Dear marketing data scientist (or data scientist of any discipline, really), If you’ve been thinking about going the entrepreneurial route with your data career, BUT – secretly you’re doubting whether you have what it takes to “make it” as a business owner… this post is for you, because I’m about to share a story about one of my most successful clients, a marketing data scientist, who:

  • Doesn’t have a massive online presence,
  • Didn’t have an email list, and
  • Didn’t even have a business website when we started 

Yet still, he managed to land $370k in contracts in his first 18 months using the methods I taught him in my course and mentorship program.

Make sure you read to the end of this post because that’s where I’m going to give you the exact steps he took to achieve this remarkable success.

YouTube URL: https://youtu.be/cqHomUihnLU

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As far as why I’m sharing this story now, I’ve been hard at work with the release of my latest book, Data Science For Dummies 3rd Edition, and with that, I recently revisited Kam to collect his story for the pages of that book. He had new, and even more exciting updates then the last time we spoke so I decided to share them with you here.

If you’re new around here… Hi, I’m Lillian Pierson and I support data professionals to become world-class data leaders and entrepreneurs.

Oh and by the way, we are having a launch party for the book release. It’s scheduled for September and if you’d like to get in on the festivities, I encourage you to sign-up here so we can let you know when the party’s starting!

First let me start by introducing this client, and then I will dig into what he’s done to generate that kind of wealth in such a short amount of time.

Meet Kamarin Lee, Marketing Data Scientist Extraordinaire

Meet Kamarin Lee. Now, Kam Lee is all the trappings of success. A multi-six figure income as a marketing data scientist. A posh apartment in New York City. A glorious reputation that precedes him. Long and illustrious tales about his traveling adventures across the far reaches of the planet. His work has been featured by media sites like Bloomberg and Foundr Magazine. Whatsmore, he’s driven upwards of $2 billion in revenue for his clients, and has produced 40% Year-Over-Year growth for some of the fastest-growing companies in FinTech, SaaS, e-commerce, and cloud security with econometrics, Media Mix Modeling, Lead Scoring, and AI.  In short, Kam Lee’s data science career is more fabulous and exciting than most data scientists would ever dare to dream possible.

But, less than a decade ago he too was new in the data science field. By taking the time to learn to do data science, he made a complete 180-degree in his career. He went from “Marketer” to “Marketing Data Science Leader”. 

Let’s take a brief look at how he did it, and what words of wisdom he has to offer new and aspiring data science professionals.

Kam Lee’s Backstory

It was 2015 when he first started reading the books, and taking the online courses, that helped him develop skills at statistics and machine learning. In fact, Kam is the exact prototype of the person I had in mind when I first created “The Self-Taught Data Scientist Curriculum”.

The self-taught data scientist curriculum

I’ve also included the curriculum in my free Badass’s Guide To Breaking Into Data if you just want to download that.

Anyway, Kam was doing some data science freelancer work, but really floundering between low-budget jobs and yucky clients. Back when Kam Lee started in my course in 2019, he had no email list. He had a website, but it was outdated and not focused on his current offering. And he barely had 2k followers on LinkedIn.

He signed up for my course and mentorship program, and wouldn’t you know it…

The Story Of How This Marketing Data Science All-Star Exploded (Almost Overnight)

Just a few short months after implementing my methods, Kam secured a $96,000 contract for his marketing data science services. Then, when we caught up last September, Kam Lee’s situation had only gotten better. He’d been able to successfully grow that contract to $200k ARR (annual recurring revenue) AS WELL as secure an additional $150k in ARR.

Meaning, only ten months into his business, Kam Lee already had a $350k business.

Then in June of 2021, for the book I checked in on him again, and discovered that his marketing data science business has blossomed even more…

Kam Lee success story

He said that, using my methods so far, he’d closed 15 contracts valued at $310K. He’d also pre-sold $60K worth of annual contracts for his upcoming marketing optimization SaaS company, which is expected to go live in Q4 of 2021. Of the sales he generated since joining, 67% has been pure profit. With the optimization of new custom tools, I estimate that by the end of Q4 2021 (when we launch our software), we should be able to achieve anywhere between a 75% – 80% profit margin. These products and services are all packaged, sold, and delivered through Kam’s marketing data science business, Finetooth Analytics.

Now that’s a LONG way from when we first met at the start of 2020 – back when, Kam was working as a freelance marketing data scientist on lots of small, highly customized projects, often with low budgets, and under his previous brand name, K Lee Studios.

Now if you’re thinking, “This sounds too good to be true – what’s the catch? Does he have a big team or crazy high expenses?”

Perhaps even more impressive than the way Kam Lee has been able to scale his revenues is his ability to keep his profit margins tight. 

For reference, retail stores tend to hover around .5% to 7.5%. So a profit margin of 67.7% and over $300k in profits in just 18 months – that’s extraordinary success both in data science and in business! 

Retail stores tend to hover around .5% to 7.5%. So a profit margin of 67.7% and over $300k in profits in just 18 months – that’s extraordinary success both in data science and in business! 

