. Understand the Different Marketing Avenues
There are so many marketing avenues where AI and machine learning can be deployed. Chatbots are the obvious ones. Then, there’s the rise of tools like ChatGPT and using AI to create content for your startup’s blog or website. You also get tools that help you to work out the best SEO keywords and how to incorporate those into your website.
AI has many uses – it can monitor social media, analyze the sentiment around your brand, and make recommendations about sales and marketing content based on this information. It can also monitor your website and media site traffic and then use that data to predict customer movement.
One of the biggest ways to deploy AI in your marketing—and possibly the most crucial for a startup where you don’t have a big team to do sales and marketing—is in the automation of manual tasks.
You can set up email templates and schedule those to go out at specific intervals or based on actions a customer takes on your website. You can automate media and keyword buying for PPC ads. All of these tasks are essential but take a lot of time. Once they’re automated properly, you can free up time to work on other aspects of your new business.
2. Choose the Right Tools for a Marketing Campaign
Once you’ve familiarized yourself with AI and machine learning options, it’s time to sort out what your business needs. You don’t want to and probably don’t have the capital to go out and buy every single option there is. It’s also important to remember that getting your AI tools set up properly takes time and effort.
It’s a much better idea to take a moment to analyze where your business really needs help first. Look for what’s going to make the biggest impact on your startup and go for that tool first. When you have it set up and working, you can look at what other tools will benefit your business.
However, you need to remember that you probably want these tools to be able to talk to each other and integrate into the systems that you already have in place. When looking at the available technology, take the time to look at integration features and see how each element can work together with other tools. This will further streamline your AI marketing strategy and increase your output without increasing your human effort or hours.
3. Refine and Perfect the Use of AI in Marketing
To get the most out of your AI tools, you need to know how to use them fully. This means really exploring all of the options available and training yourself and any team members that will use them. It will take a bit of time and effort, but the results will be worth it.
If the tools are complicated, schedule proper training sessions with the supplier or with the user guide material they supply. When you have time set aside to concentrate on the tool, you’ll be able to actively engage with the features. If you’re trying to learn and deploy on the fly, then you’ll always be on the back foot.
An added bonus of consistently going back to your AI tools and learning about the features in scheduled sessions is that you can closely track your progress. You can monitor how the tool is improving your marketing efforts and discover new ways to refine how you use it within your startup.
As you go through this process of learning and refining, remember to include your employees and their feedback. They may be working on a different aspect of the tool or see a different side of it if they need to use it more often than you do. Their input and buy-in to the technology will help your business to get the most out of it.
Implement Your AI Marketing Strategy with Ease
AI has really revolutionized the way we run our businesses. It’s not replacing humans—yet—but it is helping businesses to refine the way they operate and put their employees to better use. It’s great to take away the time-consuming manual tasks like tracking employee hours for campaigns or sending out follow-up marketing emails.
With a solid AI marketing strategy in place, you can let your employees focus on the tasks that add major value to your business. As a founder, this is a step in the right direction and greatly improves your chances for long-term success.
Hey! If you liked this post, I’d really appreciate it if you’d share the love. Click one of the share buttons below!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## How to Boost Startup Value with UI/UX Design
URL: https://www.data-mania.com/blog/how-to-boost-startup-value-with-ui-ux-design/
Type: post
Modified: 2026-03-17
Have you ever thought about the idea on how to boost startup value with UI/UX design? If YES, then you’re reading the right article! Read on to discover the advantages and importance of UI/UX design in increasing your startup’s value.
User Interface (UI) and User Experience (UX) design are no longer considered just fancy buzzwords in the tech world. They have become critical factors that can shape the success of your startup.
A well-thought-out UI/UX can enhance your product’s usability and significantly increase your startup’s value. Let’s delve into how.
The Power of First Impressions: UI Design
Unfortunately, the saying, “Don’t judge a book by its cover,” doesn’t hold true in the digital realm. Users often judge your product by its interface. Thus, an intuitive and aesthetically pleasing UI can leave a lasting impression. Here’s how a good UI design can ramp up your startup’s value:
Aesthetics and branding: An attractive, consistent design helps build a strong brand identity, making your product memorable.
Ease of use: A well-designed interface makes it easy for users to interact with your product, enhancing their overall experience.
Reduced development costs: Effective UI design can minimize the need for extensive coding and reduce development costs in the long run.
Nielsen Norman Group, a leader in the user experience field, highlights the importance of aesthetics in user interface design, emphasizing that it significantly affects user satisfaction and loyalty.
Source: Unsplash
Bridging the Gap: The Role of UX Design
UX design, on the other hand, focuses on creating a product that provides meaningful and relevant experiences to users. It is vital in determining how a user feels about your product. Here are some benefits of investing in UX design:
Increased user satisfaction: Good UX design makes your product enjoyable to use, leading to increased user satisfaction and loyalty.
Reduced customer acquisition costs: A well-designed UX can lead to word-of-mouth referrals, reducing the need for aggressive marketing.
Increased conversion rates: UX design helps streamline the user journey, leading to higher conversion rates and more business.
Forbes considers UX design a crucial factor for business success, underscoring that it can directly affect your bottom line.
From Theory to Practice: Implementing Good UI/UX Design
Understanding the importance of UI/UX design is one thing, but implementing it effectively is another. Here are a few tips to help boost your startup’s value through UI/UX design:
Know your users: The key to good UI/UX design is understanding your users. Conduct surveys, interviews, and usability testing to get insight into what your users want and need.
Keep it simple: Simplicity is the ultimate sophistication. Your design should be intuitive and easy to navigate, removing any potential barriers for users.
Consistency is key: Keep your design consistent across all platforms. This creates a seamless user experience, making your product more reliable and user-friendly.
Iterate and improve: UI/UX design is not a one-and-done process. Continually test and refine your design based on user feedback and behavior.
The Human Element in UI/UX Design
What sets UI/UX design apart from other aspects of product development is its focus on the human element. While technical efficiency is vital, understanding the human psychology behind user interactions is what makes a design truly shine.
This involves exploring how users think, what motivates them, what their pain points are, and how they perceive value. It’s about understanding cognitive psychology principles, like how users respond to colors, shapes, and the layout of elements.
In essence, good UI/UX design is about empathy. It’s about stepping into your user’s shoes and seeing your product from their perspective. This human-centric approach can significantly enhance user satisfaction and product usability, ultimately boosting your startup’s value.
It Pays to Hire the Best of the Best
Hopefully, the significance of UI and UX design isn’t lost on anyone reading this article. If you understand the importance of high-quality UI/UX design, you also know it is worth hiring the most competent professionals to take care of this side of the business.
Home to many innovative startups and tech companies, Florida is a hotbed for top-notch UI/UX design. It just so happens that there are platforms dedicated to showcasing the best companies dealing in UI and UX in Florida and other states, providing a comprehensive look into the exciting work being done in this field.
Websites like the one linked above offer a window into the creative world of UX design, giving budding startups a chance to find inspiration and potential collaborators for their projects.
When one browses the best UX/UI businesses, it quickly becomes evident how much value these companies place on intuitive and innovative design.
The results speak for themselves, with these companies regularly achieving high user satisfaction rates and strong brand loyalty. This focus on UX has made some Florida businesses shining examples of how strategic design can elevate a company’s success and impact.
Wrapping Up: The Tangible Value of UI/UX Design
Investing in UI/UX design is about more than just creating a pretty interface or a feel-good user experience. It’s about boosting the tangible value of your startup.
A great UI/UX design enhances user satisfaction, reduces development and customer acquisition costs, and increases conversion rates. It’s a strategic investment that can set your startup apart from the competition and drive its success.
Many renowned companies, like Apple, have understood the importance of UI/UX and have made it an integral part of their business strategy. Their success is a testament to the potential of excellent UI/UX design.
Remember, a startup’s success often hinges on delivering a product that not only meets users’ needs but also provides a seamless and enjoyable user experience. And that’s where UI/UX design comes into play.
Hey! If you liked this post, I’d really appreciate it if you’d share the love. Click one of the share buttons below!
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## MRR vs ARR: Which Metric to Use for Your Tech Startup?
URL: https://www.data-mania.com/blog/mrr-vs-arr-which-metric-to-use/
Type: post
Modified: 2026-03-17
As a tech founder, you’ve no doubt heard the battle cries of “MRR vs ARR!” echoing through the startup landscape. It’s no secret that these metrics are critical, but do you really have a solid grip on how to use them effectively?
If not, don’t worry – I’ve got you covered.
The information you need to know about startup metrics (like these) shouldn’t require you to give up equity. This information should be made freely available to all founders – and that’s why I’m writing this blog.
In this post, we’re going to de-mystify these crucial metrics, shine a light on their significance, and I’ll also guide you through the step-by-step process of calculating them.
By the end of this article, you’ll have a comprehensive understanding of ARR vs MRR. You’ll come away feeling empowered and relieved, knowing you’ve dodged the common errors made by those who misjudged these metrics.
So, are you ready to elevate your understanding of ARR vs MRR calculations without the costly price tag of your startup’s equity? Let’s unravel this essential topic together because, in the world of tech startups, knowledge is your greatest asset!
Follow me on:
Table of Contents:
MRR vs ARR: What’s The Difference?
MRR and ARR: Revenue, Not Profit
When To Use MRR
When To Avoid Using MRR
When to Use ARR
When to Avoid Using ARR
When to Avoid Using ARR
MRR vs ARR: How to Calculate Them
How to Calculate MRR
How to Calculate ARR
Common Pitfalls When Calculating MRR and ARR
FAQs in Relation to MRR vs ARR
What is the main difference between ARR vs MRR?
MRR vs ARR: which one should I use?
Why are ARR and MRR so important to the cloud business?
How does customer churn affect MRR?
What is the difference between contracted annual recurring revenue and annual recurring revenue?
Can Annual Recurring Revenue be higher than revenue?
What other metrics should I consider in addition to MRR and ARR?
Final Thoughts
If this topic is right up your alley, then be sure to sign up for the Data-Mania Substack newsletter.
That’s where we share cutting-edge insights, trends, and impartial perspectives that help data & technology service providers, SaaS founders, and consultants to harness the potential of applied AI, build strategic data-intensive solutions, and catalyze rapid business growth.
↓↓↓
MRR vs ARR: What’s The Difference?
MRR and ARR, while similar, serve very unique purposes in a startup’s financial toolkit.
Monthly recurring revenue (MRR) is your financial pulse check, providing a snapshot of the recurring revenue generated from subscriptions each month. It’s a dynamic and responsive metric, able to reflect shifts in your customer base and revenue patterns on a monthly basis.
On the other hand, annual recurring revenue (ARR) broadens the perspective to a yearly view. It offers an annualized estimate of recurring revenue, providing a stable and long-term picture of your financial health. It’s ideal for assessing year-on-year growth and making strategic decisions that require a more long-term view.
In essence, MRR provides granularity to navigate short-term, monthly trends, while ARR offers a comprehensive annual perspective for long-term planning. Understanding when to use each is key.
Editor’s Note: MRR and ARR are two of the most essential metrics for tech startups to monitor, with MRR tracking short-term performance while ARR gives a longer-term view on growth. To accurately measure success over time, it is necessary to assess other indicators such as AOV and LTV in addition to using these two recurring revenue figures.
MRR and ARR: Revenue, Not Profit
I often get asked… “Are MRR and ARR about revenue or profit?”
So, before we get into the nitty-gritty, let’s set the record straight – MRR and ARR are all about revenue, not profit.
Here’s why this distinction matters:
Revenue-Focused: When we talk about MRR or ARR, we’re focusing on the recurring revenue you’re earning from your customers. It’s like the fuel that keeps your business engine running month after month, or year after year.
Doesn’t include costs: MRR and ARR don’t take into account your operational costs. So while they give you a solid snapshot of your revenue performance, they don’t tell you anything about your profitability.
So while MRR and ARR are crucial for understanding the health and growth of your business, it’s equally essential to keep a close eye on your costs and profitability. After all, the real fun begins when your recurring revenue comfortably covers your costs, and you’re making a tidy profit!
When to Use MRR
MRR is more than just a number – it’s a critical ally, charting your growth, helping you peek into the future of your revenue, and decode the puzzle of customer churn rates.
Understanding MRR is a straightforward process with substantial benefits. You start by taking the total recurring income generated in a given month and dividing it by the number of customers. What you’re left with is an average monthly revenue per customer.
This figure is a reliable benchmark, facilitating comparisons of performance over time or against your competitors. It’s like a financial health check-up for your business, offering insights that can steer you towards more informed decisions.
Before you jump on the MRR bandwagon, it’s important to take a beat. Ask yourself this: is your product or service dependent on a subscription-based pricing model, or is it more of a one-time purchase deal?
If your answer leans towards the former, MRR is going to be your new best friend, reliably predicting future monthly revenues from those recurring customer payments.
But hey, let’s not put MRR in a box. It’s not just for subscription-based businesses. If there’s a gap between customer acquisition and their first purchase, MRR can still play the role of the super sleuth, uncovering valuable insights.
By keeping tabs on MRR trends over time, you’ll gather an intel cache on the impact of your marketing campaigns, the responsiveness of customers to specific products or services, and the need for any strategic pivots.
While MRR is a powerful tool for tracking your business’s performance trajectory, it’s not a standalone success measure.
Editor’s Note: MRR is a key metric for measuring the success of tech startups, as it provides insight into customer churn rates and future revenue. Tracking changes in MRR over time can help keep businesses ahead of the game by fine-tuning their strategies to meet market demands.
When to Use MRR
While MRR can be a valuable tool for tracking the progress of a tech startup, it’s not always the most suitable metric for gauging success.
Here are a couple of examples…
When evaluating customer lifetime value (LTV), MRR falls short. It solely focuses on one-time purchases, excluding any additional products or services customers may acquire over time. This oversight means MRR doesn’t provide a comprehensive measure of LTV. For a fuller picture, metrics like CAC (customer acquisition cost), AOV (average order value), and ROI (return on investment) should be in the mix.
And when it comes to analyzing the impact of marketing campaigns, MRR doesn’t hit the mark either. Its focus on recurring revenue means it doesn’t consider any new leads or conversions borne from a campaign.
To accurately assess the effectiveness of your marketing, metrics like cost per lead and conversion rate deserve a starring role, sidelining MRR.
For startups offering different pricing tiers and service levels, annual recurring revenue (ARR) could be a better fit than MRR.
ARR includes all contracts, irrespective of their duration, giving you a comprehensive overview of your revenue, while still offering insights into monthly earnings from existing contracts.
So while MRR can serve as a useful yardstick to measure your tech startup’s performance and growth, it’s not universally applicable. Knowing when to swap MRR for more relevant metrics ensures a data-driven approach, boosting your startup’s growth prospects.
Editor’s Note: Though MRR is often a useful metric for tracking tech startups’ progress, other metrics such as CAC, AOV, and ROI may be more suitable in certain circumstances, while ARR could provide greater insight when there are multiple pricing tiers. For evaluating customer lifetime value and marketing campaigns, metrics like CAC, AOV, and ROI should be used instead. In addition, if your startup offers multiple pricing tiers with varying levels of services then ARR might be more beneficial than MRR as it takes into account all contracts regardless of duration.
When to Use ARR
When you’re plotting your tech startup’s course, ARR should be a part of your navigational tools.
Offering a snapshot of your company’s financial well-being and growth trajectory, ARR is invaluable to investors determining the worthiness of their investment and to founders seeking to optimize operations.
^^ This won’t be you after reading this blog!
Take, for example, entrepreneur Kam Lee.
Kam employed the principles of ARR to impressive effect in his business. Through the savvy application of techniques learned inside my Data Creatives & Co. Course, Kam closed 15 B2B contracts valued at $310,000. In addition, he pre-sold $60,000 worth of annual contracts for his forthcoming marketing optimization SaaS company.
For Kam, ARR was an essential tool in the kit that guided his decisions and ultimately drove substantial profit.
Primarily, ARR is your go-to when examining long-term revenue trends. It encompasses all recurring annual payments made by customers, like subscription fees or membership dues.
For tracking customer commitment and retention, nothing beats ARR. These metrics hinge on the number of customers renewing their annual contracts. Plus, ARR can help pinpoint areas ripe for enhancement or growth within your business model.
Projecting future cash flow or planning your budget? Let ARR take the wheel. This metric enables startups to foresee more accurately than methods considering only current income.
Furthermore, ARR can lend a hand in comparing your performance against industry competitors – especially those with similar pricing models. This comparison can provide valuable insights to help fine-tune your pricing strategies.
Using ARR requires a long-term vision, necessitating a balance between immediate benefits and future gains. Opting out of ARR demands a more meticulous analysis, considering various facets that should be thoughtfully evaluated.
Editor’s Note: ARR is the key to understanding a tech startup’s financial health and future growth potential, providing an accurate measure of customer loyalty and retention. ARR can be employed to allocate resources and contrast performance with industry peers, thus offering insight into pricing tactics in the future.
When to Avoid Using ARR
Although a favorite metric among tech startups for gauging success, ARR isn’t a one-size-fits-all solution. It has its limitations and certain situations demand alternative metrics.
Firstly, ARR doesn’t reflect short-term revenue growth with precision. Because it aggregates revenue over an annual period, it can overlook non-recurring income acquired within that timeframe.
If your startup has seen sporadic sales or one-time deals during the year, they might fly under the radar in ARR calculations, potentially skewing your results.
Another scenario where ARR might falter is in measuring customer churn rates. Instead of leaning on ARR to measure customer attrition, I’d suggest using customer lifetime value (LTV) instead.
LTV takes into account the duration of a customer’s engagement and their average spend per visit or monthly payment, offering a more nuanced understanding of why customers leave and how long they stick around before doing so.
Recognizing when to give ARR a miss is vital to avoid skewed performance assessments. In such cases, MRR might be a more suitable candidate for measuring your startup’s success.
Editor’s Note: ARR isn’t ideal for gauging quick income expansion or client attrition, since it disregards one-time payments and does not provide enough info about why clients are departing. Therefore, LTV is a more suitable option to get an accurate picture of your business performance.
MRR vs ARR: How to Calculate Them
Navigating the road to success in the tech startup landscape requires a firm grip on your metrics.
Now, with a solid grasp of MRR and ARR, it’s time to turn our attention toward accurately calculating these numbers.
Next, we’ll dissect the steps to precisely measure both MRR and ARR. And to ensure you’ve got all the practical know-how, here’s a short video from Eric Andrews, demonstrating these calculations in action…
Here’s a more detailed step-by-step of the process…
How to Calculate MRR
MRR provides a snapshot of your business’s growth and can help gauge the effectiveness of your strategies.
Here’s how to calculate it…
Step 1: Identify Recurring Revenue Sources
To calculate MRR, begin by identifying all recurring revenue sources.
These could be monthly subscription fees, service contracts, hosting fees, or any regular payments your business receives each month.
Step 2: Gather the Required Information
Next, you’ll need two key pieces of information: the total number of customers and the average amount they pay each month, often referred to as average revenue per user (ARPU).
This data can typically be sourced from your billing system, CRM software, or even customer surveys.
Step 3: Calculate MRR
Once you have the total number of customers and ARPU, you can calculate MRR using the following formula:
Total Number of Customers x Average Amount Paid = MRR
This gives you a rough estimate of the monthly revenue your company generates exclusively from recurring sources.
If there are additional one-time payments made during the month, add them separately after the MRR calculation to ensure you account for all income your business generates.
Now, let’s delve into calculating ARR for a more comprehensive view of your business growth…
Editor’s Note: Multiply the count of patrons utilizing your services by their usual expenditure each month to work out a firm’s MRR (Monthly Recurring Revenue). This will give you an accurate representation of how much money is being generated from recurring sources on a monthly basis, and any additional one-time payments should be added separately afterwards.
How to Calculate ARR
Annual recurring revenue (ARR) offers a glimpse of your tech startup’s anticipated yearly income, proving helpful to both founders and investors.
Here’s how to calculate it…
Step 1: Total Up MRR
To calculate ARR, begin by adding up all your monthly recurring revenue (MRR). This includes regular charges like subscription fees and usage-based fees.
Scroll up to the previous section for a more detailed view on calculating MRR.
Step 2: Convert MRR to ARR
Next, multiply your total MRR by 12.
This gives you the annualized version of your MRR, which is your ARR.
Step 3: Include One-Time Payments
For a more precise understanding of your yearly income, include any one-time payments like setup fees or other non-recurring charges made at least once per year in your ARR.
So, the calculation would look something like this:
(Total MRR x 12) + One-Time Payments = ARR
Understanding and calculating ARR equips founders to comprehend their business more profoundly and to plan more effectively for future revenue.
Moreover, monitoring ARR changes can highlight shifts in customer churn rates and flag potential product or service issues before they escalate
“Calculating ARR for tech startups is easy. Start with MRR, multiply by 12, divide by 12 again, and add one-time payments. Track changes in ARR to understand customer churn & identify product service issues #GTM #GrowthMarketing #TechStartups”
Common Pitfalls When Calculating MRR and ARR
Navigating through the world of metrics isn’t always straightforward. ARR and MRR are incredibly powerful tools for understanding the health and potential of your startup, but they’re also easy to misinterpret.
Here are some of the most common pitfalls that startups often stumble upon while calculating MRR and ARR:
Misclassifying revenue: Some startups mix in one-time payments (like set-up fees or non-recurring add-ons) with their recurring revenue. MRR and ARR should only include recurring revenue that’s predictable and reliable.
Ignoring churn: Failing to account for churn can paint an overly optimistic picture of your MRR and ARR. Remember to deduct churned revenue from your calculations.
Counting future upgrades: Some startups count upgrades or upsells that haven’t happened yet. This can inflate MRR and ARR figures. Only include revenue from upgrades or upsells once they’ve been realized.
Overlooking discounts: If you offer discounted annual plans, be sure to adjust your ARR accordingly. The same goes for any other price reductions or concessions.
Ignoring sales cycle: Startups often overlook the sales cycle duration, especially those with longer cycles. When a deal is closed, the revenue should be spread across the duration of the contract for ARR.
Avoiding these common mistakes can help ensure that your MRR and ARR calculations provide an accurate and useful perspective on your startup’s financial health.
Achieving Business Health: The Rule of 40 and ARR
When you’re steering a tech startup, you’re likely going to encounter the Rule of 40. Now, this rule isn’t just a bit of industry jargon, it’s a compass guiding the balance between growth and profitability.
You might be wondering how this relates to annual recurring revenue (ARR). Well, let’s dive into that…
The Rule of 40 states that a healthy tech startup’s growth rate and profit margin should total 40% or more.
But what’s the measuring stick for this growth rate? You guessed it, it’s your ARR.
Your ARR growth rate is an indicator of your startup’s momentum, and when combined with your profit margin, it’s a powerhouse of insights about your company’s health. If these two add up to 40% or more, you’re sailing smoothly.
So why does this matter?
Well, the Rule of 40 and ARR go hand-in-hand. ARR is all about predicting future revenue based on current customer subscriptions. It’s like the heartbeat of your business’s growth. The Rule of 40, then, gives context to this heartbeat, placing it in the larger picture of overall business health, which includes profitability.
Remember, while ARR and the Rule of 40 are closely intertwined, they aren’t the be-all-end-all of your business metrics. They’re important, yes, but they’re part of a broader dashboard of metrics you should monitor.
It’s all about finding the right balance and charting the course that’s right for your tech startup. So, use ARR and the Rule of 40 as part of your navigational tools, but don’t lose sight of the broader horizon.
FAQs in Relation to MRR vs ARR
What is the main difference between ARR and MRR?
ARR (annual recurring revenue) and MRR (monthly recurring revenue) are both key metrics used by businesses with subscription models to measure the total predictable and recurring revenue.
The main difference between them is the time period they consider:
A startup’s MRR is its predictable income every month. It’s calculated by multiplying the number of customers by the average billed amount during a month.
For example, if a business has 100 customers, each paying $10 per month, the MRR would be 100 (customers) x $10 (average monthly payment) = $1000.
In contrast, ARR measures the recurring revenue from your term subscriptions normalized to a one-year period. It’s calculated by taking the MRR and multiplying it by 12. Simple.
According to the above example, if the MRR is $1000, the ARR would be $1000 (MRR) x 12 (months in a year) = $12,000.
MRR vs ARR: which one should I use?
MRR suits startups with monthly subscriptions or quick sales cycles, helping you spot changes and adjust your tactics pronto.
ARR is your go-to for annual subscriptions or longer sales cycles. It’s your compass for strategic planning and perfect for wowing investors with the bigger picture of your financial growth.
But hey, why choose?
MRR and ARR are two sides of the same coin. Use MRR to stay nimble and make swift moves, and ARR to steer your long-term strategy. With both in your arsenal, you’re in control of your business’s financial story.
Why are ARR and MRR so important to the cloud business?
ARR and MRR are like the bread and butter for gauging your startup’s endurance.
Think of ARR as your year-long financial barometer, measuring the money you’re pocketing from recurring sources across 12 months. Then there’s MRR, its monthly counterpart. These two metrics are your trusty GPS, honing in on the viability and growth trajectory of your cloud operations.
They’re your private detectives, sniffing out invaluable intel on customer retention, customer lifetime value, cash flow forecasting, and the nitty-gritty of your financial health.
And it doesn’t stop there. Keeping tabs on ARR and MRR means getting an up-close and personal look at your customer base. Imagine having clear-cut visibility into how different customer segments engage with your services or how usage trends shift over time.
What is a good ARR growth rate?
According to Metric HQ, a good ARR for SaaS businesses depends on how much revenue you’re generating…
If you’re generating an ARR between 1-5M, a good median year-on-year (YoY) ARR Growth Rate to aim for falls between 52% and 59%. But if you want to be in the top echelon, you should be looking at rates between 102% and 154%.
For those with an ARR in the range of 5-15M, the goalposts shift slightly. Here, the median YoY ARR Growth Rate lies between 46% and 55%. The top quarter of businesses in this bracket are boasting growth rates between 100% and 131%.
But remember, these are just industry averages and high benchmarks. Your company’s specific situation, the competitive landscape, and your strategic goals can all impact what a good ARR Growth Rate looks like for your business. As always, strive for growth, but stay rooted in your unique reality.
What is a good MRR growth rate?
Industry insiders peg the sweet spot at around 10-20%. If you’re consistently hitting those figures, you’re certainly on a promising trajectory.
But don’t stop there – curbing customer churn can provide a major boost to your MRR growth. Meanwhile, seizing opportunities for upselling, cross-selling, and add-ons can take you further toward your optimal MRR growth rate.
Just keep in mind, these are broad benchmarks. Your specific business context, including your model, customer behavior, and market conditions, can influence what your ideal MRR growth rate should be. Keep striving for better, but stay rooted in your unique business reality.
How does customer churn affect MMR and ARR?
Churn is the percentage of your customers that cut ties with your service during a certain time frame. A high churn rate is the arch-nemesis of your MRR and ARR. It’s like a leak in your revenue bucket, and if you’re not careful, it can drain your MRR or ARR fast.
So here’s the deal – a lower churn rate equals healthier MRR and ARR. You retain more customers, meaning you keep the recurring revenue flowing in, boosting these metrics. On the flip side, a rising churn rate could be a red flag, a potential indicator that something in your business might need tweaking.
Remember, it’s not just about bringing new customers on board, but also about keeping the ones you already have. This balance is key to maintaining and growing your MRR and ARR.
What is the difference between contracted annual recurring revenue and annual recurring revenue?
Contracted ARR, that’s the money you’re certain about, the sure thing. You’ve got contracts, promises, and commitments.
Imagine stacking up all your customer contracts and multiplying them by their contract lengths – that’s your contracted ARR.
Now, ARR, that’s your real-time financial snapshot. It’s what your company’s actually pocketing from clients during a specific period. It’s the combo of your contracted ARR and any new business you’ve managed to reel in.
So, the crux of the matter? One is focusing on what’s promised, while the other is your reality check, based on performance. Just like your bank balance and your paycheck, they’re two sides of the same coin.
Can annual recurring revenue be higher than revenue?
No, annual recurring revenue (ARR) cannot be higher than revenue. ARR is a metric that measures the total value of contracted recurring revenue from customers over a 12-month period and does not include one-time sales or nonrecurring income.
ARR will remain constant, regardless of any short-term fluctuations in income from month to month; it is a cumulative figure based on recurrent payments over the course of one year. Therefore, ARR can never exceed overall revenue because all other sources are included in the latter but not necessarily accounted for in the former.
What other metrics should I consider in addition to MRR and ARR?
Other important metrics include customer acquisition cost (CAC), average revenue per user (ARPU), customer lifetime value (LTV), and churn rate. These can help you understand the cost-effectiveness of your marketing efforts, the profitability of each customer, and customer retention, respectively.
Final Thoughts
MRR and ARR aren’t just fancy tech startup jargon; they’re the key to cracking your growth strategy. Got ’em in your toolkit? Good. You’re armed and ready to measure the impact of your business strategies.
But remember, these metrics aren’t a one-size-fits-all solution. You need to be savvy about when and where to use MRR vs ARR, depending on your current needs.
Eager to unlock your startup’s potential? Join my newsletter on substack to get more valuable free training.
↓↓↓
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## The Role of Data in Effective Sales Pipeline Management
URL: https://www.data-mania.com/blog/sales-pipeline-management/
Type: post
Modified: 2026-03-17
Every business needs a consistent sales pipeline to grow. Effective sales pipeline management and a predictable pipeline ensures new leads are abundant so you can meet or exceed revenue targets.
However, amidst the chaos of the sales cycle, many companies overlook one of the most critical elements in sales pipeline management: data.
Data is critical in pipeline management and is present at every stage of the sales journey, from prospecting and lead generation to nurturing and closing deals. Yet, despite its undeniable importance, it has to be more actively managed so it isn’t neglected.
Today, we’ll explore data’s critical role in sales pipeline management and how effective data governance can make or break your pipeline’s performance — especially its impact on email marketing campaigns and sender reputation.
What is Data Governance?
Data governance refers to your company’s guidelines around data policies and procedures to manage its quality, security, and integrity. It also includes internal policies around collecting, storing, and disposing sensitive data.
For example, you collect personal information from lead generation forms on your website. Your data governance ensures this information is only handled by those with proper authorization and that the information is accurate and current.
Without proper data governance, your sales pipeline can quickly become cluttered with inaccurate information, leading to missed opportunities, wasted resources, and strained customer relationships.
We can categorize data governance into five main pillars:
Ownership: Who is responsible for data within the organization, and who can make decisions about data-related matters?
Quality: How do you maintain data quality, and what standards are in place to ensure accuracy and reliability?
Access: Who can access and modify data, and what permissions do you grant to individuals or departments?
Security: What measures are in place to protect sensitive data from breaches or unauthorized access?
Lifecycle: How is data created, stored, used, and eventually retired or archived?
The Link Between Data and Sales Pipeline Management
As with most things, being proactive is always more efficient than reacting. Imagine navigating the busy streets of rush hour New York traffic (isn’t it always rush hour in NY?) for the first time without a GPS. Every wrong turn is a minute wasted and a dollar in the curse jar.
A GPS with up-to-the-minute accurate information would save time and frustration.
However, being proactive in sales is only beneficial when operating with correct data. In the sales world, your pipeline is like the GPS for your sales teams. It guides them through a network of leads, prospects, and customers and helps them navigate the journey from the first contact with a new prospect to a closed deal.
A sales pipeline is a visual representation of your sales process, consisting of various stages that reflect the customer’s buying journey.
These stages typically include the following:
Lead generation: Data collected with each new lead is the foundation of your sales pipeline. It needs to be accurate.
Lead qualification: Sales teams rely on data to determine which leads are genuinely interested and have the potential to become paying customers. They use data points like lead source, demographics, engagement history, and past interactions to make informed decisions.
Nurturing: Automation tools and customer relationship management (CRM) systems leverage data to send targeted emails, personalized content, and follow-up reminders.
Sales Analytics: Data is analyzed and interpreted to make informed decisions. These insights guide strategic decisions, such as adjusting sales tactics, optimizing marketing campaigns, or reallocating resources.
Closing Deals: Sales is a cyclical process and doesn’t end at a closed deal. Post-sale data, including customer feedback and usage patterns, can inform customer retention strategies, upselling opportunities, and product improvements.
In the end, clean and reliable data makes it easier to see how you can improve the outcomes of your sales pipeline. It’s the key to avoiding missed opportunities and stunted growth.
Five Steps to Implement Effective Data Governance
Implementing effective data governance in sales pipeline management requires a holistic approach that involves people, processes, and technology.
Here are the key steps to ensure your data governance practices are healthy and sustainable:
1. Identify who is responsible for data governance
Managing data effectively is a team effort. It requires sales and marketing teams, IT, and data management teams to come together and adopt a data-centric culture. These teams play a valuable role in understanding the significance of data in the sales pipeline context and how it actively contributes to its integrity and quality.
Each team brings its unique perspective and expertise, ensuring the development of data governance is well-rounded, covers all relevant aspects of data management, and supports your organization’s overall mission and goals.
2. Establish clear policies and procedures
Once you identify your team, the next step is to establish clear and concise data governance policies and procedures. These documents should outline data collection, storage, sharing, and usage standards in plain language that everyone in your company can understand. The more straightforward your documents, the less room you leave for misunderstandings or misinterpretations.
3. Ensure data quality through cleansing and validation
Data can get old fast. And considering your sales team relies on this data to make sales, it must be current. Regular data maintenance is necessary to check for errors and inconsistencies, such as email format validation, to help keep your database accurate and reliable.
This consistent data maintenance, in turn, helps your sales team work more efficiently and effectively, leading to better results in your sales pipeline. So, it’s a critical step in maintaining the integrity of your data within the sales process.
4. Monitor and audit data regularly for accuracy
Data governance continues after the implementation of policies. It requires continuous monitoring and auditing. Regular data audits are necessary to help identify issues, such as outdated records or security vulnerabilities, so you can act quickly to fix them. Monitoring data usage patterns can also uncover potential areas for improvement or areas where additional training is needed.
5. Train employees to ensure compliance
Frontline employees are typically the ones using the data most often. This consistency of use means their understanding of data governance policies is critical. However, without the proper training, they may not understand their responsibilities regarding data handling.
For this reason, your company should have a comprehensive training program and accessible resources for all employees, especially those who may not have in-depth knowledge of tech.
Training should cover data security best practices, the importance of accurate data entry, and non-compliance consequences. Regular training refreshers and resources like manuals or online guides are invaluable to help reinforce these principles.
Data Governance and Email Marketing
Email marketing campaigns are an inherent part of any digital marketing strategy. Your sales pipeline is incomplete without one. Email marketing allows you to engage with your audience, nurture leads, and turn them into clients or customers.
However, the effectiveness of email marketing heavily relies on your company’s sender reputation.
What exactly is email sender reputation?
It’s the credibility of a sender’s IP address and domain. Internet Service Providers (ISPs) use the sender’s reputation to decide whether to deliver an email to a recipient’s inbox or divert it to the spam folder. As you can imagine, a positive sender reputation is vital for email deliverability and open rates. With a good reputation, there’s a greater chance your marketing emails will reach the intended recipients and they’ll open and engage with them.
Effective data governance practices directly influence the sender’s reputation. Clean, accurate, and permission-based email lists, a product of stringent data governance policies, result in lower bounce rates and spam complaints. Only sending marketing emails to people who opted in to receive them helps maintain a positive sender reputation.
This balance is where buying email lists can hurt your business. If someone receives an email from a company they’ve never heard of, there’s a greater likelihood they’ll hit the spam button. And the more spam complaints you receive, the more you risk getting banned from using email marketing services (EMS).
Additionally, data governance helps segment email lists based on recipient behavior and preferences. This segmentation allows for targeted and relevant email content, boosting engagement and sender reputation.
Here are a few tips to help you maintain a strong sender reputation:
Use permission-based lists: Always obtain explicit consent before adding someone to your email list.
Clean your list often: Regularly remove inactive or bounced email addresses from your list to maintain its quality.
Send engaging content: Craft engaging and relevant content that encourages subscribers to open and interact with your emails.
Follow a consistent sending schedule: Whether you send emails once a week or monthly, maintain a consistent sending schedule and volume to build trust with ISPs.
Monitoring and analysis: Analyze bounce rates and spam complaints and adjust strategies accordingly.
Integrating these email marketing best practices into your data governance strategy will enhance your sender reputation and increase the effectiveness of your email marketing campaigns.
Embrace data governance
A predictable and efficient sales pipeline is your closest guarantee to a continuous flow of leads. It enables your sales teams to convert new customers and clients to meet revenue targets consistently.
For this reason, it’s essential to understand the role of data in effective sales pipeline management. Embracing data governance ensures that your sales team’s information is accurate, reliable, and up-to-date. From lead generation to closing deals, every stage of the sales process relies on clean and precise data.
Effective data governance strengthens your sales pipeline management. From identifying responsible collaborators, establishing clear policies, ensuring data quality, and providing training, your business can use data to develop a successful sales pipeline and grow your business.
