June 19, 2012

In a recent article, David Aaker discusses why game changing innovations never see the light of day.  He claims that innovations often are killed off because of “gloomy-picture bias created by decision makers that are motivated to be risk-sensitive.”  In his article, he provides seven reasons for this gloomy-picture bias.  At the end of the article he points to the importance of making “sure that the assumptions and judgments are based on data and analysis that have depth and credibility.”  This certainly speaks to the importance of marketing research in product innovation.

This article got me thinking about how important it is to set up a research project properly so that marketing research can turn innovative ideas into successful ventures. Concept and product development is at the heart of many research studies.  A well designed study can help identify the stars and the duds among a long list of ideas.  It can help refine an idea so it is appealing to a large number of consumers.  It also can help assess the fit of a product with the concept.  But a poorly designed research study can kill off an innovative idea, leaving you with a lost opportunity for a competitive edge.

One of the key mistakes that could be made early on in concept and product research is the sample definition.  The sample definition refers to the characteristics of the respondents selected to participate in the research surveys.

If the sample is defined too broadly, the resulting research could indicate, perhaps falsely, that the idea has limited appeal.  The idea tested may in fact, not test well among a nationally representative sample, but does perform well among a niche group, to which it could be successfully targeted.  If that niche group is never identified and examined, you may never know you had a potentially successful product.  The best way to identify a niche group is to look at the results by key subgroups and identify a group that finds the idea more appealing.  However, I have seen clients take this to extremes, essentially searching for a needle in the haystack.  They break data down in multiple ways trying to find the group their idea appeals to, so the project isn’t killed off.  They often come up with an impracticable group that is either too small or can’t be targeted easily in the marketplace.

On the other hand, the sample definition could be too narrow, effectively eliminating a key group of respondents.  Over the past several years, we have seen an increasing number of projects with low incidence sampling requests.  The Internet has made it easier to target very specific groups, and marketers are eager to utilize this new capability.  However, the benefits of this approach can come at a cost.  Sample constructed too narrowly could exclude groups you hadn’t thought of, but that find your idea appealing.  Again, a missed opportunity.

Clearly, careful thought and consideration must go into defining a sample for a concept or product test in order to strike a balance between broad and narrow.  And that’s where experience makes a big difference.  Experienced researchers can work closely with you to determine the appropriate sample definition and ensure your decisions – particularly around new and innovative ideas – are based on accurate data.

 

 

Want to learn more about data quality?  Read Reality Show: Ensuring the Authenticity of Survey Respondents and Survey Results

Category:Data Quality, Survey Best Practices
Posted on June 19, 2012



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