The profit margin of a marketing data scientist

Just TALKING about Kam Lee’s story gives me goosebumps.

 

The Imminent Release Of Kam’s AI SaaS

I’ve said it before and I’ll say it again… Although offering services is the easiest way to get profitable fast in your data business, it is also the least scalable business model. 

Although offering services is the easiest way to get profitable fast in your data business, it is also the least scalable business model. 

At first Kam adapted his business model by taking the agency approach to service delivery, which is more scalable but still extremely high-maintenance as a business model. To scale his impact further, Kam needed to build out a business model that’s lower-maintenance and that demands less in terms of project management overhead. Enter, his AI SaaS solution. 

Over the past year, he’s been successful in productizing his deep marketing data science expertise into a SaaS product that delivers both lead scoring and marketing mix modeling. The signature framework around which he is developing this AI SaaS has already helped several clients realize over $121 million in opportunities for predictable ROI and cost-savings. 

By developing this marketing mix modeling and marketing optimization software, what he’s really doing is building clever ways to automate much of the existing successes he has already generated with data science services

 

By developing this marketing mix modeling and marketing optimization software, what he’s really doing is building clever ways to automate much of the existing successes he has already generated with data science services he offers in marketing attribution and marketing mix optimization, customer segmentation, customer lifetime value models, predictive models, cross-sell / up-sell models (Buy-Till-You-Die for CLTV), market basket analysis, pricing and promo optimization, as well as analytics of web and digital data.

By delivering the services first, he developed a business, a client list, and a working knowledge of exactly what his customers need from him – basically all the things he needs to deliver a SaaS product that actually sells. Kam takes the “dogfooding” approach to building his SaaS product… he uses it to lower the manual work required to deliver his services. As he is building the product, he’s also pre-selling it – so that when it’s ready for release, it hits the ground running. 

Now that’s the way to self-fund your AI SaaS company!

how to create a data product

 

Speaking of super profitable data science products, I’ve published a video on How To Create A Data Product That Generates At Least $450000 Per Month and left it on my blog here, if you care to watch! 

 

The Exact Steps Kam Took To Reach Rapid Success

Using the knowledge from the course, Kam Lee was able to leverage the power of tech, automation and outsourcing and create an incredibly profitable business that runs like clockwork – even without him having to be on stand-by. Here’s how it happened, in a step-by-step approach, according to Kam.

Step 1 – He took online training courses on data science implementation

For example, in 2018, Kam first took my LinkedIn Learning course on “building a recommendation system with Python machine learning & AI”. Since he was already working in ecommerce at the time, the course helped him adopt a more data-intensive approach to his marketing career. In other words, he became a marketing data scientist, instead of the other clear option, which would have been to become a marketing strategist.

Step 2 – He started taking contract work as an independent marketing data scientist

In 2019, he began consulting on marketing analytics projects, delivering these services as an agency. After getting caught up in the “low budget” hamster wheel, he could clearly see the error in his ways, which were these: 

* His offers were not differentiated from market competitors,

* His ideal customer profiles were not clear, and

* He was not clearly presenting the monetary value of the results his services could generate.

Step 3 – He invested in taking his data business to the next level 

Of all the work he did throughout the course of the mentorship, he attributes his remarkable success to the following cores:

Market research. He developed a concrete customer research study that helped him validate his minimum viable product. He built a competitor matrix, and established clarity on his ideal customer profile. He then used that profile to clearly define his circle of influence – in other words, the influencers, team members, and other close associates who have the ability to influence his outcomes. He also clarified the true Total Addressable Market (TAM) – the total market demand for his offers, expressed as an annual revenue estimate. After estimating his TAM, he crafted and executed a plan for meeting individuals within that market where they are.

Offer development and positioning. He improved his offers per his research findings, and productized many of the services he’d previously been offering. He also made sure he was demonstrating his value to his growing audience. 

Operations optimization. He innovated new ways to apply systems and technology such as Python, RPA and data engineering to drive back-office operations for his company. As a result, he’s able to keep costs down and drive a high profit margin. 

If you like this post on Kam Lee’s entrepreneurial success, and you want to look further into building your own data science business, then I definitely recommend you download my free toolkit, The Data Entrepreneur Toolkit here. Inside the toolkit I share the 32 very best tools and processes for growing your data business fast!

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! 

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Lillian Pierson, P.E.

Lillian Pierson is a CEO & data leader that supports data professionals to evolve into world-class leaders & entrepreneurs. To date, she’s helped educate over 1.3 million data professionals on AI and data science. Lillian has authored 6 data books with Wiley & Sons Publishers as well as 8 data courses with LinkedIn Learning. She’s supported a wide variety of organizations across the globe, from the United Nations and National Geographic, to Ericsson and Saudi Aramco, and everything in between. She is a licensed Professional Engineer, in good standing. She’s been a technical consultant since 2007 and a data business mentor since 2018. She occasionally volunteers her expertise in global summits and forums on data privacy and ethics.

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