Guest post from Bryan Ruiter – Golden Egg Check.
You won’t be surprised that having a ‘strong team’ is an important factor for any startup. But what makes a strong team? In this post we’ll share our most important research findings with you.
Golden Egg Check’s research suggests an ideal startup team is composed as follows: the startup has 3 founders, with master degrees and experience from 4 prior startups. They all work 45-50 hours a week and throughout their careers they obtained 4-8 years of industry and managerial experience. To enable the best possible business decisions, they support their decision-making with customer metrics and with a rich network of helpful mentors.
Over 95% of the studied venture capitalists (VCs) in the Netherlands, regards the team as one of the most important factors to predict whether a startup will become successful. However, very often VCs assess the entrepreneurial team on mere gut feeling. Golden Egg Check, together with the University of Twente, conducted research towards quantifying startup performance from a wisdom of crowds perspective.
According to our sample of nine experts, with backgrounds in venture capital, business incubation and acceleration, business development, and entrepreneurs, the top eight of the most important team criteria are shown on the right. Prior startup experience is seen as the criteria with the most predicative power for success. Obviously, not every prior startup is a successful startup. However, some VCs said that they like to invest in startups consisting of founders with experience from failed startups “because these are the entrepreneurs who have seen the pitfalls of entrepreneurship”.
It’s not surprising to see level of education as the least predictive of the eight, “entrepreneurship is a trial and error process and is not necessarily learned in school”. The third place finisher, number of founders might be a bigger surprise. The number of founders determines what is in reach for a startup. Two or three founders can do a lot more than a single founder, and can complement each other in skills and network. Especially for startups it is important to be fast. But the more is not always the merrier; watch out for conflicts of interest. Investors find it often difficult to realize an interesting investment opportunity when many individuals have a stake in the startup.
It’s of course nice to know that prior startup experience contributes the most, but on its own it’s not enough to make a quantitative model. For a quantitative model we need to know for example, how much prior startup experience is best. That’s why we asked experts to score different levels of performance for their utility. With this method one has to rate each level on their relative contribution. The worst level scores 0 and the best level scores 100. The individual utility functions are subjective estimates but the median of a group is a lot more accurate. This phenomenon is referred to as “the wisdom of crowds”.
#1 Prior startup experience – Having at least a little bit of experience helps a lot As mentioned before, experts regard prior startup experience as the most important criteria. They identified four prior startups as the best level of performance (utility of 86.3) and no experience as the worst level of performance (utility of 3.9). The step from no experience to 1 prior startup (utility of 66.5) is a huge leap forward. The utility function is declining after the fourth prior startup, indicating that experience after the fourth prior startup decreases the probability that a startup becomes successful. We were somewhat surprised to see this. Some experts might think that, when it takes 5-10 years to make a start-up successful, these entrepreneurs are relatively old or are giving up easily when the tide is turning against the startup.
Concluding, some prior startup experience has a huge benefit to the probability of success. But, too much experience decreases this probability.
#2 Industry specific experience – Think outside of the box
Four to eight years of industry specific experience is the most desired level of performance (utility of 91.1), while no experience scores the worst (utility of 4.4). On first sight, the function looks linear, but the scale on the horizontal-axis is logarithmic. After four to eight years of experience the function declines, 16+ years of experience (utility of 64.1) scores worse than one to two years of experience (utility of 65.2). Striking is, when an industry guru is the founder of a startup the probability of startup success is not better than with one to two years of experience. Experts might associate an industry guru “with an older person as a founder” or as being “crusted in the industry and not being able to ‘think outside of the box’”.
As is the case with prior startup experience, some industry specific experience adds a lot of utility while more is not necessarily better.
#3 Multiple founders – Three founders perform better than four
Now it becomes even more interesting, the shape of this graph is very insightful. Three founders is the most optimal founding team size (utility of 91.9), while 8 or more founders is a “recipe for disaster” (utility of 7.8). From a single founder startup (utility of 43.2) to a two founder startup (utility of 78.8) is the biggest possible improvement while from three founders (utility of 91.9) to four founders (utility of 59.8) is the biggest possible decline. In the end the utility function seems to flatten out.
It is very clear that three founders is the most optimal startup size. With 95% certainty it can be said that three founders perform better than four founders. So better think twice when adding a founder after the third to the team.
#8 Level of education – A doctoral degree is not necessarily best
Founders with a master’s degree perform best (utility of 94.8). But, the biggest improvement is from an associate’s degree (utility of 41.7) to a bachelor’s degree (utility of 84.9). Striking is that a doctoral degree (utility of 63.1) scores significantly worse than a master’s degree. Experts might think that “Entrepreneurs with a doctoral degree are too analytic”, that “they are not leaving their basements/labs” and that “they think that their product is never good enough to enter the market”.
Concluding remarks
Although the answers of the experts were remarkably consistent, to be fair we should emphasize that the sample size of nine experts is relatively small. The results that we presented are probably not something revolutionary new but more something that confirms what you already knew. The next step is to proof whether these characteristics will actually lead to startup success. In order to do this you need to create an extensive dataset in which you follow startups for years. We did not do this, but we do think that the methodology used in this article leads to a great indication.
So what should startups and investors do with the information just provided? Well, as a startup you can study the criteria and the performance levels and you can immediately see how you can improve your team. If your startup consists of one founder, you might want to look for one or two extra founders. If your startup lacks experience you might want to add some experience to the founding team. As an investor you can grade every proposal with the information we just provided as a baseline. You could also develop your own quantitative approach with some similar methodology.