Think "high-tech entrepreneur," and you're likely to envision Bill Gates, Mark Zuckerberg or Steve Jobs. What you're not likely to imagine is a woman. Dow Jones VentureSource reports that of the U.S.-based companies receiving venture capital last year, only 6 percent had a female CEO and 7 percent had a female founder. It's true that fewer women have the engineering and science backgrounds that often spark startup creation -- but the disparity may also be the result of "pattern recognition" among investors, according to Eric Ries, author of The Lean Startup. And the downside of pattern recognition, he says, is "it's another word for bias. It's literally a biased way of thinking that can cause you to miss opportunities that don't match the pattern."
"Venture capitalists will often boast about their ability to spot entrepreneurs," says Ries, who cites a scene in the movie Moneyball in which a group of team leaders evaluated prospects by debating who "looks like a baseball guy" and other superficial aspects. But just as the movie's hero discovered that objective metrics could be used to build a powerhouse team, Ries contends that it's time for startups to up their A-game by embracing a more scientific understanding of what actually works. With that understanding, he suggests, funders will be better able to evaluate entrepreneurs on their own merits, rather than simply falling back on alumni and personal networks, or candidates' previous track record in Silicon Valley -- which tends to replicate the white (or sometimes Asian) male power structure. After all, who "looks like a startup guy?"
But with so much money at stake, why has it taken so long for the startup world to codify best practices? Says Ries, "That system of pattern recognition has worked OK for quite a while, because the magnitude of success is so great, it doesn't have to be that efficient overall as long as you don't mind wasting people's time and money along the way." In other words, venture capitalists -- like baseball players -- don't mind batting .300, as long as the hits they do get are Google or Facebook-quality. But with lower barriers to entry -- you can start a tech company much more cheaply than in the past -- and more companies seeking funding, the pattern is breaking down. The best ideas may not come from the usual suspects. "As the world is getting more and more unstable and disruptive, patterns aren't holding," says Ries. "The things that made you successful in past don't make you successful in future, and it's produced a crisis, a vacuum."
The antidote, he says, is to reject traditional ways of measuring startups' success. Have they been following the plan carefully? Who cares, as long as the possibility exists that the entire premise may be flawed? What if no one even wants what you've been so carefully developing? The frightening reality for many startups, Ries learned during his tenure as a Chief Technology Officer, is that many companies have no idea whether they really are successful -- or even what success might look like. Inspired by Lean Manufacturing, which transformed the auto industry, he created the "Lean Startup" methodology, which he describes as "management discipline for situations of high uncertainty." When a company is new and lacks a track record of customer demand, "beating the plan isn't cause for celebration," he says. "In fact, successfully executing it often leads to failure because you're fulfilling a bad plan. You have to learn to tell the difference between value and waste, but unlike in Lean Manufacturing, it's not so simple: the creation of high quality stuff is no longer a virtue. The value in a startup situation is validated learning, learning if we're building a sustainable business. You can use Lean Manufacturing strategies but the goal is efficiently figuring out what stuff to make."
In other words, you're subjecting startups to the scientific method -- crafting hypotheses and testing them, rather than clinging to "vanity metrics" like page views, as many wishful entrepreneurs often do. "There are a lot of people for whom 'scientific' means rote, formulaic, and uncreative," says Ries. "But you don't get to be Einstein by sitting in your office imagining stuff. You have to make predictions about experiments that then come true -- and if you make mistakes, you have to be willing to correct them. The definition of a great scientist is vision and a willingness to put that to the test, and the same is true for entrepreneurship."
Being certain about what really works means you're selecting the best people -- without the potential bias of pattern recognition. And that means a better return on investment. The Lean Startup metholodogy, says Ries, "makes entrepreneurship accessible to everyone. The whole point of science vs. astrology or alchemy is to strip away bits of folk wisdom that sound good but aren't true."
Dorie Clark is a marketing strategist who teaches at Duke University's Fuqua School of Business. She is the author of Reinventing You and Stand Out, and you can receive her free Stand Out Self-Assessment Workbook.