It is a well-known fact that 90% of startups fail. We all hear lack of funding as a key reason for startup failure. But have you ever wondered if there was research done on reasons for startup failure? What are the structural reasons that result in startup failure? What can startups do to avoid the path of death? These questions led me to interview Thomas R. Eisenmann who is the Howard H. Stevenson Professor of Business Administration at the Harvard Business School, Peter O. Crisp Chair, Harvard Innovation Labs, and Faculty Co-Chair of the HBS Rock Center for Entrepreneurship.
Being a young entrepreneur, my goal in this research is to help fellow entrepreneurs avoid known pitfalls. That is why, I was intrigued by Professor Tom Eisenmann’s book titled, “Why Startups Fail: A New Roadmap for Entrepreneurial Success.”
The article below is based on my interview with Professor Tom Eisenmann.
After learning about the six reasons why startups fail that are outlined in Professor Eisenmann’s book, I was curious to get his opinion on the top reason within these six. Professor Eisenmann was quick to name the top reason as diving in too fast without studying the market for the product. He elaborated that a lot of the startups jump too quickly to build a product without doing market analysis. This wastes capital and human resources for at least four months. As Professor Eisenmann explained this, my mind raced to examples of several startups I have seen in Silicon Valley that end up changing their direction. While I admire startups being agile and shifting their focus area either adjacently or orthogonally, I agree with Professor Eisenmann that more diligence on the upfront market study could alleviate a lot of pain that startups and their investors go through when they do not find a product-market fit eventually. The Professor termed it the false start and found it analogous to how an athlete jumps too soon to get an edge over competitors but ends up losing the race.
During the discussion, we did not stop there on the topic of a false start. Professor Eisenmann delved into the human psychology behind the false start. He mentioned that given the natural impulse of entrepreneurs, speed is their currency. As entrepreneurs want to get going on building the company and engineers love designing products, it is natural for startups to conduct minimal customer interviews and get going on progressing with their startups. But this still does not justify the lack of research that startups need to do before defining what the product should look like.
Has Professor Eisenmann implemented his methods to prevent startup failure? Professor gave a humble response by saying that he hasn’t provided a diving catch to an entrepreneur whose company was on the brink of failure. But in general, Professor Eisenmann has received positive feedback about the book from entrepreneurs who have thanked him and made alterations to their business plans.
Having grown up in Silicon Valley, I have always heard of successful entrepreneurs as demigods. Hence, I was puzzled by Professor‘s book, where he calls out bold vision as a reason for startup failure. So I asked him about this contradiction. Professor explained that entrepreneurship is about storytelling, so having a big vision is important for entrepreneurs to be able to convince strategic partners, investors, and potential employees to believe in their vision. But the issue springs up when the vision is so strongly felt by the entrepreneur that he or she does not see the signals from the universe that the vision is flawed and needs to be conceived again. This is a natural and ego-defensive response by any human being to ignore such signals. We teach entrepreneurs to stay on the course and believe in their point of view and the approach for building a solution. So, being able to filter out the signals that suggest a change becomes very difficult for an entrepreneur who is naturally taught to be stubborn in his approach. Being persistent is very important for an entrepreneur, so he feels that he should try one more feature to convince the customer, and pitch to more investors to look for funding despite all the rejections. But the issue becomes when the entrepreneur has gone too far. This is where big vision becomes a cause of failure for the startup.
Professor Eisenmann explained how hyper-speed could spell disaster for startups. Entrepreneurs love growth. Investors love growth. Infact, Y-Combinator says growth is core to a startup. So, it is possible to grow too fast in a direction that can be chronically unprofitable. Unless the next wave of customers has the same needs as the initial ones, it gets tougher to acquire them. You may have to market harder, reduce the price, and do more to stay in the growth trajectory. You’ve got to hire, maintain control, and scale. This is all done in a startup that has none of that, to begin with. If you go too fast, you get into the stream where customers are less likely to stick around or spread the word about the product. The operation foundations start to wobble. The company culture can take a serious downturn. The first set of employees may believe in the long-term vision, but the next hires may not – “new guard” vs. “old guard” conflict. The startup can turn south very quickly, especially if you’ve burnt the initial cash. Before you know it, your startup has vanished.
Having discussed with Professor Eisenmann on causes of startup failure, I was curious if we can create a mathematical formula that can predict startup success using attributes of entrepreneurs and investors. Using econometrics and statistical models, it looks pretty doable to predict a startup’s success by pouring data and measuring factors such as the founders’ background, target market, competitors, and so forth. Professor Eisenmann has tried that. In the appendix to his book, he explains his survey of 470 early-stage founders of mostly software-based businesses who launched startups between 2015 and 2018 and raised at least half a million dollars but no more than 3 million. He asked these founders 50 questions about who they were and how they were managing the first year of their business. By following the businesses till 2020, Professor had a few years to see how the venture was evolving and if the next round was a down round or an up round. There are some variables that are predictive of statistically-based success. But his results showed that the things people assume are highly predictive of startup success are not necessarily so, for example, where the founders went to school, their MBA degree, gender, age, or experience as a serial founder. None of these factors really moved the needle. Each factor seemed significant when viewed alone. But when you put all the founder attributes into a multivariate model, no factor proved to be extremely game-changing. The only predictor that was statistically significant was the number of late-stage rivals faced by the startup. You don’t want to launch a ride-sharing service if Uber and Lyft are out there. Overall, at best, you can explain only some of the variance in startup outcome by throwing all the variables in. The R-squared (reflective of variance) is quite low.
Can Artificial Intelligence (AI) predict success rate? Professor Eisenmann doesn’t believe so and explained that the problem of training the AI models is with the use of historical startup data. Historically, an awful lot of startup data is collected towards a certain type of founders due to gender bias or bias towards the underrepresented minorities. AI software may recommend finding a 25-year old South Asian, white, or East Asian Computer Science major as the founder. But that goes against other studies that have shown that older founders perform better as entrepreneurs due to richer networks and experience. It takes tremendous sacrifice to be an entrepreneur. When you’re a 20-year old and unmarried, you can probably afford to take more risks. But as a 45-year old with family responsibility, you would take the plunge only if the startup idea has legs. So, probably, the success of older entrepreneurs is based on appropriate risk aversion.
Do you think ethics have a role in startup failure? Professor Eisenmann said “no doubt about it.” The slippery slope is always there as an entrepreneur. Entrepreneurs at every step of the way have to make ethical decisions of the representations they make. Usually, they’re constantly selling basically nothing, just a vision. So, with ongoing pressure, it is tempting to misrepresent the progress they are making and punch above their weight. The pressure is only that much greater when you’re running out of money. Hence, lying becomes an occupational hazard. “Fake it till you make it” is often talked about in Silicon Valley. But there are ethical ways to fake it. Folks who coach entrepreneurship are way more sensitive to ethics today than they were before. People at colleges are teaching entrepreneurial ethics.
In sum, I enjoyed learning about startup failure during the discussion with Professor Tom Eisenmann. While discovering a structural framework and a deeper thought process, I felt that his book does an excellent job of laying down the fundamental reasons behind startup failure. I hope this interview and the book are helpful to founders who are entering the fascinating world of entrepreneurship. Go big but think first!
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