Bio:
Amy Remark is a versatile copywriter with seven years experience in conversion-driven content. Lately, she’s honed her skills in writing about SaaS and financial products. Having run her own ecommerce venture (and her own copywriting business), Amy is familiar with the ins, outs, struggles, and praises of founder life. Curious and solution-oriented, she aligns herself with value-driven projects, combining her degree in Human Kinetics and digital marketing savvy to create valuable, narrative-forward prose.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Business Growth and Digitalization: Implementing a Payroll Software for Startups
URL: https://www.data-mania.com/blog/payroll-software-for-startups/
Type: post
Modified: 2026-03-17
As a SaaS founder, you’re more than familiar with the challenges of building and scaling a technology-driven startup in today’s competitive market. However, in the midst of focusing on product development and advertising, payroll management often gets overlooked – which goes in no one’s favor. That’s why, in today’s blog we’re looking at what you need to know about payroll software for startups, and decision factors that are likely to impact your startup’s overall growth.
Approximately 50% of Americans have experienced payroll issues, which is problematic considering that 65% of the workers are living from paycheck to paycheck. Why make things hard on yourself when there’s a simple solution?
If startup owners in the past could manage with sheets and documents, you can definitely manage with the help of advanced payroll software. And to put my money where my mouth is, let’s delve into how to implement payroll software for startups.
Payroll software for startups: How it works
I don’t know about you, but many small businesses still create templates with columns to calculate everything from the employees’ working hours to taxes. This works but it’s not nearly as effective as using payroll software for startups accounting requirements.
Payroll systems are designed with simplicity in mind, and that’s what your startup should be based on. As a SaaS founder, you already appreciate the value of technology, so embracing this automatic way of managing payroll should be a piece of cake.
You know how people often say that tasks have become so automated that we’ll forget how to do them manually, for example, basic math? Well, let me agree to disagree.
The goal of implementing a payroll software for startups isn’t just to replace your manual efforts, but also to free up your valuable time.
It’s true that some might become lazy and only depend on automation. But see, it’s a matter of personal preference. The goal of implementing a payroll software for startups isn’t just to replace your manual effort, but also to free up your valuable time.
That said, payroll systems will streamline the process of managing payroll as well as enhance overall productivity. It’s not only about paying your employees on time but also complying with tax regulations, which is crucial for a startup’s success.
Benefits of implementing payroll software for startups
The benefits of implementing payroll software go beyond just automating the calculation part when managing salaries. It can forever change the way you handle your startup’s finances and human resources. Here’s how this software can help your startup business grow:
Reduce human errors. Regardless of how experienced your employees are, payroll management is simply too complex. All the tasks that it involves, including tax regulations and precise working hours calculations, are prone to human errors. By implementing payroll software, startups can automate these calculations and improve overall accuracy.
Save valuable time. Time, time, time. It’s the one thing everyone lacks, especially if you’re a business owner. With the use of payroll software, you can regain the time that would otherwise be spent on manual payroll management.
Store and access data. Payroll systems make it so much easier to store your data securely in the cloud, eliminating the need for physical storage. Once in the cloud, losing this data afterward is practically impossible.
Scalability. The goal of every startup is for the business to grow, and the key advantage of payroll software is that it will grow as your business grows. As soon as you start hiring more employees, the software will start managing payroll for a larger team. Not to mention, you won’t need to put much extra effort into it.
Data-driven decisions. Payroll software will also provide you with real-time data analytics and reporting that can help you make data-driven decisions. And making decisions based on accurate data, means they’re informed and aligned with your business goals.
Now, these are the general advantages of payroll software for startups, we want to get more into the nitty-gritty details – the exact aspects in which the software can assist your SaaS business. +
Keeping up with cryptocurrency tax regulations
Helping you comply with tax regulations is an obvious benefit of payroll software but when it comes to cryptocurrencies, this is a game-changer. I don’t know if you’ve embraced this trend of paying your employees in crypto; what I do know is that many have.
It definitely has its own benefits but it doesn’t come without challenges. And currently, complying with crypto tax regulations is the biggest one. However, you can use Toku to streamline your crypto payroll management and make sure you don’t end up on the wrong side of the law.
Here’s why keeping up with crypto tax regulations can be such a complicated process:
Evolving Regulations. Abiding by the rules can be difficult sometimes but it’s even harder to abide by rules that aren’t transparent. As governments are trying to adapt to this changing financial landscape, crypto tax regulations are constantly evolving, making it challenging for businesses to keep up to date.
Various Approaches. Different jurisdictions have different approaches to cryptocurrency taxing, as some consider them a property while some categorize them as a form of currency. China and Saudi Arabia, on the other hand, have banned them completely.
Record-Keeping. Each cryptocurrency transaction must be carefully tracked for tax regulations, and this becomes a problem if the transactions are too many. But as already mentioned, record-keeping is payroll software’s forte, so problem solved.
Lack of Clarity. Obviously, the tax regulations surrounding cryptocurrencies aren’t entirely clear. But it’s not only about that. You also have to be aware that not all are familiar with cryptocurrencies. And while all have heard about them, not everyone has used them or understands how they work.
This is why leaving employees to handle cryptocurrency taxes on their own isn’t such a smart idea, and is best done with the help of payroll software.
Crypto compensation provides faster transactions and lower transactional fees but comes with the challenge of tax regulation. On the other hand, payroll software can help you streamline the process and navigate these potential challenges.
HR payroll software
Payroll software can also ease the workload in HR, which doesn’t only help HR professionals but benefits your entire organization. The administrative burden can sometimes be too much, which easily leads to human errors. And we can all agree this isn’t a good thing, especially when we’re talking about sensitive information such as employees’ salaries, taxes, and credit card information.
By implementing payroll software for startups, you help the HR team accurately calculate salaries and taxes, while also providing them with more time to focus on employee development and engagement. The software will also provide you with real-time analytics into how everyone is doing with their jobs in terms of every little aspect – from attendance to employee satisfaction and overall productivity.
As a startup, your HR team is likely on the smaller side. But even if it’s one person, HR professionals play a crucial role in business growth and payroll management, which is why freeing up some of their time by automating processes can help you a great deal.
Enhancing employee engagement
Implementing payroll software will also have a positive impact on how engaged your employees are. Regardless of whether it’s a remote workspace we’re talking about or a traditional office setup, your employees must be motivated. Achieve that and your entire business will flourish.
So, how does payroll software impact your employees exactly?
By seeing how their salaries are calculated, and witnessing no salary issues, they’ll have peace of mind about their earnings. This builds transparency and trust. The financial stress they felt would decrease significantly, which in turn would allow them to focus more on work.
As for HR employees, I’ve already mentioned how payroll software lightens up their administrative burden. However, this also positively affects other employees.
First, they’ll get their salary on time, which will motivate them to do their jobs with satisfaction. Satisfied employees are more than likely to stay with your company and be more productive.
Business growth through payroll software
To put it simply, implementing payroll software for startups creates a domino effect that leads to employee satisfaction and business growth.
It goes something like this – the software reduces potential human errors in salary and tax calculations, ensuring employees are paid the exact amount they’ve earned and more importantly, on time. Without the financial stress, your employees experience higher job satisfaction. Naturally, more satisfied employees tend to be more productive, which ultimately goes in favor of your startup business.
You see how it’s all connected. Such a simple and cost-efficient step can lead to substantial business growth. As a SaaS founder, you’re already deep into the digital world, so you understand the power of streamlining processes. Now, the decision is in your hands.
Author bio
Makedonka Micajkova contributed this piece on payroll software for startups. She is a freelance content writer and translator, always bringing creativity and originality to the table. Being multilingual with professional proficiency in English, German, and Spanish, it’s needless to say that languages are her biggest passion in life. She is also a skilled communicator, as a result of having three years of experience as a sales representative. You can find her on Linkedin.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## The Radical Future of Spreadsheets: Equals is Pioneering the Next Generation
URL: https://www.data-mania.com/blog/future-of-spreadsheets/
Type: post
Modified: 2026-03-17
The data analysis world has evolved drastically. As business, financial, and data professionals, we’ve relied heavily on traditional spreadsheets. But with the ever-growing need for real-time, agile decision-making and comprehensive analysis, it’s clear we’ve been asking spreadsheets to perform tasks they clearly weren’t designed to do. That’s where Equals steps in to redefine the future of spreadsheets.
In case you missed my earlier post, Equals is a next-generation tool that’s about to radically disrupt EVERYTHING you know about data analysis and reporting.
Spreadsheets have evolved, and you’re about to see just how much.
The data landscape is constantly changing, but the tools many professionals use? Not so much.
“For analysts and data professionals of all kinds, traditional spreadsheets are growing more restrictive and clunky on a daily basis. They simply can’t keep up with our growing data needs.”
For analysts and data professionals of all kinds, traditional spreadsheets are growing more restrictive and clunky on a daily basis. They simply can’t keep up with our growing data needs.
Over the last few weeks though, I’ve gotten the chance to get up-front and center with Equals myself – and I’ve got to say! Equals has absolutely revolutionized the future of spreadsheets.
One of my favorite things about Equals is its built-in functionality that allows users to push analysis directly to stakeholders, in whichever format needed and on a manual or automated basis!
Instead of pulling information manually, and building reports on a piecemeal basis, Equals enables you to directly publish critical insights to platforms like Slack, Email, or even directly into presentation slides.
This of course increases the speed of decision-making, but it also ensures that key stakeholders have a real-time pulse on business happenings!
The Equals Dashboard launch
As you’ll recall from my previous post, Equals recently launched their Dashboards feature on Product Hunt and it seems like the ENTIRE data community really came alive to rally behind them in that launch.
So much so that, Equals won #1 on Product Hunt that day!!
Massive thanks to everyone who showed up and supported this incredible startup. I really loved all of your insightful comments!
Here were some of my absolute favorites:
Follow Josh Hubball on LinkedIn.
Follow Khalid Khan on LinkedIn.
Follow Bruno Haag on LinkedIn.
As you can see, Equals is already making waves in the industry.
So many users are reporting improved efficiency, reduced overhead costs, and faster decision-making processes.
Better yet, the tool is bridging the gap between data scientists and business professionals, which in turn fosters a more inclusive data-driven culture.
This is just a taste of what’s in store for the future of spreadsheets
As businesses increasingly recognize the limitations of traditional spreadsheets, tools like Equals are set to dominate the industry.
With the continuous addition of new features and integrations, Equals is setting new standards for all. Here are some of the things I love most about Equals…
Equals’ unparalleled enhanced functionalities & user experience
Equals has tapped into the very essence of what modern data professionals, business analysts, and financial analysts crave.
Its features are truly unparalleled. Here’s what I like most:
Directly connected data: Equals isn’t just any spreadsheet tool. It’s the future of spreadsheets. It’s a data powerhouse, pulling real-time data from your company’s databases and connecting it directly to your sheets. No more manual data entries or importing CSVs. Data is live, updated, and always ready for analysis.
Comprehensive data analysis: Say goodbye to third-party tools or software for data visualization or analysis. With Equals, everything is available and ready to go in one place. Advanced query functions, complex data visualizations, and in-depth reporting tools are all built in.
Seamless integrations: Equals understands that modern businesses use a plethora of tools. That’s why it seamlessly integrates with communication tools like Slack, Email, and Google Slides, ensuring that you can push your analysis to stakeholders, where it matters most.
Take a look at just some of its advanced functionality below. 🤯
Equals’ was clearly built for collaboration and flexibility
Collaboration is at the heart of Equals. Whether it’s sharing real-time insights, working on a shared dashboard, or even pushing data to various platforms, Equals ensures that collaboration is intuitive and straightforward.
Intrigued? The best part is yet to come…
Equals is so confident in the value they bring that they’re offering a free 14-day trial.
Go ahead and experience firsthand how Equals is changing the future of spreadsheets.
Don’t let your business or your team lag behind. Be part of the future, be part of Equals.
Click here to start your free 14-day trial at Equals.com
With functionalities that allow for real-time data updating, comprehensive analysis, seamless integrations, and the unparalleled ability to push data directly to stakeholders, Equals stands out as the future of spreadsheets.
Gone are the days when spreadsheets were just static cells and numbers. In this dynamic business landscape, tools like Equals are not just a luxury—they’re a necessity. Join the ranks of data professionals who’ve embraced the future and never looked back.
Ready to take the plunge?
Dive into the world of Equals and experience the future of spreadsheets.
Start Your Free 14-Day Trial Now!
Pro-tip: If you like this post on Equals and the future of spreadsheets, then you’ll also probably love to learn more about Equals’ Dashboards here, and about its conversion tracking capabilities here.
Disclaimer: This post may include sponsored content or affiliate links and I may possibly earn a small commission if you purchase something after clicking the link. Thank you for supporting small business ♥️.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## The Definitive Guide to Building a B2B SaaS Dashboard for More Efficient Product-Led Growth
URL: https://www.data-mania.com/blog/b2b-saas-dashboard/
Type: post
Modified: 2026-03-17
Over the years I’ve spent advising B2B tech startups, I’ve seen it proven time-and-time again that there’s nothing that’s more crucial to your company’s growth than data-driven decision-making. For that reason, in this blog post I’ll be sharing with you my top tips on the metrics you need to include in your B2B SaaS dashboard in order to best steer your product-led growth (PLG) strategy. Prepare to elevate your growth leadership skills to the next level…
Modern growth leaders must go beyond traditional marketing tactics and really master the art of delivering strategic data-driven growth acceleration. This is where a well-structured growth dashboard comes in… A growth dashboard is like a compass. You should always consult with yours before making any significant marketing or growth decisions. A B2B SaaS dashboard done right will easily steer a SaaS startup through the intricate maze of market performance, user engagement, and business growth complexities that they need to navigate. But, get it wrong and you can expect to waste untold quantities of time and money pursuing a strategy that never would have worked in the first place.
Before digging into my recommendations, let me introduce myself and why I’m qualified to advise on B2B SaaS dashboard design for smart growth strategies. My name is Lillian Pierson. I have spent the last 18 years delivering strategic marketing services for tech companies of all shapes and sizes, from Fortune 100 companies like Amazon and Dell, to VC-backed startups like Domino Data Lab and Cloudera, from bootstrapped tech consulting agencies to self-funded marketing SaaS giants like ClickFunnels, and everything in between.
I’m a Fractional CMO for B2B tech SaaS startups and consultancies. Although I have ample experience advising bootstrapped data and AI consultancies, my main focus is on supporting high-growth VC-backed SaaS startups.
Please note: The advice I offer in this blog post assumes that you are leading growth for a VC-backed B2B SaaS company. The metrics and discussion points are tailored around that key point, and may have limited relevance to other business or funding models.
The advice you’re getting in this post is about what growth metrics you need to monitor to steer your PLG strategy in the right direction.
Structuring a B2B SaaS Dashboard for Maximum Impact
I’m about to share with you the must-have metrics for a B2B SaaS dashboard to inform your strategic growth and marketing decisions. My assumption here is that, if you’re reading this, then you’re a growth or marketing leader who’s looking to make better informed PLG decisions – and subsequently get ahead in your growth leadership career.
But to any founders who are reading this, please take caution. While it’s relatively simple for a tech startup founder to build a dashboard for tracking PLG growth metrics, you also need to know how to interpret these metrics in the context of a broader growth strategy. This type of interpretation requires a significant backdrop of education and experience in both growth and marketing. Even with this B2B SaaS dashboard in place, trying to DIY your growth and marketing strategy is a recipe for disaster. My advice to founders, hire an expert to help you with your strategy if you don’t have one already.
With that out of the way, here’s how I approach structuring a PLG dashboard for maximum impact. First you need to break down your metrics by category, of course.
I suggest the following 6 categories for your B2B SaaS dashboard:
North star metrics
Acquisition metrics
Activation and engagement metrics
Retention metrics
Growth and expansion metrics
Referral and advocacy metrics
It’s always more helpful to show rather than tell, so we went ahead and created an Equals dashboard that shows you some effective ways to visualize PLG metrics so that they’re easy to understand with just a passing glance.
Let’s look at each of these categories a little closer…
North Star Metrics
If you’re leading a B2B SaaS company, I recommend focusing on Payback Period as your primary metric. While the LTV/CAC ratio has traditionally been used for tracking the long-term profitability of your customer acquisition strategies, be sure to prioritize metrics that offer quicker and more reliable insights into customer acquisition efficiency.
Payback Period
The Payback Period is the amount of time it takes for a company to recoup its investment in acquiring a customer. In other words, it’s the time it takes for the net profits from a customer to equal the initial Customer Acquistion Cost.
Using a line chart here within your B2B SaaS dashboard helps depict the trend of the Payback Period, thereby offering instant insights into the efficiency of investment in customer acquisition.
This metric is crucial for understanding cash flow implications and the immediate financial impact of customer acquisition. Naturally you want a short Payback Period, as shorter a Payback Period means that the company regains its investment faster. This is particularly important for startups and companies with tight cash flows, or the need to secure further VC-funding.
From my experience, the ideal Payback Period varies depending on the business model and industry. For B2C SaaS companies, a Payback Period of less than 3 months is often desirable. For B2B SaaS, it may extend to less than 8 months. If your Payback Period is longer than these benchmarks, you’ve either got a leaky funnel, a positioning / relevance problem, or worse – a product problem.
Personally, I love Payback Period as a northstar metric because:
It focuses on the speed and efficiency at which you can make back your investment and invest more into acquisition.
It is a wonderful metric for aligning marketing with product growth.
Here’s how to calculate your Payback Period:
And if you’re interested in more industry benchmarks regarding Payback Period, here’s what Lenny Rachitsky has to say:
Source: https://www.lennysnewsletter.com/p/payback-period
LTV/CAC Ratio
Another good north star metric for any B2B SaaS dashboard is the LTV/CAC Ratio, This is a key metric for understanding the long-term value of customers relative to acquisition costs.
This ratio compares the Lifetime Value (LTV) of a customer to the Customer Acquisition Cost (CAC). LTV represents the total revenue a company expects to earn from a customer throughout their relationship, while CAC is the cost of acquiring a new customer.
The LTV/CAC Ratio is useful for assessing the overall efficiency and sustainability of a company’s sales and marketing efforts, where a higher ratio indicates that the company is generating more revenue per customer relative to the cost of acquiring them.
Generally, a healthy LTV/CAC Ratio is considered to be 3:1 or higher, meaning the lifetime value of a customer is three times the cost of acquiring them.
Note: In the context of a SaaS business model, LTV is generally realized over a long period, typically between 5 to 10 years.
Acquisition Metrics
When evaluating acquisition metrics, I look for trends and patterns that indicate the effectiveness of our initial user attraction strategies. The relationship between site visits, sign-ups, and the resulting CAC offers invaluable insights.
This is really about understanding the story behind the numbers. For example, a spike in sign-ups without a corresponding increase in engaged users might signal a need to refine our targeting strategy.
The acquisition metrics I suggest you track on B2B SaaS dashboard like this are:
Free Trial Signups (#): This measures the effectiveness of initial user attraction strategies.
Website Visits (#): Tracks the effectiveness of online presence and marketing campaigns in driving traffic.
Website Conversion Rate (%): This measures the conversion of site visitors to registered users. This is a measure of how well targeted your traffic is to the UVP of your product, and how effective your website is at converting visitors.
Customer Acquisition Cost (CAC, $): This metric reflects the cost of acquiring new customers.
While CAC is an essential and foundational metric to every B2B SaaS dashboard, it should not be used as a north star metric, simply because it doesn’t account for the cost efficiency of your customer acquisition activities. Furthermore, CAC is unable to account for the lag in positive brand effects that result from your marketing spend (things like PR, organic SEO blog asset creation, organic social media and newsletter activities, advocacy, and referrals that happen outside of the predefined CAC sales cycle).
Activation and Engagement Metrics
In PLG, activation and engagement are the lifeblood of growth. Metrics like free-to-paid conversion rates and feature adoption rates are reflections of a product’s value proposition.
Time-to-value (TTV) is particularly close to my heart, as it directly correlates with first impressions. A short TTV often leads to higher engagement and retention rates.
Free-to-Paid Conversion Rate (%): This metric tracks how well your funnel is converting free users into paying customers. It’s a key measure of initial user activation.
Feature Adoption Rate: This indicates how frequently users are engaging with various features of the product. This reflects on product value and user activation. I recommend using a bar chart to display this metric.
Time-to-Value (TTV, #): This measures the speed at which users are able to generate value from your product in a self-serve environment. It’s an important metric for understanding early user experience and engagement. TTV is best measured in days, of course.
Retention Metrics
Retention metrics like user retention rate, product stickiness (DAU/MAU), and churn rate are crucial barometers of long-term success. In my experience, these metrics are the true test of product-market fit.
High retention and low churn signify that we are meeting customer needs effectively. These metrics also guide our product development, indicating where enhancements or changes might be necessary.
User Retention Rate (%): Measures how well the product keeps users engaged over time, indicating long-term user satisfaction and product fit. This measures the rate at which a company retains its customers over a given period of time. It is calculated by subtracting the number of new customers acquired during that period from the number of customers at the end of that period, dividing by the number of customers at the start of the period, and then multiplying by 100 to get a percentage. A high retention rate, of course, indicates that more customers are staying.
Product Stickiness (DAU/MAU, %): This indicates how often users engage with the product, a key indicator of its ‘stickiness’ or ongoing user engagement. A high ratio of DAU to MAU (aka; “DAU/MAU ratio”) indicates that users are returning to the product frequently, suggesting high stickiness.
Churn Rate (%): This metric tracks the rate at which customers stop using the product, which is crucial for understanding user retention challenges. This measures the rate at which customers or subscribers stop doing business with a company over a given period of time. It is calculated by dividing the number of customers lost during that period by the initial number of customers at the start of that period. A high churn rate indicates more customers are leaving.
Growth and Expansion Metrics
Growth and expansion metrics, including Product Qualified Leads (PQLs) and expansion revenue, are key indicators of a product’s scalability and market acceptance. Consider including them on any B2B SaaS dashboard.
PQLs, in particular, have been a game-changer in our marketing strategies because they allow us to focus our resources on the most promising leads. Expansion revenue, on the other hand, reflects our success in not just acquiring but growing customer accounts, a critical aspect of sustainable business models.
Key growth and expansion metrics include:
Product Qualified Leads (PQLs, %): This metric identifies which percentage of leads are highly engaged with the product and likely to convert, which is important for evaluating growth-focused marketing strategies. Defining who qualifies as a “qualified lead” is a subjective process, and the criteria and scoring for PQLs will vary depending on the product, target market, and specific user journey. The goal is to identify those users who are not just interested in the product but are also actively benefiting from its use, thus indicating a higher likelihood of converting to paying customers.
Expansion Revenue (%): This measures revenue growth from existing customers through upselling, cross-selling, or upgrades. This reflects the success of account expansion efforts and is a critical metric for many businesses, particularly those operating on a subscription model. It’s measured as a percentage of revenue expansion from your existing customer base over a given interval of time.
Referral and Advocacy Metrics
Finally, referral and advocacy metrics, like Net Promoter Score (NPS), provide a window into your customer experience and loyalty.
A high NPS is often a precursor to organic growth through word-of-mouth and customer advocacy. It’s a metric that goes beyond the dashboard. It influences everything from customer support strategies to product development.
Net Promoter Score (NPS, #): This metric gauges customer loyalty and satisfaction, which can indicate the potential for customer referrals and organic growth.
By focusing on these key metrics and interpreting them through the lens of your overall business objectives, you can make informed decisions that drive sustainable growth.
In my role, I’ve seen firsthand how a well-structured B2B SaaS dashboard is a powerful tool for decision-making and strategy development. Each metric we track is a piece of a larger puzzle, and it’s my job to put these pieces together to form a comprehensive picture of our growth trajectory. By focusing on these key metrics and interpreting them through the lens of your overall business objectives, you too can make informed decisions that drive sustainable growth.
Moving Beyond the Basics with Enhanced Dashboard Utility
In the evolving landscape of SaaS startups, having a basic B2B SaaS dashboard is just the starting point. For the dashboard to be useful, you need some advanced features and integrations.
Let’s look at a few of the simple requests you can provide your team in order to elevate your dashboard from a simple data display to a dynamic tool for strategic decision-making.
Customization and Filters
If there’s a budget, it’d be ideal for your B2B SaaS dashboard to also be useful for team members other than yourself. The first step to making that happen is to request customization and filtering capabilities.
A one-size-fits-all approach rarely suffices in the nuanced world of SaaS metrics.
Customization will allow each team member, from marketing to product development, to tailor the dashboard to their specific needs. Customizations could include custom views for different user segments, time periods, or product lines.
Filters, on the other hand, enable users to drill down into the data to focus on specific metrics or time frames that are most relevant to their current objectives. This level of personalization ensures that the B2B SaaS dashboard remains relevant and useful for all stakeholders.
Real-Time Data and Alerts
In our fast-paced industry, outdated information often causes missed opportunities and misguided strategies.
From my perspective, the integration of real-time data feeds for your B2B SaaS dashboard is an absolute non-negotiable.
But coupled with this, you also need a robust alert system to notify you and other team members when certain thresholds are met or when unexpected patterns emerge. Alerts like this are essential in situations where swift and proactive responses are required. Whether it’s a sudden spike in user churn or an unexpected drop in feature usage, real-time alerts help in maintaining a constant pulse on the product’s performance.
Integration with Product Analytics Tools
For a B2B SaaS dashboard to be truly impactful, it must seamlessly integrate with other product analytics tools. This integration ensures a continuous and automated flow of data, thus eliminating the need for manual updates and reducing the risk of errors.
Whether it’s pulling in data from customer relationship management systems, marketing platforms, or user feedback tools, integration is key to achieving a holistic view of your product’s performance and customer experience.
I really like Equals Dashboards for these capabilities, because – with Equals – you can get all the integrations you need for your B2B SaaS dashboard, in an environment that makes it easy for your development team to build and deliver.
Other Equals features that I love:
✅ 𝐄𝐚𝐬𝐞-𝐨𝐟-𝐮𝐬𝐞: With its intuitive interface, you can easily connect a variety of data sources like PostgreSQL, Snowflake, and Stripe. The Query Builder and SQL Editor cater to both beginners and advanced users, allowing for complex data manipulations without the need for extensive SQL knowledge.
✅ 𝐅𝐚𝐦𝐢𝐥𝐢𝐚𝐫𝐢𝐭𝐲: Equals’ pivot tables, charts, and Excel-based formulas bring a familiar yet enhanced experience to data analysis.
✅ 𝐀𝐈-𝐢𝐧𝐟𝐮𝐬𝐞𝐝 𝐚𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐜𝐞: Equals’ AI Assist feature offers capabilities like automated text summaries and smart dashboard generation.
✅ 𝐇𝐢𝐠𝐡𝐥𝐲-𝐜𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐯𝐞: Collaborative features like multiplayer collaboration, comments, and access controls make Equals an ideal tool for team projects.
✅ 𝐄𝐟𝐟𝐨𝐫𝐭𝐥𝐞𝐬𝐬 𝐫𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠: With Equals, you can transform raw data into insightful reports effortlessly, and on auto-pilot.
Start A Free Equals Trial Today >>
Enhancing your B2B SaaS dashboard involves a strategic blend of customization, real-time data, and seamless integrations. These enhancements help transform your dashboard from a static data repository to a dynamic tool that you and others can use for improved decision-making.
Feel free to borrow the recommendations from this blog post when instructing your executional team on growth dashboard requirements so that you can position yourself as the proactive SaaS growth leader we both know you are.
Conclusion
Building the ultimate B2B SaaS dashboard involves an intricate dance of both art and science. It requires a lot more than just data aggregation. You’re building a strategic command center to guide yourself and your teams through product growth and marketing complexities.
This guide has illuminated the path towards building a dashboard that embodies the principles of product-led growth by blending insightful analytics and intuitive design. A well-designed dashboard in Equals transforms the approach of a SaaS growth leader by providing you a panoramic view of the product’s journey, user engagement, and financial health.
The right B2B SaaS dashboard empowers growth leaders to make decisions that are not just reactive to market changes but are proactive and strategic, thus ensuring sustained growth and success.
As a fellow growth leader in the SaaS domain, let me tell you, our biggest challenge these days isn’t just staying ahead of the curve – We have to redefine it.
Use the insights and strategies outlined in this guide as requirements that your executional team can use to build a B2B SaaS dashboard that stands as a beacon of innovation and efficiency. Leverage the power of Equals to build a dashboard that not only tracks your growth but accelerates it.
A B2B SaaS dashboard is a reflection of your strategic vision and commitment to growth. Start today by assessing your current dashboard and identifying areas for enhancement. Implement the strategies discussed above, and watch as your dashboard transforms into a dynamic tool that propels you to new heights in your growth leadership career.
🤍 This blog post was produced in proud collaboration with Equals. 🤍
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## The Data and AI Imperative with Lillian Pierson [PODCAST]
URL: https://www.data-mania.com/blog/the-data-and-ai-imperative-with-lillian-pierson/
Type: post
Modified: 2026-03-17
This was a conversation with Neil Wilkens about my upcoming book, The Data & AI Imperative.
Hit play below to watch it on YouTube.
Or, listen to the episode below.
Episode Show Notes
Lillian Pierson, P.E., author of Wiley’s groundbreaking new book “𝘛𝘩𝘦 𝘋𝘢𝘵𝘢 & 𝘈𝘐 𝘐𝘮𝘱𝘦𝘳𝘢𝘵𝘪𝘷𝘦”, is a celebrated growth strategist, advisor, and fractional CMO for B2B tech startups, scale-ups, and consultancies that want to drive more consistent, reliable revenue growth. Lillian and Neil discuss platforms like CastMagic and Fathom as well as favourites ChatGPT and the possibilities afforded by private AI systems to protect your commercial or sensitive data.
Lillian shares her STAR framework, a powerful method for leveraging data and AI and introduces her new book, ‘The Data & AI Imperative: Designing Strategies for Exponential Growth’, a comprehensive guide for tech-savvy leaders who want to harness the power of AI and data to drive extraordinary business growth.
Find more content like this here.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## AI, Data Strategy, and Product-Led Growth: A CMO’s Playbook with Lillian Pierson [PODCAST]
URL: https://www.data-mania.com/blog/ai-data-strategy-and-product-led-growth/
Type: post
Modified: 2026-03-17
This was a conversation between Lillian Pierson and Mehmet Gonullu about AI, data strategy, and product-led growth.
Hit play below to watch it on YouTube.
Or, listen to the episode below.
Episode Show Notes
In this episode of The CTO Show, we’re joined by Lillian Pierson, a fractional CMO and marketing strategist with an impressive track record of supporting 10% of Fortune 100 companies and educating two million learners on data science and AI. With a unique background as a licensed professional engineer and two decades of marketing experience, Lillian brings a distinctive perspective to the intersection of technology and growth strategy.
Lillian discusses her upcoming book, “The Data and AI Imperative: Designing Strategies for Exponential Growth,” which aims to protect business leaders in their decision-making around technical strategies while helping data professionals advance into more strategic roles. She shares valuable insights on how organizations can avoid common pitfalls in AI implementation, emphasizing the importance of aligning technology initiatives with business objectives rather than adopting new tools simply because they’re trending.
About Lillian:
Lillian Pierson, P.E., author of Wiley’s groundbreaking new book “𝘛𝘩𝘦 𝘋𝘢𝘵𝘢 & 𝘈𝘐 𝘐𝘮𝘱𝘦𝘳𝘢𝘵𝘪𝘷𝘦”, is a celebrated growth strategist, advisor, and fractional CMO for B2B tech startups, scale-ups, and consultancies that want to drive more consistent, reliable revenue growth. Leveraging the strategic, data-driven methodologies she’s used to support 10% of the Fortune 100, Lillian eliminates the guesswork from marketing to deliver predictable and measurable results. With a formidable reputation as a leading educator and consultant in data, AI, and growth marketing, Lillian has empowered over 2 million learners through her dozens of books and courses, produced in partnership with Wiley and LinkedIn Learning.
Links:
https://www.data-mania.com/blog/the-data-ai-imperative/
https://www.linkedin.com/in/lillianpierson/
00:00 Introduction and Guest Introduction
01:07 Lillian Pierson’s Background and Journey
02:20 Initial Spark in Data, AI, and Growth Strategy
03:40 Discussing Lillian’s New Book
06:05 Five Signs Your AI Strategy Isn’t Ready to Scale
07:34 Challenges in Adopting New Technologies
15:35 Data Monetization Strategies
18:25 Scaling Strategies for Long-Term Success
21:15 Product-Led Growth and AI Integration
26:01 Understanding Product-Led Growth
26:56 The Role of a CMO in Product Development
27:46 Marketing Strategies for Product Validation
28:40 Leveraging Community and Buyer Perspectives
29:37 Integrating Product and Marketing for Growth
31:18 Enterprise Sales and Outdated Playbooks
32:10 The Power of Personal Branding and Influencers
35:04 Exploring Partnerships and Monetization
38:07 The Impact of AI on Marketing
41:42 Final Words of Wisdom for Founders
44:53 Conclusion and Contact Information
Find more content like this here.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## How To Improve Email Marketing Performance Using AI
URL: https://www.data-mania.com/blog/how-to-improve-email-marketing-performance/
Type: post
Modified: 2026-03-17
The past few years have brought new ways for companies to connect with customers, but email marketing remains a powerful way to engage. Even after the appearance of social media and messaging apps, customers prefer emails to interact with brands. According to the State of Marketing research by Salesforce, the number of outbound emails increased by 15% last year. The volume of sent emails is high because it’s driven by high customer engagement, and now AI in email marketing is helping to increase results. This article discusses how to improve email marketing performance of your business using AI.
AI has already changed this sphere by introducing new tools for personalization, campaign strategies, and automating tasks, but it’s still in the early stages. However, still, there are lots of questions that marketers need to answer. How can we integrate AI into existing strategies? Will our existing email marketing platforms incorporate AI? How AI in email marketing will target our audience?
The Benefits of AI in Email Marketing
So how to improve email marketing performance and what makes AI so great for email marketing? It’s the ability to process massive datasets with speed and precision. Thus, AI is a super-efficient strategist that can analyze customer behavior, predict trends, and customize messages for maximum impact.
Firstly, nowadays, personalization is the most important factor in email marketing. Generic emails are not interesting to consumers anymore as they expect brands to know their preferences, anticipate their needs, and communicate as though they’re speaking directly to them. AI is helping here greatly because it offers text insights and crafts personalized messages.
Moreover, AI improves repetitive tasks like audience segmentation and A/B testing, letting marketers focus on strategy and creativity.
AI-Powered Tools and Technologies for Email Marketing
New wonderful AI-driven marketing tools are appearing every year. Platforms like Mailchimp, HubSpot, and ActiveCampaign are among the front-runners, offering features like advanced segmentation and predictive analytics.
Predictive analytics tools, such as Optimove and Phrasee, take things further by forecasting customer behavior. They allow marketers to plan campaigns that align with their audience. Meanwhile, natural language processing (NLP) tools like Grammarly ensure your email content is grammatically correct and error-free, which helps an email look more professional.
Key Features to Look For in AI Email Tools
When evaluating AI email marketing tools, consider the following features:
Predictive Analytics: Tools that can predict customer behavior and outcomes.
Segmentation Capabilities: Advanced options for audience segmentation based on behavioral data.
Dynamic Content: Tools that allow the creation of personalized, on-the-fly content.
How to Improve Email Marketing Performance: Use AI
The following are some important points on how to improve email marketing performance and how AI can help in achieving this:
Audience Segmentation
Audience segmentation is a marketing strategy based on identifying subgroups within the target audience in order to deliver more tailored messaging and build stronger connections. Audience segmentation plays an important role in successful email campaigns, with AI taking it to a new level. AI can analyze personal, behavioral, and demographic data to create precise audience segments that traditional methods could only dream of achieving.
For example, a retail business uses AI to categorize customers into groups based on purchase frequency, preferred product categories, and browsing habits. This segmentation allows to create and send targeted emails that speak directly to each group’s interests. Companies like Amazon have mastered this, offering personalized product recommendations.
Creating Email Subject Lines
Subject lines are crucial for email campaigns. The largest percentage of people getting emails decide whether to open an email based on the subject line. AI is assisting greatly here with tools like Phrasee and Persado generating great subject lines optimized for open rates.
A/B testing, another critical component of subject line success, is also simplified with AI. Instead of manually testing two options over days, AI tools can analyze multiple variations in real-time, delivering actionable insights faster than ever before.
Personalized Content
Another way on how to improve email marketing performance is creating personalized content. Sending tons of generic emails is not working anymore. AI-powered tools help marketers to create personalized content that adapts to individual preferences. For example, an online bookstore might use AI to recommend book titles based on a user’s browsing history or past purchases.
But it is not all AI can do here, namely AI-based predictive analytics helps to anticipate future customers’ needs. Picture a retailer noticing a customer’s consistent search for winter gear. As the season approaches, AI prioritizes emails showcasing winter essentials, dramatically increasing the likelihood of a purchase.
