With a nod to the Clayton Christensen book of the same name, I wanted to revisit the concept of the innovator’s dilemma from the perspective of marketing research. I do this on the heels of attending the recent Edison Awards (a combination awards ceremony / meet the innovators forum and showcase).
During the two-day session, I heard speakers and panelists talk time and again about the reasons why they don’t use research (though virtually all of them did, as they went on to talk further). 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.
So, to reframe the innovator’s dilemma, I would state it as “Why has the role of research lost so much share of mind in the innovation space, and what can it do to reclaim its rightful place?” Or, put another way, how do we help smart people become, well, smarter?
I think we need to acknowledge that market researchers have partly marginalized ourselves.
We’re still sitting around wondering whether this internet thing is for real or not, whereas 9 out of 10 marketers use social media to promote their business.
We worry that mobile is too new to use for research, when in fact about 6 billion people have smartphones whereas only 5.8 billion have clean drinking water.
I’m actually convinced the majority of us still believe the earth is flat, and that babies are delivered by storks.
The Edison Awards, and listening to a whole bunch of great innovators (including Ipsos, which won a bronze for our Innovation Archetypes), suggests to me we have to change the way we act and how we analyze.
Changing the Way We Act
If I think about changing the way we act to be more relevant to innovators, there are a number of guidelines we can follow to make an immediate impact. These include recognizing:
- You must use the available data first: Not every business question requires new primary data, many can be answered with secondary data or data that are already in-house.
- It’s more important to be right than precise: Often, our quest for perfection causes us to miss the bigger answer.
- The decision rule is preponderance of the evidence, not beyond a reasonable doubt: A corollary to the last guideline, we have to increase our comfort with making judgments.
- Timeliness, not cleanliness, is next to godliness: The right answer delivered 3 months too late is the same as a wrong answer.
Changing the Way We Analyze
These guidelines can be directly translated into a new continuous approach to data analysis, rather than the ad hoc approach we’ve taken previously. They allow us to move to what I’m going to call Systems Thinking – how all the individual parts work together within the context of the broader business objective. In this world, data no longer exist on their own; they are all connected to the core business problem – and to each other.
The future of innovation research looks very different when engaging in Systems Thinking:
- You have to start with a strategy: Innovation requires a purpose, there are too many pieces of data to wander aimlessly.
- Your base case derives from your strategy: A base case is an idea, concept, prototype or other representation of an innovation that serves as your first baseline for evaluation.
- You have to assess your base case: This research is designed to tell you (a) where you are, and (b) what to do next.
- You need to look for analogs: Analogs are similar types of introductions that can serve as a guide for what you are doing. They also open up new data streams for analysis.
- Finally, you have to adjust the base case…continuously: Whenever you have a question, you use data (could be already available, could be a quick primary read) to update your current assumption and (hopefully) improve your proposition.
Systems Thinking forces research practitioners to implement those things we know we ought to be doing, but have been too conservative to pursue. It makes us relevant. And it enables us to help smart people – the innovators – become smarter. And that is how you solve the innovator’s dilemma.