The client provided me with a limited amount of market research and interview data that they’d collected during the process of building the product. I did additional market research on SEMRush, Twitter, LinkedIn, and Product Hunt.
Based on my analysis of their existing interview data, the market research, and the readily available product features, I identified that the best market for the product at that stage was the financial services industry.
Being a spreadsheet product, this product was of course up against spreadsheet behemoths, Excel and Google Sheets. Within that market, however, there was a significant trend wherein spreadsheet-based analysts are looking for more powerful, robust spreadsheeting capabilities than are offered by the traditional giants.
This product promised to provide lightning-fast, powerful compute power within a spreadsheet environment so that spreadsheet analysts could analyze massive datasets without needing to learn SQL or Python or to move into a new analytical environment.
Consequently, this product was well positioned to capitalize on that trend in demand.
Competitor products included Equals and Rows.
The problem this product solved was Excel’s and Google Sheets’ inability to run complex calculations on massive datasets without freezing or crashing.
The largest market challenge we faced with this product was one of pricing and value proposition against the mainstream spreadsheet products, Excel and Google Sheets. Because Excel and Google Sheets are essentially free products, our unique value propositions – and the pain points that the product solved – needed to be substantial enough to justify charging for the product.
This challenge represented an opportunity for us to drive messaging and product differentiation such that it was focused almost exclusively on the product’s superior spreadsheet processing speed and its ability to analyze larger volumes of data than are possible within the free counterparts.
This differentiation also meant that we needed to target “spreadsheet superusers” exclusively – these were analysts who had enough frustration and pain around the slow spreadsheet problem and who were ready to spend money to have that problem solved.
This was a complex SaaS product and we utilized 3 distinct campaigns to drive customers, users, and leads for the product. Those campaigns included:
For brevity’s sake, I will summarize the buyer’s journey in aggregate below.
Touchpoints: Email, Sales Page, Live Chat, Google Ads, LinkedIn Page, Twitter Page, SEO-Optimized Blog, Live Launch Event
Pain Points: Slow, crash-prone spreadsheets that can’t meet the analytical processing demands of Spreadsheet Modeling Professionals
Data indicated that the “Spreadsheet Modeling Professionals” segment represented the largest opportunity in terms of revenue, market share, or strategic value – especially given the product resources and capabilities at that time.
I wrote the following user narrative which we used on the sales page in order to connect with our ideal customer persona and convert them into paying customers.
“TO: All Spreadsheet Modeling Experts & Analysts Who Are Sick Of Dealing With Slow, Frozen, and Crashing Spreadsheets
Stop looking for new add-ins and formula best practices to try to solve your Excel spreadsheet problems. I have big, exciting news for you about the #1 thing you’re missing in your analytical toolset.
You were taught that spreadsheets should be powerful enough to execute any complex mathematical operation. Everyone’s using them after all… as advanced modeling tools, as databases, as personal finance trackers… Just load the data, add a header, and you’ll be good to go.
What went so wrong?
Year after year, datasets got larger, and your manager’s expectations got higher. But the bigger that datasets got, the more problems you ran into with your spreadsheets.
They’re slow, they’re clunky… and frankly, most of them just crash and burn when you go to run a complex analysis across a large spreadsheet.
If you would’ve known that you’d spend so many of your waking hours building complex mathematical analyses in spreadsheets that simply can’t handle your data processing requirements, you probably would have thought twice about getting that math degree.
That’s exactly what almost every spreadsheet rockstar we’ve spoken to feels like, anyway.
No matter how many Add-Ins they’ve tried or formula best practices they put into place, the slow Excel spreadsheet problem could not be overcome.
God forbid they bring that data over to Google Sheets for some real-time collaboration with peers… That move almost always spelled disaster.
Maybe you’ve passed your busted Excel spreadsheets off to a programmer more times than you’d care to admit…
Maybe your company spent $10k+ on a custom-built application to crunch your data for you…
On certain days you may have wasted more time waiting on Microsoft Excel than you did with your own children…
We’ve all been annoyed by extremely misguided statistics, like:
“In Europe, advanced Excel users are wasting ~€55 billion every year by “misusing Excel” – IDC UK
WE SAY: THAT STAT IS PURE RUBBISH
Maybe if advanced Excel users had spreadsheets powerful enough to do their jobs, none of that would be an issue.
Stop blaming analysts for spreadsheet limitations that they didn’t create!
If you’ve completely given up hope of ever using the full range of your hard-earned analytical expertise…
Please don’t feel bad.
It’s not your fault.
After enduring so many of these same spreadsheet frustrations myself, I almost gave up on the idea of using a spreadsheet to analyze large datasets or collaborate with peers.
After significant A/B Testing + experimental optimizations, I was able to identify ideal segments for ads and to provide data that justified adjusting the ideal customer persona from Financial Modeling Professionals to Spreadsheet Modeling Professionals.
I built a testimonial funnel and quiz funnel which were both successful in generating useful user feedback. We also implemented a built-in product review module which was successful in generating a large list of 5-star user ratings for the product.
REQUIREMENTS: Mission + Vision + Brand Values Articulation, Brand Story Formation, Market Research & Competitive Analysis, Define Minimum Marketable Product / Packaging / Pricing, Opt-In Freebie, Pre-Launch Website Setup, Sales Page Copy + Design, Community Setup, Email Setup + Welcome Sequence, Retargeting Ad Setup, Members Area Setup, Hiring, Team Management
Minimum Marketable Product:
REQUIREMENTS: Channel Management, Team Management, Content Management, PPC Ads Management, Regular Nurture Content, Ads Creative, PPC Ads Set-Up
Weeks 1, 2, 3: Pre-Sale + Soft-Launch
The point of this launch is to get some customers and to build the bank of launch materials for reuse in the next launch. Warm-up advertising pixels. Execute pre-sale email launch to current email list.
EMAIL LAUNCH to current subscribers
Weeks 4, 5, 6: Customer Onboarding + Delivery / Post-Launch Evaluation & Regrouping / Email List Nurture
Weeks 7, 8: Customer Onboarding + Delivery / Launch Regrouping / Nurture Audience
Weeks 9, 10: Simulated Launch
Weeks 11, 12, 13: Customer Onboarding + Delivery / Launch Regrouping / Nurture Audience
Weeks 14, 15, 16, 17, 18, 19: Live Launch To Warm Leads / Onboard Affiliates
To facilitate risk analysis and contingency planning I always perform detailed alternatives analyses (via a multi-criteria decision-making matrix).
Before proposing a minimum viable product or launch strategy, I did a thorough evaluation of existing assets (ie; existing email list, interview data, and available product features). I followed that up with extensive market research and competitive analysis.
I then defined 3 alternative, high-potential product-market-fit hypotheses. I did a complete multi-criteria analysis of these alternatives against 10+ mission-critical go-to-market criteria… and based on the weighted scores of the 3 alternatives, I suggested the minimum marketable product that we decided to launch.
This minimum marketable product represented the lowest-risk, highest-reward alternative for bringing the product to market immediately.
I personally was the influencer that supported this product launch. My audience is comprised of people from the data analytics, data science, and AI industry. By publishing a few of our pre-launch emails to my own channel, I was able to drive ~60 targeted leads for the product. Most of these people were data analysts and business analysts.
Evergreen Publishing Plan:
Engagement / Success Metrics