Automating Email Campaigns
Email automation isn’t new, but AI makes it smarter. Triggered emails like cart abandonment reminders or re-engagement messages are now more effective thanks to real-time behavioral analysis.
AI doesn’t just send emails on autopilot; it learns from performance data and adjusts campaigns on the fly. It adjusts the content, rethinks the timing, or refines the audience targeting to ensure better results. This adaptability ensures campaigns stay relevant and effective, even as market conditions shift.
Email Deliverability
Crafting the perfect email is only half the problem; marketers need to make sure that emails get to the right inbox. Using artificial intelligence helps to ensure that carefully composed messages arrive exactly where they belong. AI improves email deliverability by keeping a keen eye on factors like the sender’s reputation and the nuances that often trigger spam filters. Platforms like SendGrid and Litmus help to get deliverability metrics, offering smart recommendations to improve campaigns.
Integrating AI with Customer Relationship Management
Combining AI with customer relationship management (CRM) platforms unlocks a wealth of possibilities. Systems like Salesforce Einstein provide a 360-degree view of customer interactions, enabling more personalized and efficient campaigns.
For instance, AI can identify upselling opportunities by analyzing CRM data and making sure that emails are both timely and relevant. This integration improves the overall customer experience and drives better results.
Implementing AI in Your Email Marketing Strategy
Now that we’ve explored how to improve email marketing performance and the significant benefits of AI in email marketing, how can you adopt these strategies effectively? Here’s a simple, structured approach to integrating AI into your email campaigns:
Step 1: Audit Your Current Email Strategy
Begin by assessing your existing email marketing strategy. Identify effective elements and areas in need of improvement. Gather data on open rates, click-through rates, and conversion rates to establish a baseline for future performance.
Step 2: Choose the Right AI Tools
Select AI-driven email marketing tools that align with your specific goals. Familiar platforms like Mailchimp, SendGrid, and HubSpot offer robust AI features designed to enhance personalization, automation, and analysis. Do thorough research to find the best fit for your needs.
Step 3: Experiment with Personalization
Once you’ve integrated AI tools, focus on enhancing personalization in your email campaigns. Begin small by tailoring subject lines based on customer segments, then expand into personalized content and product recommendations.
Step 4: Leverage Predictive Analytics
Use AI for predictive analytics to determine the optimal times to send emails. Test different timings to identify patterns that yield the highest engagement, refining your strategy accordingly.
Step 5: Continuous Optimization
Make a habit of A/B testing various elements of your email campaigns. Use insights gathered from AI tools to inform your decisions, allowing for dynamic and ongoing optimization as you learn what resonates most with your audience.
Step 6: Monitor and Adjust
Establish key performance indicators (KPIs) to track your email marketing effectiveness continually. Regularly monitor these metrics, adjusting your strategy based on performance data and customer feedback. Remember, the goal is to foster a cycle of continual improvement.
Conclusion
Including AI in email marketing strategies isn’t just a trend; it’s a necessity nowadays. Thanks to AI’s capabilities, marketers can deliver campaigns that are not only effective but also deeply personalized.
As consumer expectations continue to grow, adopting AI-driven strategies will be crucial for staying competitive. Whether it’s through smarter segmentation, real-time adjustments, or enhanced deliverability, AI offers the tools to elevate email marketing to new heights.
For businesses looking to stay ahead, the message is clear especially on how to improve email marketing performance: embrace AI or risk being left behind. After all, the future of email marketing isn’t just about sending messages – it’s about making every message count.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## AI Agents in Marketing – Without Them You’re Leaving Revenue on the Table
URL: https://www.data-mania.com/blog/ai-agents-in-marketing/
Type: post
Modified: 2026-03-17
As AI evolves, its transformative potential for marketing becomes ever more apparent. AI agents in marketing are emerging as autonomous digital constructs, capable of automating complex tasks, augmenting creativity, and redefining traditional frameworks.
This blog post is based on an explorative conversation about the unprecedented potential of AI agents in marketing, drawing on insights from an illuminating conversation between Madhukar Kumar, Chief Marketing Officer at SingleStore, and Lillian Pierson, a seasoned data-driven marketing strategist.
🎙️ Watch & listen to the full episode above or directly on YouTube here. Or read on if you prefer consuming your content that way.
If you’re leading a technology company or business unit and are eager to explore the forefront of AI-driven marketing strategies – or the use of AI agents in marketing – this is your roadmap.
Decoding the role of AI agents in marketing strategy
Probably you’re wondering, what is an AI marketing strategy?
Good news! It’s pretty simple.
An AI marketing strategy integrates advanced machine learning and data science principles to drive efficiency, personalization, and scalability.
At its core, an AI marketing strategy integrates advanced machine learning and data science principles to drive efficiency, personalization, and scalability. Madhukar outlines three critical pillars that underpin effective AI-driven strategies in the tech sector:
Product-Led Growth (PLG):
Objective: Accelerate user acquisition by integrating AI to streamline repetitive marketing and operational tasks.
Execution: AI-powered platforms automate tasks like campaign iteration and customer onboarding workflows, enabling faster feedback loops and freeing resources for strategic thinking.
Outcome: Increased agility in responding to customer needs and reduced time-to-value for marketing initiatives.
Pipeline optimization
Objective: Bridge the gap between marketing and sales functions to enhance lead nurturing and conversion.
Execution: AI tools provide real-time insights into pipeline health by analyzing CRM data, forecasting opportunities, and automating follow-ups.
Outcome: Seamless pipeline alignment leads to improved lead-to-revenue conversion rates.
Branding at scale
Objective: Amplify brand presence while maintaining consistency across channels.
Execution: AI systems like GPT-based content engines create personalized, high-quality content tailored to audience personas, ensuring cohesive branding without additional overhead.
Outcome: Expanded brand reach with minimal manual intervention, translating into stronger market positioning.
Improving creativity and automation with AI in marketing
AI is a game-changer in modern marketing. By integrating AI into workflows, businesses are able to achieve more efficient operations and unlock new possibilities for creative expression. Here’s how AI is transforming both automation and creativity in marketing.
Automation: AI excels at handling repetitive, data-heavy processes that previously bogged down marketing teams. Tools like n8n and marketing orchestration platforms reduce the operational load by automating tasks such as:
Keyword research and campaign analytics.
Dynamic landing page creation.
Automated segmentation and personalized messaging.
Creativity: While automation shines in repetitive tasks, AI also empowers human creativity. Using platforms like Jasper and Canva, marketers are able to:
Generate data-driven content outlines informed by advanced keyword and audience sentiment analysis
Produce on-brand visuals and multimedia assets in minutes, freeing creative teams for more strategic endeavors.
The role of multi-agent systems in AI marketing
Multi-agent systems in AI marketing use multiple specialized AI “agents” working together to handle tasks like data analysis, content creation, and campaign management. Acting as a coordinated network, these systems help businesses achieve better results with fewer resources.
What are multi-agent AI systems?
Multi-agent systems consist of autonomous AI entities, each specialized in specific tasks, working together to achieve overarching marketing goals. Madhukar shares insights from SingleStore’s experience with their multi-agent setup, which mirrors the cognitive diversity of human marketing teams.
Coordination and control: Tools like LlamaIndex enable streamlined collaboration among AI agents, defining roles and workflows for maximum efficiency.
Cost efficiency: By mimicking human team structures, multi-agent systems deliver scalable solutions that reduce operational costs while maintaining high performance.
Case study highlights: SingleStore’s AI agent “SQrL”’:
SingleStore’s AI chatbot, SQrL, is an advanced tool designed to assist users with SQL-related queries. As AI technology progresses, tools like SQrL are expected to play a pivotal role in refining marketing strategies through predictive analytics.
SingleStore’s AI chatbot, SQrL, exemplifies the practical application of AI agents in B2B marketing such as:
Customer engagement: Squirrel uses advanced NLP capabilities to interact with users in real-time, providing SQL-related solutions and reducing friction in user journeys.
Enhanced productivity: Automating routine queries allows human teams to focus on strategic challenges.
Future potential: Adaptive AI agents like SQrL are poised to autonomously refine marketing strategies, leveraging predictive analytics to anticipate market trends.
The future of AI agents in marketing
AI agents in marketing are the future of business growth, offering unmatched capabilities to automate, optimize, and innovate at scale. Their role in gaining a competitive edge in the ever-evolving marketing landscape is crucial. Data-driven leaders thrive by embracing these technologies to create dynamic, high-impact marketing ecosystems.
To explore the full scope of AI’s potential in marketing, join Madhukar Kumar, Chief Marketing Officer at SingleStore, for an in-depth conversation in our latest podcast episode.
Tune in now to discover how AI agents in marketing can elevate your growth strategy to new heights…
🎙️ Or, watch the full episode below.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Why Every Business Needs an AI Marketing Strategy to Stay Ahead in 2025
URL: https://www.data-mania.com/blog/ai-marketing-strategy/
Type: post
Modified: 2026-03-17
If you’re not leveraging an AI marketing strategy yet, you’re letting your competitors win! AI agents in marketing are transforming how marketing professionals and tech leaders steer technology-driven organizations, with automation being a critical component of modern marketing success. Understanding and leveraging this approach is essential to maintain a competitive edge.
This blog post breaks down what automation in AI marketing strategy entails, why it matters, and how it transforms your marketing operations by driving unparalleled efficiency and effectiveness.
If you’re curious to dive deeper into how AI and automation are shaping the future of marketing, be sure to watch the insightful conversation between Madhukar Kumar, Chief Marketing Officer at SingleStore, and Lillian Pierson, a seasoned data-driven marketing strategist.
They explore key aspects of automation, share their expert perspectives, and discuss practical strategies you’d apply in your own marketing efforts.
🎙️ Watch & listen to the full episode above. Or read on if you prefer consuming your content that way.
Why should you care about automation in AI marketing strategy?
The complexity of managing modern marketing operations overwhelms even the most sophisticated teams. Automation goes beyond just improving efficiency. It serves as a strategic tool for scaling personalized engagement and achieving better business outcomes.
If you’re leading a data-intensive business, consider this: the volume of actionable insights buried in your data grows exponentially. Without automation, your team struggles to extract and apply those insights in time to make a meaningful impact.
What is automation in AI marketing strategy?
Automation in AI marketing strategy involves leveraging advanced AI systems to execute repetitive and data-heavy marketing tasks with precision. This empowers your team to focus on higher-value strategic decisions, including campaign ideation, customer experience design, and business development.
AI automation enhances your team’s capabilities by complementing human ingenuity, rather than replacing it. By automating processes like content personalization, customer segmentation, and performance tracking, your team is able to achieve more in less time, with greater impact.
Key benefits of AI-driven automation
AI empowers marketing teams to focus on high-level strategy while ensuring precision and productivity. Let’s explore some of the top benefits this technology brings to the table:
Streamlined workflows: AI automates the heavy lifting in campaign execution—from scheduling social posts to orchestrating complex workflows. Tools like HubSpot or ActiveCampaign ensure timely, accurate execution.
Cost and time efficiency: By handling time-intensive tasks like A/B testing or audience segmentation, automation frees up your team’s bandwidth for innovation, cutting operational costs in the process.
Elevated quality: AI-powered tools like Grammarly for copy or Adobe Firefly for visuals enhance output quality without the need for constant human intervention.
Data-driven insights: AI systems like Tableau or Looker consolidate and analyze massive datasets, offering actionable insights that inform and refine your strategies.
How automation revolutionizes key marketing processes
In today’s fast-paced digital world, marketing teams are under constant pressure to deliver high-quality results efficiently. As consumer expectations grow and competition intensifies, companies are turning to automation to streamline processes and elevate performance.
Here’s how automation is revolutionizing key marketing processes:
1. Automating repetitive tasks
Use cases: Social media scheduling, email drip campaigns, and data cleansing.
Impact: Consistency in execution and reduced human error.
2. Personalization at scale
Use cases: AI tools like ChatGPT or Jasper craft tailored messages based on audience data, improving relevance and resonance.
Impact: Boosts customer engagement and conversion rates.
3. Campaign orchestration
Use cases: Workflow tools like Apache Airflow or Zapier automate end-to-end campaign processes.
Impact: Seamless execution across platforms, reducing coordination overhead.
4. Enhancing creative development
Use cases: AI tools like MidJourney for imagery or Descript for video editing generate and optimize creative assets.
Impact: Faster production cycles and improved asset quality.
5. Advanced analytics and reporting
Use cases: Dashboards powered by AI analyze campaign performance in real-time, identifying bottlenecks and opportunities.
Impact: Empowers decision-makers with actionable, up-to-the-minute insights.
Real-world example of using AI to scale marketing efforts
Consider a SaaS company launching a new product. Here’s how it uses AI to enhance every phase of a product launch:
Audience segmentation: AI analyzes customer data to segment audiences by behaviors and preferences, ensuring highly targeted marketing.
Personalized email campaigns: AI personalizes email content at scale, boosting open rates by 30% through tailored messaging based on individual needs.
Automated webinar reminders: AI automates webinar reminders and follow-ups, maximizing attendance and post-event engagement.
Real-Time feedback analysis: AI quickly processes feedback, allowing the team to optimize messaging and product features in real-time.
The result is a faster go-to-market, higher customer engagement, and a measurable ROI uplift, showcasing AI’s role in improving marketing efficiency and effectiveness.
Your next step: Embrace automation to unlock exponential growth
Automation in AI marketing strategy is no longer optional—it’s imperative for businesses aiming to scale sustainably. By integrating automation into your marketing stack, you position your organization for efficiency, agility, and precision.
🎙️ To dive even deeper into the transformative power of automation in AI marketing strategy, don’t miss our conversation with Madhukar Kumar, SingleStore CMO, in the latest podcast episode where he shares all of these insights.
Tune in now to learn how the right AI tools revolutionize your marketing processes and elevate your business to new heights. Listen or watch the full conversation here.
Don’t forget to share your thoughts—what part of AI automation excites you the most? Your perspective is invaluable, and we’d love to hear from you.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Finding The Right Path: Exploring Funding Options For Tech Startups
URL: https://www.data-mania.com/blog/funding-options-for-tech-startups/
Type: post
Modified: 2026-03-17
Launching a tech startup is an exciting journey filled with ambition, innovation, and big dreams. However, even the most groundbreaking ideas need the right financial support to move forward. Without sufficient funding, scaling operations, hiring talent, and developing products can quickly become difficult. Choosing the right funding approach is one of the earliest and most important decisions a startup founder must make. Understanding the different funding options for tech startups can set the stage for long-term success.
Traditional and Modern Funding Options For Tech Startups
Many founders immediately think of venture capital when considering how to fund their startups. While venture capital can provide substantial resources and strategic advice, it often comes with expectations for rapid growth and equity stakes. This model works well for startups with high scalability and disruptive potential, but it is not the only route. Bank loans and small business lines of credit can also support startups that have a more steady growth plan, although they usually require a strong business plan and personal guarantees.
Angel investors offer another funding avenue. These are individuals who invest their own money into early-stage businesses they believe in. Often more flexible than venture capital firms, angel investors may be willing to back startups based on vision and potential rather than proven revenue. Crowdfunding has also gained popularity, allowing startups to raise small amounts of money from many people. A successful crowdfunding campaign can validate your product and build a loyal community even before launch.
Creative Financing for Greater Flexibility
Some startups prefer financing methods that avoid giving up equity. Bootstrapping, or self-funding, remains a powerful strategy for founders who want to maintain full control of their business. Although it can limit growth speed, it promotes discipline and sustainability. Another flexible option involves invoice factoring services, which allow startups to sell their accounts receivable at a discount to improve cash flow without taking on debt. This solution can be particularly helpful for tech startups working with large clients who have lengthy payment terms.
Grants and competitions provide non-dilutive funding opportunities. Winning a grant or an innovation competition can give a startup credibility, publicity, and essential financial support without the need to repay or share ownership. However, these opportunities are highly competitive and often require detailed applications and strong proof of concept.
Choosing the Best Fit for Your Vision
Each funding option carries its own advantages and risks. The best choice depends largely on your startup’s stage of development, revenue model, growth goals, and appetite for outside involvement. Some founders benefit from combining several funding sources to create a more stable financial foundation while preserving flexibility.
Securing the right funding is about more than just finding cash. It is about aligning your financial resources with your business strategy and long-term vision. Thoughtful selection among the many startup financing options can help your tech venture move from idea to reality with greater confidence and resilience. With careful planning and smart decision-making, the right financial partnership can open doors to growth, innovation, and lasting success. For more information, look over the accompanying infographic.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Employee Retention Strategies for Small Businesses: How To Keep Your Best Employees From Leaving
URL: https://www.data-mania.com/blog/employee-retention-strategies-for-small-businesses-how-to-keep-your-best-employees-from-leaving/
Type: post
Modified: 2026-03-17
In a competitive tech landscape, retaining top employees is becoming more difficult. That’s why today we’re talking about employee retention strategies for small business.
Startups and established companies alike are constantly offering new incentives to attract skilled professionals. While salary often grabs the spotlight, benefits play an equally important role in how employees decide where to work and whether they stay. Offering strong benefits is one of the smartest employee retention strategies for small businesses that want to stay competitive without engaging in costly salary wars.
Employee Retention Strategies for Small Businesses & Why Benefits Matter More Than Ever
Today’s employees expect more from their employers than a paycheck. They look for a full package that supports their quality of life both inside and outside of work. Health benefits, flexible schedules, mental health support, career development opportunities, and retirement planning all contribute to an employee’s decision to stay or leave. Workers want to feel that their employer genuinely cares about their well-being, growth, and long-term success, not just their productivity. Even in tech roles where remote work and flexible hours are common, comprehensive benefits are often the deciding factor in staying loyal and committed.
The Financial and Emotional Impact of Strong Benefits
Good benefits do more than protect physical health. They reduce stress, improve job satisfaction, and help employees feel secure about their futures. Health insurance carries significant weight. Without it, employees may face financial burdens that could drive them to seek more secure opportunities. Working with a knowledgeable health insurance broker in Texas, or wherever your business is based, can make it easier to offer attractive coverage options without stretching your budget too thin. Well-structured benefits give your team peace of mind, allowing them to focus on innovation and growth instead of worrying about personal expenses.
Customizing Benefits for a Competitive Edge
Different employees value different perks. Some may prioritize strong healthcare plans, while others appreciate generous parental leave, wellness programs, or tuition reimbursement. Listening to what your team values most and adjusting your benefits offerings accordingly shows that you respect their needs and care about their personal and professional growth. Offering choices within your benefits package also empowers employees to select options that fit their lifestyles, creating a more satisfied and committed workforce. A flexible approach allows you to meet diverse needs across different stages of life, from young professionals starting families to seasoned employees planning for retirement. This thoughtful customization can significantly boost loyalty.
Strong benefits are an investment in your company’s future, not just a current expense. Employees who feel valued are more likely to stay, perform at a higher level, and recommend your company to others. This creates a positive cycle where attracting and keeping top talent becomes easier over time. Building an environment where people feel supported ensures that your best employees are less tempted to leave for other opportunities, even those that appear exciting on the surface. With the right benefits in place, your business can maintain its competitive edge and build a foundation for long-term success. To learn more on how benefits can keep your best talent from leaving, check out the resource below.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## AI in Investment Management: How AI Is Transforming Communication Between Fund Managers And Investors
URL: https://www.data-mania.com/blog/ai-in-investment-management/
Type: post
Modified: 2026-03-17
The relationship between fund managers and investors depends on the timely exchange of accurate, actionable information. Traditional methods of communicating fund performance, strategy changes, and market outlooks can no longer meet the speed and scale required in today’s financial environment. The use of AI in investment management is changing that dynamic by streamlining data flows and creating new ways to personalize and optimize investor communications.
5 Powerful Uses of AI in Investment Management
There are 5 powerful ways that the use of AI in investment managment is changing the game…
(photo credit: Microsoft Stock Images)
AI Enhances Transparency and Timeliness
AI in investment management is reshaping how fund managers collect, interpret, and present data to investors. Rather than relying on quarterly updates or static reports, managers can now deliver near real-time insights powered by machine learning algorithms. These systems are capable of continuously analyzing portfolio performance, tracking market trends, and updating investors with precise summaries without manual input. This creates a more dynamic and transparent relationship, where investors are informed faster and with greater accuracy.
Automated Reporting Improves Accuracy
One of the most significant advantages of artificial intelligence is the ability to eliminate human error in reporting. AI tools can extract relevant data from internal systems, market feeds, and compliance platforms, then organize that information into investor-ready formats. Natural language generation can create personalized summaries for different audiences, from institutional stakeholders to individual clients. These tools allow fund managers to maintain consistency in communications while scaling outreach to large investor bases.
Personalization at Scale
Investors vary in their risk tolerance, investment goals, and communication preferences. AI helps managers address this diversity by segmenting audiences and delivering information relevant to each group. A retail investor may receive simplified performance breakdowns, while a large institutional client might receive a detailed technical report. This kind of targeted engagement improves investor satisfaction and builds trust. AI also supports sentiment analysis, allowing managers to gauge investor reaction and fine-tune future messaging accordingly.
Predictive Analytics Strengthen Decision Support
Investors vary in their risk tolerance, investment goals, and communication preferences. AI helps managers address this diversity by segmenting audiences and delivering information relevant to each group. A retail investor may receive simplified performance breakdowns, while a large institutional client might receive a detailed technical report. This kind of targeted engagement improves investor satisfaction and builds trust. AI also supports sentiment analysis, allowing managers to gauge investor reaction, track behavioral trends over time, and fine-tune future messaging accordingly to improve transparency, client retention, and long-term relationship value.
Secure Data Management and Compliance
Using AI in communication does not compromise security. Advanced systems follow regulatory requirements and maintain strict access controls. Many solutions are integrated with hedge fund management software, allowing seamless compliance with data-sharing rules while automating many back-office functions. This reduces administrative burden and ensures that all communications meet industry standards.
Artificial intelligence is quickly becoming a central tool in investment communications. It empowers fund managers to be faster, clearer, and more strategic in how they connect with investors. As expectations grow for personalized and data-rich updates, firms that embrace AI in investment management will be better positioned to build lasting relationships and deliver consistent value. For more information, look over the accompanying infographic below.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## How Onboarding Kits Shape Company Culture
URL: https://www.data-mania.com/blog/how-onboarding-kits-shape-company-culture/
Type: post
Modified: 2026-03-17
In fast-growing tech startups, the onboarding process often moves quickly as teams focus on product development and market growth. Thoughtful onboarding kits can create structure during this transition by reinforcing company culture and providing employees with clear starting points. When implemented effectively, these kits help new hires feel connected to the organization from their first day.
Communicating Mission and Values
Startup culture often centers on a shared vision and a clear sense of purpose. Onboarding kits can introduce these ideas through concise documentation that explains company goals, product philosophy, and long-term direction. A welcome guide that highlights core values helps employees see how their roles connect to the larger mission.
Leadership messages included in the kit can also provide clarity about priorities and working style. Founders or department leaders may share insights about collaboration, decision-making, and communication expectations. This information supports alignment between employees and company leadership.
Providing Tools for Early Productivity
Tech startups move quickly, which makes early productivity essential. Onboarding kits should include resources that help new hires begin contributing without unnecessary delays. Access to information for internal systems, development environments, and communication platforms allows employees to start working efficiently.
Documentation about team workflows also supports early integration. Guides explaining sprint cycles, product release schedules, or collaboration tools reduce confusion and help employees adapt to the company’s working rhythm.
Providing small physical items can reinforce this transition as well. Items such as notebooks, workspace accessories, or apparel help create a sense of belonging. Some startups use same-day t-shirt printing to prepare branded shirts for new hires, allowing employees to participate in team activities or company events immediately.
Reinforcing Community and Collaboration
Culture grows through interaction. Onboarding kits can introduce employees to the people and communities that shape the organization. Team directories, mentorship program information, and internal communication channels help new hires connect with colleagues quickly.
Structured introductions also strengthen collaboration. For example, onboarding materials might highlight cross-functional teams, product groups, or internal communities focused on learning and innovation. These connections encourage knowledge sharing across departments.
Events and informal gatherings can also be mentioned in onboarding materials. Hackathons, learning sessions, and social meetups provide opportunities for employees to contribute ideas and build relationships within the company.
Encouraging Long-Term Engagement
Onboarding should support growth beyond the first week of employment. Kits that introduce professional development opportunities help employees see potential career paths within the organization. Information about training resources, technical workshops, and internal mentorship programs demonstrates a commitment to employee development. Feedback channels should also be clearly explained. Encouraging new hires to share ideas or raise concerns builds trust and promotes communication.
Employee onboarding kits offer startups a practical way to translate culture into everyday experiences. Clear documentation, accessible tools, and thoughtful introductions help new hires integrate quickly into fast-moving environments. When onboarding reflects company values and supports collaboration, employees are more likely to engage fully with their work and contribute to organizational growth. Look over the infographic below for more information.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Capital Crunch: The Startup Funding Lifecycle & Why Scaling A Startup Is Harder Than It Looks
URL: https://www.data-mania.com/blog/capital-crunch-the-startup-funding-lifecycle-why-scaling-a-startup-is-harder-than-it-looks/
Type: post
Modified: 2026-03-17
The early days of a startup are often marked by energy, innovation, and optimism. Founders hustle to turn ideas into prototypes then into real products with real users, without fully understanding the nuances of the startup funding lifecycle. But once the foundation is set and traction is visible, the next phase begins. Scaling a startup requires more than vision. It requires capital, and finding the right kind at the right time can be harder than expected.
Gaps in the Startup Funding Lifecycle
The startup funding lifecycle typically begins with seed rounds or early angel investments, which often depend on the founder’s personal network. Once a business gains momentum, Series A and beyond provide the fuel for hiring, product development, and market expansion. However, the gap between these stages is where many startups stall. A business may be too advanced for pre-seed investors but still considered too risky or small for institutional funds. This funding no-man’s-land can delay key decisions or force founders to dilute equity more than they anticipated.
Timing plays a significant role. Raising capital too early can create pressure to scale before product-market fit is established. Waiting too long may cause competitors to move faster or capital reserves to run dry. Investors evaluate not only performance but also the clarity of a company’s path forward. A strong pitch deck without clear financial projections or a reliable customer acquisition strategy often gets passed over.
(photo credit: Microsoft Stock Images)
Investor Expectations and the Proof Problem
As companies move through the startup funding lifecycle, investor expectations rise steeply. Early believers may back a concept. Later-stage investors expect evidence. This includes detailed KPIs, churn metrics, revenue growth, and retention rates. In sectors like fintech or healthcare, additional regulatory hurdles raise the bar even further. Founders must shift from storytelling to financial rigor.
Another common issue is that startups may misjudge their valuation. Founders influenced by early enthusiasm may pitch inflated numbers, which can scare off serious investors or set the business up for failure in the next round. A bloated valuation without the operational performance to match leads to down rounds, morale issues, and reputational damage in future negotiations.
Strategic Misalignment and Missed Opportunities
Funding can also go sideways when there is a mismatch between a company’s goals and an investor’s expectations. A startup focused on long-term sustainability may clash with investors looking for aggressive growth at all costs. In areas like clean tech investing, founders may find better alignment with mission-driven funds that understand longer development cycles and complex go-to-market timelines.
Startups that succeed in scaling often combine financial discipline with strategic flexibility. They treat fundraising as part of their business model rather than a separate task. This means staying engaged with investors even when not actively raising and constantly refining both short- and long-term plans.
Scaling a startup is rarely a straight line. There are pivot points, pauses, and recalibrations. However, startups that keep an eye on their metrics, refine their funding strategy, and maintain honest conversations with potential investors are better prepared for the road ahead. To learn more, look over the accompanying resource below.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## The Power Of Strategic Startup Partnerships In Growth
URL: https://www.data-mania.com/blog/startup-partnerships/
Type: post
Modified: 2026-03-17
Startups often operate under pressure. There are deadlines to hit, capital to stretch, and markets to reach before competitors catch up. Amid the urgency, strategic startup partnerships offer a powerful way to move forward efficiently. They allow startups to access new networks, capabilities, and resources without building everything from the ground up.
Why Startup Partnerships Matter Early
In the early stages, a startup might be rich in ideas but short on infrastructure. Forming a startup partnership with a more established company can help fill that gap. Whether it is access to technology, distribution channels, or regulatory knowledge, alliances can help young companies accelerate faster than going it alone.
Consider a startup in the telehealth space. Building a patient base and gaining trust in that field is difficult. But by collaborating with a clinic group or hospital network, the company could conduct real-world pilots, refine its offerings, and build credibility more quickly than it could alone.
(photo credit: Microsoft Stock Images)
What Makes a Partnership Truly Strategic
The most effective startup partnerships do not just plug holes. They create value for both sides and serve a shared objective. That might include co-developing a product, sharing proprietary data, or working together to enter a new market. The key is alignment of incentives and clarity of roles.
Protecting intellectual property and establishing terms upfront is essential. Startups should avoid informal arrangements and instead rely on documented agreements that define expectations. Many founders rely on legal experts and due diligence firms to evaluate potential risks and structure fair deals.
Avoiding Risky Dependencies
Startups can run into trouble when they overcommit to a single partner or treat a deal as a substitute for a business model. An exclusivity agreement with a large company may sound promising, but it can limit a startup’s ability to explore other opportunities. If the partnership stalls, growth can grind to a halt, leaving the startup locked into outdated terms or misaligned priorities.
Another risk is chasing funding disguised as partnership. If the only value a partner brings is capital, it may be more appropriate to call it an investor relationship. Strategic alliances should expand capabilities and offer new possibilities for innovation or scale, bringing access to networks, technology, or operational knowledge that would otherwise take years to develop internally.
Building With the Long Game in Mind
Founders who approach startup partnerships thoughtfully tend to build stronger companies. That means asking the right questions. Do the cultures align? Can both parties benefit in the long term? Are expectations realistic? Will the partnership support flexibility as the startup evolves, or could it create unintended constraints that hinder future decisions?
Quick deals often fade. Strong startup partnerships, built on mutual value and clear communication, can become growth engines. They open doors that might remain closed otherwise. They encourage startups to think beyond their limits, without losing focus on their core priorities or long-term strategy.
Startup partnerships are not shortcuts. They are strategies. When built with care, they can define a company’s future just as much as its product or pitch. For more information, look over the accompanying resource below.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## A Real Performance-Based Pricing Example: How Strategic Positioning Unlocked a $750K Enterprise Deal
URL: https://www.data-mania.com/blog/a-real-performance-based-pricing-example/
Type: post
Modified: 2026-03-17
Let me share a performance-based pricing example that completely transformed how one of my clients approaches enterprise deals.
This data consultant had all the technical chops you’d expect – deep specialization, real expertise, years of experience. But here’s what he didn’t have: clear market positioning, consistent inbound leads, or any systematic approach to landing enterprise clients. Sound familiar?
Then we started working together one-on-one, and everything changed.
“Pay me based on results. If I can’t solve your data problem and save you money, you don’t owe me anything. If I succeed, I earn a percentage of what you save.”
We didn’t just optimize his LinkedIn profile or tweak his messaging. We fundamentally repositioned him as THE go-to expert for enterprise data problems that others couldn’t solve. But here’s the real breakthrough – this positioning gave him the confidence to propose something most consultants would never dare:
“Pay me based on results. If I can’t solve your data problem and save you money, you don’t owe me anything. If I succeed, I earn a percentage of what you save.”
That’s confidence backed by strategic positioning. And it opened the door to a $750,000 performance-based contract that he delivered in under two weeks.
This performance-based pricing example isn’t just about creative contract structures. It’s about the power of positioning yourself as a category-of-one expert who stands behind their work with absolute confidence. Performance-based pricing is just one of many scalable options. There are other revenue models for startups in the tech and data space.
Understanding Performance-Based Pricing: Beyond the All-or-Nothing Myth
Most founders think performance contracts are either “all-risk” or “no-risk.” That’s why most attempts at performance-based pricing fail spectacularly.
What Performance-Based Contracts Actually Are: Think of them as shared ownership of outcomes. You’re not selling time or deliverables – you’re betting on measurable results. The client pays less upfront but shares meaningful upside when you hit (or exceed) targets.
The magic happens when you structure them correctly, which brings us to our next performance-based pricing example framework.
The 3-Tier Structure That Actually Works
Here’s the proven framework from our performance-based pricing example that eliminates risk for both parties:
Tier 1: Base Fee (60-70%) Covers your costs and baseline value. This isn’t charity – you’re still getting paid for your expertise and proven methodology.
Tier 2: Performance Bonus (20-30%) Tied to specific, measurable outcomes you can directly influence and control.
Tier 3: Upside Share (10%) Percentage of growth or savings you generate beyond baseline expectations.
Why This Structure Works:
For clients: Guaranteed value at the base level with explosive upside potential
For you: Cover costs while positioning for exponential returns based on real impact
The Complete Performance-Based Pricing Example Breakdown: $4M Problem = $750K Solution
Let me walk you through exactly how this performance-based pricing example played out, because the details make all the difference.
The Challenge: The enterprise client had operational inefficiencies costing them almost $4 million annually. Instead of pitching a traditional $50K consulting project, my client proposed this structure:
Complete diagnostic work at no charge
Take 20% of documented savings for the first year
Zero payment unless the problem was solved completely
The Results:
Fixed the problem and saved them $3.75M annually
Earned $750K (20% of verified savings)
Client achieved $3M net win with zero upfront risk
Project completed in under two weeks
When Performance-Based Pricing Makes Sense (And When It Absolutely Doesn’t)
Not every situation calls for performance-based pricing. Here are the three critical signs it’s right for your business:
✅ Sign #1: You Have Proven, Repeatable Frameworks
You need systems that consistently drive results – methodologies you’ve tested and refined across multiple clients. In our performance-based pricing example, the consultant had a repeatable data optimization process with documented success.
Red Flag: You’re still figuring out your methodology or this represents experimental work.
✅ Sign #2: The Client Has Clear, Measurable Goals
Your client must be willing to tie compensation to specific, quantifiable outcomes. Cost savings, revenue increases, efficiency gains – things you can measure objectively without ambiguity.
Red Flag: Vague goals like “brand awareness” or “market positioning” that can’t be clearly measured.
✅ Sign #3: You Control Enough Variables for Success
You need direct influence over the outcome, not dependence on external factors beyond your control.
Red Flag: Success depends heavily on client team execution, market conditions, or factors you can’t directly impact.
How to Structure Performance Contracts Without Getting Burned
The difference between a profitable performance-based pricing example and a complete disaster comes down to meticulous planning. Here’s your protection framework:
1. Document Current State Obsessively
Before you change anything, create an irrefutable baseline. Screenshots, reports, third-party audits – whatever it takes to establish “this is exactly where we started.”
Why This Matters: Prevents disputes about how much improvement you actually delivered.
2. Define Success in Writing (Before You Start)
Not just revenue targets – but timeline, measurement methods, and contingency plans for external factors.
Include specifics like:
Exact calculation methodology
Who measures what, when, and how
How to handle market changes or team turnover
What constitutes “completion” of your work
3. Build in Minimum Thresholds
Only trigger performance bonuses when results exceed meaningful baselines. This protects you from small improvements that don’t justify the risk structure.
Example: “Performance bonus applies only when savings exceed $500K annually”
4. Set Clear Time Boundaries
Define exactly how long the performance measurement period lasts. Typically 12-24 months for percentage shares, depending on your type of work.
Why This Matters: Prevents indefinite payment obligations or disputes about when “results” should be measured.
The Psychology Behind This Performance-Based Pricing Example
Performance-based pricing works because it completely flips the traditional consultant-client dynamic. Instead of “pay me and hope for results,” you’re confidently saying “let me prove value first.”
What This Communicates:
Supreme confidence in your proven methodology
Complete alignment with client success (not billable hours)
Lower risk than traditional consulting approaches
Laser focus on outcomes over activities
The Competitive Advantage: While competitors pitch hourly rates or fixed projects, you’re offering shared success. This positions you as a true strategic partner, not just another vendor.
The Strategic Positioning Component: Why This Client Was Ready
Our performance-based pricing example only worked because we built the positioning to back it up. Here’s what made the $750K contract possible:
1. Category-of-One Expertise
We positioned him not as “a data consultant” but as “THE data consultant for enterprise operational efficiency.” Narrow, specific, defensible.
2. Proven Methodology
His data optimization framework became a reusable asset – something he could confidently bet on across multiple clients.
3. Risk-Absorption Confidence
Strong positioning gave him the confidence to absorb client risk because he knew his system worked consistently.
The Lesson: Performance-based pricing isn’t just a payment structure – it’s a positioning strategy that demonstrates unshakeable confidence in your value delivery.
Your Performance-Based Pricing Readiness Assessment
Before you consider your own performance-based pricing example, honestly evaluate these critical factors:
Do You Have:
A proven, repeatable methodology for your service?
