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

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having better performance than others in its field. Disruptive means it is cheaper and worse in performance, or that it creates an entirely new market. (This is different from the common notion of “disruptive” as meaning any innovation that is game-changing or radically better; Christensen and Thurston often mean the opposite of that, at least in the short term.) The second factor to consider is whether the company is an “incumbent” or a “new entrant.” Intel or Microsoft would usually be the former, while a startup would be the latter.

If you’re an incumbent, a sustaining strategy is usually successful, Thurston says. But if you’re a startup, he says, you are 30 to 40 percent more likely to survive if you have a disruptive strategy than if you shoot for higher performance. “This is where VCs and entrepreneurs make the biggest mistake,” he says. “If you’re sustaining and a new entrant, that’s probably the worst strategy—you are almost guaranteed to fail.”

And in fact, that is precisely why most startups fail, he says. “Their pitches are always ‘cheaper and better.’ But that’s only half right. Cheaper is good, but better is actually a con because it will invoke a competitive response.”

Why? “When the big guys see startups that are better than them, they’re very, very threatened,” Thurston says. “If they do nothing, they lose. They have to act aggressively, and they’re usually pretty good at that. They’re probably going to win that fight.”

But if a startup hangs around and doesn’t threaten the big players right away, but instead gradually gains market share and keeps improving, then it has a good shot. Some classic examples: Toyota in the 1950s and ‘60s, EMC and NetApp in data storage in the late 1990s, Netflix,, and some broader technologies like cell phones vs. land lines.

OK, so some of this is common sense. But if it’s so successful, why haven’t more people—entrepreneurs and investors in particular—adopted disruption theory? Probably because models for predicting how companies will do are a dime a dozen, so Christensen gets lost in the noise; and Thurston’s studies are not widely known yet, though parts have been peer-reviewed and published. (An upcoming book by Michael Raynor will cover some of this research.)

And second, Thurston says, the actual prediction process involves a fair bit of number crunching. “For four years we’ve been refining [the model] with lots of data,” he says. “It’s a lot more technical than in Clay’s books.” In other words, not everyone can apply the model correctly. But the real proof will come from the predictions he makes about new companies whose fates are unknown.

Then again, the model is dead wrong 15 percent of the time. Lest you think Thurston won’t admit to failures, he points out several instances where his own predictions are wrong. Take the Apple iPhone, he says—if you apply the model to this specific product, instead of the company … 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] Follow @gthuang

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