It’s not the size of payoff but the size of worst case that drives some entrepreneurs

In the past I wrote about the difference between the mind of an analyst and that of an entrepreneur,

It is impossible for someone who rationally estimates net present value of all options, stress tests their assumptions, meticulously conduct sensitivity analysis, determines market sizes and customer demands to start a new venture. (Dan Ariely calls this Optimism Bias, Gavin Cassar attributes this to selection bias)

Corollary: To start a venture one needs to be risk seeking and at the very least be willing to suspend their  rational mind to make the leap.

A research conducted by Saras Sarasvathy, Assoc Prof at University of Virginia points to evidence on how entrepreneurs evaluate outcomes, that it turns out it is indeed different from the methods of a rational decision maker,

Sarasvathy interviewed 45 successful entrepreneurs, all of whom had taken at least one business public (see caveat at the end of this post).  It was not the planned outcome, carefully constructed from comprehensive business plan and understanding of customer needs that drove these founders. They started with resources they had and imagined the possibilities.  Instead of estimating the size of rewards from a venture from successful outcomes or the likelihood of such outcomes, they asked, “what is the worst that can happen if they failed”.

If the failure was not all that bad they went right ahead.

In fact the failure is indeed not bad. Another research conducted by Pfeffer of Standard GSB, found that

Few of the participants in entrepreneurial activity suffer significant consequences from unsuccessful decisions, and therefore many players have less incentive than one might expect to improve their decision-making  – VCs get guaranteed principal and Entrepreneurs often, although not always, are working with other people’s money, so their financial downside, except in terms of the opportunity costs of their time, are also limited

Since entrepreneurship is already viewed and accepted by all as a high risk activity, failure is not only accepted but glorified as example of risk taking.

It’s not the size of payoff that drives them but imagining what is the worst that can happen. And it appears the worst case is not worst at all.

Caveat: Sarasvathy interviewed 45 successful entrepreneurs whose venture went public and more importantly agreed to talk to her for her study. Clearly there is survivor, selection and availability biases here. The results are also based on the conversations with those entrepreneurs which is prone to hindsight and narrative biases.

Take this for what it is worth.

How do VCs decide to invest in a startup – Regression Analysis -Part 1

This is a multi-part article. I decided to make it lot more technical (as in statistical analysis) so this would not only serve as a prediction model for startup funding but also serve as a model for you when you see similar such predictions. You will not see the results of the regression analysis in this article but you can read it all here if you can answer a statistics question.

Imagine you were asked to invest in ten startups. Given numerical ratings on the Team, Product, Market and Traction but knowing nothing about the specifics of the team, the exact product or the domain they play in, can you pick those that actually received a term sheet? Take this quiz and see how you do.

What characteristics of a startup make it attractive for venture capitalists to invest in it? If you are a startup founder preparing for that pitch, wouldn’t it be nice to know the answer so you can prepare well to maximize your chances of getting that coveted term sheet? For those who are listening, there is no scarcity of advice. Everyone from VCs, startup founders who secured funding at significant valuations and others on the sideline, all have something to say.

Are any of these relevant to startup founders? What is noise and what is signal? Do any of these have hard numbers behind them?

Until now there was no hard quantitative data on startups that pitch to VCs and the outcome. Thanks to data from Jay Jamison, partner at BlueRun Ventures, I have data on 216 startups that pitched to his firm. Jamison rated them on four metrics, Team, Product, Market and Traction using a 5-point scale and also noted the outcome of their pitch. The outcome is rated as likelihood of getting term sheet on a five-point scale, with 5 meaning they got it.

Armed with this data we now can model if any of these traits of a startup influence its ability to get term sheet using statistical analysis. While Jamison did his initial analysis himself, it was not rigorous enough and pointed to incorrect reasons. He later shared his data with me that enabled me to do not one but two ways of analysis of this data to come up with a prediction model.

The results indeed hold surprises compared to his previousanalysis. In the next part I will go into details of regression analysis, its metrics and pitfalls.

Again, see if you can predict which startups got funding, take this quiz.


Please don’t embarrass us by having a business model for your startup – VCs

When I wrote the valuation model for Pinterest, many people wrote to me to point out that Pinterest stopped using SkimLinks and hence stopped making any revenue. Making revenue it turns out is not good for your startup (don’t quote this line out of context).

How do you value an investment? Any investment, be it a farm , shares of an established enterprise or a tech startup?

Simple answer is, you look for profits they generate now and how it is expected to grow.

But that level of transparency and simplicity is a problem for venture capitalists investing in Silicon Valley’s startups. The New York Times Bits blog says why VCs don’t want the startups to show any viable business model let alone profits,

“It serves the interest of the investors who can come up with whatever valuation they want when there are no revenues,” explained Paul Kedrosky, a venture investor and entrepreneur. “Once there is no revenue, there is no science, and it all just becomes finger in the wind valuations.”

With any hint about business model one can come up reasonable valuation models for any business. Granted one has to make assumptions to get there but we can quantify the uncertainties in the assumptions and state our valuation in terms of probabilities which can be used to place bets (I mean investment).

That is not good because

they’re interested in pumping up enough hype and valuation to find a quick exit through an acquisition at an eye-popping premium.

How else can you justify $200 million valuation for Pinterest when its chances of making revenue that justifies such a valuation is less than 0.25 percent?

This seems to explain why VCs advice startups to give their product away for free and why VCs don’t advice startups about customer segments and filling an urgent need. As Stanford’s Pfeffer says,

These companies are simply being founded to be bought.

Not to fill an urgent need and to take their fair share of value created.