At least 3 successful case studies with measurable results?
The ability to measure and document outcomes objectively?
Control over the key variables that drive success?
Financial runway to wait for performance payments?
Does Your Client Have:
Clear, quantifiable problems you can solve?
Decision-making authority to approve non-traditional contracts?
Willingness to share upside when you deliver results?
Systems in place to measure the outcomes you’ll improve?
If you can’t check most of these boxes, stick with traditional pricing until you can build this foundation.
The Implementation Roadmap: Creating Your Own Performance-Based Pricing Example
If you’re ready to test performance-based pricing, here’s your step-by-step approach:
Phase 1: Build Your Foundation (1-2 months)
Document your methodology and proven results
Create case studies with specific, measurable outcomes
Identify your “minimum viable improvement” thresholds
Phase 2: Find the Right Opportunity (1 month)
Target clients with quantifiable problems (cost inefficiencies, revenue gaps)
Focus on prospects who’ve tried traditional consulting unsuccessfully
Prioritize relationships where you have established credibility and trust
Phase 3: Structure the Deal (1 week)
Use the 3-tier structure (base fee + performance bonus + upside share)
Document everything: baseline, measurement methods, timelines, thresholds
Include clear exit clauses and dispute resolution processes
Phase 4: Deliver and Document (Ongoing)
Track progress obsessively with shared dashboards
Provide regular updates showing improvement trajectory
Build this success into your next performance-based pricing example positioning
What Changes When This Becomes Your Story?
This performance-based pricing example wasn’t just about money – though $750K in two weeks certainly didn’t hurt. It was about the strategic advantage of positioning yourself as someone who stands behind their work with absolute confidence.
Performance-based pricing isn’t right for everyone. But when it’s right, it creates a competitive moat that traditional consultants simply can’t cross.
The deeper principle: When you have proven systems, clear positioning, and unshakeable confidence in your value delivery, you can restructure entire market dynamics in your favor.
What’s your next move?
Ready to engineer your own growth breakthrough? I am taking on 1 new client for next month – drop your details in this form and let’s look to see if we’re a fit.
Want more strategic insights like this delivered weekly? My growth frameworks have helped tech startups scale from pre-revenue to $6M+ ARR by building predictable, data-driven systems instead of hoping marketing tactics work. Join my private newsletter here.
FAQs
What is Performance-Based Pricing?
Performance-based pricing is a compensation model where you get paid based on measurable results you deliver, not just time spent or deliverables completed. Instead of charging traditional hourly rates or fixed project fees, you’re essentially betting on outcomes – taking on shared ownership of your client’s success.
Think of it as moving from “pay me and hope for results” to “let me prove value first, then get rewarded based on actual impact.” You’re not selling time or tasks; you’re selling measurable business improvements like cost savings, revenue increases, or efficiency gains.
This approach demonstrates absolute confidence in your methodology while positioning you as aligned with client success, not just another vendor. When structured correctly, it covers your costs while creating exponential upside potential based on real value delivered.
How Does the Performance-Based Pricing Model Work?
The most effective approach uses a 3-tier structure that eliminates risk for both parties:
Tier 1: Base Fee (60-70%) covers your costs and baseline value delivery – what you’d typically charge for the methodology, expertise, and professional execution. This is paid regardless of performance outcomes.
Tier 2: Performance Bonus (20-30%) is tied to specific, measurable outcomes you can directly control. This only triggers when results exceed meaningful baselines, often with graduated tiers like 10% at 50% target achievement, 20% at 75%, full 30% at 100%+.
Tier 3: Upside Share (10%) provides percentage participation in growth or savings beyond baseline expectations, creating long-term alignment with exceptional results.
Critical success factors include documenting baseline measurements obsessively, defining success criteria in writing with exact calculation methodology, building in minimum thresholds for meaningful improvements, and ensuring you control enough variables to directly influence outcomes.
Do You Have More Performance-Based Pricing Examples?
Performance-based pricing works across multiple industries where outcomes are measurable. Sales optimization consulting might use $45K base + 15% of revenue increase above baseline for 18 months instead of a traditional $75K fixed fee.
Marketing efficiency projects could structure as $25K base + $50 per qualified lead above current baseline for 12 months, while operational cost reduction might use $35K base + 25% of annual cost savings verified by third-party audit.
The model thrives in operations consulting (cost savings, efficiency improvements), sales training (revenue increases, conversion improvements), marketing strategy (lead generation, customer acquisition cost reduction), and technology implementation with measurable productivity improvements.
What makes these examples successful is having quantifiable outcomes that can be measured objectively, direct consultant influence over key variables, established baselines for comparison, client systems capable of measuring improvements, and meaningful impact potential that justifies the contract complexity.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## How Startups Can Deploy TTS for Scalable Support and Onboarding a New Customer
URL: https://www.data-mania.com/blog/onboarding-a-new-customer/
Type: post
Modified: 2026-03-17
A startup’s journey towards expansion is often defined by its ability to efficiently scale operations, but a major challenge often arises with the manual process of onboarding a new customer. As more users are added to a growing company, the normal customer support and onboarding processes prove to be a major hindrance.
Processing a large number of requests manually and educating new users requires an equally proportional increase in human resources, which proves to be expensive and challenging to maintain. It is here that contemporary AI-powered technology presents a feasible route to scalability.
Among the most effective of these technologies is text to speech (TTS), which enables startups to automate and customize customer interactions so that support and onboarding continue to be seamless and effective despite the rapidly increasing size of the business. With the use of TTS, organizations can develop a solid, accessible, and cost-effective base for their growth.
The Strategic Value of Text-to-Speech for Startups
For a startup, every available resource matters. Text-to-speech technology, which renders written text into high-quality, human-sounding voices, offers a means of providing an even tone and professional voice without using costly voice actors or studio space. Unlike vintage, robotic-sounding systems, contemporary TTS is driven by deep machine learning models that are capable of replicating human intonation, rhythm, and emotion. This enables the use of brand-specific voices that are engaging as well as authentic.
The main strategic advantages of implementing a text-to-speech solution are:
Cost Efficiency: TTS dramatically lowers the costs of operation by automating the normal support tasks. Rather than employing a big team of customer service agents to assist with frequent queries, startups can implement automated voice agents and interactive voice response (IVR) systems.
24/7 Accessibility: A human support team is circumscribed by business hours and time zones. AI-based TTS offerings can offer support 24/7 so that customers can contact them or onboard at any time, anywhere globally.
Scalability: TTS solutions are capable of processing an unlimited volume of concurrent conversations with no loss of quality. As customer numbers increase, the system automatically scales, providing uniform performance without the need for extra staff.
Brand Consistency: All voice engagements, from onboarding tutorials to customer support calls, can employ the same brand voice. This consistency reinforces brand identity and ensures that every customer interaction feels integrated and professional.
Streamlining New Customer Onboarding Using TTS
The process of onboarding a new customer is essential for ensuring long-term user retention. A clear and informative process enables the user to quickly learn about the product and helps promote long-term usage. TTS can be utilized to build a highly efficient onboarding process that is both personalized and scalable.
Some of the primary use cases for TTS during customer onboarding are:
Interactive Product Tours: As a part of the process of onboarding a new customer, instead of static text or video, startups can utilize TTS to create dynamic voice-guided product tours. The computer can “read” step-by-step directions, explain features, and provide context as users navigate the product. This is a more interactive, engaging experience that will better reach auditory learners.
Personalized Welcome Messages: New users can be greeted with a customized welcome call or message produced by a TTS system upon registration. The voice may sound professional and friendly, and the message can be customized to the individual user’s particular requirements or declared goals, thus making them feel special right at the beginning.
Automated Walkthroughs: Startups can produce in-depth, multi-part walkthroughs for sophisticated products. In place of one lengthy video, TTS can be used to produce a series of short, bite-sized audio guides where users can listen to the content at their own convenience while being able to multitask.
Accessibility: By offering an audio alternative, TTS makes onboarding content accessible to more users, including visually impaired or readers who have difficulty reading. This guarantees a more inclusive and user-friendly experience for all.
Boosting Customer Support using Text-to-Speech
Customer support is a vital aspect of every business, but it can be a significant expense center for an expanding startup. By incorporating TTS into support channels, startups can automate and streamline their support processes, allowing human agents to work on complex, high-value cases.
Practical uses of TTS in customer care are:
Intelligent IVR Systems: Advanced IVR systems, driven by TTS, can have natural, free-flowing conversations with the caller. Rather than a forceful menu, the system can recognize a customer’s question in natural language and give a sharp, informative answer. For instance, if a customer asks to know his/her account balance, the system can look it up and announce it verbally.
Automated Chatbots: TTS can be employed to incorporate a voice aspect in text-based chatbots. When a customer prefers to have a voice conversation, the chatbot can translate its text inputs into vocal speech, making the conversation more human-like and engaging.
Proactive Outreaches: Startups can utilize TTS to create automated, customized calls or messages for payment reminders, order status updates, or satisfaction surveys. The proactive effort decreases inbound support load and keeps customers informed.
Information Dissemination: In place of answering the most frequent questions, a startup may build a large repository of articles and FAQs. TTS can be utilized to transform this material into a searchable audio database, enabling customers to receive instant, verbal responses to their queries without needing to browse lengthy documents.
Conclusion
For rapidly growing startups, text-to-speech technology is no longer a nicety but a strategic imperative. TTS removes tedious support and onboarding tasks from personnel, freeing up time and resources to invest in core business development and innovation. It offers a cost-effective, scalable, and very personalized means of engaging with customers, guaranteeing that a startup’s support and processes for onboarding a new customer are able to scale as quickly as its customer base. By adopting TTS, an organization can offer top-notch customer experiences that are not only efficient and cost-effective but also engaging, setting the stage for a brighter future.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## Branding Blind Spots: The Hidden Mistakes That Hurt Growth
URL: https://www.data-mania.com/blog/branding-blind-spots-the-hidden-mistakes-that-hurt-growth/
Type: post
Modified: 2026-03-17
Every company believes it understands its brand. Yet even the most successful businesses have gaps between how they see themselves and how customers see them. These gaps can weaken trust, slow growth, and reduce market relevance. Identifying and addressing these blind spots is one of the most important steps toward building a resilient brand.
Misaligned Messaging
A brand’s message should reflect both its purpose and the audience’s expectations. Many companies develop a tagline or mission statement that sounds appealing but fails to connect with what customers actually value. For instance, a tech company that promotes innovation but delivers a confusing user experience sends a mixed message. Consistency across marketing channels, tone, and customer touchpoints is essential.
Misalignment often happens when leadership teams shape messaging in isolation. Internal enthusiasm for a product or service can lead to exaggerated claims or overused industry jargon. Regular brand audits help identify whether external communication still matches internal intent. Listening to customer feedback can reveal how far the message has drifted.
Ignoring Internal Brand Culture
Brands do not exist solely in advertisements or websites; they live through employees. An inspiring brand message fails if the workforce does not believe it. Internal culture is often overlooked as a driver of brand credibility. When staff are disengaged, customers can sense it through tone, service quality, or lack of enthusiasm.
Leadership should communicate the brand’s purpose clearly and consistently to employees. Internal workshops and storytelling sessions can connect everyday work to broader brand goals. Recognizing staff who exemplify brand values reinforces alignment and creates a sense of shared ownership.
Overlooking Visual Consistency
Visual identity is more than a logo. It includes typography, color palette, imagery style, and layout choices. Inconsistent visuals weaken recognition and make the brand look fragmented. This often happens as teams grow or outsource creative work without central brand guidelines.
Brand managers should develop and maintain a digital asset library with approved templates, graphics, and examples of correct usage. Routine checks of social media posts, packaging, and advertisements help ensure visual cohesion across platforms. Even small inconsistencies can reduce brand clarity over time.
Data Without Context
Marketing teams have access to more data than ever, but data alone can distort brand strategy. Metrics such as clicks or impressions can create an illusion of engagement without true connection. A sudden increase in traffic does not necessarily mean brand strength if the visitors do not convert or return.
To avoid this blind spot, combine quantitative data with qualitative insight. Interviews, focus groups, and customer reviews reveal the emotional dimension behind numbers. Understanding why audiences engage or disengage provides context. Brand consulting firms often emphasize this balance, ensuring decisions reflect both evidence and emotion.
Every business has blind spots. What separates lasting brands from struggling ones is the willingness to look for them. Identifying weak points in messaging, visuals, culture, and data interpretation strengthens every layer of communication. When leaders focus branding mistakes as learning opportunities, they build organizations that are both self-aware and adaptable. Check out the infographic below to learn more.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## AI Visibility Playbook: How to Show Up in AI Answers
URL: https://www.data-mania.com/blog/how-to-show-up-in-ai-answers/
Type: post
Modified: 2026-03-17
Marketers don’t just compete for blue links anymore; they compete for answers. That’s why everyone is looking into how to show up in AI answers. In 2026, high-intent buyers increasingly start (and end) their journey inside AI answers across ChatGPT, Perplexity, Gemini, and AI Overviews. That shift rewrites the rules of discoverability. Traditional SEO still matters, but AI visibility, which is essentially showing up as a cited or summarized source in these answers, depends on how clearly your content expresses user intent, how easy it is for large models to parse, and whether it’s verifiable and fresh.
This piece builds on Data-Mania’s 5 Steps to Optimize for AI Search and focuses on what actually drives AI search ranking in practice and how to operationalize it with Bear’s Blog Agent so teams can scale visibility without adding headcount.
How to show up in AI answers today?
You don’t need an extremely technical schema. You need content that makes it easy for answer engines to trust and reuse your work:
Intent clarity over keyword stuffing. Titles and H2s that mirror real questions (“what is…,” “how to…,” “best…for…”) map directly to the way users prompt and the way models retrieve.
Readable structure. Clear hierarchy (H2/H3), short paragraphs, direct definitions, and “TL;DR” sections help models extract and cite.
Verifiability. Outbound citations to credible, primary sources and consistent entity signals (brand, product, author) increase confidence.
Freshness. Recency matters. Content updated on a predictable cadence is more likely to be re-ingested and recited.
Topical cohesion. Internal links that cluster related posts reinforce authority for specific themes.
This isn’t new magic; it’s disciplined editorial craft, expressed in a way that LLMs can understand at a glance.
From large-scale data: what the numbers say
From Bear’s corpus of 20M+ prompt/response pairs and 80M+ analyzed citations across leading answer engines, several patterns emerge:
Question-led structure correlates with higher citations. Pages that use question-oriented H2/H3s (“How does pricing work?” “What’s the difference between X and Y?”) are cited more often than comparable pages organized around broad marketing claims.
Verifiable sources win. Posts that link to primary research (datasets, peer-reviewed studies, official docs) outperform those relying on generic secondary roundups, especially for non-branded queries.
Freshness boosts inclusion. Recency signals (particularly clearly labeled updates within the last ~90 days) correlate with higher inclusion in AI answers for competitive topics.
Concise summaries get reused. When a post offers a crisp definition or numbered list, models frequently lift or paraphrase that segment, making your page the source of record. As a rule of thumb. LLMs strongly prefer the beginning and end of articles.
These are directional relationships, not guarantees. But, they’re consistent enough at scale to inform an editorial operating system for Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO). And, they’re the ultimate answer to how to show up in AI answers.
How Bear’s Blog Agent turns best practices into repeatable workflows
Most content teams already know the right moves – but they still aren’t exactly sure about how to show up in AI answers; also, the gap is execution at scale. Bear’s Blog Agent closes that gap by transforming your strategy into a managed, end-to-end workflow:
Inputs: The agent ingests your existing blog posts, knowledge base, and a short content questionnaire so it actually understands your product, audience, pain points, and proof points.
Intelligence:
Maps user intents to question-led outlines that align with how people prompt, and how models fetch.
Surfaces high-value internal links to strengthen topical clusters (and fix orphaned pages).
Pulls credible external studies and sources to underpin claims with verifiable evidence.
Benchmarks successful competitor posts to identify structural and topical gaps, without entirely copying.
Drafts editor-ready copy that’s SEO-aligned and AI-readable: clear headings, concise summaries, and embedded FAQs.
Packages essential on-page elements (FAQ block, table of contents, meta details, and JSON-LD for common patterns like FAQ/HowTo/Article) so your page ships ready for AI answers, with no dev lift required.
Outputs: Editors receive a polished draft with link recommendations, E-E-A-T signals (author expertise, references), and refresh prompts for ongoing recency. Coming soon: direct CMS integration for one-click publish and scheduled refresh.
Proof of work: A B2B SaaS client used Blog Agent to refactor a set of legacy posts. In six weeks, they went from zero AI citations to frequent mentions on 25+ non-branded prompts, with measurable gains in AI Visibility and qualified lead volume attributed to answer-engine traffic.
What should marketers do this quarter?
If you’re executing manually, here’s a pragmatic checklist:
Pick 5–10 cornerstone pages that align with real buyer questions; rewrite H2/H3s to mirror those questions.
Add user-intent FAQs (3–5 per post) and create keyword-rich answers, to increase the likelihood of LLMs citing those snippets.
Provide a crisp summary and a definition box or numbered steps for easy reuse in AI answers.
Establish a 90-day refresh cadence for competitive topics; visibly update the timestamp and context.
Track two simple metrics: AI citation rate (how often you’re referenced across engines) and visibility % (share of prompts where you appear).
Prefer not to do this by hand? Bear’s Blog Agent automates the heavy lifting, turning the list above into an always-on editorial system for AI Search Ranking. It writes with intent clarity, bakes in verifiability, and ships a structure that models can parse instantly. Then it keeps pages fresh. In short: you get scalable GEO/AEO without adding headcount.
Book a demo of Bear’s platform
The next era: GEO/AEO becomes your editorial operating system
The winning teams won’t treat visibility in AI as a one-off project. They’ll treat it as an editorial OS:
Briefs start with intent clarity (what exact questions we must answer) then move to messaging.
Content ships structured and verifiable by default, not retrofitted later.
Continuous optimization replaces “publish and forget.” Pages evolve alongside the questions customers ask and the evidence the market produces.
Agents move inside the CMS, monitoring freshness, surfacing gaps, and updating content before rankings slip.
When that happens, “AI Visibility” stops being a buzzword. It becomes compounding distribution: your best ideas get discovered and reused precisely when buyers are asking for them.
FAQs
How to show up in AI answers?
AI visibility is a measure for how frequently your content is used, cited, or summarized in AI answers (e.g., ChatGPT, Perplexity, AI Overviews). We track it via AI citation rate and visibility % across a fixed set of non-branded prompts.
Does JSON-LD actually help?
Yes. Paired with clear, question-led structure and credible sources, JSON-LD (e.g., FAQ/HowTo/Article) improves how parsers and models understand your page, which correlates with inclusion.
How often should we refresh content?
For competitive topics, aim for meaningful updates as frequently as possible. Recency correlates with improved inclusion in AI answers.
What does Bear’s Blog Agent do differently?
It operationalizes GEO/AEO: ingesting your context, producing intent-aligned drafts with verifiability baked in, packaging on-page elements for AI readability, and maintaining freshness (soon directly in your CMS).
Want to show up when buyers ask AI tools about your category? Get the AI Visibility Playbook.
---
## I Thought “GTM Engineer” Was Marketing BS. Then I Found 89 Startups Hiring for It.
URL: https://www.data-mania.com/blog/gtm-engineering/
Type: post
Modified: 2026-03-17
I’ll be honest: when I first heard “GTM Engineer,” I rolled my eyes so hard I nearly strained something. “GTM Engineering,” I thought, “give me a break.”
It sounded like yet another attempt by marketers to rebrand themselves with a sexier title. You know the type: “growth hacker,” “revenue architect,” “demand generation ninja.” Just marketing people trying to sound technical without actually building anything.
So I ignored it. For months.
Then yesterday, I was researching outbound training. I can’t even remember why the term popped back into my head, but something made me search “GTM engineer” instead of just looking for sales playbooks.
Here’s what I found:
Over 4,000 monthly searches for the term (so clearly not just me wondering)
Nearly 90 active job postings on LinkedIn as of this week, most of them in San Francisco, most of them at well-funded startups
Job descriptions asking for things like “AI automation platforms,” “workflow integrations,” “data-driven experimentation,” and “building repeatable playbooks”
I stared at my screen for a solid minute.
Then I did something I rarely do: I asked ChatGPT (which has memory of all the work I’ve done with clients) a simple question:
“Am I a GTM engineer?”
Its response: “Yes. You’ve been doing GTM engineering for years. You just didn’t call it that.”
Holy cow.
Turns out, I’m not just a licensed Professional Engineer of the environmental variety. I’m also, apparently, a GTM engineer. And if I didn’t know this role existed, I’m betting a lot of you don’t either.
So here’s what I learned, why it matters, and how to know if you actually need one on your team.
WTF is GTM Engineering? (And Why I Thought It Was Fake)
Let’s start with why I was skeptical.
The term sounds like marketing jargon. “GTM” (go-to-market) is already overused. Add “engineer” to it, and it feels like someone trying to make growth hacking sound more legitimate by borrowing credibility from actual engineering.
But here’s the thing: it’s not marketing. It’s actually engineering for revenue systems.
A GTM engineer doesn’t run ad campaigns or write blog posts (though they might build systems that do those things). They architect, test, and optimize the infrastructure that turns prospects into customers. Think of it like this:
Traditional marketing says: “Let’s launch a campaign and see what happens.”
GTM engineering says: “Let’s build a system that predictably generates qualified leads, measure every stage, identify bottlenecks, and optimize for repeatability.”
It’s the difference between throwing spaghetti at the wall and running controlled experiments with clear hypotheses, instrumentation, and iteration loops.
Here’s what actually convinced me this is real:
I looked at the job postings. Not the titles, but the actual requirements. Here’s what companies are hiring for:
From a Senior AI GTM Engineer role at Calendly:
“Hands-on experience with AI, automation platforms, and LLMs using Clay, OpenAI (ChatGPT), Anthropic (Claude), Zapier, and Hightouch”
“Comfortable designing APIs, building webhooks, authoring cloud functions, and querying data in SQL”
“Proven track record integrating workflows across GTM systems and partnering with systems-admin teams for tools like Salesforce, Outreach, Marketo, Braze, and Gong”
“A self-starting, data-driven mindset with commercial bias and hacker mentality: run experiments, prototype fast, and measure impact”
From a GTM Engineer role at Beacon Software:
“Launch 20+ experiments per quarter testing messaging, channels, targeting, pricing, and sales processes”
“Deploy AI tools and custom automation to eliminate manual GTM work”
“Diagnose pipeline bottlenecks by analyzing where deals stall and why conversion rates drop”
“Reduce average sales cycle length by 20% through systematic process improvements and funnel optimization”
These aren’t marketing jobs. These are systems engineering jobs for revenue operations.
And suddenly, I realized: this is exactly what I’ve been doing for clients. I just called it “fractional CMO work” or “growth strategy.”
How I’ve Been a GTM Engineer Without Knowing It
Let me show you what I mean with two recent projects.
Case Study 1: AI Consulting Startup – $5,160/Month Saved, 8x Faster Execution
A fast-growing AI consulting startup was spending over $5K/month on freelancers to create content. The founder would record a video or send bullet points, then wait days (sometimes weeks) for polished LinkedIn posts, emails, and short-form video scripts to come back.
The bottleneck: Manual handoffs. No consistent voice. Slow turnaround. Expensive execution.
My GTM engineering approach:
I built a custom Claude MCP agent integrated with Airtable that auto-generates multi-channel content from founder-recorded videos or bullet docs. The system pulls brand voice guidelines, case studies, and positioning frameworks from a structured database (Airtable), runs them through Claude with custom prompts, and outputs LinkedIn posts, email sequences, and video scripts. All in the founder’s voice.
I connected it with n8n for workflow automation so the system runs without the founder touching it.
Results:
$5,160/month saved in freelancer costs
8x faster content development (hours instead of weeks)
43% increase in engagement (better quality and consistency)
3.5x more qualified leads from organic content
Less than 60 minutes/week of founder time required
This isn’t marketing. This is systems architecture for growth. I diagnosed the bottleneck (manual execution), designed a modular solution (Claude MCP + Airtable + n8n), instrumented it with quality checks, and optimized for repeatability.
That’s GTM engineering.
Case Study 2: Groe Solutions – First Inbound Leads in 60 Days
Groe Solutions is a B2B tech startup in the health and fitness analytics space. When they came to me, they had zero inbound interest. Their market was tough, their messaging was vague, and they were stuck in founder-led outbound mode.
The bottleneck: No clear positioning. No defined ICP. Content that didn’t resonate.
My GTM engineering approach:
I didn’t start by “doing more marketing.” I diagnosed the root cause: their messaging didn’t differentiate them, and they hadn’t validated who most cared about their product.
So I rebuilt the foundation:
Defined their ICP through team conversations
Clarified their unique value proposition with positioning that actually mattered to buyers
Built an AI-driven content generation framework that, when executed, systematically delivered strong, clear messaging across channels
Results:
First inbound leads within 60 days
Measurable increase in prospect engagement
Increased brand awareness in a previously low-inbound niche
Again: this isn’t “content marketing.” This is diagnostic analysis, constraint optimization, and systematic execution. I treated their GTM like an engineering problem: identify the failure mode (weak positioning), fix the root cause, build a repeatable system, and measure outcomes.
That’s GTM engineering.
What Real Engineers Bring That Marketers Don’t
Here’s the thing most people calling themselves “GTM engineers” get wrong: they’re not actually engineers.
They’re growth marketers borrowing engineering language. And there’s a huge difference.
1. Engineers Design for Failure Modes
Real engineers don’t just build systems for ideal conditions. We ask: What breaks when volume 10x’s? What happens when this input is missing? Where are the single points of failure?
When I built that Claude MCP content automation, I didn’t just auto-generate and pray. I embedded:
Brand voice validation checks (does this sound like the founder?)
Context verification (does the system have the right case study to reference?)
Human approval gates (flag outputs that need review before publishing)
If the system can’t find the right context, it flags the gap instead of publishing garbage.
Most “GTM engineers” would just automate and hope it works. Real engineers build in fail-safes.
2. Engineers Do Root Cause Analysis
Marketers treat symptoms. Engineers diagnose structural problems.
When Groe Solutions wasn’t getting inbound leads, most consultants would’ve said: “Post more on LinkedIn! Run some ads!”
I didn’t touch a single marketing channel. I fixed the root cause: unclear positioning and an undefined ICP. The market wasn’t the problem. The messaging was.
That’s engineering thinking: identify the constraint, fix it at the source, then optimize execution.
3. Engineers Build Modular, Reusable Systems
Marketers build one-off campaigns. Engineers build frameworks that scale.
I don’t create custom solutions for every client. I’ve developed modular playbooks: my ACT Method (Anchor, Construct, Test), my Demand Engine framework, my PLG validation systems. I’ve tested them across dozens of clients.
When a new startup hires me, I’m not starting from scratch. I’m adapting proven systems to their specific context.
That’s the difference between engineering and guessing.
4. Engineers Understand Constraints & Trade-Offs
Every engineering problem involves trade-offs: speed vs. cost, precision vs. scalability, risk vs. reward.
Most “GTM engineers” promise growth without understanding resource constraints. They’ll recommend hiring a full team when a startup can’t afford it, or tactics that require 40 hours/week when the founder is maxed out.
I offer tiered fractional packages ($2K, $5K, $10K/month) precisely because I understand startup constraints. A bootstrapped founder at $0 ARR needs different support than a Series A company at $2M ARR.
Real engineers design for the environment they’re operating in. Not some theoretical ideal state.
5. Engineers Measure What Matters
Marketers track vanity metrics: impressions, engagement, clicks.
Engineers measure leading indicators tied to revenue: pipeline contribution, CAC payback period, activation rates, LTV.
When I ran a campaign for Ericsson that generated 1.2M+ impressions, I didn’t stop at awareness metrics. I tracked all the way to 850+ MQLs and attributed website visitors from specific content pieces.
Real GTM engineering connects top-of-funnel activity to bottom-line revenue.
The Parallel Between Environmental Engineering and GTM Engineering
Here’s what really clicked for me: GTM engineering uses the exact same principles I learned in environmental engineering.
Systems Thinking & Optimization
In environmental engineering, you can’t just fix one pollution source and call it done. You have to understand the entire watershed: how inputs flow through the system and where interventions have maximum impact without creating unintended consequences. And you’re always working within constraints: budget, regulations, existing infrastructure, environmental impact limits.
GTM works the same way. You can’t just “fix the funnel” or “improve SEO” in isolation. I look at the entire customer journey: how positioning affects conversion, how content feeds pipeline, how product experience impacts retention. Everything is interconnected.
And startups have even tighter constraints than environmental projects: limited budget, small team, competitive markets, founders wearing too many hats. My job is to architect the leanest, highest-impact growth engine possible. That’s why I helped an anonymous data consultant land a $750K performance-based contract by repositioning him strategically. Not throwing money at paid ads.
Maximum traction with minimum waste.
Measurement & Iteration
In environmental engineering, you monitor water quality, flow rates, treatment efficiency. Then adjust processes based on real-time data.
I take the same approach with GTM. Every playbook I deploy has built-in KPIs and feedback loops. When validation tests don’t hit benchmarks, we don’t just “try harder.” We analyze what failed, adjust targeting and messaging, and run iterative experiments.
A system that isn’t measured can’t be improved.
Long-Term Sustainability Over Short-Term Fixes
Environmental engineers don’t chase quick fixes. We design for decades, treating root causes instead of symptoms.
Same with GTM. I don’t burn budgets on unsustainable paid acquisition or chase viral hacks. I build repeatable, scalable systems that work whether I’m there or not.
That AI consulting startup didn’t just save $5,160/month. They built a content engine that keeps working, gets smarter over time, and freed the founder to focus on growth instead of execution.
Should You Hire a GTM Engineer? (The Framework)
Here’s how to know if you actually need one.
The 5 Core Skills of a Real GTM Engineer
Systems Architecture – Designing interconnected marketing components that work together like a well-engineered machine, not isolated tactics
Diagnostic Analysis – Identifying root causes of growth blockers rather than treating surface symptoms (like understanding whether your conversion problem is actually a positioning problem)
Constraint Optimization – Maximizing impact within real-world limitations of budget, team size, and founder bandwidth
Instrumentation & Measurement – Building feedback loops that track leading indicators (pipeline quality, activation rates), not just vanity metrics
Modular Design – Creating reusable, testable frameworks that can be adapted across different contexts rather than rebuilding from scratch every time
📦 Steal This: The 3-Question GTM Engineer Vetting Framework
Before you hire anyone calling themselves a “GTM engineer,” ask:
Can they show you a system they built (not a campaign they ran)?
Can they diagnose your growth blocker in 15 minutes without immediately selling you a solution?
Do they measure leading indicators (activation rate, time-to-value) or lagging ones (MRR, traffic)?
If they can’t answer all three convincingly, they’re a marketer with a new title.
What Tools Should You Learn First?
If you want to think like a GTM engineer before hiring one, master these four tools:
1. Claude or ChatGPT – AI-powered content and strategy work that replaces expensive agencies. Learn to write prompts that generate positioning frameworks, messaging variations, and customer research insights.
2. Airtable – Build structured context databases that feed your AI systems with brand voice, case studies, and positioning frameworks. This becomes your growth operating system.
3. n8n (or Zapier) – No-code workflow automation that connects your tools and eliminates manual handoffs. Start simple: automate lead enrichment or content distribution.
4. A simple analytics stack – Google Analytics + your CRM’s native reporting. Measure what actually drives pipeline, not what makes pretty dashboards.
The biggest mistake I see is founders buying a dozen tools before they understand which marketing motions actually convert. That AI consulting startup saved $5,160/month by building one well-architected system using just Claude MCP, Airtable, and n8n. Instead of paying freelancers to do it manually.
Green Flags: You Actually Need a GTM Engineer If…
✅ You have product-market fit but inconsistent revenue growth
✅ You’re overspending on paid acquisition with unclear ROI
✅ Your founder is stuck executing marketing instead of leading strategy
✅ You have data but can’t attribute what drives revenue
✅ You need someone who builds systems, not just runs campaigns
Red Flags: You Don’t Need a GTM Engineer If…
❌ You haven’t validated product-market fit yet (you need customer development first)
❌ You’re looking for someone to “just run ads” or “post on social” (hire a specialist)
❌ You want quick wins without investing in long-term infrastructure
❌ You expect one person to replace strategy, sales, and marketing execution
The ONE Thing You Can Do This Week to Think Like a GTM Engineer
Here’s your tactical next step:
Map your current customer acquisition process end-to-end.
Grab a whiteboard or open a doc. Draw every step from first touchpoint to closed deal. Include:
How prospects discover you
What happens when they engage
Where they convert (or don’t)
Where you personally get stuck
Then identify the single biggest bottleneck where leads stall, deals die, or you’re doing manual work that should be automated.
Most founders jump straight to tactics: “I need better ads!” “I need more content!” “I should be on TikTok!”
But real engineering starts with understanding the system before optimizing it.
When I start a client engagement, I don’t touch a single marketing channel until I’ve done ICP validation and messaging audits. That’s why Groe Solutions got their first inbound leads in 60 days. Not by “doing more marketing,” but by fixing their foundational messaging and ICP clarity first.
Strategic diagnosis beats tactical execution every single time.
What I’m Taking Away From All This
A week ago, I thought “GTM engineer” was a made-up title for marketers who wanted to sound technical.
Now I realize it’s a real discipline. And one I’ve been practicing for years without knowing it.
The engineering mindset I developed over 20+ years as a licensed P.E. is exactly what makes my growth work different. I don’t guess. I don’t chase trends. I design systems, test hypotheses, measure outcomes, and optimize for repeatability.
And apparently, there’s a name for that now.
My prediction? This role will stick around as long as any job is “here to stay” these days. But it will evolve very quickly, especially as generative AI makes automation and experimentation faster, cheaper, and more accessible.
The startups that win in 2026 won’t be the ones with the flashiest campaigns. They’ll be the ones with the best-engineered revenue systems.
P.S. How a P.E. Accidentally Became a GTM Engineer
I spent the first part of my career designing environmental systems: wastewater treatment, pollution control, infrastructure optimization. Then I pivoted into marketing and growth, thinking I was leaving engineering behind.
Turns out, I never left.
I just started applying the same principles: systems thinking, root cause analysis, constraint optimization, measurement, iteration. To a different domain: revenue generation.
When that ChatGPT conversation told me “You’ve been doing GTM engineering for years,” it wasn’t validating a job title. It was validating an entire approach.
The best part? My engineering background is my unfair advantage. While most marketers are guessing, I’m building. While they’re chasing trends, I’m diagnosing. While they’re celebrating vanity metrics, I’m tracking pipeline.
So if you’re a founder wondering whether you need a GTM engineer, or whether the person calling themselves one is legit, ask yourself this:
Are they building systems, or are they just rebranding marketing?
Because there’s a huge difference.
And if you want someone who actually engineers growth, not just talks about it, let’s talk.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## How Marcos Rivera Built a 25-Person Pricing Firm on Word of Mouth
URL: https://www.data-mania.com/blog/how-marcos-rivera-built-a-25-person-pricing-firm-on-word-of-mouth/
Type: post
Modified: 2026-03-17
Six months before Marcos Rivera left Vista Equity Partners, he did something most founders never think to do.
He started saying hello.
Not pitching. Not asking for introductions. Not teasing his upcoming business. Just reaching out to old colleagues, former bosses, people he’d worked alongside and genuinely liked. He sent messages that basically said, “hey, what’s up?” And then he listened.
By the time he actually announced he was going independent as a pricing consultant, he wasn’t cold-launching into the void. He was announcing to a warm room of people who already had him on their radar and actually cared how he was doing.
I thought about that when he told me the story, because I did the opposite when I started my fractional practice. An accelerator coach told me to ‘reach out to my network’ and I did exactly that, to people I hadn’t talked to in two and a half years. You can guess how it went. They were polite. Nobody called back.
Marcos’s approach works because it’s not a strategy. It’s just how humans actually operate.
Warm the Network Before You Need It
Reconnect before you have anything to ask for. That’s the whole discipline.
Marcos spent that pre-launch window checking in with his network the way a decent human being does. No agenda. He wanted to know if people had changed jobs, had kids, moved on. And because he genuinely liked a lot of these people, it didn’t feel calculated. It just felt like catching up.
Then, when he announced he was going independent, he had four to six months of pipeline warmth already baked in. People reached out to him. Some offered referrals. A few became first clients.
Cold network outreach asks people to do you a favor before you’ve re-established any relationship capital. It works occasionally, but it feels bad for everyone involved. The warm-up approach inverts the dynamic entirely. You’re not asking. You’re already in a relationship.
If you’re thinking about going independent, or launching something new, or pivoting your practice: start the warm-up now, six months before you need anything.
Your First Clients Are Your Marketing Team
Marcos’s first clients came from Vista. Former colleagues, portfolio company contacts, people who knew his work and trusted his judgment. Standard enough.
What surprised him was what happened next.
