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

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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, 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.”

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

  1. Mr. Gunn says:

    The theory isn’t wrong about the iphone. The iphone was cheaper and inferior to the technology available at the time (v1 couldn’t send SMS, had poorer quality camera, etc), but while Nokia sat on their hands and didn’t aggressively pursue the US market, Apple did and they had no qualms about advertising “now, for the first time, Internet on your Phone!!” as if that were something new.

    So the theory works, especially if you add skilled marketing (arguably Apple’s core competency).

  2. Mike R. says:

    My understanding is that disruption is a theory of competitive response. In that sense, the iPhone definitely posed a serious threat to the big handset manufacturers. As an employee of one of them at the time, I can tell you there was a 5 alarm fire. In that way it was more “sustaining” than disruptive, as Christensen would put it.

  3. Samantha Irons says:

    Timely article. Thurston was just at my firm last Thursday sharing some of his work. Our pathfinding team is using it to look at some of our project portfolios to figure out what to keep funding, what to wind down, or what to possibly spin out.

    What strikes me is that it forced us to use more objective bases for sorting through opportunities, rather than the emotional and intuitive horse trading that usually goes on. Without this data-driven approach we were relegated to endless circular debates about opinions that could never be tested. The biggest voices won the day.

    What I’d like to know is why more startup and angel investors don’t use more similarly data-driven approaches. I know some do, but they seem to be a minority.

  4. Sajol G says:

    Very interesting research. It appears that the “sustaining” model works best in an established market, when market penetration based on lower cost is advantageous. However the product must at least meet the current performance levels in the areas of customer value add (not necessarily in all areas).

    It would be interesting to understand how customers model/value performance vs. price?

  5. David Locke says:

    An existing market cannot be found for an innovation that may potentially be disruptive, but certainly discontinuous. If a market does exist, the innovation will be continuous or sustaining. This is research question #1.

    An innovation that does not have an existing market is potentially disruptive, because it has one more hurdle to clear, the slope of its price-performance curves, or S-curves. S-curves have been around long before Christensen mentioned them. Moore’s Law is the slope of an S-curve at a point in time.

    Disruption occurs at a point in time. That point in time is relative to the S-curve of the incumbent’s technology. You are not disruptive from day one. And, you can potentially become disruptive only to see that slip away when the incumbent raises the bar price-performance wise. Disruption is not a static attribute.

    Any discontinuous innovation must go through Moore’s technology adoption lifecycle, and cannot be held to the 18 month to success window of continuous or sustaining innovation. Not working on adoption and going straight to mass markets is what kills most discontinuous innovations. The failure to gain marketshare will also kill any potential disruptive technology.

    Forget disruption. Pay attention to adoption, and to framing the underlying innovation. If it disrupts great, if not you can still create a category, value chain, and the wealth involved in that.

    Most of what we do these days in the software industry is so far away from disruption that disruption has become a buzzword.

  6. An iPhone can be considered a simpler version of a computer, and Apple leveraged it’s existing competencies in technology and marketing to make it a success. So even though Apple were entering a new market, it wasn’t a complete departure from what they’d done in the past.

    I think the Disruptive Innovation model makes sense for some start ups – look at 37signals as an example: their products have fewer features, but are hugely successful. But for some start ups it isn’t applicable. Think about a biotechnology company – some of the most successful strategies have involved developing Orphan Drugs. This is a Blue Ocean strategy, which avoids direct competition altogether.

  7. Mike R. says:

    I think some of us may be missing the point here. Apparently this Thurston is more than 80% ACCURATE at using disruption, however he does it, to predict if businesses will succeed or fail! If it’s true, that’s HUGE!