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How to measure disruptive innovation and why traditional surveys alone don’t work

It’s a funny thing about disruption – it tends to shake things up. So how do you accurately measure disruptive innovation?

Even “helpful” disruptions, like the new operating system on your phone or the improved, easy-to-use packaging for your favorite box of cookies, can feel off-putting at first. As Rob Hernandez pointed out here recently, people like what they know; it is, after all, the essence of brand equity and product trust.

Disruptive innovation thrives on out-of-the-box insights; traditional approaches alone do not work

This “disrupt-aversion” creates problems not just for startups and new product developers. It challenges anyone trying to get a read on what people think about innovation, and especially disruptive innovation. To get a true understanding of the potential of disruptive innovations, the traditional research assumptions and methods may not be enough. In fact, they may send you down the wrong path completely.

The most obvious example is the traditional survey. We know that asking people questions can be a great way to get at the feelings and beliefs that drive behavior; they can provide an indispensable why complement to passive data, which tends to focus on dimensions such as what, where, and when.

But disruptive innovation (ie new product or service concepts which force us to rethink or relearn familiar behaviors) often do not fare well in standard surveys. They are so responsive to people’s inner experiences that they may capture overreactions – feelings of discomfort and confusion surrounding something unfamiliar – rather than a balanced picture of how consumers will react over the long term.

A three-dimensional approach to defining consumer reactions to disruptive innovation

To get a better understanding of an innovation’s promise and marketplace value, we have developed a three-dimensional approach to defining consumer reactions. The fact is that marketers today have a wealth of data at their disposal; but combining it to create a sharper, trustworthy lens onto people’s feelings requires a mix of science, experience, and finesse.

What consumers say

We look first to what consumers say, in quantitative or qualitative research – whether it be how they feel inside, what they wish for, or how they think they believe they would react in certain circumstances. This candid perspective has been the backbone of the market research industry, and remains an essential component of behavioral analysis.

What consumers mean

Next, we look to what consumers mean. Using voice analytics, for example, we can learn more about how convinced people are of what they are saying. And by leveraging different analytical techniques, such as multivariate key drivers analysis, we can get a deeper look at the motivations and contexts behind certain behaviors.

What consumers do

Finally, through passive tracking and secondary data sources, we can find out what consumers really do. From loyalty card and POS (point of sale) data to web browsing and online purchase records, we can get a real-world perspective that rounds out the picture we have about people’s ideas and beliefs, and ultimately how they behave or act.

Conclusion

Integrating these and other data sources helps marketers and researchers get beyond the limitations of any one dimension of consumer data or insights. In the process, it can take us beyond the “gut reactions” that people often have to something new, and reveal whether a disruptive product truly has marketplace appeal, or is destined for the long list of seemingly great ideas that never made a dent in people’s hearts, minds, or wallets.

Contact us for further details on how our approach works within your specific business areas and needs – or download our white paper on rethinking concept testing.

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1 Comment

The proposed approach (measuring what consumers say, mean and do) is an ideal approach. Measuring the do (the “as marketed test”) is also a traditional research I’ve used since 30 years. So nothing new. The problem is one cannot always do this (2, 3, 4 or more concepts) because of cost and time constraints. Therefor the say and mean can be used for screening purposes when taking into account the distinction between innovators, early adopters, early majority, late majority and laggards consumer profiles in the sample.