How Smart Quantitative Research Optimizes Corporate Innovation

How Smart Quantitative Research Optimizes Corporate Innovation

As discussed recently in the blog Forging a Clear Path to Corporate Innovation, involving consumers in the innovation process leads to a richer pipeline of new product ideas. Even in the absence of this approach, companies are constantly generating ideas for new products. With all these ideas coming in—from consumers, employees, management, consultants—on which should management develop into full concepts for testing?

Given the relatively low rate of success for new product launches (less than 3% of new consumer packaged goods exceed first-year sales of $50 million—considered the benchmark of a highly successful launch; HBR, April 2011), and the cost and time in developing and testing concepts, selecting the right ideas for deeper concept testing is critical.

The simplest method for choosing which concepts to test is to pick management’s favorites. Of course, the faults are many but, most importantly, we aren’t typical consumers. But let’s say management does choose their 10 favorite out of 100 possible ideas. Ten concepts are developed and tested, and maybe two of those 10 will meet some threshold for further development. But what of the other 90? Even if you’re really good at predicting what consumers want, you have to imagine that there were maybe two of those other 90 that would have cleared the threshold for further development as well. Had you the time and money to test all 100, perhaps you would have uncovered those other two in the concept testing phase, and your success would have doubled from two to four potential offerings. But developing and testing concepts for each new idea is not feasible.

The solution is a quick, low-cost, customized concept screening system. The idea is to share brief concept descriptions and images with consumers and to use a standard set of metrics to measure and track high potential concepts over time. The primary objective of this quantitative research is gathering and evaluating key metrics to ascertain strength of these rough concepts and developing only those that meet a pre-determined screening threshold.

The system converts key metrics into an individual level Purchase Propensity Score (likelihood to purchase the product—adjusted for overstatement, the top drivers of purchase intent, and category frequency of purchase). Additionally, the analysis can incorporate additional adjustments, based on appropriateness for product category or client circumstance, either through up-front survey design or as final adjustments based on marketing performance statistics such as actual sales data. The score is then indexed to the norm or a control concept.

If you’re interested in learning how Market Strategies can help your product development research, email me.

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