VCs expect the companies they invest in to use data to improve their decision-making. So why aren’t they doing that when evaluating startup teams?
Sure, venture capital is a people business, and the power of gut feeling is real. But using an objective, data-backed process to evaluate teams — the same way we do when evaluating financial KPIs, product, timing and market opportunities — will help us make better investment decisions, avoid costly mistakes and discover opportunities we might have otherwise overlooked.
An objective assessment process will also help investors break free from patterns and back someone other than a white male for a change. Is looking at how we have always done things the best way to build for the future?
Sixty percent of startups fail because of problems with the team. Instinct matters, but a team is too big a risk to leave to intuition. I will use myself as an example. I have founded two companies. I know what it takes to build a company and to achieve a successful exit. I like to think I can sense when someone has that special something and when a team has chemistry. But I am human. I am limited by bias and thought patterns; data is not.
You can (and should) take a scientific approach to evaluating a startup team. A “strong” team isn’t a vague concept — extensive research confirms what it takes to execute a vision. Despite what people expect, soft skills can be measured. VCVolt is a computerized selection model that analyzes the performance of companies and founding teams developed by Eva de Mol, Ph.D., my partner at CapitalT.
We use it to inform every investment decision we make and to demystify a common hurdle to entrepreneurial success. (The technology also evaluates the company, market opportunity, timing and other factors, but since most investors aren’t taking a structured, data-backed approach to analyzing teams, let’s focus on that.)
VCVolt allows us to reduce team risk early on in the selection and due diligence process, thereby reducing confirmation bias and fail rates, discovering more winning teams and driving higher returns.
I will keep this story brief for privacy reasons, but you will get the point. While testing the model, we advised another VC firm not to move forward with an investment based on the model’s findings. The firm moved forward anyway because they were in love with the deal, and everything the model predicted transpired. It was a big loss for the investors, and a reminder that hunch and gut feeling can be wrong — or at least blind you to some serious risk factors.
The platform uses a validated model that is based on more than five years of scientific research, data from more than 1,000 companies and input from world-class experts and scientists. Its predictive validity is noted in top-tier scientific journals and other publications, including Harvard Business Review. By asking the right questions — science-based questions validated by more than 80,000 datapoints — the platform analyzes the likelihood that a team will succeed. It considers:
- Personality traits: Certain traits are essential to a team’s ability to grow and scale a company. These relate to motivation, growth mindset and flexibility, fear of failure and obsessive passion.
- Passion: The platform also considers what entrepreneurs are most passionate about: investing, developing or funding. A person’s passion will inform their short and long-term decision-making, so the different types of entrepreneurial passion in a team need to align to ensure success.
- Human capital: This is one of the strongest predictors for future venture success. We consider prior startup experience, industry experience, management experience, shared experience, education and look if there are complementary knowledge and skills.
- Team dynamics: Strong team dynamics are crucial to leveraging individual skills and knowledge. We look at team cohesion, shared strategic vision, task-conflict and psychological safety. You might think a “big happy family”-type team is an investor’s dream, but research shows you want a bit of conflict and tension. When people challenge each other, it shows diversity of thought and leads to better outcomes.
In addition to assessing the quality of team composition at the start of the investment, the platform continuously reassesses to confirm whether the team is ready for the next phase of company growth. This serves as a powerful diagnostic tool for identifying when human intervention may be needed. We share the results of the analysis with the teams we back and the ones we don’t. Because it is entirely objective and backed in science, it leads to fruitful, less emotional discussions.
Using data to overcome unconscious bias
Of course, as investors, we aren’t just looking to mitigate failure. We are looking for outliers — the teams and products that defy the odds and create billion-dollar payouts. As investors, we might think that quantifying that special something or creating too rigid of an evaluation process will take the magic out of what we do or hinder us from finding — yep, I am going to say it — the next unicorn. That is not the case. Using data doesn’t stop you from investing in outliers — our own thought patterns do.
Data can help overcome unconscious bias. This year, the global dialogue about racial injustice forced us (I hope) to look inward, to challenge ourselves to get real about our own investment patterns and prejudice. Bias is hard to identify and to overcome — but data helps.
This is not a newsflash: the majority of VC dollars go to white, college-educated men. As an industry, we know this, yet we are still failing to back entrepreneurs with diverse backgrounds. Last year, U.S. VC investments in female founders reached an all-time high of … hold your applause … 2.8%. Research shows that female founders deliver higher returns than males and that diverse teams improve performance and outcomes. Again, we know this. So where is the disconnect? Using data to support decision-making is one way to overcome investment bias.
Another misconception we make as investors is assuming the unicorns are in our network. Warm introductions are still the norm, but what are the odds that the next Affirm or Airbnb is going to get in front of you? What are the chances the next Spotify or Adyen is just a referral away? Statistically, the chances are small. We need to think more broadly and find entrepreneurs outside of our network to improve the diversity of the companies we support, as well as the chances of finding a team with that special something.
Investors see an entrepreneur’s ability to get an introduction as a testament to their hustle. But it is really difficult to get into networks you are not a part of, especially high-profile VC networks. It takes a different skill than building a company and getting in front of clients. We want entrepreneurs to focus on their business, not trying to get an intro to us. We are fine with a cold email, because a platform like VCVolt helps us analyze large volumes of inbound inquiries quickly. Any entrepreneur can log on to take the assessment, which takes about 15 minutes to complete.
Only the best teams can execute on a vision and scale a company. VCVolt is a science-backed process for identifying those teams and for spotting red flags and potential pitfalls. By recognizing challenges upfront, we are better prepared to offer support and suggest changes. Fixing errors can be very time-consuming, especially when it comes to teams. The model saves entrepreneurs that time, a crucial commodity for a tech startup.
Again, a data-based approach to team assessment doesn’t replace one-on-one interviews or take away from the importance of intuition and experience — it complements it.
Yes, this is a people business, but by overlaying gut feeling with facts, we can improve our decision-making and make progress in removing bias to foster more diverse — and more profitable — investing.