At the 2004TED conference, Malcolm Gladwell tells the story of Dr. Howard Moskowitz, a man who revolutionized the prepared food industry through a new kind of analytical thinking. Long story short: Dr. Moskowitz was one of the first people to argue that companies should pursue multiple products targeted at customer subsegments rather than try to create the perfect product for all customers. He realized that an attempt to create a "platonic ideal" —whether it was pickles, mustard, or pasta sauce—would be a suboptimal result for most consumers. Consumers are individuals with preferences that are better clustered than averaged. Mr. Gladwell states that this change in business thinking (spurred by Moskowitz’s study of pasta sauce) mirrors a more general scientific shift from a focus on universal truths to the study of variation.
The prepared food industry gets it—as evidenced by nine variations of Ragu sauce on the grocery shelves—but I’m not convinced that these lessons have permeated the rest of the business analytics landscape. In particular, I am struck by the inability of most survey analyses to reveal insights about respondents.
The tools may be part of the problem. Here’s an example of what WebSurveyor provides its users to help them analyze online surveys:
Their site tells us:
"Each question is graphed independently allowing you greater flexibility in customizing the layout of reporting for each question...Filter results based on specific responses or cross-tabulate results from two different questions, giving you powerful tools for detailed analysis."
Powerful? Flexible? More like barebones. WebSurveyor is putting the analyst in a very constrained box that won’t help deliver an better understanding of respondents. WebSurveyor’s tool demands "question-centric" not "customer-centric" analysis.
Consider how this typical survey approach would serve you in an effort to understand the passengers of Noah’s Ark. A surveyor would ask each animal to fill out basic information about their height, weight, number of legs, food preference, etc. The results would then let us know that the average animal weights 23 pounds, has a height of 1.2 feet, 5.6 legs, 30% omnivore and so on. All of which would miss the essential insight about the animals on board: there are two of each.
Unfortunately, the kind of analysis needed to reveal personality / needs / behavior clusters in your respondent population isn’t well supported by out-of-the-box analytical tools. One approach is factor analysis—a statistical technique that is used in marketing to "identify the salient attributes consumers use to evaluate products in a category" (Wikipedia). Another approach is to examine individual visual representations of individual respondents—a technique that we term (rather clumsily): customer flashcards.