Why VCs should hire an engineer instead of another investment professional
DDVC #34: 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.
Current subscribers: 8,052+, +105 since last week
Brought to you by VESTBERRY - A tool designed specifically for data-driven VCs
Watch our 7-minute product demo, showcasing the platform's powerful features and intuitive interface. Gain valuable insights and make data-driven decisions with unparalleled ease.
84% of VCs want to ramp up their resources and become more data-driven, yet less than 1% of firms do have dedicated engineers working on such initiatives. This is the result of two recent polls and research we have conducted as part of the upcoming Data-driven VC Landscape 2023.
What explains the gap between ambitions and reality?
Over the years, I’ve talked to hundreds of aspiring data-driven VCs and repeatedly heard the same reasons preventing them from achieving their goals:
No consensus among Partners to allocate the budget
Too busy with day-to-day to focus on new initiatives
Stuck in “buy versus build”, tried external off-the-shelf solutions and also worked with freelancers, but not happy with either outcome
Don’t know where to start, whom to hire etc.
How to close the gap?
Looking at 1. and 2. above (the majority of VCs), I’m convinced that results will speak louder than words, meaning that data-driven VCs with dedicated initiatives will outperform the market in the long run, eventually convincing skeptical Partners and investment professionals who are not willing to sharpen the saw (=step back, level up their tool stack and get more done with less). With sufficient sample size and control variables, we’ll hopefully be able to prove causality outside-in. The first episode of the upcoming Data-driven VC Landscape will be another building block in this picture.
Inside-out, on the other side, will be even more explicit as data-driven VCs themselves can easily attribute which deals were sourced manually versus through data-driven initiatives. This is also true for solutions purpose-built across the VC value chain. At Earlybird, for example, we measure efficiency, effectiveness and inclusiveness across several dimensions, as described in the “How to measure productivity and identify potential for improvement” episode.
Unsurprisingly, many of the leading data-driven VCs don’t speak about their initiatives, trying to keep the positive impact for themselves. Only a few openly speak about it, which of course reduces the incentives for other firms to do the same. Intentionally, these silent but highly active data-driven VC firms try to leave their competitors behind. There are pros and cons, just like closed-source software versus open-source software. I personally believe a lot in the power of community.
Looking at 3. and 4. above (the minority of VCs), I see that the respective VCs have the conviction and budget but are frustrated as there is no blueprint out there. Therefore, firms typically start with the lowest cost (to reduce downside) and test off-the-shelf solutions, in most cases for sourcing and lead gen. Clearly, this is better than doing nothing, yet if everyone uses the same tools, it won't be easy to create alpha long-term. Moreover, customization needs oftentimes need to be left behind.
As a natural next step, most VCs take the next “least downside” step by engaging a freelancer to stitch together some web crawlers, APIs and a simple database to meet the customization demands, getting one step closer to uniqueness and the ability to generate alpha.
Some months into the project, I can ensure you that from my own experience as well as the experiences of many befriended VCs, you will get frustrated. Most freelancers lack an understanding of the VC job and have no incentive to take ownership and learn the basics. Therefore, the project either gradually pulls someone from the investment team in (👋🏻 my friends) or gets killed completely.