Data-driven VC #9: Patterns of successful startups
Where venture capital and data intersect. Every week.
👋 Hi, I’m Andre and welcome to my weekly newsletter, Data-driven VC. Every Thursday I cover hands-on insights into data-driven innovation in venture capital and connect the dots between the latest research, reviews of novel tools and datasets, deep dives into various VC tech stacks, interviews with experts and the implications for all stakeholders. Follow along to understand how data-driven approaches change the game, why it matters, and what it means for you.
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The last few episodes were centered around a hybrid approach to startup screening where we combine deterministic, rule-based systems with ML-based approaches. While deterministic logic gets infused by human biases and the perceived importance of different selection criteria, the ML-based approach derives the screening criteria weights directly from the data. Combining both approaches is, in short, a two-way street that unifies the benefits (objective, less biased, more inclusive, efficient, effective etc.) of both individual approaches and avoids their respective drawbacks (mirroring the past into the future, limited sample size etc.)
To better understand both concepts and explore the patterns behind successful companies, I will first look at the deterministic approach through a more academic lens (in order to remove my own biases) before then drawing insights from ML-based screening models.
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VC selection criteria - What are investors looking for?
VC selection criteria are a well-researched field with several papers examining the different dimensions. The two tables below summarize the most important studies in the field and rank different selection criteria accordingly. Among them are team, problem/market (size, growth, timing pull/push, fragmentation), solution/product (USP, IP, etc), business model, go-to-market motion, traction (product, financial), competitive landscape, defensibility, cap table and round structure.
Clearly, the evaluation criteria and their relative importance differ across studies but there are some that predominate all across. Namely the team, product, market and traction (#biasON which are also the most important ones for us at Earlybird #biasOFF).
I supervised a Master’s thesis in 2019 to understand the major VC selection criteria in more detail. For example, when thinking about the team, we aimed to understand both the underlying sub-criteria like educational background, professional background, age, gender or social media presence, but also the relative balancing across these sub-criteria. Unfortunately, results were wide-spread and statistically not significant, showcasing once more the diversity of investor perspectives.
That being said, deterministic approaches have benefits but also clear shortcomings and instead of asking investors what they look for based on their experience and limited sample size, why not flipping it around and analyzing the data to understand what successful startups have in common?
Startup success patterns - What does the data tell us?
Let’s have a look at some independent analyses to get a well-rounded picture. First off, I’d like to share selected results from a scoring model we have developed at Earlybird. Due to the overall high relevance of the founder and executive team, I’ll focus the subsequent deep dive on this dimension.