Those clients presented his pricing work to their boards. And board members from other portfolio companies (people Marcos had never met) started reaching out directly: hey, who helped you with that? Can I get an intro?
He hadn’t planned for this. He’d just done excellent work and put it in front of people who sat on multiple boards.
Nail your first few clients. Put everything in. Don’t hold back. Not just because of goodwill, but because good work placed in front of the right people at the right moment becomes its own distribution channel.
For most specialist consultants, the referral engine doesn’t start with cold outreach or content or ads. It starts with one board member telling another.
Marcos eventually layered on more credibility signals like, the Vista name, a growing client logo list, and then a book. Each one compounded the authority from the last.
Ditch the Deck. Give Them the Playbook.
The big consulting firms deliver a 300-slide deck with six-point font, colorful charts, and a million-dollar invoice. Dense. Beautiful. Completely unactionable.
His deliverable is the playbook which includes what the pricing model is, what features belong in which plans, what’s an add-on, what the price points are, and exactly how to test and roll it out. Steps. Decisions. Specifics. Stuff you can actually execute the week after the engagement ends.
He adapted Google’s Sprint methodology into a cross-functional pricing workshop. Everyone relevant is in the room: finance, product, sales, engineering. The session is intense and collaborative. By the end, everyone knows what the pricing is and, critically, why.
When clients co-create the pricing with him, they walk into their next board meeting with high conviction that they can execute it. They’re not presenting something a consultant handed them. They’re presenting something they helped build.
The Mr. Miyagi Technique
A sales leader in one of his workshops pushed back on tiered packaging, insisting every customer should get every feature so they could just be successful. Marcos didn’t argue.
He asked, “why do you feel that way?”
He listened. He empathized with the intent (wanting customers to win). Then he asked a single question, “have you ever paid for something you never used?”
The leader said yes, he hated that. Marcos walked him through what happens when customers are paying for 11 features and only using 3: discount pressure, churn risk, renewal nightmares. The sales guy nodded. He got it. He became an advocate.
The secret move in the workshop is cross-examination between team members, not between Marcos and the team. He asks the group to comment on each other’s ideas before he weighs in. People listen differently to peers than to consultants. And while they’re challenging each other, Marcos is teaching them, almost invisibly, how to think about pricing.
That’s the Mr. Miyagi method. You’re guiding, not correcting.
Price Your Own Services Like a Founder, Not a Freelancer
Marcos made two decisions on his own pricing from day one: no hourly billing, fixed fee only.
Hourly billing breaks the incentive structure. You get paid more for working more hours, but the client just wants the problem solved. Fixed fee puts the risk on you. That’s exactly the point. It forces you to scope tightly, deliver efficiently, and stay disciplined about what’s in and out of the engagement.
His initial pricing audit offer, a two-week diagnostic to find what’s broken, flopped. People said they liked it. Nobody signed. When he dug in, he realized: clients don’t actually want to find their problems. They want them fixed.
So he pivoted. He kept the diagnostic but credited its cost back to the project if the client moved forward. Now the diagnostic isn’t a standalone offer. It’s an on-ramp. Conversion improved immediately.
Three words that now anchor his service delivery philosophy: simple, fixed, clear outcomes. No ambiguity about what gets built, when, or for how much.
Scale Without Disappearing
The hardest part of growing a specialist consulting firm isn’t finding clients or hiring talent. It’s solving the founder bottleneck where clients want you, not your team.
Marcos hit this wall around five to six people. He was in every meeting, every sales call, every delivery session. When he started turning away business because his lead time was getting too long, he knew something had to change.
Before he hired anyone, he ran a Google Calendar time audit. He tagged every meeting and task and used the analytics feature to see exactly where his hours were going.
He was spending 18 to 20 hours a week doing Excel analysis for pricing work. That was his signal. Not ‘I’m busy.’ Specifically: here’s the low-leverage work I can hand off, and here’s the profile of person I need to find.
He started with contractors on Upwork and Fiverr, ran interviews, and hired the ones who could take that analysis block off his plate. Only later, once the right people proved themselves, did he bring them in as full-time employees.
The hard part isn’t hiring. It’s convincing clients who signed because of you to trust someone else.
Marcos did it gradually. In every engagement, he started working alongside his senior associates, present in the same sessions, giving them credibility by proximity. Then, methodically, he started stepping back.
Eight sessions became six became five became four. He didn’t disappear. He just stopped attending the ones his team could handle alone. Clients still saw him at the critical moments. They still felt the expertise. The handoff was invisible because it was gradual.
He told me he thought about a dentist he used to love who grew her practice too fast. One day he showed up and got a junior associate instead of her. It felt like a downgrade. That sensation is what he’s always worked to prevent.
Every Monday, Marcos meets with each of his six senior strategists to review active client work. Each strategist presents where a client’s pricing stands and where they’re taking it. Then the room picks it apart: why this structure? What’s the evidence for a price increase? Have you considered this instead?
Marcos stays sharp. The client gets the firm’s collective thinking, not just one person’s. And the strategists get better every week by watching each other get challenged. He’s never marketed this externally, but he should. His clients aren’t getting one expert. They’re getting six.
What AI Can (and Can’t) Do for Specialized Consultants
AI has made Pricing I/O faster. A lot faster. The firm has built dozens of custom GPTs for internal analytical workflows. Document consumption, interview summarization, pattern extraction, code optimization. Things that used to take hours now take minutes.
What it hasn’t replaced is the judgment call. When Marcos tested AI on real client pricing strategy, it gave competent generic advice. It couldn’t apply the firm’s proprietary frameworks to specific business contexts, or navigate the organizational dynamics that make a pricing rollout actually stick.
Where it gets interesting is in the middle ground. Marcos has been training AI on his pricing page breakdown framework, uploading pricing pages and videos of himself analyzing them, feeding the AI his annotation patterns until it started recognizing the same signals he does. It’s getting better with every upload.
His roadmap involves feeding all 400 engagements plus his full framework library into AI systems that can eventually replicate his analytical lens at scale. Phase one is faster analysis. Phase two is firm-specific decision support.
The nuanced take I’d add to this is – It only works because he already has everything documented. His frameworks are written down. His 400 engagements are organized. Most consultants I talk to are carrying their best knowledge entirely in their heads, which means AI can’t touch it.
What’s Next: Pricing Deserves a Home
Right now, for most companies, pricing lives in spreadsheets and old slide decks from projects that ended months ago. There’s no infrastructure for making ongoing pricing decisions, tracking whether the model is working, or knowing when it needs to change.
Marcos is building that infrastructure.
This summer, Pricing I/O is launching a software product layered with expert services: a permanent home for pricing decisions. Not just a SaaS tool. A system with expertise baked in so clients don’t just get software; they get validation, accountability, and guidance on what to do with what it shows them.
It’s the natural arc: one-time project, to retainer, to ongoing operating system.
The best consultants eventually productize their judgment. Marcos is doing that with 400 engagements and a decade of frameworks behind him.
Things Worth Stealing
Start the network warm-up six months before you need anything. No pitch, no agenda. Just genuine check-ins with people you actually like. When you eventually announce your move, you’re landing in a warm room, not a cold one.
Nail your first few clients completely. Make the work practical and executable, not impressive and dense. The board room is your distribution channel. Good work placed in front of the right people at the right moment creates referrals you can’t buy.
Workshop over deck. Co-creation over reveal. Fixed fee over hourly. Give clients the playbook, not the presentation. Define scope tightly, deliver clean outcomes, and make sure they leave knowing exactly what to do next.
Run a calendar audit before you hire. Find the low-leverage hours and build the job description around them. When you bring people in, work alongside them first, step back gradually, and never disappear entirely. Authority transfers through proximity, not announcement.
Use AI to move faster on analysis. Don’t expect it to replace judgment. And if you want AI to eventually think like you, start documenting your frameworks now. It can only learn what you’ve already written down.
You can find Marcos at pricingio.com or follow him on LinkedIn, where he posts pricing breakdowns, frameworks, and tactical content. His book, Street Pricing, is the no-BS practical guide to pricing he wished existed when he started.
If you’re working through GTM strategy, positioning, or content at a B2B SaaS or AI startup, I’d love to connect. Find me at data-mania.com or on LinkedIn.
P.S. After this conversation, I immediately started reconsidering my own delivery model and how I could improve it based on what I learned. It’s been less than a week, and I’ve already brought in a new client where I’ve transitioned my pre-strategy development check-in call to alignment call where I fully intend to co-create with the client and start building that buy-in from the very beginning.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## The Anti-Networking Strategy That Works Wonders for Technical Founders
URL: https://www.data-mania.com/blog/the-anti-networking-strategy-that-works-wonders-for-technical-founders/
Type: post
Modified: 2026-03-17
I thought I knew how to network.
I’d paid for an accelerator. Practiced the scripts. Learned to “find common ground” by asking about someone’s city or their weekend plans.
It worked well enough to get through doors, but something felt off. The conversations were fine. Professional. Forgettable.
Then I talked to Jonathan Green for an hour, and he completely rewired how I think about building relationships.
Jonathan runs Fraction AIO, where he builds AI automation systems that free founders to spend more time on high-value human interactions. He also hosts The Artificial Intelligence Podcast. But here’s what makes him interesting: he’s a self-described introvert whose favorite hobby is reading, who had zero friends until age 17, and who now networks with unbelievably wealthy people and closes major clients through what he calls “being without intent.”
In other words: He learned to network the same way you’d debug code or optimize a system. He reverse-engineered charisma.
What follows isn’t your typical networking advice. This is for technical founders who thought they could code their way out of face-to-face relationship building, who hate the transactional feel of most networking events, and who want a system that actually works.
Why Technical Founders Can’t Code Their Way Out of Networking
Here’s the uncomfortable truth.
When Jonathan started his first online business, his goal was simple: “I’m going to start an online business so I never have to talk to another person in person again.”
If you’re a technical founder, you’ve probably thought some version of this. Build a great product, let it speak for itself, avoid the squishy human stuff.
Here’s what might surprise you: That strategy caps your growth at a level far below your potential.
Jonathan talks to investors constantly. What he hears from them is eye-opening: “Most of it’s emotional. They don’t invest as logically as you think they do. If they like the person or they catch a vibe, then they’ll give them $50,000 or $100,000 or a million dollars.”
The same pattern shows up everywhere. Promotions aren’t just about technical excellence. They’re about soft skills.
If people don’t like you, it doesn’t matter how brilliant your code is. You won’t move up because there’s always that human element.
The hard part is: This feels unfair to technical people. You’ve spent years building expertise, and now someone’s telling you that being likable matters more than being right?
Not quite. What Jonathan figured out is that networking and social skills aren’t innate talents.
They’re learnable systems. And if you can learn to build a tech stack, you can learn to build relationships.
The Tao of Not Being a Value Vampire
Jonathan’s entire networking philosophy comes from the Tao: be without intent.
Here’s what that means in practice: Most people approach high-value contacts like vampires. They think, “I have one shot with this person. I need to shoot my shot and get maximum value before they escape.”
You’ve seen this at conferences. Someone meets a successful founder and immediately launches into their pitch, asks for an intro, requests advice on their startup.
The interaction is transactional. Single-serving.
Jonathan calls this the “value vampire” mentality, and it guarantees you’ll never build a real relationship.
The alternative? Approach every conversation without an ulterior motive.
When Jonathan met Russell Brunson (ClickFunnels founder) on a cruise years ago, he didn’t recognize him at all. While 10 other people kept interrupting to pitch Russell, Jonathan just had a conversation.
He even told Russell he thought he always wore a sailor suit in photos (he doesn’t). Jonathan confused a polo shirt with a Navy uniform.
At the end of an hour, Russell asked: “Don’t you know who I am?”
Jonathan didn’t. And that honest naivete became the foundation of their friendship and eventual business relationship.
Here’s the pattern: When you approach successful people without thinking “what can I get from them?”, you differentiate yourself from the 99% who are extracting value.
You become memorable. Trustworthy. Someone they actually want to help.
Jonathan never asks Warren Buffett about investing (hypothetically, though he does this with wealthy people he meets). He asks about kids, hobbies, origin stories.
Whatever someone is strong in, he avoids talking about. Everyone else goes after that. He goes after what they care about.
How to Reverse-Engineer Charisma (Even If You’re an Introvert)
Think networking is an innate talent? Here’s why you’re wrong.
Jonathan had no friends until age 17. He’s deeply introverted. His entire personality is what he calls “an artifice,” a learned creation of who he wanted to become.
At 17, he met Nathan Ellis, the first popular person he’d ever encountered who was also genuinely kind. Jonathan spent hours studying Nathan’s patterns.
How he started every conversation with a compliment. How he treated people with consistent warmth. How he showed “forgetful kindness” by giving the same compliment multiple times to the same person.
This observation became the foundation of Jonathan’s business approach. He estimates 90% of his success comes from systematically saying nice things to people.
Wait, isn’t that manipulative?
No. And here’s why: “It’s not that I don’t mean it. It’s that I wouldn’t naturally say it if I didn’t. It’s a learned skill. It’s not fake. It’s a creation. It’s learning, oh, this is how to be the person you want to be.”
The key insight is that nobody compliments men. The bar is incredibly low.
Jonathan recently told a guy who’s a thousand times more successful than him: “I really appreciate you taking the time to be on my show. It really means a lot to me. I appreciate the effort you put in what you said.”
The response? “Oh my gosh.” Instant rapport.
The guy immediately asked if Jonathan takes referrals and said he had projects that might be a great fit.
Jonathan has also written unsolicited LinkedIn testimonials for people doing interesting work. One guy messaged him: “Dude, me and my wife just cried for an hour because I was thinking of quitting.”
Jonathan hasn’t spoken to him since. There was no follow-up, no ask, no business relationship. Just a small gesture that had profound impact.
The hard part is: You have to actually mean it. You can’t fake genuine curiosity or kindness. But you can learn to express what you genuinely feel, which most technical people have been trained not to do.
The Most Powerful Question You Can Ask (And Why People Won’t Answer It)
Jonathan learned everything he knows about business from dating.
He didn’t date until age 27. He was terrified, had no natural social skills, so he read books and studied the structure of attraction like he was debugging a system.
The most powerful question he discovered: “What’s your favorite thing about yourself?” or “If you never had to work again, what would you spend your time doing?”
These questions reveal who someone really is. Not their professional persona. Not their pitch. Their actual self.
However: Nobody wants to answer these questions first. They’re too vulnerable.
The solution? You go first.
Jonathan believes in leading from the front. If he’s going to ask a personal question, he gives a personal answer first.
He’ll talk about his typhoon experience (the worst thing that ever happened to him). He’ll share uncomfortable stories about learning to be social or struggling with dating.
When you share something real, people respond with something real. And whoever’s talking builds rapport with themselves and you. The vulnerability creates connection.
Or put another way: Most networking conversations are performance. Two people performing their professional personas at each other.
When you lead with vulnerability, you break that pattern and create an actual human interaction.
The other crucial element: you have to actually care about the answer.
Jonathan is obsessive about remembering personal details. Not business details. Personal ones.
He remembers more about someone’s kids, hobbies, and origin stories than their company metrics.
“If you ask a question and you don’t care about the answer, someone’s like, my kid is sick, dude, write that down. You cannot forget that because now it’s the most important thing. If you forget that, you reverse the value.”
He uses CRM systems to track: How many kids someone has. Their ages. Their hobbies. Their current challenges.
He admits he’s terrible with names but compensates with tools and by being honest about it.
The framework: Remember what no one else remembers. Ask questions no one else asks. Care about things no one else cares about.
Networking as Seduction: The Multi-Touch Rapport Playbook
Jonathan thinks of networking as seduction. Not in a creepy way. In a strategic way.
The goal isn’t to close in one interaction. It’s to move through stages: Attention → Reply → Phone call → Relationship → Referral/Opportunity.
Instead of traveling to conferences, Jonathan talks to 10 people per week for 15-minute calls. If someone’s boring or not a fit, it’s 15 minutes.
If they’re great, he talks for an hour. He approaches it as farming, not hunting. Plant seeds. Harvest years later.
“Sometimes someone will be like, hey, we talked for 15 minutes three years ago. I have a client that might be a good fit for you.”
Here’s how it works in practice: Jonathan does pre-interview calls for his podcast. These are really screening calls disguised as fit checks.
He talks to almost everyone who applies, then decides whether to feature them.
The surprising part? Most people who do the pre-interview never book the actual recording. They self-select out.
Jonathan doesn’t have to reject them directly. Natural attrition handles it.
For direct competitors, he’s upfront: “Oh, I noticed we kind of do the same thing. Let’s hop in and see if there’s something we can help each other with.”
He diverts to a different type of conversation focused on potential collaboration.
But here’s what shocked me: Jonathan talks to B-tier prospects too. Not just superstars or direct ICPs. People who seem mid-tier, who might not be obvious wins.
Why? “You never know if someone’s going to be bigger later or if someone could be the right person. I’ve met people whose dad was super successful, really well known. And people didn’t know who he was.”
“I was talking to him for a long time, and then someone would talk to him, and they’d hear his last name, and you could watch them change.”
That shift is visible. And disgusting. The person who was dismissed suddenly becomes everyone’s best friend.
Jonathan refuses to do that. Every person gets 15 minutes of genuine attention.
Value isn’t just about immediate ROI. It’s about building a network where people remember you treated them like a human being.
AI Should Give You MORE Face Time, Not Less
Here’s where Jonathan’s business model gets interesting.
He builds AI automation systems, but not to replace human interaction. To create more capacity for it.
“Everything I do in my business is to let people spend more time doing this part of the face to face.”
His AI systems handle outreach and lead generation. But the second a lead replies, Jonathan (or his client) takes over personally.
The human part is where deals close. Where rapport builds. Where trust forms.
One example: Jonathan built a system for a solar maintenance client. When a solar company declares bankruptcy (which happens frequently), his system finds all customers who filed permits with that company and emails them same-day.
The message: “We noticed your company just went bankrupt. We’re not monsters. We know you’re in a tough situation. We’ll just do maintenance at fair prices.”
Another trigger: 20-year warranty expirations. When someone’s solar system warranty ends, automated outreach offers maintenance before they even realize they need it.
The pattern: Identify high-intent moments where someone just discovered they have a problem. Reach out with empathy, not a hard sell.
Then switch to human conversation to close.
A lot of people approach Jonathan wanting to automate their social media or email. He says no.
“That’s when you’re talking to people. That’s when you’re building maximum rapport. You don’t… everything else.”
Automate the stuff that frees you to be more human. Not the human part itself.
Steal This: Your Networking Playbook for This Week
If you implement nothing else, try these:
The Nathan Ellis Compliment Pattern: Start your next three conversations with a genuine, specific compliment. Not generic (“great to meet you”) but specific (“I loved your point about X in that post”).
Men especially never hear this. The bar is low. The ROI is high.
The Vulnerability-First Protocol: Before asking someone a personal question, answer it yourself first. Try: “If you never had to work again, what would you spend your time doing? For me, it’s [your answer]. What about you?”
The CRM Humanity System: Add three non-business data points to every contact: number of kids (if relevant), current hobby or interest, one personal challenge they mentioned. Review before your next interaction.
The 15-Minute Coffee Alternative: Instead of one conference this quarter, schedule 10 fifteen-minute calls with people in your ecosystem. Focus on B-tier prospects, not just superstars.
Ask them what they’re genuinely interested in.
The Exception Memory Principle: Pick one thing you’re genuinely curious about (jobs, origin stories, hobbies). Ask everyone about it. Write down their answer.
This becomes your memory anchor for each person.
The Anti-Pitch Rule: For your next five networking conversations, ban yourself from pitching. Focus entirely on learning about the other person. See what happens.
P.S. I’m still processing this conversation.
I’ve spent years thinking networking was about “common ground,” finding safe topics like cities or weekend plans. Polite. Professional. Low-risk.
What Jonathan showed me is that low-risk equals low-reward. The people who break through aren’t the ones with the best elevator pitch.
They’re the ones who make you feel seen. Who remember your kid’s name. Who write you an unsolicited testimonial when you’re thinking about quitting.
The small gestures compound in ways you can’t predict.
I’m going to try the compliment pattern this week. I’m going to ask one person “What’s your favorite thing about yourself?” after answering it myself first. I’m going to remember what no one else remembers.
What about you? What’s one networking strategy from this piece you’ll implement this week?
Reply and let me know. I actually want to hear.
Want to connect with Jonathan? Find him on LinkedIn or check out Fraction AIO to help automate your own outreach.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## The AI Founder’s Guide to Pricing Transparency (With Real Product Teardowns)
URL: https://www.data-mania.com/blog/pricing-transparency/
Type: post
Modified: 2026-03-17
Pricing transparency, or lack thereof, can be a huge make-or-break deal with customers. But when an AI product lacks pricing transparency AND almost gets your 380k-follower LinkedIn account nuked? That’s an absurdly bad customer experience that all AI startups can and should learn from…
The notification appeared while I was mid-research session.
“Important notice from LinkedIn.”
My stomach dropped. With 380,000 followers and 92,000 newsletter subscribers, a LinkedIn account restriction was potentially catastrophic.
The message was direct: “We noticed activity from your account that indicates you might be using an automation tool. This can sometimes be caused by browser extensions or third-party applications that run in the background.”
The LinkedIn warning that nearly destroyed my account
Here’s what terrified me: I had no idea I’d done anything wrong.
I was using Perplexity’s Comet browser.
The thing that’s most unnerving is that I had no idea I’d done anything wrong. I was using Perplexity’s Comet browser for legitimate work. Their marketing literally told me to use it inside LinkedIn, as you can see below 👇
Comet’s own marketing encouraged using the product to take actions within LinkedIn!
LinkedIn’s warning was clear: “To protect our members’ privacy and help foster authentic interactions on LinkedIn, our User Agreement doesn’t allow the use of automated software. Use of these tools may lead to your account being restricted.“
A single trust violation destroyed any confidence I had in the tool.
I stopped using Comet that day. Not because the product wasn’t powerful, but because a single trust violation destroyed any confidence I had in the tool.
Oddly enough, I only started using Comet in the first place because Perplexity had reached out about wanting to do a paid partnership – I guess this what not the user feedback they were looking for, and this definitely isn’t paid.
This incident is part of a broader pattern I’m seeing across AI pricing and product design that’s quietly destroying user adoption.
And here’s the thing that keeps me up at night… This wasn’t an isolated incident. It’s part of a broader pattern I’m seeing across AI pricing and product design that’s quietly destroying user adoption.
Let me show you what I mean.
Over the past 6 months, I’ve tested dozens of AI tools – the one’s I’m covering here include Perplexity Comet, Claude, OpenArt, Gamma, ChatGPT, Bolt, and LinkedIn integrations – to understand what pricing transparency actually looks like in practice. I’ve identified 3 frameworks that separate trust-building products from churn-generating ones.
Here’s what I learned from real product teardowns.
The Invisible Limits Problem: Pricing Transparency & Why Your Users Can’t Trust You
Think AI credit systems are just a pricing decision?
They’re actually a trust signal. And most AI tools are sending the wrong message.
LinkedIn sent the warning a few days after I ran out of Comet credits (not while I was actually using Comet 🤷♀️). 36 hours before I recieved the warning from LinkedIn, I was researching summer home locations using Comet. The tool was performing beautifully.
The tool was performing beautifully. Surfacing insights, comparing markets, pulling together data I’d have spent hours finding manually.
Then suddenly: “You’ve hit your daily limit.”
A moment later, the message changed: “You’ve used all your credits for the month.”
I’d burned through my entire monthly allocation in a single day. One research session. And I had zero warning it was coming.
No meter showing credits depleting. No alert at 75% usage. No way to pace my work or know I was approaching a limit. The only option presented: upgrade to Perplexity Max for $200/month (billed annually).
The only option after burning through credits in one day: $200/month
Here’s what most AI founders miss: This wasn’t a conversion opportunity. It was the moment I decided to stop using the product entirely.
The same pattern played out with Claude. I’ve maxed out my Claude Pro credits 7 times since September. Every single time, I had no visibility into how close I was to the limit. No way to manage my usage strategically. No ability to optimize my workflow around the restrictions.
When you work less than 30 hours per week with sporadic afternoon sessions like I do, you don’t need consistently high daily limits. But you do need to know where you stand.
Claude recently introduced a pay-as-you-go option for daily overage, which is genuinely innovative.
Instead of forcing me into a $100/month Max plan (where I’d overpay by about $70/month for my actual usage), I can now pay only for the days I need extra capacity.
However, The core problem remains: I still can’t see my daily credit balance. I still hit limits without warning. And it still feels like the system is designed to create surprise friction at peak productivity moments rather than empower me to manage my usage.
Or put another way: it feels like they want you to run out so they can upsell you.
The Invisible Meter Problem Framework
When AI tools hide usage limits, they trigger a predictable user behavior sequence:
Surprise friction at peak engagement → Users hit limits during their most productive or valuable work moments, creating negative associations with the product itself.
Impossible usage optimization → Without visibility into remaining credits, users can’t pace work strategically or make informed decisions about when to use the tool versus when to wait.
Trust erosion → Hidden limits feel manipulative rather than helpful, especially when paired with aggressive upsell prompts at the moment of friction.
Product avoidance → Users begin rationing usage or avoiding the tool entirely to prevent surprise interruptions, reducing overall engagement and limiting their discovery of the product’s full value.
The psychological impact is profound. You’re not training users to upgrade. You’re training them to find alternatives.
The Trust Builders: How OpenArt and Gamma Get Visibility Right
Not every AI tool gets this wrong.
Every time I generate an image in OpenArt for my Convergence newsletter graphics, I check my credit balance first. I can see exactly what I have: 9,586 credits displayed in green in the top corner.
OpenArt shows my exact credit balance – no guessing, no surprises. This is good what pricing transparency looks like.
I know exactly what I’m spending: 60 credits for a high-quality nanobanana pro image.
And I make the conscious decision that it’s worth it. And honestly, it’s fun.
This feels empowering.
I’m choosing to spend credits on quality because I can see the trade-off clearly. I’m not worried about surprise limits. I’m not anxious about running out unexpectedly. The visibility creates confidence, and the confidence creates loyalty.
OpenArt costs $29/month for 12,000 credits. More than some alternatives. But the transparent credit system makes me feel in control rather than manipulated.
Gamma does something similar with their 10,000 monthly credits. When they transitioned from unlimited to a credits system, I wasn’t thrilled. But at least they show you your remaining balance as you use the tool.
Gamma’s credit visibility – another instance of spectacular pricing transpareny. I always know where I stand
Transparency alone doesn’t solve everything though.
I’ve noticed I experiment less with Gamma now. The credit anxiety crept in. Each iteration carries a cost I can see depleting in real-time. Instead of testing 5 layout variations to find the perfect format, I’ll do 2 or 3 and call it good enough.
The psychological shift from “unlimited exploration” to “managed resource” changed how I engage with the product. Less innovation discovery, more conservative usage.
The hard part is that credits systems, even transparent ones, create resource scarcity that makes users more strategic but less experimental. If your product’s differentiation depends on users pushing boundaries and discovering novel applications, visible credit depletion might protect revenue while sacrificing advocacy.
But I’d still choose OpenArt’s transparent system over Perplexity’s invisible limits any day. At least with visibility, I’m making conscious trade-offs instead of getting surprised by restrictions I didn’t know existed.
The All-or-Nothing Upgrade Trap (And How Claude Escaped It)
Here’s what might surprise you: The problem with most AI pricing isn’t the price itself. It’s the mismatch between pricing tiers and actual usage patterns. Better pricing transparency allows user to more easily identify that gap (which may be why many products actively avoid pricing transparency).
Let’s look deeper at this…
36-hours before I got the warning from LinkedIn, I hit Perplexity Comet’s monthly limit after just a single day of summer home research, the only option was $200/month. No middle tier. No flexible option. No way to pay for just the research sessions I actually needed.
For someone with sporadic usage (a few intensive days per month, not consistent daily needs), this felt absurd. The gap between “free tier that runs out in one day” and “$200/month enterprise pricing” left me in product limbo.
Compare that to Claude’s new pay-as-you-go option. Instead of forcing me into a $100/month Max plan for capacity I’d rarely use, I can pay only for the specific days I need extra credits. This matches my actual work pattern: heavy usage a few days per week, minimal usage the rest of the time.
Or look at Bolt’s approach: free tier with optional $25-50/month upgrades only for months when I’m actively building projects. Project-based pricing that matches usage patterns. Clear warnings before hitting limits. Predictable, transparent, and actually useful.
Even ChatGPT’s model works, though for different reasons. At $20/month, I never run out of credits. The limits are generous enough that I don’t need visibility because I never hit friction. Abundance eliminates the need for meter-watching.
The Usage Pattern Mismatch Framework
AI founders building pricing systems need to ask:
Do your pricing tiers match how users actually work? Sporadic heavy users need different options than consistent moderate users. One-size pricing forces overpayment or creates surprise limits.
Are you creating mid-tier gaps? The jump from free/basic to enterprise often leaves your best potential customers (growing startups, freelancers, small teams) with no good option.
Does your upgrade path feel like a choice or a trap? Presenting only one expensive upgrade option at the moment of peak frustration doesn’t optimize conversion. It optimizes churn.
Can users see what they’re paying for? If you’re charging based on usage, show the usage. If you’re setting limits, show the limits. Hidden metrics create hidden friction.
Think about your last SaaS pricing decision. Did you upgrade because the product delighted you, or because you felt trapped by artificial limits? That feeling matters.
The Hidden Liability Gap: When Your Product Breaks Other Platforms’ Rules
Let’s go back to that LinkedIn warning.
This wasn’t just a UX problem. This was a legal and professional risk transfer that I had no idea I was accepting.
Comet’s marketing showed LinkedIn recruiting use cases. Their safety documentation warned about “suspicious content” and encouraged users to “carefully review Comet actions.” But nowhere did they mention that using their browser on LinkedIn could trigger automation flags.
Comet’s safety messaging… notice what’s missing: any warning about LinkedIn automation detection
Nowhere did they warn that my 380,000-follower account could be restricted.
When AI tools market use cases that violate third-party platforms’ Terms of Service without explicit warnings, they’re transferring massive risk to users. And most users, like me, have no idea until they get the scary warning message.
This destroys trust immediately and permanently. I won’t return to Comet even after account restrictions are lifted. The near-miss was enough.
The Third-Party Risk Transfer Framework
If you’re building AI tools that interact with other platforms, here’s your checklist:
Audit every third-party integration for ToS conflicts. Don’t just assume your use cases are safe. Actually read the platform agreements. LinkedIn explicitly prohibits automation. Discord has specific bot policies. Twitter/X has API restrictions. Know the rules.
Explicitly warn users about risks before they take action. Not buried in documentation. Not implied in safety tips. Direct, clear warnings: “Using this feature on LinkedIn may trigger automation detection and result in account restrictions.”
Never market use cases you haven’t verified are safe. If your product page shows LinkedIn recruiting prompts but LinkedIn prohibits automation, you’re setting users up for account risk. That’s not just bad UX. That’s negligent.
Consider building platform-specific guardrails. If certain platforms have strict automation policies, disable those integrations or require explicit user acknowledgment of risk before proceeding.
Here’s what most founders miss: When users face account restrictions because of your product, they don’t just stop using your tool. They actively warn others. They write angry reviews. They become anti-advocates.
The reputational damage far exceeds any revenue you’d have gained from that user.
Steal This: The AI Pricing Transparency Checklist
Want to build an AI product with usage-based pricing transparency? Copy this checklist:
Real-time usage visibility. Display current credit balance, remaining daily/monthly limits, and usage trends in a persistent, easy-to-find location. Users should never have to wonder where they stand.
Progressive warning thresholds. Alert users at 75%, 90%, and 100% of their limits. Give them time to save work, wrap up tasks, or upgrade before hitting hard stops. Warnings prevent surprise friction.
Flexible upgrade paths. Offer multiple tier options beyond “free” and “enterprise.” Pay-as-you-go for sporadic users. Mid-tier monthly for growing teams. Let users choose the model that matches their actual usage pattern.
Third-party integration risk disclosures. If your product interacts with other platforms (social media, communication tools, productivity apps), explicitly warn about potential ToS violations before users take risky actions.
Experimentation vs. conservation trade-offs. Recognize that visible credit systems reduce experimentation. If innovation discovery is core to your value prop, consider generous limits or sandbox environments where users can explore without credit anxiety.
Abundance vs. transparency strategies. You can build trust through complete transparency (OpenArt’s visible meters) or through generous limits that eliminate need for tracking (ChatGPT’s effective abundance). Pick one and commit. Half-measures create the worst of both worlds.
Warning communication that builds trust. When users approach limits, don’t just show “upgrade now” CTAs. Explain what’s happening, why limits exist, and what their options are. Treat it as helpful information, not sales pressure.
Usage pattern matching. Study how your actual users work. Do they have consistent daily usage or sporadic intensive sessions? Build pricing tiers that match reality, not your ideal customer profile.
Each item on this list addresses a specific trust break I’ve experienced across my AI tool stack. The founders who get this right aren’t just building better pricing; They’re building competitive moats.
The pricing transparency checklist
The Real Cost of Pricing Opacity
AI tools are operating in a narrow loyalty window right now.
Most founders and operators are testing 25+ AI tools simultaneously. We’re in evaluation mode, comparing capabilities, trying out workflows, deciding what stays in our stack and what gets churned.
One surprise friction event is enough to trigger permanent churn.
I stopped using Perplexity Comet after the LinkedIn warning and the 1-day credit burnout. Not because the product lacked features. The pricing UX and risk transfer destroyed my trust.
I reduced my Gamma experimentation after they introduced credits, limiting my ability to discover their best use cases. The tool is still valuable, but credit anxiety changed how I engage with it.
I hit Claude’s limits 7 times without visibility into my usage, creating a pattern of frustration that makes me consider alternatives despite genuinely preferring Claude’s capabilities.
In this market you shouldn’t just be optimizing for short-term revenue generation. You should also focus on whether you’re building (or destroying) long-term product-market fit. Because …
👉 When users experience surprise limits at peak engagement moments, they don’t think “I should upgrade.” They think “I should find a tool that respects my time and doesn’t manipulate me.”
👉 When users face account restrictions because your product violated third-party ToS without warning them, they don’t just churn quietly. They actively warn their networks.
👉 When users can’t manage their usage because you’ve hidden the metrics, they don’t optimize consumption. They ration engagement and limit their discovery of your product’s value.
The question every AI founder should be asking: What are we optimizing for?
The founders who win are the ones who build trust through transparency, match pricing to actual usage patterns, and treat users like intelligent adults making informed decisions rather than conversion targets to be manipulated.
I know which products I’m recommending to my network. And I know which ones I’m quietly removing from my stack.
The difference is trust.
P.S. This morning I opened OpenArt to generate a header image for this newsletter.
The familiar green indicator showed 9,586 credits in the top corner. I selected nanobanana pro quality. The interface showed 60 credits would be deducted. I clicked generate.
The image rendered beautifully. I checked the balance again: 9,526 credits remaining.
This tiny interaction, repeated dozens of times across my content creation workflow, is what good pricing UX feels like. I’m not being surprised. I’m not being manipulated. I’m not anxious about hidden limits or wondering when I’ll hit a restriction.
I’m making conscious trade-offs about quality versus cost, knowing exactly what I’m spending and what I’m getting. It’s empowerment, not friction. Trust, not anxiety.
That’s the feeling you want your users to have.
If you’re building an AI product and struggling with GTM strategy, pricing architecture, pricing transparency, or how to build trust in a crowded market, let’s talk. I work with founders to transform technical capabilities into go-to-market strategies that actually drive adoption.
Because in the AI era, the best product doesn’t always win. The most trustworthy one does.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## The AI Due Diligence Checklist: Why Your Series A Could Take 60+ Days Longer
URL: https://www.data-mania.com/blog/ai-due-diligence-checklist/
Type: post
Modified: 2026-03-17
The Federal Court Notice That Changed Everything
December 2, 2025. 1:03 PM.
I opened an email with the subject line “Notice of $1.5 Billion Proposed Class Action Settlement Between Authors & Publishers and Anthropic PBC.” My first thought? Spam filter failed me again.
But then I saw my name. My books. My settlement claim ID.
Two of my Data Science For Dummies editions (2nd Edition from 2017 and 3rd Edition from 2021) were in Anthropic’s pirated training dataset. The court had already ruled. All I needed to do was wait for my share of the settlement to arrive. About $6,000 for two books.
A welcome Christmas bonus for a solo entrepreneur who doesn’t usually get these. This moment highlights why an AI due diligence checklist is no longer optional for startups that are training models on third-party data.
But here’s what kept me up that night: If my $6k is a line item in someone’s quarterly legal expenses, what’s your exposure when VCs start asking where your training data came from?
Your Series A timeline could extend by 60+ days. Here’s exactly why, and what you need to prepare now.
