Correlation Ventures is using data to take the instinct out of venture capital investing. Typical venture capitalists depend on “gut feel” built through years of experience to make investment decisions, but this can lead to unintentional adverse effects. Laura Huang, a professor at Wharton who researches how intuition can affect VC investments, believes that “gut feelings might just be a cover for our bias…driving most of our decisions.”
Founded in 2006, Correlation didn’t make a single investment for 4 years. Instead, the founders (HBS alums) David Coats and Trevor Kienzle, amassed the most complete database of VC financings in existence, by meeting with other VCs, entrepreneurs, and data providers. Using this data (consisting of financing details, investors, board members, management, industry segment, business stage, exit details, and more), Correlation built a predictive model that enables them to make many investments very rapidly using disciplined analytics.
While typical VCs may take months to make an investment decision, Correlation leverages their model to make decisions in as little as two days. For entrepreneurs and VCs seeking co-investors, the firm offers quick and easy access to VC capital without ever taking a board seat. The decision-making speed enables Correlation to make many more investments than a typical VC; in September 2015, they had a portfolio of 112 companies for their first fund. The model is built to achieve smaller successes. Co-founder Coats described the model as “kind of like counting card in blackjack” in that “it’s really designed to tilt the odds in our favor a little bit. If you play enough hands you should win.” The use of data to drive investment decisions seems to be paying off; Correlation has significant investments in Casper and invested in Virsto, which exited for $200M, resulting in a substantial windfall for Correlation and evidence of their ability to capture value they create.
Correlation is not alone in using analytics to identify good investment opportunities. Several other firms have employed similar approaches and amassed databases of information they use to create models. Correlation believes their database to be the “most complete, comprehensive database on venture investments and their outcomes.” Assuming no other companies can amass a comparably complete database, Correlation should be able to maintain a competitive advantage relative to competitors in making quick and profitable VC decisions. The data ownership also presents several opportunities for the firm. For example, the database could potentially be sold as a product, enabling Correlation to become the data provider to other VCs, a potentially lucrative opportunity.
If Correlation were to productize and sell their data, other VC firms would face an interesting organizational challenge like the one seen in Moneyball. Venture capitalists have historically relied on instinct and gut feel to make investment decisions. It could be challenging to convince these individuals to trust a computer model, particularly if they fear being replaced by the machine. It will be crucial to get senior leaders to believe that data analytics are a benefit to their role rather than a threat, and to recognize that their intuition is likely introducing bias that could be impacting results. Leveraging a predictive model in VC could help firms eliminate much of the bias that exists in today’s investment environment (for example, investing in male entrepreneurs over female entrepreneurs) while also increasing returns for firms.
Time will tell if predictive analytics lead to investment success for Correlation and other data-driven VC funds. With $350 AUM, Correlation is worth keeping an eye on in the coming years to evaluate the role data can play in VC.