The Venture Capital industry is a poster child for cognitive biases. Most VC firms are judged not for their decisions but for their outcomes. If you have one Google or Facebook in your portfolio, you are thought of as a genius with no attention paid to any of your other investments. Feedback loops in the VC business are broken, and there is no incentive to make better decisions. However, a little attention to industry data reveals a sobering statistic. Research from Cambridge Associates shows that as an industry, VCs pick winners only 2.5 percent of the time. More than a decade of data reveals that out of more than 4,000 VC investment rounds annually, the top 100 generate between 70 and 100 percent of industry profits. The batting average of the best VC firms is modestly better, closer to 4.5%. All of this creates a huge need for a higher-quality decision-making process. What better method to fall back on than the five-decade old discipline of Decision Analysis (DA) that draws from Decision Science and helps us avoid common pitfalls in decision-making.
The Value of DA in Venture Capital
The application of DA in VC delivers on several fronts:
- Helping us avoid cognitive biases: In Ulu's first fund, we did not use DA in 20 out of our 60 investments. Of those 20, half went out of business. In the other 40, we used DA and only 5 folded. This is also the cohort with our big exits. We have some experiential evidence that DA has helped us identify risks early on that we would otherwise be blind to. The enthusiasm of the entrepreneur is infectious, and every time we meet a bright and dedicated entrepreneur, we feel like we should have invested yesterday. DA helps us slow down, go back to the fundamentals in a discipined manner, and we end up discovering that things are not as attractive as they first seemed. This helps us make investments with our eyes open, fully cognizant of the risks we are taking.
- Making our decision architecture transparent to our entrepreneurs: Nothing is more frustrating to an entrepreneur to receive a "No" from a VC without a clear rationale. Decision Analysis makes clear our rationale in painstaking detail, and entrepreneurs are invited to challenge our assumptions with better logic and evidence. This creates a learning frame on both sides. Even when the answer is no, our entrepreneurs feel that the decision was fair, and we often get referrals from those entrepreneurs we have declined to fund. In fact, culturally, we are optimized around this metric -- the # of referrals we get from those entrepreneurs we have declined to fund. This requires us to show up with transparency and clarity and DA helps us deliver on that.
- Making our decision architecture transparent to our investment team: Any investment opportunity has too many details to keep in one person's head and it is very hard to discover knowledge gaps in our investment team. Decision Analysis allows us to discover key differences of opinion through quantification, which drives great internal conversations around what we believe and why. As we go through this process collaboratively, we get a shared sense of the decision architecture and are able to get behind a decision as a team with clarity.
- Making our decision architecture transparent to other investors: After we have invested in a startup, our entrepreneurs are able to use our decision analysis to make the decision architecture clear to other investors participating in the round. These investors can put in their own assessments into our structure and discover whether the investment is a good decision for them or not.
- Diversity outcomes: In a counter-intuitive way, it turns out that having such a rigorous process of evaluation ensures that merit shines, and merit has many colors. We believe that our disciplined use of DA is behind our off-the-charts diversity statistics in our portfolio. In fact, "entrepreneurship as equity" is a creed for us and diversity is part of our core outcome metrics on which we evaluate ourselves. DA is what helps us deliver on those outcomes. This is not that hard to explain after the fact. Orchestras in the US started seeing a rise in women members in the 70s and 80s after they started adopting blind auditions. DA helps us level the playing field by helping mnimize our biases.
- Delivering Value in the VC conversation: Most entrepreneurs get nothing back for all of the engagement with VCs, other than the funding check if they are successful. However, we find when we do a DA with the entrepreneur, they love the process as it yields critical insight into the key drivers of their business. 20% of the time, startups will alter their go-to-market strategy based on the insights that emerge from our DA process, which we call market-mapping.
DA and Portfolio Construction
Click on the video above to watch Dr. Clint Korver's guest lecture on DA in VC at Stanford University's class "Professional Decision Analysis," Feb 2018
The mathematics of DA leads to two counter-intuitive results:
- If we think we are best-in-class VCs with 4.5% chance of an outlier success, we should be investing in at least 50 companies for a 90% chance of an outlier
- The cheapest stake we can get in a company is from our first investment, and therefore, we don't hold back our investments in tranches
Both of these insights emerge from the math and are counter to contemporary VC practice. The first principle implies that we will have a very different approach to boards. Instead of staying with companies throughout their life, we aim to be their Seed-to-Series A partner and exit after they have secured Series A funding. This frees us up for other companies, and it also makes our companies more attractive for a Series A investor. The second principle implies that we put our best shot in our first investment without any plans to participate in the future. If that company were to come back to us in a future round, then we treat it as a new deal and go through our analysis again to see if it makes sense (as in, does it give us a 10x PWMOIC -- see Market Mapping for details).