How to Predict Whether a Startup Will Succeed or Fail: Testing the “Disruptive Innovation” Model

Thomas Thurston is a startup predictor. Tell him about your company, and he’ll tell you whether it will survive or fail.

No, he’s not an investor, or a psychic. By day, Thurston is a mild-mannered researcher and consultant whose training is in law and business. He’s the founder of Portland, OR-based Growth Science International, a research firm that works with entrepreneurs, investors, and corporations on their business strategy. By night, though, he’s testing every possible angle of a theory that could change the way a lot of people think about startup strategy.

Here’s the upshot of Thurston’s recent research, and why it’s important. Pretty much every startup you’ll ever meet will say it is better than its competitors. However you want to measure it—speed, technology, revenue model, whatever—a young company will say it outperforms others in its class. What’s more, it’s smaller and nimbler than the big companies, so it will be able to innovate faster and stay ahead of the curve.

Just one problem: That’s exactly why it will fail.

What a startup should do instead—to give itself the best chance of surviving—is enter the market at the low end of performance, Thurston says. That is, offer a product that’s not necessarily as good as its competitors, but is cheaper and more accessible. “Lower cost, lower performance, and gets better over time,” is how Thurston puts it.

If this sounds familiar, you’ve probably read Clayton Christensen’s books on business innovation. Christensen, a Harvard Business School professor, is the author of The Innovator’s Dilemma, The Innovator’s Prescription, and Disrupting Class, and he is coming to Seattle on May 17 to give the keynote at the Technology Alliance’s annual State of Technology Luncheon. The connection to Thurston is that he and Christensen have collaborated on testing predictions about startups and other companies.

In 2005, Thurston was working at Intel Capital when he got interested in whether a mathematical model could predict startup success or failure better than chance. He plowed through obscure academic papers and popular books, tried different things, and settled on building a sophisticated model based on Christensen’s principles of “disruptive innovation” (more on this definition shortly). Thurston got a hold of 48 business plans from within Intel—new businesses that had corporate funding—and checked how they did (survive or fail) against what Christensen’s model would predict. To his surprise, the model made accurate predictions more than 85 percent of the time, and the results were highly statistically significant.

Thurston decided to take a year off from his job in 2007 to continue the research with Christensen in Boston, co-sponsored by Intel and Harvard. They expanded their analysis to include all new businesses Intel has supported (roughly 100), as well as hundreds of outside companies across different industries and geographies. The result was the same: 85 percent accuracy.

Skeptics would say the model was tested by its own proponents, so it’s not surprising they would find it accurate. But Thurston maintains he is an independent researcher; he would happily switch to another model if it worked better, he says. He has since returned to Portland and continued the work at Growth Science, where doing the modeling is part of his consulting gig. He says he’s been getting lots of interest from companies and venture capitalists seeking advice.

So here’s how the predictions work, in a nutshell. First, a company is classified according to whether its market strategy is “sustaining” or “disruptive.” Sustaining means it is positioned as … Next Page »

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Gregory T. Huang is Xconomy's Deputy Editor, National IT Editor, and Editor of Xconomy Boston. E-mail him at gthuang [at] xconomy.com. Follow @gthuang

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