Around the early ’90s, a new job title began to appear in many of the companies I consulted with–“Manager of Customer Insights” (and variations on this theme).  In many cases, this involved “rebadging” of many managers of market research.  The presumed goal of this renaming exercise was worthy–shifting the focus of market research from process to content–and from data collection and analysis to knowledge.  

These days, purchasers of market research services are likely to say that the one thing they most want from their research investment is insight.  Ask these buyers what they mean by insight, however, and they may be unable to answer.  A couple of years ago the CEO of a major market research company conducted a series of “client advisory” forums with the directors of market research (or “customer insights”) from several of the firm’s key clients.  Over the course of these sessions, the clients stressed again and again that they wanted their research partners to provide “insights.”  Finally, the research firm CEO asked them to define insight.  Of the dozen or so clients participating in the forums, only two could offer any type of definition.  One of these was a dictionary definition (see two representative dictionary definitions of “insight” at the end of this post).  

In my own experience, clients who complain that research offered no insight often say “I didn’t learn anything new” or “this doesn’t tell me what action I should take.”  This gives us a clue to the nature of insight and, perhaps, a method for achieving insight.  At one level, insight is seeing something that we have not seen before–a pattern or a connection between things.  As one example of pattern detection, check out a brief article in the New York Times (“Fast Arriving Fads Quick to Flame Out,” May 17, 2009) about a study conducted by Jonah Berger of the Wharton School and Gael Le Mens of Stanford University that looked at the prevalence of first names, as recorded by the U.S. Census.  Looking at data going back to 1880, they found that the faster a name becomes popular (based on number per one million children), the faster it declines to “pre-fad” levels.  Names with distinctive spikes (fast rise and decline) included “Dewey” (c. 1900), “Debra” (c. 1960), and “Amy” and “Jeremy” (1970’s-1980’s).  Some names (Patrick and Katherine, for example) are relatively stable over time.  And, if you’ve recently named a child “Ava” or “Aiden,” you’re part of an uptick in popularity for these names that might not last long.  I want to make two points about this study.  First, the “pattern” is only apparent when looking across a large number of names over a long time period (the  rise and fall might take a couple of decades or more) and second, visual examination of the data makes it easy to see the pattern (there’s a nifty graphic accompanying the Times article). 

In fact, proper visual display of quantitative information is sometimes crucial to drawing insight from data.  Edward Tufte makes this point powerfully in his description of the “failed” data analysis that preceded the disastrous launch of the space shuttle Challenger (see “Visual and Statistical Thinking:  Displays of Evidence for Decision Making” by Edward R. Tufte, 1997).

Patterns are not detectable when we look at data elements in isolation.  The typical survey-based research report is a linear summary of the answers to the survey questions, sometimes with responses reported by various subgroups (i.e., “banner points”). While it’s possible that such simple summary analysis is informative (“I didn’t know that so many of my customers are also buying from my competitors!”), patterns emerge only when we can see how the answers change across relevant dimensions (e.g., time, geography, attitudinal segments, and so forth).  

There’s another aspect to insight.  Sometimes we “see” something that completely changes our understanding.  The Tower of Hanoi puzzle provides an example.  You have a wooden base with three identical dowels.  On the first dowel are stacked several disks of increasing size (smallest on top, largest on the bottom).  The task is to move all of the disks to the third dowel, but…  you can move only one disk at a time AND you cannot place a larger disk on top of a smaller disk.  Solving this problem involves a specific insight–that you can move disks back and forth between all three dowels, as long as you move only one at a time and never put a larger disk on top of a smaller one.  The framing of the puzzle–in the physical design and the task instructions–appears to lead most people initially to attempt a solution in which disks are only removed from the first dowel.  After all, if the solution were immediately obvious, it would not be much of a puzzle.  

While we often can find patterns and connections using systematic, analytic approaches (like the analysis of census data on first names), the type of insight required to solve a puzzle like the Tower of Hanoi is qualitatively different–much more like an “aha” or “eureka” experience.  Once you’ve figured out the solution, you can solve similar puzzles quickly by recognizing the form of the problem.  

“Aha” insights sometimes happen in market research, but in my experience they are most likely to occur when we use qualitative methods, such as case histories, in-depth interviews, and immersion.

The second client complaint (“this doesn’t tell me which action to take”) usually reflects a failure to align the research with the business problem.  Most often, the link between the research activities and the actions available to the firm is missing.  I think this occurs because, by the time the customer insights department begins working with the market research partner, the process is two or three steps removed from the business problem.  It’s important to have a line of sight from the data to the actions that the firm can take.  Consider the auto industry as an example.  Once a model is introduced (that is, the vehicle is designed and engineered, the assembly line has been built, parts have been ordered, and so forth), the automaker has only two ways to impact the choices of consumers: advertising and price.  An attitudinal segmentation at this point might be nice, but unless it directly informs advertising or pricing decisions it’s not likely to help a manufacturer decide on a course of action.

So, it’s important to specify, up front, what we mean by “insight.”  If we’re looking for new knowledge, we need to know “what we know” as well as what we don’t know–and it’s important for clients to share this knowledge with their research partners.  We also need to recognize that insight often results from looking across multiple sources of information, enabling us to see patterns or connections that are not otherwise apparent.

Copyright 2009 by David G. Bakken

Here are the dictionary definitions of insight:

 From the Concise Oxford English Dictionary (Oxford University Press, 2004).  

insight n. 1 the capacity to gain an accurate and deep understanding of something; an understanding of this kind. 2 (Psychiatry) awareness by a mentally ill person that their mental experiences are not base in external reality.

From Merriam Webster’s Collegiate Dictionary 1oth Edition (Merriam-Webster, 1993).

insight n. 1: the power or act of seeing into a situation: PENETRATION  2: the act or result of apprehending the inner nature of things or of seeing intuitively  syn see DISCERNMENT.

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