If we’re going to create a pathway to customer knowledge, we ought to start with some kind of definition, so we’ll know where we’re trying to go.  One might argue that anything we “know”–any facts that we have about customers–constitutes “knowledge.” At one level that’s true.  But, as I’ve suggested in an earlier post (“The Advantage in Customer Knowledge,” May 21, 2009) knowledge is more than just a collection of facts.  In our ESOMAR paper “Creating Customer Knowledge” (ESOMAR Consumer Insights, 2004), Mike Lotti and I defined customer knowledge as “the understanding of customer motivations, attitudes, perceptions, and experiences such that we can predict customer behavior.”  In other words, customer “knowledge” really represents our “theory of the customer.”

Almost all marketers have one or more implicit models of customer behavior that guides their decision-making.  In many cases, these models are developed on the basis of limited information, combined with specific psychological processes that we use to help us explain and predict the behavior of other people.  These processes are sometimes referred to as implicit psychology and they affect our perceptions of others in predictable and biased ways.  For a good introduction see Implicit Psychology:  An Introduction to Social Cognition by Daniel M. Wegner and Robin Vallacher (Oxford University Press, 1977, still in print).

To create our theory of the customer we need to mimic the explicit process represented in the scientific method we all learned in school.  Around the time Mike and I were preparing our paper, an article by Clay Christensen and Michael Raynor appeared in the Harvard Business Review (“Why Hard-nosed Executives Should Care About Management Theory,” September, 2003) describing this process as applied to management theory.  Theory building begins with observation and description of the phenomenon of interest.  This has been a major role of market research–to observe and describe the behavior of customers.  The second step is categorization of the observations and descriptions, perhaps revealing patterns of association or correlations in the data.  The third step is the development of predictions or hypotheses about causal relationships.

Despite all the data that are available about customers from transactional databases, surveys, focus groups, and so forth, many organizations are unable to capitalize on opportunities to create customer knowledge.  There are at least four reasons for this.

  • Much data gathering is aimed at answering specific and urgent tactical questions.  Research may be conducted to populate marketing dashboards, to measure reactions to new product ideas or to gauge the impact of specific marketing actions (ad copy, price changes, etc.), but research is seldom conducted specifically to develop and test theories of customer behavior. That does happen in the academic community, but sometimes such work is too theoretical or focused on too narrow a problem.  At any rate, once the immediate question is answered, management’s attention quickly turns to the next tactical problem.
  • Most data gathering is project-specific.  This is a consequence of the combined effects of the tactical and decisive nature of most research and the budgeting process in most firms.  I helped create business unit market research budgets when I worked for AT&T, and it was a process of estimating the number of concept tests we would do in a year, ad copy tests, customer satisfaction tracking studies, and so forth.  No one ever said “How much do we need to spend to understand our customers?”  This makes it difficult to integrate multiple studies into the kind of programmatic research–typical in the academic world–that is essential to the development and refinement of theory.  Granted, some firms are adopting knowledge management technology to facilitate retrieval of information that has been gathered across multiple projects, but that’s a far cry from systematically designing research to build on what’s known and to test specific propositions about customers.
  • Market researchers and users of market research too often equate correlation with causation (there’s a related problem in that we sometimes see illusory correlations–associations that we expect to find based on our implicit models that are not actually present in the data.  If you don’t think this happens, take a look at How Doctors Think by Jerome Groopman).  As our analytic methods and our computers have become more powerful we’ve come to rely almost exclusively on number crunching to to develop “predictive models” of customer behavior.  
  • The internal politics of the organization may derail even the best intentioned and executed knowledge creation efforts.  Different stakeholders have different motivations, and they often have different ways of gathering and interpreting data from customers (think of the way the sales force thinks about customers compared to the way a product marketing manager or brand manager thinks about customers).  In some organizations, much of the customer data is “owned” by the management information systems or information technology groups.  

Market segmentation research offers a good example of the ways in which companies can squander opportunities to create customer knowledge.  There are many varieties of market segmentation, but the original paper on the topic by Smith (“Product Differentiation and Market Segmentation as Alternative Marketing Strategies,” Journal of Marketing, 21, 3-8, 1956) described an orientation based on understanding the varying motivations that buyers bring to the marketplace.  In this view, segmentation requires an in-depth exploration of consumer motivations outside the marketplace.  This is a critical distinction. As Greg Allenby of The Ohio State University and his collaborator Geraldine Fennell point out, demand creating conditions–the problems that consumers are trying to solve to improve their well-being–are spawned upstream of the marketplace.  

Best-practice market segmentation requires a multi-stage research solution, beginning with broad qualitative research to understand the potential variability in consumer motivations, followed by more qualitative to refine the hypotheses generated in the first round, and finally, carefully designed quantitative research to put those hypotheses to the test.  What we’d really like to see is quantitative that allows us to test competing hypotheses about the drivers of consumer behavior, rather than confirmation or disconfirmation of a single hypothesis.  This tends to make best-practice market segmentation (where the goal is understanding variability in consumer motivations, rather than simply targeting consumers based on differential response to marketing efforts) complex and expensive, especially in today’s environment when firms need to understand their customers on a global basis.

In my recent experience, many firms are “doing” segmentation.  That is, they are commissioning their research supplier partners to conduct segmentation research.  All too often that involves a kitchen sink set of questions and whatever method the supplier recommends for clustering survey respondents based on the answers to those questions.  Good segmentation work is still being done, but there is a lot of poorly conceived segmentation work happening as well.

What’s missing in most segmentation research today is the systematic upfront development of theory to explain variability in consumer motivations.  Quantitative segmentation studies should be confirmatory, not exploratory.  If you don’t have a pretty good idea of the conceptual dimensions (or “constructs”) that will define the segments going in, you’re likely to be disappointed with the results of a quantitative segmentation study.

Look for more on best practices in market segmentation in future posts.

Copyright 2009 by David G. Bakken.  All rights reserved.