A framework to grow your start up data driven from day one

My usual go-to topic is data-driven VCs but for this piece, I’m switching sides and imagining what I’d do if I were a founder and how I’d make sure my team and company were data-driven from day one.

Recently, I was part of a panel at the LEVEL UP 2024 in Eindhoven - awesome event, by the way! It is not always possible to stay within the panel topic during discussions though; experienced the same while I was stepping in and out different sessions.

That experience, plus a thought-provoking post I came across from Jeroen Coelen , really got me thinking. (His post challenged the common and slightly depressing narrative that only 1 in 10 or 12 startups make it. Recommend: https://iwantproductmarketfit.substack.com/p/do-9-out-of-10-startups-fail)

So, I scrapped my original idea of writing about applying a start-up approach in VC firms to build a data driven culture—don’t worry, that’s coming next—and instead decided to tackle how startups can leverage data to grow and succeed.

For this aim, I will use Gritd’s approach to start-up phases: Customer validation, problem solution fit, market entry, product-market fit, repeatable sales and business-model fit. After the last phase, Gritd argues, scaling starts so I also won’t cover those phases. I refer to their report on their website for more explanation and definitions (www.gritd.nl). Credits to Gijs van de Molengraft

?I will map data drivenness dimensions (business need, data scope, data infrastructure, data governance, data talent and data culture) to these phases to offer you a framework as a data drivenness matrix for startups.

Customer Validation: Listen Before You Build

In the early days, your biggest task is figuring out if there’s really a market for what you're offering. What you need is customer feedback—lots of it.

Data at this stage comes from simple, hands-on sources: interviews, surveys, and direct conversations. Pay attention to the patterns you hear: How many customers face the same issue? How desperate are they for a solution? You're not just gathering opinions; you're collecting valuable insights to validate if you’re solving the right problem. On the other hand beware of the qualitative data pitfall in product development strategy from day one: verify qualitative input with quantitative data as soon as you can, in the later stages.

Your "data infrastructure" is basic: spreadsheets, forms, notes. And your only governance challenge is privacy related issues. Put importance to it!

In this stage data culture is owned by the founder(s) of course, like many other challenges! Founders or one of the founders should foster data literacy and drivenness in the team, if there is a team. But this is a once -in-life chance: you can start a data driven culture from the scratch, which is easier to transform an established culture. Believe me!

Talent? Again, the founder! If you are looking for a co-founder, then finding a data savvy one will be a perfect start for data driven future.

Problem-Solution Fit: Are You Solving the Right Problem?

Once you know customers want a solution, the next step is figuring out if your solution works. Here, your data scope expands slightly. Now you're looking for feedback from your early adopters, those who are willing to try new solutions because they feel the pain the most. (I skip the discussion of asking the right questions for collecting data instead of biased information.)

Gather data on how well your product or service meets their needs through solution demos, usage stats, and direct feedback. It's still qualitative and small-scale, but it helps you fine-tune the product. You don’t need fancy data tools yet most probably—just make sure you're capturing all the feedback in an organized way so you can iterate quickly. But since more information about people and your solution is being collected, start thinking about security issues also. You will potentially rely on some basic, easy to use cloud solutions.

Market Entry: Find Paying Customers

Now that you’ve nailed the problem-solution fit, it’s time to take your solution to a wider audience. At this stage, your focus is on finding those willing to pay, which is a whole new challenge. Data becomes your ally in understanding market trends and customer behavior.

Start tracking lead data: Who’s interested? What percentage of your leads convert to paying customers? Count also the number of product demonstrations.?

Here, tools like simple CRM systems and web analytics become useful, giving you the insight you need to understand where your customers are coming from and what they respond to.

At this stage you might think of hiring data engineering talent (an intern maybe) to keep the data clean and processable. I always say hire a data engineer first rather than a data analyst because the data analyst you hire first will be doing more engineering stuff.

Product-Market Fit: Keep Your Customers Happy

If you’ve made it this far, congratulations—you’re building something people are willing to pay for. Now comes the real test: Do they love it? This is the phase where you optimize for retention.

Your data scope shifts to usage metrics: How often are customers using your product? Are they referring others? Retention is key here, and data can help you spot problems before they turn into churn (customers leaving).

This is where you might need more advanced data infrastructure—things like relational databases to track user interactions. This puts more importance on data engineering talent.

Once you start selling your product, you might already have grown a team. So it is the time to think about data culture in a more structured way. You need to have a recruiting strategy accordingly but also need to keep your team data-literal. I would suggest democratizing data among the team, which means giving right access to right people so that they can keep analyzing information themselves rather than asking to a data analyst every time. This is another way to keep your team data aware.

Repeatable Sales: Create a Scalable Machine

As you grow, you need to systematize your sales and marketing processes. At this point, you’ve got some established customers, and your challenge is scaling that up without massively increasing costs. Data can help you optimize your funnel—understanding how long it takes to close a deal, what percentage of leads turn into customers, and how to keep acquisition costs down.

You’ll need more complex data pipelines now, tracking customer behavior at scale and automating certain parts of your sales process. Your goal is to build a repeatable engine that turns prospects into paying customers efficiently. At this stage you might need data analyst talent as a separate workforce for understanding customer behavior in depth.

Business Model Fit: Ensure Long-Term Profitability

Finally, your focus shifts to the financials. It’s not just about getting customers—it’s about ensuring your business is profitable. Metrics like Customer Lifetime Value (CLTV) vs. Customer Acquisition Cost (CAC) become critical. You want to ensure that the money you’re spending to acquire a customer is worth it in the long run.

Data governance and data quality become more critical at this stage as you're dealing with more complex financial data. Ensure your data policies are robust, and your team is equipped with the right talent, like data scientists, to keep scaling efficiently.

New pitch deck is alternative data

In this framework, start-ups considered to be aiming customers, a data-drivenness matrix towards fundraising would be different. Though, I would like to raise one issue in this regard at least: new pitch deck is the alternative data!

VC industry is progressing towards data driven strategies, thus I believe the traditional pitch deck alone is no longer sufficient for start-ups to attract best class VCs. With the growing influence of AI and large language models (LLMs) in decision-making, a startup's true story extends beyond slides—it's also reflected in the data they generate across digital platforms. This data is increasingly used by investors for verification at least.

Investors are now turning to alternative data sources—ranging from transaction records and web traffic to founder data and publicly accessible information about a startup's product—to scout, assess, and automate their processes, reducing bias in decision-making. From gauging product-market fit to evaluating team dynamics, financial health, and market traction, every aspect of a startup is being analyzed through a data lens by VCs. As a result, the most persuasive "pitch" a startup can offer isn't just its slide deck but the quality, depth, and transparency of the publicly available data. Startups that manage, curate, and showcase strong data stand out more effectively.

I believe that a company’s "scrappable" data is becoming the new pitch deck. While data included in pitch decks has always been important, the public data that can be gathered from various sources is gaining even more significance. It's the evidence of a startup’s potential, progress, and sometimes even validation of its vision. The more comprehensive and accurate the data you emit, the better your chances with predictive models.

So, I strongly recommend that startups focus not only on their narrative but also on the "data profile" that supports it.

Final Thoughts: Build a Data-Driven Culture

Being data-driven is not just about having the right tools; it’s about building the right mindset. Encourage everyone in your startup, from top to bottom, to embrace data in their daily decisions. When your culture is data-centric, you move faster, adapt better, and grow smarter.

Everybody finds being data driven very hot! But we easily lose focus while we deal with many challenges in start-up lifespan. Hope this framework and the data drivenness matrix help you at least to check yourself along the way.

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