What the $1.5B Settlement Actually Settled
Let’s do the math that every VC is now doing:
$1.5 billion ÷ 500,000 works = ~$3,000 per work
That’s not a penalty. That’s the new baseline cost structure for unlicensed training data. And Judge William Alsup made something crystal clear in his June 2025 ruling: fair use only applies to legally obtained content.
Think using pirated books for “research purposes” creates a loophole? The court said no.
Anthropic downloaded over seven million books from LibGen and Pirate Library Mirror. Judge Alsup called this “inherently, irredeemably infringing.” The transformative use argument that AI companies relied on? It only works if you started with lawfully acquired materials.
In other words: You can’t fair-use your way out of piracy.
Chad Hummel from McKool Smith put it plainly: “This is very sobering for other AI companies. The content-licensing market will accelerate, and the dollars will be bigger.”
Peter Henderson, a professor at Princeton University, confirmed the pattern: “$2,000 to $3,000 a book is a recurring theme across the contracting space, across the settlement.”
This isn’t one company’s problem. This is the new floor price for content licensing in AI. For founders, this ruling quietly reshaped the AI Due Diligence Checklist investors now expect before funding.
So what does this mean for you if you’re raising money right now?
The AI Due Diligence Checklist VCs Are Now Using
This AI Due Diligence Checklist breaks those questions into concrete documentation requirements most startups are unprepared to produce.
Here’s what changed since the settlement. VCs and enterprise buyers added a new section to their evaluation process, and it comes with documentation requirements that most AI startups aren’t prepared to meet.
When investors or procurement teams ask “Where did your training data come from?”, they’re actually asking seven different questions:
In practice, an AI Due Diligence Checklist translates that single question into specific documentation requirements most startups aren’t prepared to produce.
1. Data Provenance Documentation
Complete inventory of every training dataset by source
Acquisition method for each dataset (purchased, licensed, scraped, synthetic)
Date ranges showing when data was acquired
Chain of custody documentation if datasets were transferred between entities
What VCs actually want to see: Spreadsheet or database showing every training source, with acquisition receipts and licensing agreements attached. If you scraped public data, show the Terms of Service analysis that confirms you’re compliant.
2. Licensing Agreement Archive
Signed licensing agreements for all commercial datasets
Open source license documentation (MIT, Apache, GPL, etc.) with usage terms
Publisher permissions for any copyrighted materials
API Terms of Service for scraped data
What disqualifies you immediately: Saying “we scraped it, so it’s fair use.” That defense died with this settlement.
3. Fair Use Analysis per Dataset
Legal memo documenting fair use justification for each dataset
Analysis of transformative use specific to your model’s purpose
Documentation showing data was lawfully obtained first
Assessment of commercial impact on original copyright holders
The hard part is: Fair use isn’t a checkbox. It’s a legal argument that requires documentation showing you even qualify to make it. At this point, the AI Due Diligence Checklist stops being theoretical and becomes a documentation-heavy legal exercise.
4. Third-Party Audit Trails
External legal review of data sourcing practices
Technical audit showing no shadow library sources in training pipeline
Compliance certification from recognized standards body (if available)
Regular audit schedule showing ongoing compliance monitoring
What this signals: You’re not just compliant today. You’ve built systems to stay compliant as you scale.
5. Legal Representations and Warranties
Formal legal opinion letter on training data compliance
Indemnification terms you can offer to enterprise customers
Insurance coverage for IP infringement claims (if available)
Documented process for responding to DMCA takedowns or copyright claims
Why this matters: Enterprise buyers want to know you’ll protect them if a lawsuit emerges. They’re not just evaluating your current compliance. They’re evaluating your ability to shield them from your past decisions.
6. Regulatory Compliance Proof
GDPR compliance documentation if training on EU personal data
CCPA compliance for California resident data
Industry-specific regulations (HIPAA for healthcare, FERPA for education, etc.)
International data transfer agreements if applicable
Or put another way: Data origin isn’t just about copyright. Privacy regulations create a second layer of exposure that compounds the risk.
7. Ongoing Monitoring Process
Documented process for evaluating new training data sources
Internal review board or legal sign-off requirements for dataset additions
Training for technical team on compliant data acquisition
Incident response plan for discovering problematic data in existing datasets
What separates winners from losers: Companies that treat this as a one-time checklist versus companies that build it into their development culture.
Why Your Funding Timeline Just Extended 60 Days
Startups without an AI Due Diligence Checklist ready are discovering that fundraising timelines now stretch weeks longer as investors force retroactive documentation.
Let me walk you through the new math of raising a Series A in the post-Anthropic settlement world. The absence of a prepared AI Due Diligence Checklist is now one of the most common causes of extended Series A timelines.
Here’s how it used to work: You’d spend weeks 1-2 on initial VC meetings and pitch refinement. Weeks 3-4 on term sheets. Weeks 5-8 on due diligence (mostly financial and technical). Then weeks 9-10 wrapping up legal docs and closing.
Now? Add this to your calendar:
New timeline (post-settlement):
Weeks 1-2: Initial VC meetings and pitch refinement
Weeks 3-5: Data governance package assembly (new)
Weeks 6-7: Legal review of training data compliance (new)
Weeks 8-9: Term sheet negotiations
Weeks 10-13: Due diligence including data provenance verification
Weeks 14-15: Additional legal documentation for data licensing (new)
Weeks 16-17: Closing
That’s 7-8 additional weeks. And that assumes you already have your data provenance documentation ready. If you don’t? Add another month minimum.
Here’s what this means practically:
Cash flow impact: If you planned for a 10-week fundraising process and built 4 months of runway, you’re now cutting it close. That forces emergency extensions, bridge rounds, or unfavorable term sheet negotiations when VCs know you’re desperate.
Competitive disadvantage: While you’re assembling data governance packages, competitors who prepared earlier are closing deals and launching features. Every week matters in AI.
Deal erosion: The longer diligence takes, the more likely deal terms deteriorate or investors get cold feet. Extended timelines create opportunities for competitors to launch similar features, market conditions to shift, or investors to find alternative deals.
However, companies that built data governance into their foundation aren’t seeing these delays. They’re using compliance as a sales accelerator.
If Books Cost $3K Each, What’s Your Code Repository Worth?
Let’s follow the logic to its uncomfortable conclusion.
If 500,000 pirated books triggered a $1.5 billion settlement, what happens when someone applies the same math to code repositories?
GitHub hosts hundreds of millions of repositories. Stack Overflow has over 24 million questions and answers. If each code file, function, or answer represents a copyrighted work, and if the $3k per work precedent applies…
The math gets uncomfortable fast.
I’m not fear-mongering. I’m reading the trajectory. GitHub Copilot faces parallel legal scrutiny over code training data. Stack Overflow’s Terms of Service create licensing ambiguity that no one’s fully tested in court yet. And synthetic data generation might not eliminate copyright risk if the source data feeding those synthetic generators was unlicensed to begin with.
Here’s what might surprise you: OpenAI and Meta should be paying licensing fees for new content creators generate, and they should be retroactively compensating creators for content they’ve already used without permission. That’s not a controversial position among creators. It’s common sense when you see the settlement amounts.
The hard part is that most AI companies built their models first and figured out licensing later. That worked when everyone assumed fair use would protect transformative AI applications. The Anthropic settlement proved that assumption wrong.
So what do you do if you’re sitting on models trained with code from repositories with ambiguous licensing?
Proactive licensing isn’t defensive. It’s a competitive moat. Companies that can demonstrate clean code provenance will win enterprise contracts that competitors can’t even bid on. Government agencies, Fortune 500 companies, and regulated industries aren’t going to risk vendor relationships with companies that can’t prove their training data is lawfully sourced.
Think of it like security certifications. SOC 2 compliance is expensive and time-consuming. But once you have it, you can compete for deals that uncertified competitors can’t touch. Data governance compliance works the same way.
The companies that will win aren’t waiting to see how these cases play out. They’re treating data governance as a competitive moat. Here’s how.
Why Compliant AI Commands Premium Pricing
This shift explains why an AI Due Diligence Checklist now directly influences pricing power, not just legal approval.
Most AI startups view data governance as a cost center. That’s backward.
Compliant AI is premium AI. Here’s why enterprise buyers will pay more for it, and how to position it in your go-to-market strategy.
The Risk Elimination Value Proposition
When you sell to an enterprise, you’re not just selling features. You’re selling risk mitigation. Every vendor relationship creates potential liability for the buyer. If your AI tool gets sued for copyright infringement and they’re using it in production, that’s their problem now.
But if you can demonstrate comprehensive data governance, you’re eliminating a category of risk that keeps legal teams awake at night. That’s worth paying for.
For buyers, a documented AI Due Diligence Checklist reduces vendor risk in ways features alone cannot.
Frame it this way in your sales materials:
“Our training data is 100% licensed and documented. Here’s our data provenance package. Here’s our legal opinion letter. Here’s the indemnification we can offer you. We cost 30% more than competitors, and here’s exactly what that premium buys you: zero legal exposure from our training data.”
Tiered Pricing That Reflects Compliance Costs
Don’t hide licensing costs in your overall pricing. Make them transparent and let customers choose their risk level. Each tier implicitly reflects how complete and defensible your AI Due Diligence Checklist really is.
Here’s how I’d structure it if I were pricing your product:
Tier 1: Standard Model (trained on mixed sources, best-effort compliance, no indemnification)
Tier 2: Enterprise Model (trained on licensed sources only, full documentation, limited indemnification)
Tier 3: Regulated Industries Model (trained on fully licensed and audited sources, comprehensive documentation, full indemnification, ongoing compliance certification)
This approach does three things:
Justifies higher pricing for compliant offerings
Segments your market by risk tolerance
Creates upsell paths as customers grow and face more scrutiny
The Marketing Narrative That Wins Enterprise Deals
Data governance isn’t a checkbox in your security documentation. It’s a headline feature in your positioning.
Compare these two positioning statements:
Before: “Our AI platform delivers 40% faster insights using advanced ML algorithms.”
After: “Our AI platform delivers 40% faster insights using advanced ML algorithms trained on 100% licensed data with full legal documentation, eliminating IP risk for enterprise deployments.”
The second version signals that you understand what enterprise buyers actually care about. Speed matters. But legal exposure matters more. Messaging only works when it’s backed by a real AI Due Diligence Checklist, not aspirational claims.
When you’re competing for six-figure or seven-figure contracts, the company with clean data provenance wins even if their model is slightly less accurate. Because legal is a veto function. Your champion in Product might love your tool, but if Legal can’t sign off, the deal dies.
Make it easy for Legal to say yes.
What to Audit This Week
This five-day sprint is designed to help founders assemble an initial AI Due Diligence Checklist before diligence begins, not while it’s already blocking a close.
You don’t need to solve this overnight, but you do need to start now. Here’s your tactical checklist for the next seven days.
By Friday afternoon, you’ll have three things most founders won’t: a documented risk assessment, a compliance budget, and messaging that positions your startup ahead of the competition.
Monday-Tuesday: Inventory Your Training Data
Specific actions:
Create a spreadsheet listing every training dataset currently in use
For each dataset, document: source, acquisition date, acquisition method, file/record count
Flag any datasets where you don’t have clear documentation of how you obtained them
Identify datasets that came from web scraping without explicit licensing
Deliverable: Complete training data inventory spreadsheet
Wednesday: Assess Licensing Gaps
Specific actions:
For each dataset, determine current licensing status: licensed, open source (with specific license), scraped (with ToS review), unknown
Calculate percentage of your training data that’s fully licensed vs. ambiguous
Identify your three highest-risk datasets (largest, most recently added, least documented)
Research licensing costs for those high-risk datasets if you were to properly license them today
Deliverable: Risk assessment ranking your datasets by legal exposure
Thursday: Document What You Can Prove Today
Specific actions:
Gather all existing licensing agreements, purchase receipts, API Terms of Service
Create a folder structure organizing documentation by dataset
Write down your current data acquisition process (even if it’s informal)
Identify gaps where you don’t have documentation and can’t recreate it
Deliverable: Organized evidence folder showing current compliance status
Friday: Budget for Compliance Costs
Specific actions:
Calculate estimated licensing costs for closing your highest-priority gaps
Factor this into your next fundraising amount (if pre-Series A)
Estimate time required to build data governance processes (legal review, internal training, ongoing monitoring)
Add 45-60 days to your next fundraising timeline to account for extended due diligence
Deliverable: Updated financial model including data governance costs
Weekend: Draft Your Data Governance Messaging
Specific actions:
Write the “Training Data Compliance” section of your pitch deck
Update your website security/compliance page to mention data governance (even if you’re still building it)
Prepare your answer to “Where does your training data come from?” that you’ll use in sales calls
Sketch out what a “compliant AI” positioning strategy would look like for your specific market
Deliverable: First draft of compliance messaging you can refine with your team
This week of work won’t solve everything, but it will put you ahead of 90% of AI startups who are still pretending this isn’t their problem. By Friday, you should have the first defensible version of your AI Due Diligence Checklist, even if it’s incomplete.
The Unique Position of Creator-Advisors
I’m in an unusual spot right now.
As a creator, I’m benefiting from a settlement that compensates me for IP that was used without permission. As a Fractional CMO serving AI startups, I’m helping companies navigate exactly this kind of risk in their go-to-market strategy.
I’m not claiming guru status from either side. I’m a fellow traveler who happened to see both perspectives, and what I see is this: From both sides, the absence of an AI Due Diligence Checklist is now an obvious and avoidable failure.
The companies that will win in the next three years aren’t the ones with the best models. In practice, winning teams treat an AI Due Diligence Checklist as a growth asset, not a legal afterthought. They’re the ones who figured out data governance early enough that it became a competitive advantage instead of a compliance nightmare.
The hard part is making compliance interesting enough to talk about. Most founders don’t want to spend board meetings discussing licensing agreements. But when you reframe it as “Why we can win deals that our competitors are legally disqualified from bidding on,” suddenly it gets strategic attention.
Most of those positioning conversations now start with an AI Due Diligence Checklist, whether founders realize it or not. If you’re building in AI right now and you’re wondering how to position your startup in this new landscape where training data origin matters as much as model performance, let’s talk about how compliance becomes your differentiation strategy.
I help AI startups translate technical capabilities into messaging that resonates with enterprise buyers and investors who are now scrutinizing data governance. Having seen both sides of this settlement gives me a perspective that pure marketing consultants don’t have.
Book a consultation focused on compliance as competitive differentiation
P.S. I’ve been testing Nanobanana recently and I love it. The UI is smooth, the outputs are solid, and it’s genuinely useful for rapid prototyping. But here’s what I kept thinking while using it: What was this trained on?
I couldn’t find training data disclosure anywhere. Not in the docs. Not in the settings. Not buried eight clicks deep in some legal page. Maybe it’s there and I missed it. Or maybe it’s not there because they’re betting no one will ask yet.
That’s the world we’re leaving behind. The world where “we’ll deal with licensing later” was a viable strategy.
In the world we’re entering, enterprise buyers are asking about training data sources before they ask about features. And if you can’t answer clearly, you don’t make it to the next meeting.
The $1.5 billion settlement just made that world official. And if you’re not ready to answer the data provenance question, you’re already behind.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## Why 97% of GenAI Projects Succeed at Align AI (While 95% Fail Everywhere Else)
URL: https://www.data-mania.com/blog/why-97-of-genai-projects-succeed-at-align-ai-while-95-fail-everywhere-else/
Type: post
Modified: 2026-03-17
You’re staring at your SaaS stack dashboard again.
$4,300/month for Zapier. Another $899 for HubSpot automation. $299 for that AI sales tool everyone’s raving about. You’ve got the subscriptions. You’ve got the logins. You’ve even assigned someone on your team to “figure it out.”
But here’s what’s actually happening… your sales team is still manually copying leads from one spreadsheet to another. Your marketing automation sends emails to the wrong segments. And that AI tool? Nobody’s touched it in three weeks because “we’ll get to it when things calm down.”
Here’s what might surprise you: The problem isn’t your tools. The problem is that you’re trying to automate chaos.
I sat down with Nadav Wilf, CEO of Align AI, who’s built a consulting practice with a 97% project success rate. That number stopped me cold. In an industry where AI implementations fail more often than they succeed, Nadav’s team is batting .970.
The secret? They refuse to automate anything until the underlying process actually works.
The Real Reason Your AI Projects Keep Failing
Most companies Nadav encounters don’t have an AI problem. They have a process problem that AI can’t fix.
“Companies lack basic SOPs and operational frameworks,” Nadav told me. “They’re already paying for automation tools but don’t know how to extract value from them.”
You can’t automate your way out of operational chaos. You can only automate chaos faster.
Think about it. If your sales process is “call whoever seems interesting and hope they convert,” no amount of AI is going to magically create a repeatable system. You’re just going to burn through leads faster with worse results.
The companies that succeed with AI automation do something counterintuitive first: they stop buying new tools and start documenting what actually generates ROI.
The hard part is admitting you’re not ready yet. I’ve watched this play out dozens of times. A startup gets a sudden influx of users (maybe they won an award, got featured somewhere, landed a big client). Their systems immediately crack under the pressure because the processes underneath weren’t solid enough to scale.
Success breaks broken systems. AI just breaks them faster.
What a 97% Success Rate Actually Looks Like
Nadav’s 97% success rate isn’t about using better AI models or fancier automation platforms. It’s about a radically different approach to implementation.
Here’s the Align AI framework: Align, Automate, Achieve.
Align means getting crystal clear on what process you’re actually trying to improve. Not “we need better sales,” but “we need to reduce the time between qualified lead identification and first sales call from 72 hours to 24 hours.”
Automate means layering technology on top of that validated, documented process. Only after you know it works manually.
Achieve means measuring specific outcomes tied to business metrics that matter, like revenue per lead, conversion rates, or time-to-close.
The companies that skip straight to “automate” are the ones in that 3% failure bucket.
When projects fail, it’s almost never the technology. Nadav shared a story about a data dashboard initiative that went sideways despite solid technical execution. The issue? Misaligned expectations, unclear success metrics, and stakeholders who weren’t bought into the process from the start.
The lesson: buy-in drives adoption. Adoption drives results. No amount of technical excellence fixes a stakeholder alignment problem.
The Sales Automation Workflow You Can Actually Steal
Let me walk you through a real implementation that worked, broken down so you can replicate it.
Nadav’s team worked with a B2B company struggling with lead prioritization. Their sales team was drowning. Every lead looked equally promising (or equally terrible) in the CRM. Reps were wasting hours chasing cold prospects while hot leads went stale.
Here’s what they built…
The Lead Scoring System
First, they identified the signals that actually predicted conversion:
Behavioral signals:
Website visit frequency and recency
Content engagement (which resources they downloaded)
Email open and click patterns
Product demo requests or trial signups
Firmographic signals:
Company size (employee count)
Industry vertical
Technology stack (scraped from job postings and LinkedIn)
Funding stage
Each signal got weighted based on historical conversion data. A demo request from a Series B SaaS company with 50-200 employees scored higher than a whitepaper download from a three-person consultancy.
The Automation Workflow
The system ran continuously in the background:
Inbound lead flow:
New lead enters CRM (from web form, demo request, content download)
System automatically enriches with firmographic data
Behavioral tracking begins immediately
Lead score calculates in real-time as new signals arrive
When score crosses threshold, lead auto-assigns to sales rep with Slack notification
If lead doesn’t engage within 48 hours, automated nurture sequence begins
Outbound prospecting motion:
System identifies high-fit companies based on ideal customer profile
Finds decision-makers at those companies
Personalizes outreach based on company signals and recent activity
Tracks engagement and updates scores accordingly
Surfaces warmest prospects to sales team daily
The sales team stopped manually sorting through leads. Instead, they woke up to a prioritized list of the hottest prospects, automatically researched and ready for outreach.
The results? Sales reps spent 60% less time on lead research and qualification. More importantly, they spent that time talking to prospects who were actually likely to convert.
Steal This: Your Lead Scoring Blueprint
Want to build something similar? Here’s your roadmap…
Phase 1: Audit Your Current State (Week 1-2)
Map your existing lead flow:
Where do leads enter your system?
What data do you capture at entry?
What happens to leads after they enter?
Where do leads get stuck or fall through cracks?
Analyze historical conversion data:
Pull your last 100 closed-won deals
Identify common characteristics (size, industry, behavior)
Look for patterns in their journey from lead to customer
Calculate conversion rates by segment
Phase 2: Define Your Scoring Model (Week 3)
Pick 5-7 signals that matter most:
Start simple (you can always add complexity later)
Weight signals based on correlation to conversion
Make sure you can actually track these signals with existing tools
Example starter model:
Demo request: +50 points
Email click: +5 points
Website visit: +2 points
Right company size: +30 points
Wrong industry: -20 points
Phase 3: Build the Automation (Week 4-6)
Tool selection:
You probably already own the tools you need
Most CRMs (HubSpot, Salesforce, Pipedrive) have native scoring
Zapier or Make can connect the gaps
Clay or Clearbit for enrichment
Automation architecture:
Lead enters → enrichment API call → score calculation → routing logic → team notification
Keep it simple at first
Add sophistication as you validate the model
Phase 4: Measure and Iterate (Week 7-12)
Track these metrics:
Lead quality score (your custom metric)
Conversion rate by score tier
Time from lead-to-first-contact
Sales team adoption rate
Build a simple dashboard that updates daily. Share it with the team. Adjust scoring weights based on what actually predicts conversions.
The hard part is resisting the urge to overcomplicate. Start with a simple model that captures 80% of the value. You can always add sophistication later.
The Metrics That Actually Tell You If It’s Working
Most founders measure AI implementations wrong. They track vanity metrics like “number of automations deployed” or “AI tools adopted.”
Nadav focuses on three things:
1. Lead Quality Score (Programmatic)
Build scoring directly into your workflow so it updates automatically. Don’t rely on manual tracking or monthly reports. You should be able to see lead quality trending up or down in real-time.
What to measure:
Average lead score of new leads (trending up = better targeting)
Conversion rate by score tier (validates your model)
Score distribution (helps identify where to set thresholds)
2. Performance Over Time
The best measurement happens longitudinally. Track the same metrics weekly for at least 90 days.
Watch for:
Time-to-first-contact decreasing
Conversion rates improving
Sales cycle length shrinking
Revenue per lead increasing
3. Team Adoption and Buy-In
This is the metric nobody tracks but everyone should. If your sales team isn’t using the system, nothing else matters.
Measure:
Daily active users of the automation
Leads followed up within SLA
Manual workarounds (signals the system isn’t working)
Qualitative feedback from team
Nadav’s team uses dashboards to keep executives and teams aligned. When everyone can see the same performance data updating in real-time, buy-in becomes easier. People trust what they can see.
The Maintenance Reality Nobody Warns You About
Here’s the truth about automation that nobody wants to hear: nothing lasts forever.
That beautiful workflow you spent six weeks building? It’ll break. Not might break. Will break.
APIs change. Tools get deprecated. Your business model evolves. Scoring models drift as your ICP shifts. Integrations break when vendors update their platforms.
The question isn’t whether you’ll need maintenance. The question is: who’s going to do it?
Nadav’s approach combines two strategies:
Internal team training: Teach your team to understand and maintain the systems. This doesn’t mean everyone needs to become a Zapier expert, but someone should understand the logic and be able to troubleshoot basic issues.
Ongoing support partnership: Complex systems benefit from expert oversight. Align AI provides continued support to ensure automations keep running as businesses evolve.
Budget for maintenance from day one. A system that costs $10K to build might need $1-2K per quarter to maintain properly. Plan for it.
The 90-Day Launch Plan for Small Teams
If you’re a founder with a small team and want to launch your first meaningful AI automation in the next 90 days, here’s Nadav’s recommended sequence:
Days 1-30: Align
Week 1-2: Process audit
Document your current workflows (sales, marketing, operations)
Identify the biggest time sinks
Look for highly repetitive tasks done manually
Talk to your team about what’s breaking
Week 3-4: Pick ONE process
Don’t boil the ocean
Choose something with clear inputs, outputs, and success metrics
Validate that it’s actually working before you automate
Get team buy-in on why this matters
Days 31-60: Automate
Week 5-6: Design the workflow
Map the complete process on paper first
Identify data sources and destinations
Choose tools (probably ones you already own)
Create the automation logic
Week 7-8: Build and test
Start simple, add complexity later
Test with sample data first
Run parallel with manual process initially
Get team feedback and iterate
Days 61-90: Achieve
Week 9-10: Full deployment
Train team on new workflow
Document how it works and what to do when it breaks
Set up monitoring and alerts
Create your measurement dashboard
Week 11-12: Measure and optimize
Track your defined success metrics
Gather qualitative feedback from users
Identify bottlenecks or friction points
Make targeted improvements
The hard part here is staying disciplined. You’ll be tempted to expand scope, add features, automate everything at once. Resist. Nail one workflow completely before moving to the next.
Should You Build AI Agents Yourself?
Here’s a question I get constantly: “Should technical founders build their own AI agents and automations?”
The answer is yes and no.
Yes, you should get your hands dirty enough to understand what’s possible. Build a simple agent. Play with Claude or GPT-4. Understand the basic architecture and limitations.
Why? Because it makes you a better buyer. You’ll have more productive conversations with vendors. You’ll spot BS faster. You’ll know what’s realistic to expect.
But you probably shouldn’t build production systems yourself. Not because you can’t, but because it’s not the highest-value use of your time.
Think about it like plumbing. Understanding how pipes work makes you a better homeowner. Actually replumbing your house? That’s what specialists are for.
The balance: Learn enough to be dangerous. Delegate the implementation to experts. Stay involved enough to maintain strategic oversight.
I learned this the hard way building design automation with tools like Nano Banana. I can create a design brief generator that produces publication-quality graphics entirely through AI. But should I be doing that for every project? No. I should be building frameworks and strategy while specialists handle execution.
You’re a founder. Your job is to build a business, not to become the world’s best Zapier developer.
When You’re NOT Ready for AI Automation
Let’s talk about when to wait.
You’re not ready if…
👉 You’re pre-product-market fit. I work with startups at every stage. The earliest-stage companies shouldn’t invest heavily in automation yet. You need validated processes before optimization makes sense.
I watched this play out recently with a client running ads. “We’re getting traffic but not leads,” they told me. When I asked about attribution tracking, nobody had set it up. They were spending money on ads without knowing which channels worked.
That’s not an automation problem. That’s a foundation problem.
👉 Your processes aren’t documented. If you can’t write down the steps of your current workflow, you’re not ready to automate it. Automation requires clarity.
👉 You don’t have validated success metrics. What does “better” look like? If you can’t answer that with numbers, you can’t measure if automation is working.
👉 You’re changing strategy constantly. If your ICP shifts every quarter, your go-to-market motion is still experimental, or you’re pivoting frequently, automation will just lock in the wrong process.
Simply put, automate momentum, not experiments.
Simply put, automate momentum, not experiments.
Wait until you have something that’s working manually and you need to scale it. Then automate.
Solving the Buy-In Problem (The Real Reason Projects Fail)
What Nadav emphasized most throughout our conversation was the fact that technical implementation is rarely the bottleneck.
The real challenge? Getting executives and teams to actually adopt the new system.
Buy-in drives adoption. Adoption drives results. No amount of technical excellence fixes a stakeholder alignment problem.
Here’s how to get buy-in:
Involve stakeholders early. Don’t build in a vacuum and unveil the finished product. Get input during design. Let teams shape the solution.
Show quick wins. Don’t wait for the perfect system. Deploy something simple that solves a real pain point in week one. Build momentum.
Make data visible. Dashboards aren’t just for reporting. They’re for creating shared reality. When everyone sees the same metrics updating in real-time, alignment happens naturally.
Train properly. Budget time and resources for actual training. “Here’s how to use this” isn’t enough. Teams need to understand why it matters and what success looks like.
Celebrate adoption. Recognize team members who embrace the new system. Make adoption part of culture, not just a technical rollout.
The 3% of projects that fail at Align AI? Almost always a buy-in or adoption issue, not a technical one.
What This Means for Your Next 90 Days
If you take nothing else from this conversation, remember this: process clarity beats fancy technology every single time.
You don’t need the latest AI model or the most sophisticated automation platform. You need documented processes, clear success metrics, and team buy-in.
Start with one workflow. Something small but painful. Document how it works today. Identify what good looks like. Build the simplest automation that could possibly work. Measure obsessively. Iterate based on data.
That’s how you join the 97%.
The alternative? Keep paying for tools nobody uses while your team drowns in manual work. Keep hoping the next AI platform will magically fix your operational chaos. Keep watching competitors automate past you while you’re stuck in pilot purgatory.
The choice is yours.
If you want more tactical frameworks like this delivered weekly, subscribe to The Convergence. I’m breaking down exactly how technical founders build GTM systems that actually scale.
And if you’re ready to implement something like Nadav’s sales automation workflow but need guidance on where to start, let’s talk. I help technical founding teams translate engineering principles into revenue systems.
P.S. Remember that design automation I mentioned earlier? I built a design brief generator that feeds into Nano Banana and produces publication-quality graphics that are 100% AI-generated. Spotless. Professional. Indistinguishable from human design work.
Total setup time? Maybe four hours across two weeks.
That’s the power of smart automation in a solid process. You don’t need a design team. You need clear requirements and the right tools.
The same principle applies to everything else in your business. Clear process + right tools + proper implementation = leverage that actually scales.
Now go automate something that matters.
Resources & Tools Mentioned:
Align AI – Nadav Wilf’s AI automation consulting firm
Align AI Case Studies – Real implementations and results
Nadav Wilf on LinkedIn – Connect directly
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## When Your Customers Keep Asking for the Wrong Product
URL: https://www.data-mania.com/blog/when-your-customers-keep-asking-for-the-wrong-product/
Type: post
Modified: 2026-03-17
Daphne Tay had spent three years building a language learning platform.
Legal terminology in French. Financial jargon in Mandarin. Compliance language across a dozen European dialects. Her team had invested thousands of hours researching domain-specific vocabulary, building databases of technical terms that didn’t exist in standard translation tools.
Then customers started asking for something different.
“Can your platform translate our documents with the formatting intact?” One client was expanding into Europe and needed product collateral translated into eight languages. Not vocabulary lessons. Actual PowerPoint decks with images and layouts preserved.
Another client needed training materials. Another needed compliance documents. The pattern was clear: they wanted visual translation, not language learning.
Daphne kept saying no. She’d built a learning platform, not a document translator. That wasn’t the vision.
But the requests kept coming.
Here’s what might surprise you: The hardest part wasn’t building the new technology. It was killing the original vision after three years of investment. Daphne had to decide whether to keep building what she thought the market needed or pivot to what customers were actually willing to pay for.
She chose the pivot. Today, Bluente translates documents for Fortune 500 manufacturers, Big Four law firms, and pharmaceutical companies expanding across Europe. They process translations 90% faster and 90% cheaper than human translators while preserving every image, format, and layout detail.
I sat down with Daphne to understand how she made this work. What I learned fundamentally changed how I think about market selection, trust-building in AI sales, and staying current on AI from 7,000 miles outside Silicon Valley.
Why Most AI Startups Choose the Wrong First Market
When Daphne decided to pivot from language learning to enterprise translation, she didn’t just chase the biggest opportunity. She ran a framework. That framework:
The Vertical Selection Scorecard: TAM × Volume × Existing Expertise
Most founders pick one of these factors. Daphne insisted on all three.
TAM (Total Addressable Market): Legal and finance weren’t just big markets. They were massive. The global language services industry hit $71.7B in 2024 and is projected to reach $75.7B in 2025. Within that, legal and financial translation represents one of the highest-value segments because the cost of errors is catastrophic.
Translation Volume: These industries need constant, high-volume translation work. Marketing collateral, training materials, compliance documents, product sheets. And when companies expand to Europe, that volume explodes. The EU has 24 official languages, which means 552 possible linguistic combinations (each language translates into 23 others).
Existing Expertise: This is where most founders miss the opportunity. Daphne’s legacy language learning platform had spent years building databases of legal and financial terminology across languages. That wasn’t wasted effort. It became training data for fine-tuning Bluente’s translation engine.
She didn’t abandon three years of work. She repositioned it as a competitive moat.
The Anchor Customer Strategy
Once Daphne selected legal and finance as her beachhead, she got tactical about customer sequencing.
She didn’t chase easy wins. She targeted the Big Four law firms in Singapore first.
Why? Because in high-stakes industries, your first customers matter more than your first revenue.
Landing the Big Four established immediate credibility. When Daphne pitched other enterprise clients, she could open with: “We work with [Big Four firm names].” That single line bypassed months of trust-building.
You need to close anchor customers before you have other anchor customers. It’s a classic cold-start problem. Daphne solved it by leaning into her differentiated positioning around domain expertise and security.
Steal This: The Vertical Selection Diagnostic
Before you commit to a market, answer these three questions:
TAM Reality Check: Is this market large enough to support a venture-scale business? Be honest. Niche can work, but it needs to be a big niche.
Volume Validation: Do customers in this vertical need your product once or repeatedly? Recurring, high-volume needs create defensible revenue.
Unfair Advantage Audit: Do you have existing expertise, data, relationships, or technology that gives you a head start in this vertical? If not, why will you win?
If you can’t answer “yes” to all three, keep looking.
How Bluente Sells AI to the World’s Most Risk-Averse Buyers
Selling AI to legal and finance teams is like selling parachutes. The cost of failure is existential.
A mistranslation in a legal contract could void agreements worth millions. A translation error in financial disclosures could trigger regulatory violations. These buyers don’t care how fast or cheap your tool is if they can’t trust it.
Daphne built trust through a three-part stack: Domain Expertise + Zero Data Retention + Anchor Customers.
Part 1: The Domain-Specific Fine-Tuning Advantage
Remember that legacy language learning platform? Daphne had spent years researching legal and financial terminology across languages. Not surface-level vocabulary. Deep, nuanced, context-dependent terminology that changes meaning based on jurisdiction.
That research became a proprietary database. Bluente used it to fine-tune their translation engine specifically for legal and financial documents.
When Daphne pitched clients, she didn’t lead with “we use AI.” She led with “we’ve spent years building legal and financial language models that understand your domain.”
She positioned the AI as trained by domain experts, not as a generic tool.
Part 2: Zero Data Retention as a Feature
Enterprise legal and finance teams have a recurring nightmare: confidential documents leaking through third-party tools.
Bluente’s security positioning is simple. Zero data retention. Every document uploaded to Bluente’s portal is deleted from their servers within 24 hours. No storage. No training on customer data. No lingering copies.
This single policy eliminated 80% of security objections during the sales cycle. It gave legal and compliance teams the assurance they needed to pilot the tool.
However, this creates a tradeoff. Bluente can’t use customer data to continuously improve their models. They’ve accepted that constraint because the trust gain outweighs the optimization loss.
Part 3: The Netherlands Manufacturing Case Study
Here’s what trust-building looks like in practice.
Bluente’s client, an industrial manufacturing company based in the Netherlands with offices across Europe, needed training materials translated into French, Italian, Greek, and a dozen other European languages.
Before Bluente: Employees would copy and paste each line from PowerPoint decks into translation tools. Translate line by line. Manually reformat images. Fix broken layouts. Re-export. The process took days per document and required multiple people.
After Bluente: Upload the PowerPoint to Bluente’s portal. Select target languages. Wait 2-3 minutes. Download fully translated documents with images and formatting completely intact.
Result: 90% time savings. Documents were immediately usable without manual fixes.
The manufacturing company initially piloted Bluente for one training deck. Within three months, they’d migrated their entire European training library to the platform.
That’s the pattern Daphne sees across enterprise clients. Pilots convert to full deployments once teams realize the translation output genuinely requires no post-editing.
Why This Matters for AI Founders
McKinsey reports that 71% of organizations now regularly use generative AI in at least one business function (July 2024 survey). But adoption doesn’t mean trust.
Enterprise buyers are still terrified of AI failures. If you’re building for high-stakes industries, your positioning has to address that fear head-on. You can’t just talk about speed and cost. You need to demonstrate domain expertise, security protocols, and proof from comparable clients.
When to Fire Your Agencies and Hire In-House
By Q4 last year, Bluente had two agencies humming along nicely. One handled outbound. Another managed SEO and AEO (AI Engine Optimization).
Both were performing. Traffic was climbing. Deals were closing.
Then Daphne went to events and kept hearing the same feedback: “I wish I’d known about you earlier. I’d never heard of your brand before.”
That’s when she realized the gap. They needed to move faster on demand generation. But agencies, no matter how good, can’t iterate at startup speed.
Daphne started searching for two roles: a chief of staff/product manager to automate internal systems, and a founding growth lead to accelerate marketing. As she interviewed candidates, a pattern emerged.
Most of the systems she wanted to automate were actually go-to-market systems. Outbound sequences. SEO workflows. Content generation. Lead qualification.
Then, at a conference in San Francisco, an advisor mentioned a role she’d never heard of: GTM engineer. Someone who combines marketing strategy with technical implementation. Someone who can build and automate the entire demand generation stack.
That was exactly what Bluente needed.
