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 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, Salesforce.com, 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 as a whole. Apple was a new entrant in mobile phones. The iPhone provided better Internet performance and a better interface at a higher cost—not poorer and cheaper—yet it was very successful from the start. “When it’s wrong, it’s interesting,” Thurston says. “We hope to improve the theory.”
What about Amazon.com, which offered a much larger selection of books than the incumbents when it first appeared in the mid-1990s? “I’d argue it was a new entrant with a disruptive strategy,” Thurston says. “It sure took a long time and a lot of money. It was lower cost for the most part, and lower performance than a bookstore. It was more inconvenient—you had to wait for a book to show up, so it wasn’t instant gratification.” Then, he says, as Internet service got better and more consumers went online, Amazon started to take market share from bigger players like Barnes & Noble and Borders. Now, of course, it is a huge player in retail, cloud computing, and many other sectors besides books.
In biotech, a current example of a disruptive young company is Complete Genomics, Thurston says. This Bay Area company (backed in the Northwest by OVP Venture Partners) uses advanced computing technology to sequence human genomes very cheaply. For $5,000, it can’t do all the tests that a fully staffed laboratory can, but it is a lot cheaper. So it “really allowed a huge market of researchers to begin to incorporate genomics, when before it was too complicated and expensive,” Thurston says. “We’d predict that it would survive.”
I also asked Thurston for his take on what the biggest disruptive threats are to some of the current tech giants. For Intel, he says, the No. 1 threat is the ARM processors found in smartphones, netbooks, and other devices like the Apple iPad. “ARM processors are cheaper and worse [than Intel chips], but they’re getting faster with Moore’s Law. It’s a monster. It’s a small chip but a huge market,” he says. “Intel can’t kill it anymore. Only in the last few years has Intel realized what a threat it is. But now it’s too big to be swatted.”
As for Microsoft, he says, the biggest threat lies in mobile software. “If I was Microsoft, I would be terrified of mobile apps on Android and Apple,” he says. “Now there’s more mobile apps than desktop software. There’s a huge ecosystem on different operating systems. [The software is] cheaper, worse, but getting better all the time, and vast in number. Disruption would say it’s not Oracle that should keep Microsoft up at night—it’s the little apps.”
It’s all good fodder for entrepreneurs and investors to chew on. Still, Thurston is quick to point out the limits of his model’s predictions, especially for startups. “It’s not a verdict. It’s an observation, a diagnosis,” he says. “It’s the difference between sailing with the wind at your back, or in your face.”