Ann Lai
Among all the buzzwords startups use when pitching investors and in their marketing, “data-driven” is nearly at the top of the pile. But what does being data-driven really mean?
Investments are slowing down and VCs are tightening their purse strings. Previously trending tech startups in fields like BNPL, crypto and the delivery market are struggling to show the growth and returns they promised in their initial funding rounds.
Smaller startups with more modest goals can entice VCs looking for safer, smaller deals, but approaching an early-stage venture with a data-driven strategy is a one-sided approach — one that often disadvantages startups.
Simple but necessary shifts in mindsets can change the way startups and investors look at data when making major investment decisions. Here are a few tips:
Stop using unfiltered data
Using raw, unfiltered data is common at startups that donʼt know how to properly filter their information, and they often end up offloading data irrelevant to their company and mission.
For example, donʼt show investors the total visits to your webpage without also showing the average duration of those visits — veteran investors will pick up on this.
Unfiltered data can skew toward biases and cause more harm than good. Many fast-evolving AI programs have unintentionally developed racial or gender biases based on the unfiltered data fed to them. Understanding how to filter data to properly tell a companyʼs story is critical to understanding where a company shines and where thereʼs room for improvement.
To avoid this, segment your data and use outliers to your advantage.
Filtering data to accurately depict operations and performance ensures that you’re comparing apples to apples. Unfiltered data creates a series of inaccurate comparisons, highlights the wrong aspects of the business and muddles critical outliers that VCs look for.
What makes a company different, more innovative or stand out from the rest? Though it may seem counterintuitive, segmented data illustrates transparency by making it more difficult to hide key figures amid unfiltered noise.
Stop focusing on growth alone
When everyone was gunning for fast growth and “growth rate” was ranked as the top metric to look at for signs of success, the black box model was venture capital’s favorite child. However, this generic model doesnʼt account for differences among companies and is especially dangerous in a market where inflated growth metrics could indicate greater risk.
So instead of simply showcasing growth, show off your growth against the backdrop of the funding you’ve raised. Startups should ask themselves how they can compare apples to apples and measure mainstream metrics against more accurate depictions of success.
As an investor, I’m looking for efficiency. I’m going with the company that raised $5 million and grew by $10 million instead of the company that raised $40 million and grew by $10 million. By measuring growth against funding raised, smaller startups can better compare themselves to high-growth, high-investment companies.
Stop skewing your numbers
Unfiltered data can muddle the story, while filtered data can push a certain narrative. Both approaches risk pissing off investors. Startups who arenʼt aware theyʼre doing so risk missing key indicators of success or concern. When filtering data, the idea is to create fair comparisons not biased narratives.
I suggest starting with the full picture. Every company is different, and the data that tells the right story will be different for each. But there are common outliers that can and should be taken into account to ensure thorough reporting.
Profitability, true retention and capital efficiency are top indicators. Investors want to see true product use — not just customer retention rate or downloads but retention rate versus total users or daily active users versus the number of downloads. Itʼs critical to know how your data selection and segmentation is telling the story to ensure that you arenʼt just data-driven but data-informed.
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