The Agency vs. In-House Decision Framework
Daphne made the call to bring outbound and SEO in-house through a GTM engineer hire. Here’s why:
Speed of experimentation: Agencies operate on weekly or monthly cycles. Startups need to test hypotheses daily. When you want to try a new outbound sequence, a different ICP segment, or an experimental content angle, having someone internal means you can ship changes in hours instead of days.
Depth of capability: During interviews, Daphne realized candidates could do far more with outbound than her agency was delivering. Not because the agency was bad. Because agencies optimize for what works across multiple clients. In-house teams optimize for what works for your specific ICP.
System ownership: Agencies hand you dashboards and reports. Internal teams hand you the actual systems. When Daphne’s GTM engineer joins, they’ll own the entire stack: the workflows, the automations, the data pipelines. That ownership creates compounding advantage over time.
However, Daphne was careful about timing. She’d learned from her first product that hiring too early kills momentum. Her philosophy now: wait until you’re absolutely certain about the role before bringing someone on board.
What Makes a Great GTM Engineer
During Bluente’s hiring process, Daphne tested candidates in two ways:
Depth of execution: The best candidates used AI to build entire systems during the interview process. They’d show up with working prototypes: “Here’s the outbound sequence I’d run. Here’s the automation I’d build. Here’s how I’d instrument it.”
That level of detail gave Daphne confidence they could execute, not just strategize.
Tool fluency: Daphne asks every candidate: “What’s the latest tool you learned in the last week?”
Some candidates mention Claude or ChatGPT. The best candidates mention tools from three days ago. Tools Daphne hasn’t heard of yet.
Finding people who combine strategic marketing thinking with systems-building execution is challenging. Most marketers can’t code. Most engineers don’t understand go-to-market. The intersection is rare.
If you’re trying to hire a GTM engineer, look for evidence of both: strategic frameworks and technical implementations they’ve actually shipped.
Staying Current on AI from 7,000 Miles Away
Operating from Singapore, Daphne worried about being disconnected from Silicon Valley’s AI ecosystem.
She built a system to stay current that actually works better than living in San Francisco. Here’s how she does it:
System 1: Let Social Algorithms Work for You
Daphne follows AI tool creators on TikTok. Not LinkedIn thought leaders. Not Twitter theorists. People who actually demo tools.
Once she started engaging with that content (watching, liking, commenting), TikTok’s algorithm started surfacing more. Within weeks, her feed became a curated stream of emerging AI tools, builder demos, and technical breakdowns.
In other words, she turned TikTok into a personalized AI research engine without manually searching for content.
System 2: Follow Founders 2-3 Stages Ahead
Daphne tracks founders who are building in public on LinkedIn and X. Not peers at her stage. Founders running companies 2-3 stages more mature than Bluente.
When they share updates, she reads for tech stack mentions. When they post about new tools, she screenshots for later research. And when she’s curious, she comments directly: “Can you share what tech stack you use for this?”
Most founders respond. Those responses have led Daphne to discover tools like cursor, Windsurf, and deployment platforms she wouldn’t have found through traditional research.
System 3: Leverage Globally-Connected Investors
Bluente’s investors operate globally. They see best practices across portfolio companies in the US, China, and Europe.
Daphne structures monthly and quarterly check-ins to include a simple ask: “What are five tools or practices you’re seeing work across your portfolio?”
Her investors share insights from companies deploying AI at scale in different markets. That global perspective is actually more valuable than being physically in San Francisco because she gets pattern recognition across geographies instead of just Valley groupthink.
The Contrarian Take on Location
Think you need to be in San Francisco to build an AI startup? Here’s why that’s outdated.
Social media algorithms surface the same information globally. Founder communities share learnings publicly. Investors connect portfolios across regions. And 76% of consumers prefer buying products with information in their native language (study of 8,709 consumers across 29 countries), which means geographic diversity in founder location actually creates market insight advantages.
Daphne’s Singapore location gives her firsthand understanding of Asian and European enterprise buying behavior. For a global product, that’s strategic positioning.
Why YC Still Matters (But Not for the Reason You Think)
If Daphne were starting Bluente from zero today, she’d join Y Combinator immediately.
Not for the investment. Not for the prestige. For the first customer pool.
The YC network excels at pushing products to growing startups within the cohort ecosystem. When you’re looking for your first 10-20 customers, warm introductions from batch-mates matter infinitely more than cold outbound.
Daphne watched YC companies in her sector close their first enterprise deals through cohort connections. Those deals happened in weeks instead of months because trust was pre-established through the YC network.
Most accelerators are valuable at earliest stages. Bluente is now too late-stage to join. The window for maximum accelerator value is tight: post-product validation, pre-significant revenue.
However, Daphne’s point applies beyond YC. If you’re building B2B, warm introductions >> cold outbound for your first customer pool. Find the network that gives you access to your ICP, then optimize for relationship density over reach.
The Regional GTM Playbook: When Remote Sales Actually Lose Deals
Remote-first sales doesn’t work everywhere.
Bluente’s GTM strategy combines outbound with in-person events. Not because Daphne loves conferences. Because certain markets (Singapore, Middle East, parts of Europe) still require face-to-face meetings for enterprise deals.
Different markets have different expectations for establishing trust. In the US, you might close a deal on Zoom. In Singapore, buyers want to meet you in person first.
This isn’t about inefficiency. It’s about cultural buying behavior. Some regions equate in-person presence with commitment and legitimacy. A Zoom call signals you’re not serious about the market.
Bluente complements outbound sequences with strategic event attendance. They meet buyers face-to-face, let them see the team, and establish that they’re “out there” in the market.
Does your target market require in-person sales? Ask your first 5-10 prospects how they prefer to evaluate new vendors. If “in-person demo” or “meet the team” comes up repeatedly, adjust your GTM motion accordingly.
The Founder-Led Content Playbook for “Boring” Categories
Daphne started posting founder content on LinkedIn last year after watching successful founders build in public.
The results weren’t viral posts or massive engagement. But something interesting happened: people would come through other channels (referrals, outbound responses, event follow-ups) and mention they’d seen her on LinkedIn.
The content created indirect brand-building. It gave Bluente a personality beyond its product. It signaled that a real person was running the company, not a faceless corporate entity.
Daphne’s doubling down this year. She’s working to identify content pillars that make translation engaging despite it being a time-triggered category. Translation isn’t like sales tools where daily relevance makes content easy to create. People only think about translation when they urgently need it.
Her current hypothesis is that influencer partnerships will help uncover what resonates. By collaborating with industry voices, she can test different content angles and see what drives awareness in a “boring” B2B category.
The Attribution Challenge
Founder content impact is indirect and hard to measure. You won’t see direct conversions. You’ll see people say “I came across you somehow on LinkedIn” when they come through other channels.
That vague attribution frustrates data-driven founders. But the alternative (no founder presence) means you have zero personality in the market. And in crowded AI markets, personality is a tiebreaker.
If you’re building in a time-triggered or low-engagement category, founder content is about staying top-of-mind for the moment when your audience urgently needs you. Not about generating immediate pipeline.
What Daphne Would Do Differently
I asked Daphne: if you were starting Bluente from zero today, what would you change?
Three things:
Rapid prototyping over custom builds: The AI tool ecosystem now lets you build functional prototypes in days. Daphne would test market demand with off-the-shelf tools first, then invest in custom development only if customer willingness-to-pay justified it.
Join a strong-network accelerator early: Not for investment, but for access to the first customer pool. Warm introductions from batch-mates would have accelerated Bluente’s first 20 deals by months.
Follow customer demand signals faster: The pivot from language learning to enterprise translation should have happened sooner. Daphne spent months trying to make the original vision work when customers were clearly asking for something different.
Customer pain beats founder vision every time. The faster you internalize that, the faster you’ll build something people actually pay for.
Resources
Bluente – Try visual translation for your enterprise documents
Nimdzi 100 Report – Global language services industry data
EU Language Policy – European Union official languages
P.S. Here’s The Build vs. Buy Decision I’m Watching
I’ve been thinking about Daphne’s build-vs-buy framework a lot lately.
In my fractional and interim CMO work, I see founders making this decision constantly. Not just for product features, but for GTM systems, content engines, analytics stacks. The pattern is always the same: founders want to build custom because it feels like they’re creating a moat.
But most of the time, buying or licensing gets you to market validation faster. And market validation is the only thing that actually matters at early stages.
If you’re wrestling with a build-vs-buy decision right now (product, GTM, ops, anything), here’s my diagnostic:
Can you validate customer willingness-to-pay without building this custom?
Does building this custom create defensible differentiation customers will actually pay for?
Will building this delay your ability to test with real customers?
If you answered yes to question 3 and no to questions 1-2, you should probably buy.
And if you need someone to help you think through GTM system design with that same engineering rigor Daphne applies to product decisions, reach out. I work with technical founders who want to apply systematic thinking to revenue operations. It’s basically GTM engineering, and it’s what I do.
– Lillian
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## The 2,200 Hours/Year Problem: Why Your Business Model Might Be Working Against You
URL: https://www.data-mania.com/blog/the-2200-hours-year-problem-why-your-business-model-might-be-working-against-you/
Type: post
Modified: 2026-03-17
When Gwen Griggs finished law school, she was ready to solve problems.
She knew how to think critically. How to cut through complexity and get to answers fast. She was excited to use those skills.
Then her law firm gave her the real goal: 2,200 billable hours per year.
“The skills that I had learned in college and school to solve problems quickly weren’t the same skills that made you successful in a law firm,” she told me.
Here’s what hit me about that… She wasn’t describing a personal failure. She was describing a structural problem baked into the business model itself.
The firm wanted maximum hours. Clients wanted efficient solutions.
Those two things don’t just exist in tension; They’re fundamentally opposed to each other.
After more than 2 decades inside law firms and inside companies, Gwen spent the last 10 years figuring out how to build something different.
The Misalignment Nobody Talks About
Most professional services have this same invisible friction.
You bill by the hour. Your client wants their problem solved. Solve it too quickly? Less revenue for you. Solve it slowly? Frustrated clients who feel milked.
The hard part about it is that nobody’s really the villain here. Lawyers aren’t intentionally dragging their feet. Clients aren’t unreasonable for wanting efficiency.
But the system itself creates misalignment at every level.
Gwen saw this play out across three different contexts:
At the big law firm: 2,200 hours per year, efficiency was the enemy
As general counsel for a company: Lawyering without billing, strategic partnership
Founding ADVOS Legal: Building the engagement model she wished existed
The general counsel experience was the breakthrough. Same legal work. Same problem-solving.
But suddenly she was part of strategic business decisions. Seeing around corners. Helping solve problems before they became bigger issues.
In other words, when billing friction disappeared, the relationship transformed from transactional to strategic.
So when she founded ADVOS Legal, she asked a different question: What if we just… didn’t bill by the hour?
What Replacing Hours with Outcomes Actually Looks Like
Surprisingly, ADVOS Legal doesn’t use flat fees or fixed fees either.
They use a point-based system that Gwen and her co-founder adapted from Agile Scrum methodology – an approach shaped not only by their own experience but also by extensive input from their clients, many of whom are tech founders themselves.
Every legal project gets broken down into granular steps.
… First round letter of intent.
… Second round letter of intent. Contract negotiation. Diligence review.
Each step has a point value based on four factors:
The Four-Factor Pricing Model:
Time: Still matters, it’s just not the only thing
Criticality: How important is this to the business?
Value: What’s the strategic impact?
Urgency: Need it tomorrow? That’s more points than need it next week
Gwen can give clients a range at the start based on extensive experience. As work progresses, she tracks whether they’re on the high or low end of that range.
When new complexity emerges, like difficult opposing counsel or unexpected diligence issues, she communicates that to her client immediately.
The difference here is that clients aren’t surprised by bills. They’re informed partners in managing scope and cost throughout the process.
Take NDAs as an example. Standard NDA review might be 2 points. Same NDA but you need it in 4 hours? 4 points. Same NDA but it’s for your biggest customer and mission-critical? 3 points.
That’s outcome-based pricing. You’re not paying for hours. You’re paying for solved problems, with transparent pricing that reflects actual complexity and business value.
Steal This: Building Your Own Outcome-Based System
You don’t have to own a law firm to use this framework. The following describes how to adapt this system for your own business.
Step 1: Break every project into smallest possible units
For ADVOS, that’s “one round of letter of intent negotiation” or “IP assignment review.”
For a marketing consultant, it might be “competitor analysis,” “messaging framework,” “landing page copy.”
For a fractional CFO, “cash flow model,” “board deck,” “fundraising strategy.”
The point is granularity. The smaller the unit, the easier it is to price it consistently.
Step 2: Assign point values based on the four factors
Time matters. But, as mentioned above, so does urgency (rush work costs more), criticality (business-critical work costs more), and value (strategic impact matters).
Build a points matrix. Test it with 10-20 projects. Adjust based on what you learn.
Step 3: Give ranges, not fixed prices
Gwen doesn’t promise exactly what an M&A transaction will cost. She gives a range based on experience, then tracks progress throughout.
Clients can see whether they’re trending high or low in real-time.
This solves the “scope creep” problem without falling back into hourly billing.
Step 4: Report on deliverables, not hours
Replace time tracking with deliverable tracking. What did we actually accomplish? What problems did we solve?
That’s what clients care about.
ADVOS reports, “We delivered this contract. We negotiated this agreement. We solved this problem, and that is worth two points.”
Compare that to, “We spent 14.7 hours on your matter this week.”
Which one feels more like a partnership?
What Happens When Billing Friction Disappears
Here’s what surprised me most about Gwen’s story… when she adopted this novel engagement model. Her client relationships were radically transformed in the best of ways.
Gwen told me about clients who’d gone to other lawyers, started projects, and could never get them across the finish line. They had draft operating agreements sitting in limbo.
Why?
Because the billing model created a barrier to the very conversations needed to finish the work.
Clients withheld information to control costs. Lawyers optimized for time tracking instead of problem completion.
Projects stalled because the client didn’t want to “run up the meter” having the conversations that were truly needed to reach closure.
When ADVOS removed that friction, everything changed:
Time previously spent on time tracking and invoicing → Redirected to client service, regular check-ins, project management
Clients worried about “the meter running” → Clients sharing complete information because there’s no penalty for communication
Lawyers staying distant from daily operations → Lawyers becoming strategic partners who understand the business deeply
Projects stalling in draft form → Projects reaching actual completion with clear closure
Gwen put it simply, “If you take all of the time it takes to track your time and send out invoices and detailed invoices and make sure they match, that’s not insignificant. If you repurpose all of that time to client service, now we have meetings. We have regular check-ins. We’re making sure the projects are moving along.“
Or put another way, the administrative overhead of hourly billing isn’t just internal cost. It’s opportunity cost – the time you could be spending deepening client relationships and solving bigger problems.
The AI Adoption Paradox
ADVOS Legal is bullish on AI. Spellbook for legal-specific work. Westlaw and Lexis with AI features. Every efficiency tool they can get their hands on.
Why? Because they’re not disincentivized by efficiency.
Think about the hourly billing trap… If you’re a lawyer billing $500/hour and AI cuts your work time in half, you just cut your revenue in half. You’re literally incentivized against using the tools that would better serve your clients.
When most people hear “AI will transform professional services,” they think about job displacement.
The real story is weirder however. Hourly billing makes AI adoption a threat to your business model.
Gwen doesn’t have that problem. AI makes her and her team fast, which means she can serve more clients at the same quality level.
Or spend more time on the strategic conversations AI can’t handle – negotiation strategy, understanding human behavior, helping clients think through how to solve problems.
The thing is, AI is really good at a lot of what lawyers historically did. Document drafting. Contract review. Research. Basic analysis.
But AI isn’t good at judgment calls that require business context. Which terms in this NDA actually matter given this specific relationship dynamic? How should we position this negotiation given what we know about the other side?
What’s the strategic play three moves ahead?
That’s where ADVOS adds value. And because they’re not billing hours, they can let AI handle everything else without worrying about revenue.
Gwen and her team do several NDAs a day. AI helps with the standard elements. But the human judgment – are you the discloser or recipient? Talking to an investor, customer, or vendor? Are you the small fish or big fish in this relationship? – that still requires her expertise.
Junior staff can use AI to get work done faster, which frees them up for more interesting, higher-value work. Training accelerates. Paralegals can tackle more complex projects with AI support.
However, if you were billing their hours, this would be a problem. You’d be incentivized to keep them doing manual work longer.
Maintaining Quality Without Hourly Oversight
Of course, moving faster only works if you maintain quality. ADVOS solved that by building a QA credit system into their project management platform.
Here’s how it works:
Every deliverable has a QA field. If a paralegal delivers client-ready work to a partner, the paralegal gets the QA credit.
If the work isn’t client-ready and requires fixes, whoever fixes it gets the QA credit.
Simple. But brilliant in its incentive structure.
Non-client-ready work triggers three possible conversations. Those are:
Coaching need: You didn’t do a good job, I need to coach you on what client-ready means in this context
Training gap: You don’t actually know the material well enough yet, we need better training
System problem: Our instructions or templates didn’t give you what you needed to succeed
All three are opportunities for improvement. None are emotional confrontations about quality.
Gwen told me, “All of those are just opportunities for us to have the conversation that so many people don’t want to have. Right. They just want to go fix it, complain about it.
But in our system, we are incentivized to deliver client-ready work. It’s not going to be poor work. But now I’m training people, improving our system, and making it not all wrapped up in emotion.”
In other words, the QA system removes emotion from quality feedback by making it transactional. You either delivered client-ready work and you get the credit, or you didn’t and you don’t get the credit, plus we figure out why together.
This is how you scale quality without micromanaging hours.
The Hidden Value Killers in M&A Deals
After three decades of M&A work, including one decade at ADVOS Legal, Gwen kept seeing the same heartbreaking pattern.
Founders would get to the letter of intent stage. Everything looked good. Then diligence would start.
And their acquisition value would get whittled down because of completely avoidable issues they simply didn’t know about.
Cap tables that didn’t match the documentation. IP assignments missing from key contributors – that freelancer who helped with your logo three years ago? You never got an IP assignment.
Material contracts that couldn’t be assigned to a buyer. Workforce classification issues.
None of these were complicated.
In fact, they were simple. The founders just didn’t know.
“It was heartbreaking to watch people not knowing simple things that if they had done, we wouldn’t be having the conversation about how to reduce the value or mitigate the risk,” Gwen said.
This led her to launch Deal Advantage – a separate business focused on getting founders “deal ready” 2-3 years before acquisition.
The timeline matters. Two to three years gives you time to fix issues and demonstrate to buyers that you’re an experienced seller who knows what you’re doing.
Buyers pay more when they see clean documentation and good practices.
This is not even about getting a better deal. It’s about not losing value unnecessarily.
Buyers won’t accept risk they don’t have to accept. If your cap table is messy, they’ll reduce the purchase price or make it contingent on future performance.
If you can’t assign your major customer contracts, that’s a problem. If your IP ownership is unclear, they’ll adjust the terms.
All of those adjustments mean less cash for you at closing. And most of them are fixable with enough lead time.
Steal This: The Seven Pillars of Deal Readiness
Here’s the framework Gwen uses with Deal Advantage clients. Even if you’re not planning an acquisition, this is a useful audit of your startup’s legal foundation.
Don’t let this list overwhelm you. Most founders only have issues in 2-3 categories. But you need to know all seven to audit properly.
Financial (you probably know this one)
Clean books and records. Proper accounting. No surprises. Most founders know that this matters.
Cap Table
This trips up even large corporations. Your cap table needs to match your documentation exactly.
Every investment round, every option grant, every conversion – documented and reconciled.
Buyers look at this first. Messy cap table signals other problems.
Intellectual Property
You need IP assignments from every single person who contributed to your code, design, copy, or other creative work. That includes:
Founders
Early employees
Contractors and freelancers
Advisory board members
Anyone who touched your IP
Missing even one assignment can create complications during diligence.
Material Contracts
Your customer agreements, vendor contracts, partnership deals – buyers will review all of them. Key questions:
Can these contracts be assigned to a new owner?
Are there change-of-control provisions?
What are the terms and are they market-standard?
If your biggest customer contract can’t be assigned, that’s a huge problem.
Insurance History
What coverage have you maintained? Any gaps? Any claims? Buyers want to understand risk exposure.
Compliance
Depends on your industry, but this covers regulatory compliance, data privacy, security practices, and similar requirements. Document everything.
Workforce
Did you classify employees vs. contractors correctly? Did you pay everyone properly? Any employment issues or disputes?
These come up in diligence and can significantly impact value.
The DIY version: If you’re not ready for Deal Advantage yet, Gwen suggests using Claude or ChatGPT to generate a diligence checklist and self-audit against it.
Try asking: “Give me a comprehensive M&A diligence checklist for a B2B SaaS startup with 50 employees and $5M ARR. Break it into categories and flag the most common issues you see.”
Not perfect, but it’ll surface major gaps.
The point is awareness. Most of these issues are fixable with time. But you need to know they exist.
How These Lessons Scale Far Beyond Legal Services
I keep thinking about the broader principle here.
Gwen built a new law firm business model because hourly billing misaligned incentives. But that same misalignment exists across most professional services.
Marketing consultants’ billing hours are incentivized to spend more time, not get better results faster. Fractional executives’ billing hours benefit from longer engagements, not efficient problem-solving.
Any service business charging for time faces the same structural tension.
The question isn’t whether hourly billing is “bad.” It’s whether your business model aligns your success with your clients’ success.
When ADVOS adopted outcome-based pricing:
AI adoption became an advantage instead of a threat
Client relationships transformed from transactional to strategic
Quality improved through systems rather than hourly oversight
The firm could optimize for client outcomes without worrying about revenue
That alignment creates compounding advantages over time.
Your clients want their problems solved efficiently. You want sustainable revenue. The business model you choose either aligns those two things or puts them in opposition.
Most professional services operate in opposition without really thinking about it.
Lillian
P.S. There’s a moment in every ADVOS client relationship that Gwen watches for.
It’s when the client stops worrying about “running up the meter” and starts actually sharing what’s going on in their business. The complete picture. The messy details.
The things they’d normally hold back to control costs.
That’s when the real work can begin. That’s when she can actually see around corners and help them avoid problems before they become expensive disasters.
The billing model they chose made that moment possible. Not by being generous or charitable, but by aligning incentives properly from the start.
I think about this every time now I structure my own client engagements. The moment someone stops thinking about the clock is the moment real partnership begins.
Want to get your startup deal-ready before acquisition talks begin? Learn more about Deal Advantage
If you’re interested in legal representation and support, learn more about ADVOS Legal here.
Lastly, Gwen and her co-founder also have a program to help lawyers and other types of professional consultants to redesign their practices around the outcome-based pricing model that they developed. That program is called ADVOS Pro – learn more about it here.
Tools and resources mentioned: Spellbook for legal AI, Westlaw and Lexis for legal research with AI features, Claude and ChatGPT for DIY diligence checklists.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## From City Floodgates to AI Agents: What Mrinal Learned Building Trust Infrastructure for Autonomous Systems
URL: https://www.data-mania.com/blog/from-city-floodgates-to-ai-agents-what-mrinal-learned-building-trust-infrastructure-for-autonomous-systems/
Type: post
Modified: 2026-03-17
The sensor sitting on a city street says the water level is rising. Your system needs to decide: open the floodgates or don’t?
Here’s the problem. That sensor is physically accessible. Anyone could tamper with it. Anyone could feed false data into your infrastructure system. And if you get this decision wrong, you flood a city.
This wasn’t a theoretical security exercise for Mrinal Wadhwa. As CTO of an IoT company managing city infrastructure (parking sensors, traffic lights, water systems), this was his daily reality. Mrinal would lie awake thinking about failure scenarios. Not abstract security breaches but literal floods caused by a tampered sensor he’d trusted.
That question led to 6 years building Ockam, an open-source trust infrastructure that grew to hundreds of contributors and customers like AWS, Databricks, and Snowflake. Then in 2024, Mrinal made a bold move: he pivoted to build Autonomy, a platform for the autonomous agent products he’d been preparing for all along.
I wanted to understand what he learned about building developer communities, why open-source growth doesn’t automatically convert to revenue, and how a 3-month capability leap is forcing almost everyone to rewrite their agents from scratch.
Why Your Open Source Community Won’t Become Your Customer Base
Mrinal and his co-founder Matthew Gregory started Ockam the right way. Before writing a single line of code, they validated the problem. They presented at meetups and small conferences, talking about trust in distributed systems. They built a Slack community that grew to over 100 people, all before they had a working prototype.
When they did start building, they made a strategic choice that accidentally became a growth accelerator. They rewrote their initial prototype in Rust, a programming language that was experiencing explosive community growth at exactly that moment.
Here’s what made this work. The Rust community was hungry for examples of how to build complex systems in this relatively new language. Ockam became that example. Mrinal and Matthew made it a weekly discipline: before Wednesday, write down the week’s learnings and submit a pull request to “This Week in Rust,” a rapidly growing newsletter in the ecosystem.
Week after week, they showed up in that newsletter. Their GitHub repository became a reference for how to tackle complicated distributed systems problems in Rust. They tagged issues as “good first issue” to lower the barrier for new contributors. The community grew to hundreds of contributors and thousands of participants.
The hard part is what happened next. They assumed open-source community growth would naturally convert into paying customers. It didn’t.
The people contributing to Ockam were mostly junior developers early in their careers. They were excited about the technology, willing to contribute code, engaged in the community. But they didn’t have architectural decision authority inside their companies. They weren’t the ones with budget to buy infrastructure tools.
The actual customers (senior architects, CTOs, technology leaders) were at completely different conferences. They hung out in different circles. They needed different messaging. Mrinal had to build an entirely separate motion to reach them, largely through personal networks in the cloud computing community.
In other words, community-building and customer acquisition aren’t the same muscle.
You can have hundreds of enthusiastic open-source contributors and still need to start from scratch on your commercial go-to-market.
The initial paying customers came through direct outreach to people Mrinal and Matthew had worked with before. Once those companies started using Ockam (for connecting to private Kafka instances, for database product infrastructure, for secure service communication), the adoption was sticky. The product was hard to integrate because it sat so deep in the infrastructure stack, but that same depth made it nearly impossible to rip out once implemented.
Steal This: The Community-to-Commercial Motion Framework
If you’re building a developer tool with an open-source component, here’s the framework Mrinal learned the hard way:
Map your personas separately.
Your OSS contributors (junior developers, early career, learning-focused, no budget authority) are different from your buyers (senior architects, decision-makers, risk-averse, budget holders). Don’t expect automatic conversion between these groups.
Build different channels for each.
Community-building channels include developer-focused conferences, online communities, educational content, contribution pathways. Customer channels include enterprise conferences, direct outreach, ROI-focused content, proof of concepts with decision-makers.
Track different metrics.
On the community side, you’ve got contributors, GitHub stars, community participation, educational content engagement. One the customer side there are qualified leads, POC conversions, design partnerships, revenue pipeline.
This being said, the community still matters even if it doesn’t directly convert.
It validates that you’re solving a real problem. It creates proof points when talking to customers. It generates feedback that improves the product. Just don’t mistake community growth for a sales funnel.
How to Ride Technology Waves (And Why Timing Matters More Than You Think)
Ockam rode the Rust wave in 2020. Autonomy is riding the coding agents wave in 2024. Both times, the same pattern emerged.
Here’s how it works. When a new technology paradigm starts growing rapidly, the early community faces a common problem: not enough examples of how to actually use this thing for real work. If your product becomes a high-quality example of the new paradigm, you get visibility as the community searches for references.
For Ockam, that meant their repository became a go-to example of building complex distributed systems in Rust. For Autonomy, it means something different: they wrote documentation specifically for coding agents, not just human developers.
This might sound strange until you realize what’s actually happening. Developers are increasingly building with AI assistance (tools like Claude Code and Cursor that can write, test, and debug code autonomously). Autonomy’s documentation enables these coding agents to execute the complete development loop: build something, test it, identify what’s broken, fix it, test again, deploy it.
The user experience looks like this. Someone visits Autonomy’s website and copies a prompt that says: “Reference the Autonomy documentation at this URL, build me an app that uses agents to research facts in a news article.” They paste it into Claude Code. Twenty minutes later, they have a working application (live, hosted in Autonomy’s cloud, fully functional).
Mrinal has turned this into a go-to-market motion. He meets founders at local San Francisco events and offers to do a 40-minute pairing session. Yesterday it was someone building an AI SRE (site reliability engineer). They gave a coding agent the prompt and a reference to Autonomy’s docs, and 20 minutes later had a working implementation of their first workflow.
Even when founders don’t convert into paying customers, Mrinal learns exactly what features matter to people building that type of product. It’s product discovery through building together, not through surveys.
The broader pattern here is about timing and positioning. You can’t create these waves, but you can position yourself to ride them. Rust was going to grow whether Ockam existed or not. Coding agents are being adopted whether Autonomy exists or not.
The opportunity is to become the best example of how to use the new paradigm and to structure your product and documentation to serve both the emerging community and your commercial goals.
The 3-Month Capability Leap That’s Making Everyone Rewrite Their Agents
Something fundamental changed in the last three months. If you built an AI agent in 2023 or early 2024, you’re probably going to rewrite it.
Here’s what happened. Most agents built in the last two years are what Mrinal calls “simple scripts.” They can execute 2-3 autonomous steps. Organize your inbox. Move messages from LinkedIn into a category. File something away. These are useful, but they’re automating just minutes of work.
Starting around October 2024, agent capabilities crossed a threshold. New architectural approaches enable agents that can execute hundreds of autonomous steps. These “long-horizon agents” can automate days of work instead of minutes.
The breakthrough came from a seemingly simple change. Instead of relying primarily on vector stores to search through information, give agents a file system to work with. Give them a workspace. Give them access to traditional Unix command-line tools.
This architectural shift showed up most visibly in coding agents. Claude Code and similar tools became dramatically better than the previous generation like GitHub Copilot. They could tackle complex, multi-step tasks that earlier agents struggled with. The pattern works because working on a large codebase isn’t that different from working through complex business logic or document analysis. You’re building reasoning chains and deciding on next steps based on accumulated context.
Autonomy is designed for this new generation of agents. One of Mrinal’s recent demos is an app with 5,000 agents collaborating to solve a problem. This would be incredibly complex and expensive to build from scratch. In Autonomy, because of specific architectural decisions they made early on, it’s relatively easy and cheap to deploy large-scale agent swarms.
Or put another way, the agents most companies built in 2023 automate tasks that take minutes. The agents now possible automate workflows that take days. That’s not an incremental improvement, it’s a different category of capability entirely.
This creates an opportunity for products like Autonomy, but it also means almost everyone building agent products is facing a rewrite. The technology threshold crossed in the last few months makes previous approaches feel like toy examples compared to what’s now achievable.
Why Agents Need the Same Trust Infrastructure That City Floodgates Do
Remember that sensor on the city street that might tell your system to open the floodgates? That trust problem never went away. It just evolved.
Now instead of IoT devices making decisions about city infrastructure, you have AI agents making loan recommendations. Approving drug documentation. Analyzing body cam footage for legal cases. The stakes are similarly high, and the trust requirements are just as critical.
Here’s what trust actually means in autonomous systems.
Each agent in Autonomy possesses a cryptographic key and identity. It authenticates using that key, which makes it mathematically impossible for someone to impersonate that agent. Every decision the agent makes gets recorded in a transcript (not just what it decided, but the reasoning behind the decision and which sources it consulted).
This matters in concrete ways. One Autonomy customer is a financial institution that uses agents to make recommendations about whether to issue business loans. As those agents analyze applications and make recommendations, they’re building an audit trail. If someone later asks “why did we approve this loan?” or “why did we decline that application?”, there’s a complete record of which agent made the assessment, what criteria it used, and what sources it referenced.
The architecture represents a fundamental shift. Traditional systems relied on boundary-based trust: everything running inside this perimeter is trusted. But once you breach the perimeter, you can potentially tamper with anything inside. That model breaks down for distributed agent systems where components are running across companies, clouds, and contexts.
Cryptographic identity moves the trust guarantee to the agent level. Each agent can prove it is who it claims to be, and each decision can be traced back to a specific, authenticated agent. If an agent makes a mistake, you can trace back where the error originated. You can roll back that decision. You can audit the reasoning. You can implement verification layers.
Mrinal describes several verification patterns customers use:
Human-in-the-loop approval. The agent makes a recommendation with its evidence and reasoning. A human reviews and approves. Both the agent and human need to authenticate each other for this collaboration to be secure (which also uses the Ockam foundation built into Autonomy).
Deterministic verification of non-deterministic output. This is common in coding. An agent might use non-deterministic methods to write a program, but you can write a deterministic test that verifies the program’s behavior. If the requirement was “when I say hello, it echoes hello back,” you can reliably test that, even if the agent used unpredictable methods to write the code.
Multi-agent verification. For complex decisions, you can have multiple agents independently analyze the same input and compare their conclusions. Disagreements trigger human review or additional analysis.
The point is, reliability in non-deterministic systems doesn’t come from making individual agents perfect. They’re going to make mistakes. Reliability comes from building systems that can trace mistakes back to their origin, verify outputs through multiple methods, and maintain audit trails that let you understand what happened and why.
Agent Swarm Architecture: Why Splitting Tasks Makes Systems More Accurate
One of Autonomy’s customers is in pharmaceuticals. They’re working to compress drug approval timelines from 2 years to 1 year. A significant chunk of that time savings comes from a process that previously took weeks and now takes minutes.
Here’s the specific problem. Before submitting a drug application to the FDA or European agencies, thousands of documents need to be prepared. All of these documents reference each other (this compound is described in document 1501, that trial result is in document 2247, this analysis references document 892). Someone has to manually work through thousands of documents for multiple weeks, inserting all these cross-references.
Autonomy solves this with an agent swarm architecture. A parent agent orchestrates the work. It spins up a child agent for each document. Each child agent focuses only on its assigned document (reading it, understanding it, identifying what it needs to reference, finding those references across the document set).
Because each individual agent is only dealing with one document, its job is relatively simple. The context load is manageable. The accuracy is high. If you gave this entire task to a single large agent, it would struggle because the context is too vast. But split across hundreds of specialized agents, each with a narrow focus, the success rate goes up dramatically.
This same pattern shows up in other Autonomy customer deployments. For example:
A Public Defender’s Office uses it to process body cam footage. As footage files arrive in a folder, agents automatically transcribe them and translate them from Spanish or other languages into English. Then analysis agents assess the conversations for legally relevant content related to specific cases. Instead of attorneys manually watching hours of footage, agents do the initial processing and flag what matters.
Someone building an AI SRE uses agent swarms to analyze logs across distributed systems, with each agent focused on a specific service or component, coordinated by parent agents that synthesize findings.
The underlying principle is about context optimization. Large context load to one agent equals lower accuracy. Small, focused context per agent equals higher accuracy. Parent agents handle coordination and synthesis. Child agents handle specialized work within their narrow domain.
Steal This: Agent Swarm Design Pattern
If you’re building an autonomous system that needs to process multiple similar items (documents, logs, applications, footage, customer records), here’s the architecture:
Identify the repeated unit. What’s the thing you have multiples of? Documents, video files, customer applications, system logs?
Design the specialist agent. Build an agent focused solely on processing one instance of that unit. Keep its job simple and its context narrow. Test it thoroughly on individual examples.
Build the orchestrator. Create a parent agent that coordinates the specialists. It distributes work, collects results, synthesizes findings, and handles exceptions.
Implement parallel processing. Autonomy makes it easy and cheap to spin up hundreds or thousands of agents simultaneously. Take advantage of this. Don’t process items sequentially unless order is important.
Add verification layers. Deterministic checks, human review checkpoints, or multi-agent consensus depending on your accuracy requirements and stakes.
Create clear audit trails. Each agent should record what it processed, what it concluded, and what sources it used. The orchestrator should maintain the overall workflow history.
This pattern works because it matches how the underlying technology actually functions. LLMs perform better with focused context than with overwhelming context. Architecture that respects this constraint produces more reliable systems.
How Coding Agents Are Reshaping Who Can Build Technical Startups
Here’s something Mrinal is seeing repeatedly in San Francisco right now: founders with deep domain expertise in pharmaceuticals, logistics, legal work, or financial services, but almost no recent technical background. They haven’t written code in 10 years. But they’re starting deeply technical AI companies anyway.
The mechanism is straightforward. Someone who was a programmer 10 years ago still has the conceptual foundations. They understand how computers work, how programs are structured, how systems fit together. What they’ve lost is the syntax, the specific details, the muscle memory of daily coding.
Coding agents restore the execution capability without requiring them to relearn all those details. They can describe what they want to build at a conceptual level, and tools like Claude Code or Cursor handle the implementation specifics. They maintain strategic thinking and architectural decision-making while AI handles tactical execution.
Coding agents let them reclaim technical depth without sacrificing that breadth. Mrinal gives his own example: he now builds in programming languages he’s not fluent in. He understands computers and programs conceptually, so he can leverage specific language features through AI assistance without spending months learning syntax.
