Here we go again.
Just a month has passed since I wrote the following about my experiences at the Edison Awards, in a blog post uncreatively titled The Innovator’s Dilemma: “There was a great preponderance of what I call “brilliant individual syndrome,” wherein people stood up, said how smart they were, how they had vision to make things happen…and ignored the six failed innovation attempts that came prior to this success, when they managed to light piles of their own (and others’) money on fire and watch it go up in smoke.” What followed was a spirited defense (at least I’d like to think it was) of research as an invaluable tool for all types of innovation.
Since that time, following the publication of The Disruption Machine in The New Yorker, a cautionary tale of what happens when a reader interprets everything an author says in an absolutely literal way (and note to self: stop using the phrase “lighting piles of money on fire”), research has again found itself in innovators’ crosshairs:
- From the article just cited: “Disruptive innovation can reliably be seen only after the fact.”
- From Josh Linkner, author of “The Road to Reinvention” (and as quoted in the same article): “Predicting the future based on the past is like betting on a football team simply because it won the Super Bowl a decade ago…Let go of the past.”
- From Five myths about disruption by the authors of “Big Bang Disruption” and published in The Washington Post: “For every major transformation, we found seers who had predicted it all…”
As disruptive innovation finds itself in the midst of a good old-fashioned donnybrook, I’m here to again rush to the defense of research. Let’s take the above points one by one, using the scale from the PolitiFact truth-o-meter.
1. Disruptive innovation can reliably be seen only after the fact.
- Truth-o-meter: Mostly false
- Rationale: Through work Ipsos has done, we have identified a profile of breakthrough innovations (via what we call Innovation Archetypes) that suggests those new products most likely to be disruptive are initially viewed by consumers as having strong differentiation and meeting consumer needs, but lacking in believability. Having that profile does not guarantee a successful product (the product still has to deliver on its promise, and typically a different type of marketing support is required), but it does clearly suggest that disruptive innovations can be seen relatively early in the development process.
2. Predicting the future based on the past is like betting on a football team simply because it won the Super Bowl a decade ago.
- Truth-o-meter: Half true
- Rationale: There have been 48 Super Bowls. Forty-one (85%) of those have been won by just 12 teams (out of 32 teams currently in the league). While realizing this isn’t an entirely fair analysis, as all 32 teams have not been in the league since its formation (and I’m way too lazy to count number of seasons each team has been around to get a fair probability), the general finding holds – you would actually be better off using who won the Super Bowl previously as a predictor of success than randomly picking a team, though you could improve that probability by also looking at other variables. That’s part of why Ipsos believes that adoptions curves and information diffusion based on the past are good predictors of the future, and vital to understanding disruptive innovation.
3. Let go of the past.
- Truth-o-meter: Pants on fire
- Rationale: Understanding the past and the present allows us to better predict the future. Also, I really wanted to classify one of the assertions as “pants on fire.” Whether forecasting new product introductions or predicting the weather, the past plays a critical role. In Ipsos’ case, we reflect the past by integrating competitive context – how consumers fulfill a need – into the assessment of the future to understand whether an innovation will bring about the change (and sales) we hope.
4. For every major transformation, we found seers who had predicted it all…
- Truth-o-meter: True
- Rationale: No transformation can come to market unless someone thinks of it first. See, I too can be literal. More seriously, what this comment ignores is that for every transformation someone thinks up that works, there were thousands conceived that were either not successful or never occurred. So it’s a literally true statement, though with no practical merit. Research is a tool for increasing your odds of success. If we knew who the seers were, we’d all be friending them right now on Facebook. Searching for seers is like pulling fans out of the stands at halftime of a basketball game, having them shoot a half-court shot, then celebrating the one out of a thousand who hits it as being the best shooter. Rather than finding seers, Ipsos recommends maintaining a strong pipeline of new product ideas and pushing them past the point of consumer believability before delivering on your (initially unbelievable) promise.
If disruptive innovation is about finding brilliant seers, or letting go of the past, or only knowable through the rear view mirror, we are all playing the lottery. If innovation follows predictable patterns, with known probabilities, we are playing blackjack. And I think that’s a good representation of the role of research in developing disruptive innovation – it moves you from playing a game of chance to playing a game with predictable odds, where the decisions you make can increase your probability of winning.