Based on what I know today today, I estimate that entrepreneurship is one third talent, one third luck and one third wisdom. Talent is not teachable and, at present, is very hard to identify in first time entrepreneurs. Luck cannot be taught nor bought but plays an important role in success, and, unfortunately, is often misinterpreted as talent or wisdom. Finally, wisdom is teachable but we must be incredibly careful about following only guidelines that are borne out by data and not just a series of anecdotes.
Entrepreneurship is one third luck
We all love to believe in superstars, and superstars there are, but there is strong evidence to suggest that luck plays a big role in startup success. The best evidence I see is that entrepreneurs who are successful with their first venture have only a 30% chance of success on their subsequent venture. If luck played a weak role, then there’s no reason why previously successful entrepreneurs should continue to be successful from there-on*.
Entrepreneurship is one third talent
Data show that talent has a big influence on success. I believe this because the success rate of previously successful entrepreneurs is much higher than average. If talent wasn’t a thing then it wouldn’t matter whether your founding team had persistently been successful in the past – this simply isn’t borne out in the data. You might argue that having past-entrepreneurs on your team helps simply because they have experience. However, I don’t think this is the case, because then entrepreneurs who failed in the past would also be useful to have on your team, which isn’t true when you look at the data [in fact, their performance is very close to average].
Entrepreneurship is one third wisdom
There is also one important aspect of entrepreneurship that can be thought, which might be thought of as wisdom, rules of thumb, or, more formally, heuristics.
One way to develop rules of thumb is by carefully thinking through the fundamentals of a successful startup. Peter Thiel’s rules provide one such example. Sustainable profits are achieved by finding a way to achieve a durable competitive advantage, which, in its strongest form, is, by definition, a monopoly. A monopoly can come about as a result of enforceable patents, trade secrets, economies of scale or network effects; by optimizing for such characteristics, it seems one could improve the startups chances of success.
A second way to develop rules of thumb is to look at broad datasets but, unfortunately, there are few. Furthermore, amongst the few studies that exist, few, if any, characteristics of a startup that can be measured early in its life have been proven to lead to greater success in its later stages. Setting team composition aside, the only real correlator with success seems to be revenue, but there aren’t even enough data to prove this for startups at an early stage, and, what’s more, we know that many early stage startups simply don’t have any revenue – meaning we could really do with other new metrics.
Putting it all together
Taking the insights I have gleaned from the research and thinking of others, I have compiled a model to estimate the future success of early stage startups. For sure this model is at best weak and at worst wrong. However, I include it because I think the approach to building the model is a useful tool for others.
The model I propose has three pieces: team, traction and fundamentals – each piece worth one point, giving a maximum score of three. Importantly, the model is quantitative and relies only on information that is measurable:
1st Piece: Team (Maximum points = 1 pt.)
Raw talent – If at least founder has previously founded a startup that was acquired or went public, assign 1/2 point. Failing the above test, if at least one founder has founded a startup with current annual revenue of above $10 million, assign 1/4 a point.
Founding team size – Assign 1/6 of a point for each of the following roles on the starting team – up to a maximum of 1/2 point in total: a CEO type role; a lead technical role; a sales and marketing type role; some other distinct role.
2nd Piece: Traction (Maximum points = 1 pt.)
Sum up the total revenue earned by the startup to date. Include only revenue that has been received. Assign 1/6 pt for $0-100, 1/3 pt for $100-1,000, 1/2 pt for $1,000-10,000, 2/3 pt for $10,000-100,000, 5/6 pt for $100,000-$100,0000, and, 1 pt for above $1,000,000.
3rd Piece: Fundamentals (Maximum points = 1 pt.)
Intellectual property – Companies with chemistry or pharma based patents or trade secrets – ones that are either very hard to copy or else are really obvious if someone copies the patent so you can sue them; 1/3 pt. Companies with patents that are easy to copy but somewhat possible to track if other people copy; 1/6 pt. Other companies fitting neither previous description; 0 pts.
Economy of scale – Software; 1/3 pt. Hardware or infrastructure; 1/6 pt. Companies fitting neither of the two previous descriptions; 0 pts.
Network effects. 1/3 pt for any marketplace or communications company and 0 pts for all other companies.
Parting words – Can entrepreneurship be taught?
Before concluding, I want to point out that I’ve taken for granted the assumption that every entrepreneur wishes to optimize for success. Indeed, as an entrepreneur, you really only get to make one bet; you’re betting on the success of your own company, not a portfolio of companies. In this regard it makes sense to optimise for success. However, I really think that, while we should largely optimize for success, there must be limits. If we were all to follow the framework I have outlined above everyone would end up building a communications or marketplace based software startup. Indeed, half joking, that’s more or less what has happened. Importantly, though, not all problems we face in this world are addressed via marketplace based software startups and that, I think, is where the assumption that we should solely optimize for success might just fall down. Mission plays an important role.
Now, to conclude on whether entrepreneurship can be taught. Luck and talent each play a huge role in startup success and, by definition almost, neither is easy to teach. However, we can teach aspiring entrepreneurs to be smarter about how they build their founding team. By following the right rules of thumb – for example, focusing on building a startup with strong intellectual property, economies of scale and network effects – entrepreneurs can likely improve their chances of success. Unfortunately, as things stand today, there are relatively few good rules for entrepreneurs to follow that have been rigorously established. In my view, this stems from a lack of publicly available datasets on startups, insufficient focus on relating measurable characteristics of early stage companies (such as revenue or number of patent applications) to success, and an over-emphasis on hard to measure characteristics (like market size). With a focus on relating measurable startup characteristics to their future success, I believe there are many more rules of thumb that can be established to the benefit of future entrepreneurial generations.
*This isn’t strictly true. For example, it is possible that entrepreneurs who are successful first time round aren’t necessarily successful the second time because they fail to follow certain rules of thumb – e.g. they didn’t opt for a startup that had economies of scale and strong intellectual property – indeed there are many alternate ways of thinking about this. However, to me, it seems that a big reason is simply that luck plays a strong role, so if you’re successful first time around, there’s no guarantee of being successful the next time.