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blackswanThe Black Swan is a book that was published a few years ago and generated much publicity and at least some controversy. It occurred to me that there are lessons market researchers can learn from that book, particularly about the relationship between qualitative and quantitative data obtained from a survey format. The idea is that the framework used to analyze such data is different from that used for directly obtained qualitative data through methods such as IDIs and focus groups. Understanding the difference between quantitative and qualitative frameworks for data analysis (and in particular, the difference between statistical and managerial outliers) can help derive more value when the qualitative data are collected in a regular survey. But first, let's take a detour.

A Brief Tour of The Black Swan

In his informative (and entertaining) book, Nassim Nicholas Taleb argues that real data are either distributed normally (from "mediocristan") or not (from "extremistan"). The former are characterized by data that follow the traditional normal distribution (or bell curve). The majority of the distribution is near the middle surrounding the average and as we venture further out the number of observations becomes increasingly scarce. It is a distribution that defines many phenomena in the natural world. In fact, basic statistics shows that with a reasonable number of observations most distributions start approximating the normal.

Give Thanks for the Unknown

Posted by on in Market Research

1620 mayflower rockThis month here in the States we will be celebrating our biggest secular holiday, Thanksgiving.   Traditionally, the holiday is thought to have started when early settlers to the "new" world, the Pilgrims, sat down to have a meal to celebrate the harvest with the Native American's who had befriended them. As we begin to close out 2011 in an industry facing an uncertain future, I was struck by the similarities between those early settlers and market researchers today.

On the surface the story of adventurers seeking a better life is a bit different than the story of boring market researchers seeking to survive, but I disagree.

thanksgiving turkey dinnerLater this month, those of us in the United States celebrate one of my favorite holidays, Thanksgiving. Officially, Thanksgiving is a post-harvest celebration that was brought to the Americas by European settlers in the 16th or 17th century (depending on which historian you believe). Unofficially, it's the day where families and friends gather to feast, take naps and watch football. Oh my, even as I type this my mouth is watering...turkey, potatoes, stuffing, cranberry sauce, peas and the like, with chasers of pumpkin, apple and other assorted pies. All delicious, but I particularly love eating turkey on Thanksgiving.

As I sat down to write I realized that this is not a simple question. Consider the conventional meaning of necessities (defined as must-haves) and luxuries (defined as nice-to-haves). Which category market research falls into may depend on the eye of the beholder.

Researchers (or more accurately research sellers) may want to think of themselves as producing necessities rather than luxuries. But in the consumer world necessities are also generally commodities and often sold based on price. Researchers of course want to be seen as producing something valuable, something that is worth a premium -- in other words, a luxury.  So, which is it?

Now let's look at it from a research buyer's perspective. The buyer may think of research as a necessity, something that is indispensible for making good business decisions. But in keeping with the popular perception of necessities, perhaps they feel that more than one company can provide it and are hence unwilling to pay much of a premium for it. This view would support the many research sellers who complain about the commoditization of research.

talking_webPrior to my current tour at TRC I was a partner at a small data mining boutique that had a simple objective: support the sales and marketing goals of our clients by helping them stem attrition. While the goal may have been simple, how we set out to do this was not. See, we took upwards of 30 months of time-series data on all of their customers and applied some mind-numbing statistical techniques that identified patterns in the data that preceded an eventual behavior. In most cases that behavior was a customer terminating their relationship with our client. Once our system identified the patterns that preceded attrition, we would then continue to feed it customer data each month and it would dutifully output a list of customers that were likely candidates to attrite. In addition, for each customer on this golden list we provided the prediction "trigger", or the thing that they did that was responsible for the system flagging them as a high-risk customer.

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