Venture capital has traditionally been an industry that revolves around relationships. VCs invest in a startup’s idea but their conviction stems from the folks behind it. This largely makes sense because investing in a startup also usually entails entering a years-long relationship.
But backing companies based on the allure of the founder hasn’t always worked out. Indeed, it often gets investors tied up in companies destined to collapse for one reason or another. And depending on warm intros or networks also limits the amount of startups an investor considers, which further alienates founders who don’t have the same networks or hail from nontraditional backgrounds.
An increasing number of venture firms think the solution to cutting through the noise is by incorporating data science into their deal sourcing process. This wouldn’t be a crazy idea per se, as investors from other asset classes such as institutional investors, hedge funds and public market traders already embrace data-driven investing, but thus far venture has largely sat out of the conversation.
A few venture firms, such as Correlation Ventures, SignalFire and Rocketship.vc, have long taken this approach, but this number looks likely to grow.
Change is in the air
This week, Austin, Texas-based VC outlet Ensemble announced that it closed a $100 million debut fund to invest in early-stage startups using a data-driven approach that sorts and tracks companies based on the quality and depth of their entire team.
Ensemble’s co-founder and managing partner, Collin West — an alum of Correlation Ventures — told TechCrunch that the firm wants to back companies that have the strongest team, but that would be too difficult to track without using data science to pare the list down.
“Using software, we can track all of the people at all of the startups, and that ends up being a whole lot more information than any human brain can handle, and especially any venture firm,” West said. “We effectively sort the industry by team quality in a very objective way knowing which companies to focus on, and spend a lot more time on fewer companies.”
Telstra Ventures added a data science component to their dealmaking process back in 2017 because the firm felt that was where the industry was headed, Mark Sherman, a managing partner at Telstra, told TechCrunch+. Sherman said that the firm started to notice its portfolio companies increasingly incorporating data science in their decisions and thought there could be a lot of value in embedding it into its own investment process as well.
“He or she who has the best data tends to win,” Sherman said, speaking about how the firm realized the potential of data-driven investing. “If we started to invest in our own business and make our own tools, we will just make better investment decisions.”
Since then, the firm has built over 30 models that help track things like web traffic to a company’s website, headcount growth, and even who a company has raised money from in the past.
“We can evaluate most companies within five minutes to give us a sense of whether or not this is a company we should spend some time on in any way,” Sherman said.
While both Sherman and West agreed that the algorithms they have built aren’t a “silver bullet” solution to finding companies, they feel it does help unearth diamonds in the rough. Sherman said the firm has invested in two companies they found this way that they otherwise wouldn’t have looked into and that they have since performed very well.
The impact
If investors can use data to find companies they wouldn’t have considered otherwise, they could help the industry move away from its current, network-heavy approach and help level the playing field.
The current model of investing off warm intros and through someone’s existing network usually results in investors only investing in the people they know even if there are better companies and teams working to solve the same problems.
A data driven approach that opens the gates wider will likely also help bring more visibility to qualified teams that are overlooked due to where they are located or if they were founded by someone from a nontraditional background.
Using data also gives investors — especially smaller funds with lean teams — more time to dive deeper into due diligence on a select number of companies, which could lead to more informed investment decisions.
But it looks like the road to such a reality is long, windy and uphill.
Sherman estimates that only about 10% of VC firms are currently incorporating data science into their investing approach. But he thinks this is changing and that we will see more and more firms building this muscle in the near future.
Venture firms like Two Sigma, Khosla Ventures, Fifth Wall and Lead Edge Capital have all posted job listings for data science roles in the last three weeks alone. That’s an encouraging sign.
“Our belief is that this is one of those things where if you haven’t started to do it, you’ll be left behind,” Sherman said. “I think in 2030, every venture firm will have data science capabilities.”
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