The barrier between strategic thinking and technical execution is disappearing. The threshold for who can start a technical company and build a working prototype has shifted significantly.
This doesn’t eliminate the need for deep technical expertise entirely. Security, architecture, scale optimization (these still benefit from people who really know the ins and outs). But the entry point has changed.
Autonomy itself makes this shift possible for agent products specifically. By providing opinionated infrastructure that handles 80% of the foundational decisions (security, scale, cost optimization, deployment), it lets builders focus on the remaining 20% that’s specific to their domain and use case.
Steal This: Community Building + PLG Tactics in 2026
Mrinal built Ockam’s community through weekly discipline and Autonomy’s early adoption through AI-native documentation. Here’s the playbook you can copy and run this week:
1) Ride the wave (positioning that actually works)
Identify the growing technology community adjacent to your product. Don’t try to create a wave. Find the one that’s already rising (Rust in 2020, coding agents in 2024) and position your product as the most useful, real-world example of the new paradigm.
Make your product the best example of the new paradigm. If developers are learning Rust, show them how to build something serious in Rust with your tool. If developers are learning coding agents, publish workflows and examples that let Claude Code build with your platform. Become the reference implementation.
2) Weekly discipline (where community is actually built)
Contribute weekly in the channels your developers already trust. Consistency beats sporadic viral moments. Find the newsletter, forum, GitHub hub, or community space that already has attention. Pick a day. Show up every week with learnings, examples, and insights.
Tag contribution opportunities clearly. Use “good first issue” labels. Write contributor guides. Remove setup friction. Make it easy for someone to go from curious to first contribution without getting stuck. Smooth first experiences create repeat contributors.
Do live build sessions even when they don’t convert. Forty-minute pairing calls teach you what matters, what breaks in onboarding, and what users actually want. Even if they never buy, you still get high-quality product discovery from real use cases.
3) AI-native PLG (docs and activation for how devs build now)
Write documentation for coding agents, not just humans. Most companies have not adapted yet. Your docs should support complete loops: build, test, find issues, fix them, deploy, iterate. Test it by having Claude Code or Cursor build something using only your documentation.
Enable “copy prompt to working app” flows. Make it possible for someone to copy a prompt from your website, paste it into a coding agent with a reference to your docs, and get a working implementation fast. This is not for every product, but if you’re building developer infrastructure, optimize for time-to-value.
Track community and commercial metrics separately. Don’t confuse GitHub stars with pipeline. Community metrics validate the problem and improve the product. Commercial metrics prove you have a business. Both matter, but they are different funnels and require different strategies.
P.S. On Building for Futures You Can’t Quite See Yet
Mrinal started building trust infrastructure for autonomous systems in 2019. He didn’t know what form factor those systems would take. Would it be autonomous vehicles? Cross-cloud applications? IoT device fleets?
When LLMs emerged in 2023 and autonomous agents became viable in 2024, the form factor became clear. The trust infrastructure he’d built for an uncertain future turned out to be exactly what AI agents needed: cryptographic identity, authentication guarantees, audit trails, lineage tracking.
But he made a choice. Stay a building block that other companies use, or build the full platform for the autonomous future he’d been preparing for? Building blocks are fine businesses. They can be good businesses. But they leave you dependent on other companies succeeding with your component before you succeed.
The pivot to Autonomy was a bet that the infrastructure foundation from Ockam plus six years of thinking about trust in distributed systems created enough of a head start to go after the full opportunity. Not just the authentication layer, but the complete platform for building, deploying, and running autonomous agent products.
Sometimes you build infrastructure hoping for one future and discover it’s critical for a different future than you imagined. The hard part is recognizing when to pivot from component to platform, from building block to full solution.
If you’re building agent products (in pharma, legal, finance, logistics, or any domain where autonomous systems could compress workflows), try Autonomy for free. Copy a prompt, paste it into Claude Code, and you’ll have a working agent app in 20 minutes. No credit card required, no sales call.
Whether you end up using Autonomy or not, the exercise will show you what long-horizon agents can actually do. The capability threshold that crossed recently but most teams haven’t fully absorbed yet.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---
## The Two-Year Exit Nobody Talked About
URL: https://www.data-mania.com/blog/the-two-year-exit-nobody-talked-about/
Type: post
Modified: 2026-03-17
Here’s what might surprise you about one of the fastest exits in the AI tools space: it had almost nothing to do with the product.
Yosuke Yasuda built Algoage, a sales and marketing chatbot company, at a time when most founders were still figuring out whether chatbots were even a real GTM channel. He sold it in roughly two years for a multi-million dollar figure.
But the speed wasn’t about shipping faster, or finding product-market fit quicker, than everyone else. It was about how he priced the thing.
That single pricing decision set up everything that followed. The 10x headcount growth. The organizational chaos. The one hire that changed everything.
And now, the product he’s building to make sure no founder has to learn those lessons the hard way again. I sat down with Yosuke to unpack all of it, and what came out was one of the most tactically dense conversations I’ve had this year.
Why Outcome-Based Pricing Breaks the Budget Ceiling
When most people hear “AI chatbot for sales,” they THINK the product is the chatbot builder. You buy the tool. You configure it. You go.
However, that’s not what Yosuke sold. He sold customer acquisition.
Algoage didn’t charge per implementation, per hour, or per chatbot deployed. They charged based on how many customers the chatbot actually brought in for each client. A CPA (cost per acquisition) model, straight from the ad industry.
This sounds simple. It wasn’t. It was one of the highest-risk moves a startup can make – because if the chatbot doesn’t perform, Algoage doesn’t get paid. But Yosuke saw something most founders miss.
If it works, the budget is theoretically infinite.
Think about that for a second. When you’re paying per customer acquired, and each new customer generates revenue for the client, there’s no natural ceiling on what they’ll spend. The pricing scales with the value it creates. That’s not a pricing model. That’s a growth engine.
Here’s how it worked in practice. For each client, Yosuke would negotiate a CPA slightly below what that client was already paying to acquire customers through other channels.
The client saves a little on unit economics. Algoage captures the delta between the chatbot’s actual acquisition cost and the agreed CPA. Everyone wins – as long as the chatbot delivers.
And it did.
Replicate The Win: The Consulting Trap Diagnostic
Before you dismiss outcome-based pricing as “too risky for my stage,” ask yourself these five questions.
If you answer yes to three or more, your pricing model might be quietly turning you into a consulting firm:
Are you charging based on the time your team spends, not the result the client gets?
Do your margins shrink every time you add a new client?
Would it be easy for a client to hire your people directly instead of paying you?
Do customization requests get billed as separate line items?
Is your revenue growth roughly linear with your headcount growth?
The hard part is admitting that most “product” companies are actually running a services business underneath. The pricing model is the tell.
The 70/15/15 Rule: Hire Where Your Lever Actually Is
Here’s a hiring ratio that will make most founders uncomfortable: 70% operations, 15% sales, 15% engineering.
That’s how Algoage scaled from 10 to 100 people in under a year. And it wasn’t a guess. It was a direct consequence of the business model.
Think about it. If your revenue is tied to chatbot success quality, then the single most important function in your company is the team that makes those chatbots actually work.
Not the engineers who build the platform. Not the sales team who closes the deals. The people who deploy, monitor, and optimize each chatbot in production.
So that’s where the headcount went. 70 people, mostly non-technical, running the operation.
But here’s the part that doesn’t get talked about enough… Those 70 people only worked because of what the 15 engineers built for them.
Algoage built a custom product analytics layer in-house. They did it because nothing on the market existed for monitoring deployed chatbots at that time. Google Analytics doesn’t tell you why your chatbot just sent a buggy response to a live prospect.
The real engineering challenge was translating technical errors into language that non-engineers could act upon. When something broke in production, the ops team needed to see exactly where the problem was and how to fix it, without waiting on an engineer to explain it.
In other words, the internal tool was a product. And they treated it like one.
Replicate The Win: The Scaling Lever Audit
Before you hire your next 10 people, answer this: What is the single function that, if it got 10x better, would make your revenue 10x better?
Not your most exciting function. Not the one that feels most “startup-y.” The actual lever.
Then hire toward that lever. Let everything else stay lean. You can always add headcount to sales or engineering later. You can’t easily undo a hiring architecture that was built on the wrong assumption about where growth lives.
Don’t Delegate to Partners. Inspire Them.
Algoage grew partly through revenue-share partner companies – agencies that brought in clients and took a cut. This is a common model in the Japanese ad-tech ecosystem, and on paper it’s elegant. Low upfront cost, outsourced sales motion, shared risk.
It almost didn’t work.
The first version of the partnership strategy was pure delegation. Hand the partners the product information, set up the revenue-share terms, and let them run. Yosuke’s team expected the partners to bring in clients. They didn’t.
So they changed the approach entirely. Instead of delegating, they showed up.
Yosuke’s team started attending every single client meeting with the partners. They explained the product themselves. They showed what it could do. They demonstrated why it mattered. And gradually, something shifted.
Commitment is contagious. That’s Yosuke’s phrase, and it’s exactly right. When the partner sees the founder genuinely excited about what the product does for their clients, that energy transfers.
The partner starts believing it too. And then they start selling it.
The delegation happened later, in stages. Attend every meeting, then co-present, then let the partner present while you listen, then step back entirely. Think: Graduated handoff, instead of one-time drop.
Replicate This Win: The Partner Activation Ladder
If you’re relying on indirect channels (agencies, resellers, integration partners) and they’re underperforming, it’s probably an activation problem. Here’s the sequence:
Show up first. Attend their meetings. Do the pitch yourself. Let them watch.
Co-present. You take the lead, they fill in context.
They present, you support. Flip the roles. You’re the safety net.
They run it independently. Only after they’ve demonstrated they can close and explain the value on their own.
Warning: Don’t skip steps. The partner needs to absorb the energy before they can replicate it.
The Scaling Wall Every Founder Hits (And The One Hire That Fixes It)
This is the part of Yosuke’s story that I think every founder between 10 and 50 people needs to hear.
Algoage hit 10 people. Things started getting messy. Not in the obvious ways like “we need more engineers” or “sales isn’t closing fast enough.”
It was subtler and more corrosive than that. People were complaining about different things. Two team members would raise issues that were completely contradictory from Yosuke’s perspective.
Meetings filled the calendar. The work happening in those meetings wasn’t making progress – it was just people explaining what they did last week.
This isn’t a people problem. It’s a coordination problem. And the textbook fix – build a management hierarchy – can actually make it worse.
That’s exactly what happened. Yosuke created layers. Managers and executors. Clean reporting lines. Organizational structure that looked right on a slide deck.
It didn’t work. The managers optimized for clean org charts, not for actual progress. They cared about whether the information flow was tidy. They didn’t care whether the business was moving.
The Three Archetypes You Need to Know
When most people hear “hire a manager,” they THINK it means hiring someone who’s good at organizing people. But there are actually three distinct archetypes at play when you’re scaling, and each is independent.
The Executor is someone who’s great at getting things done themselves. They ship. They close. They build. But they don’t scale. Put them in charge of a team of 70 and they’ll try to do everything themselves until they burn out or the team stalls.
The Manager is someone who’s good at structure. They create clear processes, clean reporting lines, organized information flow. This is genuinely useful, but only if the structure is in service of progress.
A manager who’s disconnected from whether the work is actually moving the business forward is just adding overhead.
The Leverager is the one almost nobody talks about. This is the person who instinctively focuses on long-term progress above everything else. They don’t care about management best practices. They don’t try to do it all themselves.
They figure out what’s actually blocking the business from moving, and they fix that. Whether it means making a quick decision, unblocking someone else, or letting a slow thing play out because it matters more in the long run.
Yosuke hired one. An operations manager with both founding-team experience and later-stage management experience. And this person’s approach spread through the organization because he demonstrated it. Other managers watched, saw it working, and started doing the same thing.
Commitment is contagious. That phrase keeps coming back because it keeps being true.
How to Spot a Leverager Before You Hire Them
This is the part Yosuke flagged as the hardest piece – and I’ve asked him to send me more detail for a future issue. But based on what he shared, here’s what to look for:
They have a strong sense of urgency. There’s a difference between someone who moves fast because they’re stressed and someone who moves fast because they genuinely care about outcomes. The Leverager is the second type.
They have founding experience. Not just management experience. They know what it feels like when the whole thing is on the line. That context changes how they make decisions.
Their references talk about progress, instead of process. When you call their previous employers, ask specifically: “Did things move faster when this person was there?” Not “Were they a good manager?” Those are different questions.
They don’t have a signature management style. They adapt. Sometimes they jump in and do the thing themselves. Sometimes they step back and let someone else own it. The decision is always driven by what the situation needs, instead of by what feels like “good management.”
If you’re at 10 to 15 people and you haven’t made this hire yet, it’s probably the single most important thing on your to-do list.
Yosuke’s one regret when I asked what he’d tell his 2018 self was immediate and specific: hire the Leverager as soon as you see a strong PMF signal. Don’t wait for the crisis. The crisis is too late.
What Comes Next: Sharick and the AI Chief of Staff
Everything Yosuke learned at Algoage – the outcome-based model, the scaling wall, the Leverager hire, the organizational chaos that eats founder time alive – he’s now trying to encode into a product.
It’s called Sharick. Here’s the problem Sharick is built to solve. You know that feeling where you have 47 things happening at once, and your brain is trying to track all of them, and the reactive stuff keeps jumping to the top of the pile even though the important stuff is what actually needs your attention?
That feeling is what Yosuke calls the organizational chaos tax. It’s the invisible cost that drains founder agency every single day.
Sharick integrates into your email, your Slack, your communication channels. It reads what’s happening across your organization in real time. And then it does something deceptively simple: it helps you figure out what actually needs you right now, and what doesn’t.
If something urgent comes in, Sharick flags it and routes it to the right person – or to you, if you’re the right person. If it’s a low-priority to-do that can wait, it gets deprioritized.
You stop carrying the cognitive weight of tracking everything. The tool does that part.
Yosuke’s reference point for the long-term vision is the movie Her. Not in a sci-fi way. In a “what would it actually feel like to have someone you could dump everything into, who understood your context, and who proactively told you what mattered most” kind of way. That’s the experience he’s building toward.
Right now, the ICP is teams of around 50 people – the exact size where you’ve probably hit PMF, you’re growing, but you haven’t yet built the coordination infrastructure to keep up with that growth. The sweet spot of high frustration and high growth signal.
Sharick is launching in March 2026, and Yosuke is currently prioritizing product validation over distribution right. He wants to make sure it actually works before he introduces it to his network.
The GTM strategy, when it kicks in, will be product-led. Think Calendly. Think Superhuman. You use it, someone else sees it, they want to try it too. The network effects are built into the product itself.
If you want to follow what Yosuke is building, follow him on LinkedIn. Sharick’s landing page is coming soon – I’ll link it the moment it’s live.
P.S. I was actually living a smaller version of Yosuke’s problem the same week we talked. I was building a LinkedIn course on GPT apps, and in the process I built a custom GPT that pulled in my calendar, my stated priorities, and my project management board.
It mapped out my day: two hours of deep work on the thing that actually matters, delegate this to my VA, push this to tomorrow.
I’d only run it for one day when we spoke. But it had already helped me make progress on something I’d been putting off for weeks because I genuinely felt I had no time.
That’s the thing Yosuke is building, but for teams. And if you’ve ever felt like you’re too busy to do the work that actually moves your business, you already understand why it matters.
If you found this useful, forward it to a founder friend who’s between 10 and 50 people. They need this more than they know.
And if you’re not yet subscribed to The Convergence, this is your sign.
Want a clean, repeatable system for measuring B2B growth? Get the free Growth Metrics OS — a 6-day email course for technical founders and operators who want to measure growth and make better decisions.
---
## The $500K AI Readiness Question Your ERP Vendor Isn’t Answering
URL: https://www.data-mania.com/blog/ai-readiness-framework/
Type: post
Modified: 2026-03-17
Sponsored by TeamCentral
I talked to Marc Johnson and Andy Park last week… Andy told me about his friend who runs a manufacturing company and is seeking some answers about why he should modernize his ERP.
The guy’s been running Epicor from a server closet at his plant for years. Products ship on time. Processes work. The system does exactly what he needs it to do.
Then the vendor starts hounding him to migrate to their cloud version. The cost? Three times his current annual spend.
Here’s the kicker though… There’s no direct migration path. It’s not an upgrade. It’s a full reimplementation. New system, new risks, and you know the stat: 75% of ERP projects are subject to failure.
So Andy’s friend is sitting there thinking, “Why would I spend half a million dollars and risk my entire operation when what I have works fine?”
This isn’t really a story about cloud migration. There are very valid reasons to move to modern ERP, like better security patches, improved interoperability between systems, inability for vendors to support multiple platforms long-term, and yes, new recurring SaaS revenue streams.
Most mid-market companies are about to get blindsided by AI, and they don’t even know it yet
This is a story about why most mid-market companies are about to get blindsided by AI, and they don’t even know it yet terms of AI Readiness.. The problem isn’t that vendors want customers to move to the cloud for AI Readiness. The problem for AI Readiness is the timeline.
These migrations generally aren’t aligned with customer AI readiness. Companies are being forced to move on the vendor’s schedule for AI Readiness, not their own schedule, and without adequate consideration for the people, process, and budget impacts of that change.
Vendors aren’t meeting their customers where their needs are, and this creates two critical AI readiness gaps:
Customers who want to remain on legacy systems won’t be able to take advantage of AI in their current state, not without implementing a proper data infrastructure strategy first.
If they do move to the cloud without thinking strategically about data architecture, they’ll completely miss the window to position themselves for a future where AI plays a significant role in operations.
That’s the $500K question Andy’s friend is really facing. It’s not just about cloud migration costs and AI Readiness. It’s about whether he’s building the AI Readiness foundation that makes AI possible, or just delaying the inevitable while his competitors get ready.
I spent time with both Andy Park and Marc Johnson, Co-Founder) of TeamCentral. They spent almost 20 years together at a Global Consulting Firm seeing the same integration problems at every single customer before they decided to build a simpler solution. That pattern recognition matters for AI Readiness, and what they’re building could change how mid-market companies think about AI readiness.
When I asked what they wish more companies understood about AI readiness, Andy didn’t hesitate:
Growth Insight: “Undercapitalizing early is the most expensive mistake you can make. Speed is strategy, and speed requires fuel. In the world of AI and enterprise infrastructure, you can’t half-build a data foundation.” – Andy Park
Why 94% of Companies Can’t See Their Own Supply Chain
Let me give you a number that should make you uncomfortable.
Only 6% of companies have complete end-to-end visibility into their supply chain, while 62% report having only limited visibility (and not full transparency) across their operations. That’s according to the GEODIS Supply Chain Worldwide Survey.
Think about that for a second. We’re talking about the backbone of how products move from raw materials to customer delivery. And 94% of companies are flying blind when it comes to making intelligent data driven decisions.
Andy explained it to me this way, “Imagine you’re a salesperson looking at inventory in your system. You see 20 units available. A customer needs 15.
But here’s the problem. You don’t have visibility into when the next shipment arrives. You don’t know if manufacturing needs some of those units. You have no idea if another salesperson already promised them to their customer.
So what do you do? You put a hold on all 20 units.
It’s not malicious. You’re just trying to keep your customer happy. That’s literally your job.
But multiply that behavior across your entire sales team, and suddenly you’ve got inventory paralysis. Units sitting in “reserved” status that may never ship while other salespeople scramble to find stock.”
Poor data connectivity creates AI Readiness inefficiency, yeah – but it also drives otherwise rational people to make decisions that hurt the business in ways they don’t anticipate.
COVID exposed this in brutal detail. When supply chains broke down, companies without AI Readiness that couldn’t see their full value stream couldn’t respond. The ones with real visibility could reroute, adjust, and keep moving. This is exactly the kind of pain that validates a market, and it’s how TeamCentral knew they were onto something:
Growth Insight: “Product-market fit isn’t just usage, it’s willingness to pay. If they love it but won’t pay for it, you don’t have product-market fit. We didn’t find our ICP. It found us through patterns in who kept saying yes.” – Marc Johnson & Andy Park
The Band-Aid Tax: How Quick Fixes Become Technical Debt
Here’s how most mid-market companies have built their integration architecture over the last 20 years:
System A needs to talk to System B. IT finds the cheapest, fastest way to connect them. Maybe it’s a custom script. Maybe it’s a basic API call. Maybe someone literally exports a CSV every night and imports it somewhere else.
It works. It’s not elegant, but it works.
Then System C comes along. Same process. Then System D. Then your e-commerce platform. Then your EDI feeds. Then your call center system.
Andy calls these “band-aid integrations.“ And when you have 15, 20, or 50 of them, you end up with massive technical debt wrapped up in “Spaghetti Architecture” that blocks AI Readiness.
Here’s what band-aid integrations don’t include:
Logging when things break
Forensic analysis to find missing data
Redundancy for critical workflows
Any way to test changes without breaking production
So you end up with a team of people whose job is to monitor integrations and scramble when they break. And they break constantly because they’re brittle point-to-point connections that weren’t designed with any overall architecture in mind.
The hard part is that every individual decision made sense at the time. Connect the CRM to the ERP in the most cost-effective way possible. Don’t overthink it.
But architectural debt compounds just like financial debt in any AI Readiness effort. And the interest comes due when you try to do anything sophisticated, like prepare your data for AI.
When “Easy” Integration Tools Make Things Worse
Think traditional enterprise iPaaS platforms are too complex? You’re not alone.
Companies like MuleSoft and Informatica force you into architectural rigor. They make you think about decoupling systems, testability, and proper data flows. It’s heavy and complex, but it forces you to build things the right way.
The problem is that those point-to-point integration platforms, whether they’re custom scripts or basic APIs, create massive technical debt. They work in the moment but fall apart at scale.
Then the “citizen developer” platforms showed up promising to democratize integration. “Low-code.” “No-code.” Anyone can build connections between systems.
Platforms like Power Automate and Zapier are excellent at what they do… solving basic automation and repetitive tasks. Moving email attachments to files. Simple data syncs. They’re perfect for that.
But citizen developer solutions can’t solve complex data governance and data automation use cases.
Marc gave me an example: A mid-market manufacturer has customer data spread across seven different systems – CRM, ERP, e-commerce platform, EDI feeds, call center software, warehouse management, and their legacy AS/400 for financials.
Good luck building a clean, governed integration for that with a point-and-click tool.
The point is, you need to use the right tool for the right AI Readiness problem.
The Middleware Approach: Architecture Without the Complexity
This is exactly why TeamCentral built their platform differently.
Instead of just making integration “easy,” they built a middleware approach to no-code integration that’s far more scalable and cost-effective from a TCO perspective.
Think of it this way:
Point-to-point tools → Fast to build, brittle, create spaghetti architecture
Citizen developer platforms → Great for simple tasks, fail at enterprise scale
Middleware no-code platforms → Enforce data architecture while maintaining development speed
TeamCentral forces you to think about your data model first. It’s the architectural rigor of enterprise iPaaS with the speed and accessibility of no-code development, all without the brittleness of point-to-point connections. This infrastructure-first approach is harder to build and harder to fund, but Marc frames it differently:
Growth Insight: Verticalization is easy when you solve a micro-problem. Harder when you fix the foundation. We’re not selling an AI widget. We’re rebuilding the plumbing. Manufacturing and distribution don’t need another AI app, they need their systems to talk to one another.” – Marc Johnson
The Four Pillars Every AI Project Dies Without
I asked Andy and Marc what “AI ready” actually means to them from a data standpoint.
Andy laid out the AI Readiness framework: Connected, high quality, accessible, and secure.
Marc added the strategic context, “Each one of these has its own level of pain. You’re gonna get overrun by this wave if you don’t get in front of it.”
Let me break down what each of these means in practice for AI Readiness.
Pillar 1: Connected
All your systems need to speak the same language for true AI Readiness. Not JSON here and XML there and EDI somewhere else. A common business (semantic) language that translates technical data structures into something humans (and AI) can actually work with.
After 20+ years of working with ERPs, Marc and Andy knew about everything there is to know about how Oracle, Salesforce, Microsoft, and SAP model their data. Based on that knowledge, they built a common data model based on what actually works for real businesses.
Customers, vendors, orders, invoices, items, inventory – the true foundation of AI Readiness. The foundational stuff that every for-profit business needs.
In other words, instead of making you architect your perfect data model from scratch (which takes so long most companies never finish), they give you a proven starting point and let you extend it.
The platform includes over 80 pre-built, SOC 2 compliant connectors that handle thousands of data automation scenarios right out of the box. This means you’re not starting from zero, you’re starting from proven patterns that already work for companies in manufacturing, supply chain, construction, and logistics.
Pillar 2: Quality
Here’s where most AI Readiness data governance projects die: They try to define perfect data structures upfront, then spend years implementing controls that never actually get enforced.
TeamCentral’s approach is different. They deploy automated governance during synchronization.
Every time data moves between systems, business rules get applied. Deduplication happens. Validation happens. Data gets cleaned incrementally as it flows, not in one massive cleanup project that never completes.
Think of it like the medallion architecture in data warehousing. Raw data gets refined through stages until it reaches production quality. But this happens in real-time across your operational systems, not in a warehouse you query later.
TeamCentral calls this their “normalized data model with automated governance.” As data synchronizes through their embedded enterprise data model, the quality automatically increases. You’re not just moving data, you’re improving it with every transaction.
Pillar 3: Accessible
This is where natural language and AI actually matter for AI Readiness. Once your systems are connected and your data quality is solid, you need to interact with it without being a SQL expert.
Andy’s building MCP architecture that lets you use one agent experience to query data across all your connected systems.
Imagine asking Microsoft Copilot about your supply chain, and it pulls data from SAP, Oracle, Salesforce, and your warehouse management system to answer. That’s accessible.
TeamCentral’s platform is built for this hybrid infrastructure reality. It integrates and migrates data between cloud systems and legacy on-premise ERP, CRM, and WMS. Whether your data lives in a server closet or in Azure, the platform treats it all the same.
Pillar 4: Secure
None of this works without proper security and privacy controls for AI Readiness. Especially when you’re connecting multiple systems and letting AI access sensitive data.
Reality check: If you don’t have all four pillars, you’re not AI ready. And trying to build AI solutions on top of broken foundations just means you’ll automate bad processes faster.
Marc emphasized this point, “The complexity of building AI systems is already daunting. But if you don’t have the security model designed out, if you don’t have the connectivity pieces, if you don’t have frameworks in place for data governance and clean quality data, the agentic pieces will never work. That’s the AI Readiness blocking and tackling that needs to be done before you can put an LLM on top of it.”
Or as Andy put it, “We don’t want to use AI to automate bad processes and bad data. You’re just going to produce more bad data and bad processes faster.”
Start Small or Fail Big: The Incremental Governance Playbook
Andy told me about sitting with a CIO recently who was working on “AI readiness.”
The CIO’s first step toward AI Readiness? A massive data definition project. Get the entire organization to agree on what a “customer” means. Define every field. Document every standard. Build the perfect governance framework.
Sound familiar? It should, because this same project has been failing at companies for the last 20 years.
The projects take so long that by the time you’re done defining standards, business requirements have changed. So you never actually implement anything.
Here’s Team Central’s approach instead:
First, model your data holistically. Don’t think about connecting your CRM to your ERP. Think about what customer data should look like across every system.
Second, start with the smallest possible scope. Pick one specific workflow. Strip away all complexity. Make the rules as simple as you possibly can. Get that one thing working.
Third, iterate. Add the next workflow. Refine your data model. Add governance rules incrementally as you learn what actually matters.
This is particularly challenging in any AI Readiness initiative because it requires discipline to start small when everyone wants to solve everything at once. But it’s the only approach that actually ships.
Marc’s advice to customers, “Don’t worry about the end systems to start. Just model your data. Create the definition of what your data should look like. Then we’ll move into designing how to move it from one place to the next.”
This is especially critical for AI Readiness if you’re facing a legacy ERP migration. Most vendors will tell you to expect 12-18 months for a full AI Readiness reimplementation. TeamCentral’s platform delivers time-to-value in weeks (not quarters), because their customers can’t afford to have critical business processes offline for a year and a half.
Companies running GP, NAV, Sage, Epicor, SAP ECC, or JD Edwards are staring down inevitable end-of-life scenarios for AI Readiness.. TeamCentral’s no-code automation platform can streamline that data migration while keeping business-critical systems connected every step of the way.
Why “AI Ready” Still Means Different Things at Different Layers
I need to level with you about something Andy said that caught me off guard.
When I asked him about enterprise AI adoption, he told me, “Nobody really has this figured out yet. If you’re hearing people who sound like experts, there really are very few experts. Everybody wants to sound like an expert.”
He’s right, and it’s important to understand why.
Surface-level AI works great today, but it doesn’t equal AI Readiness. Using ChatGPT to draft content, scan business cards into your CRM, analyze simple datasets. These are real productivity gains using curated data that’s already in good shape.
Deep Enterprise AI is a completely different problem.
But deep Enterprise AI is a completely different problem. We’re talking about connecting legacy on-premise systems (like AS/400 financials in a server closet or datacenter) with modern cloud platforms (like Dynamics 365 and Salesforce) and manufacturing execution systems and IoT sensors on the shop floor.
Andy’s take, “We’re still like chapter 2 of 10 in AI. We’re early on.”
However, that doesn’t mean you wait. It means you focus on the foundational work that will enable AI when the technology matures.
That’s where MCP (Model Context Protocol) comes in. It’s a framework developed by Anthropic for enabling AI agents to communicate with external systems.
You can think about it this way: SAP has its own AI agent. Oracle has its own. Salesforce has Einstein. Microsoft has Copilot. Each one is built for its own tech stack, and extending them to other systems is way harder than vendors make it sound.
Most mid-market companies don’t live in a single-vendor world, which complicates AI Readiness. You’ve got SAP for financials, Salesforce for CRM, a legacy WMS in your warehouse, and manufacturing execution systems on the shop floor. Getting one vendor’s AI agent to work across all of those? That’s the problem.
TeamCentral is leveraging MCP to create a common framework for agentic AI in the enterprise. Instead of forcing you to pick one vendor’s agent and then struggle to extend it, they’re building an MCP server layer that connects all those vendor-specific agents to a single common data model and common security framework.
The result is that you can use whichever agent experience you prefer, Copilot, Einstein, whatever, to query data from any connected system.
Is building this level of AI Readiness easy? Absolutely not. As Andy said, “It’s a lot easier said and drawn on paper than it really is to build.”
But it’s the architecture that could actually make the single-pane-of-glass vision real.
Case in Point – What Happens When Your Copilot Can Talk to Every System You Have
Let me paint you a picture of what this looks like in practice.
You’re the VP of Operations at a mid-market manufacturer. It’s Monday morning. You open Microsoft Copilot and ask:
“Which orders are at risk of late delivery this week?”
Copilot pulls data from your ERP (order status), your manufacturing execution system (production delays), your supplier EDI feeds (inbound shipment delays), and your warehouse management system (inventory shortages).
It tells you: 12 orders are at risk. Three because raw materials are delayed. Five because of production bottlenecks. Four because of carrier issues.
You ask: “What’s the financial impact if we expedite shipping on the carrier delay orders?”
Copilot calculates the expedite fees, compares them to potential late delivery penalties, and tells you it’s worth it for two of the four orders.
You say: “Do it for those two. And draft emails to the other customers explaining the delay with a discount offer.”
This is what Andy means by 15-20% AI Readiness efficiency gains from integration and automation. You just made four decisions in 90 seconds that would have taken half a day of pulling reports, calling people, and doing spreadsheet math.
Team Central’s building this through their Corbi agent (stands for “Cortex of Your Business”). It includes enterprise search, task automation, and something they call Pulse, which is basically a role-specific data feed.
Think of Pulse like a social media feed for your business data. If you’re the CFO, you see progress against month-end close, profitability by line of business, aged AR compared to last quarter. You can act on it, share it, comment on it.
It’s available on mobile and desktop. Because if we’ve learned anything from consumer tech, it’s that people want to work from wherever they are.
This isn’t vaporware. It’s operational AI Readiness. TeamCentral expects, and early projects suggest, 15–20% efficiency gains from integration and automation, by eliminating manual work and by improving visibility across systems. The platform delivers rapid time-to-value because you’re not building from scratch.
Why TeamCentral Exists: Building from Midwest Infrastructure Reality
You can build anywhere, but the deepest capital pools still sit on the coasts. Marc and Andy built TeamCentral from the Midwest because that’s where their customers are. Manufacturing, distribution, construction… These aren’t coastal problems, but building outside Silicon Valley means navigating a fundamental tension:
Growth Insight: “You can build anywhere, but the deepest capital pools still sit on the coasts. The Midwest is growing momentum. The coasts still control the majority of deployable capital. There’s also a noticeable tech knowledge gap compared to coastal markets. Our customers don’t need or want AI buzzwords. They need infrastructure that works.” – Andy Park
That’s why TeamCentral’s approach is different. They’re not building for tech executives at Series C SaaS companies. They’re building for the CFO at a 50-year-old manufacturer running Epicor in a server closet who needs to modernize without betting the company.
The AI Readiness infrastructure play is harder to fund than verticalized point solutions. As Marc puts it, “We’re not selling an AI widget. We’re rebuilding the plumbing.” But plumbing is what makes everything else possible.
The Jobs AI Won’t Take (And the Ones It Will Elevate)
Here’s the question everyone’s dancing around… What happens to jobs when AI can do this much?
Andy’s take is the most grounded I’ve heard: AI eliminates low-level repetitive tasks and creates more opportunity for strategic thinking.
You’ll always need a person in the middle. AI shouldn’t make critical decisions without human review. We’ve already seen examples of what happens when companies let algorithms run unchecked, and it’s not pretty.
But here’s what changes… all the non-value-added work goes away.
The manual data entry that slows AI Readiness. The constant monitoring of integrations. The spreadsheet reconciliations. The repetitive status emails.
That creates space for people with advanced skills to do actual strategic work. The kind of work that differentiates your business and creates competitive advantages.
The companies that get AI Readiness right will invest in hiring and training strategic thinkers. The companies that don’t will try to keep doing things the old way.
And unlike previous technology waves (e.g., big data, cloud migration), this one can’t be ignored. Andy’s words: “The people that ignore it are gonna have real problems.” Where to Actually Start
If you’ve made it this far, you’re probably thinking, “Okay, this all makes sense, but where do I actually begin?”
Start with the Four Pillars AI Readiness assessment:
Connected: Can you easily get data from one system into another? Or is it custom development every time?
Quality: Do you trust your data? Or are you constantly finding duplicates, missing fields, and inconsistencies?
Accessible: Can non-technical people find the information they need? Or does everything require IT?
Secure: Do you have proper access controls and privacy protections in place?
If you’re weak on any of those four, that’s your starting point. Not the flashy AI stuff. The boring infrastructure work that makes everything else possible.
TeamCentral has built their platform specifically to address these four pillars with minimal custom code. Their approach is to connect systems with no-code integration, normalize data through an embedded enterprise model, and layer AI-powered search and task automation on top of that foundation.
They offer an AI Readiness Guide that walks through this assessment in detail, plus resources on legacy ERP migration if you’re facing that challenge. Whether you’re in manufacturing, supply chain, construction, property management, or logistics, they’ve built industry-specific solutions with pre-built templates.
But here’s the real takeaway: the companies that win aren’t the ones with the fanciest AI. They’re the ones that did the foundational data work that everyone else skipped because it wasn’t exciting.
Marc and Andy spent 20 years seeing the same integration problems at every customer before they built Team Central. They’ve worked with Oracle, SAP, Microsoft, Salesforce, and dozens of other platforms across hundreds of implementations.
That pattern recognition matters. Because while everyone else is chasing the next AI breakthrough, they’re solving the data problems that make AI actually work.
The uncomfortable truth here though is that this requires patience and discipline. You have to be willing to start small, build incrementally, and focus on fundamentals when everyone around you is talking about agents and copilots.
But that’s exactly what separates the companies with real AI Readiness that will still be here in five years from the ones that won’t.
Lillian
P.S. I keep thinking about Andy’s friend with that Epicor system in his server closet.
He’s not wrong to resist. His system works. His products ship. Why risk a half-million-dollar implementation when you’re already profitable?
But here’s what keeps me up at night: five years from now, his competitors will have AI agents managing their entire supply chain. They’ll know about problems before they happen. They’ll optimize in real-time.
And he’ll still be manually checking if inventory is available.
The window isn’t closing because the technology is ready. It’s closing because the competitive dynamics are shifting underneath us.
Start with the boring AI Readiness infrastructure work. Your future self will thank you.
This post was sponsored by TeamCentral. Want their complete AI Readiness Guide and see how their no-code platform can help you connect legacy and cloud systems? Learn more here.
Building a B2B startup growth engine? See how Lillian Pierson works as a fractional CMO for tech startups navigating GTM, AI, and scale.
---