Bad Thing! Or Why Segmentation by Consumer Attitudes May Be Dangerous

For years, B-to-B and B-to-C marketers have relied on attitudinal segmentation research to help them group their current customer base, and potential customers as well, for communication, promotion, marketing and experience initiatives. The thesis has been that, by asking a small, but meaningful, set of attitudinal questions, they would be able to develop an index, algorithm or framework equation that ranked these consumers by propensity to buy, both near-term and long-term.

For years, B-to-B and B-to-C marketers have relied on attitudinal segmentation research to help them group their current customer base, and potential customers as well, for communication, promotion, marketing and experience initiatives. The thesis has been that, by asking a small, but meaningful, set of attitudinal questions, they would be able to develop an index, algorithm or framework equation that ranked these consumers by propensity to buy, both near-term and long-term.

These frameworks—they’re arithmetic, so we can’t call them “models”—typically include questions regarding the importance of elements like value for money, acting with the consumer’s interests in mind, credit and payment terms, having knowledgeable employees, offering products which will meet the consumer’s needs, and the like. From these questions, basic segment categorization can be determined; and, once these three, four or five segments are established, we’ve often seen marketers go on to build assumptive plans and conduct further, more detailed, research around them.

The goal of these approaches is to produce attitudinal segments, which the questions can predict with high accuracy, often in the 80 percent or 90 percent range. This creates what economists would call a “post hoc ergo propter hoc” situation, Latin for “after this; therefore, because of this.” It is a logical fallacy, essentially saying that A occurred (the responses to the attitudinal questions); and then B occurred (the cuts, or segments, of consumers). Thus, A caused B. Once the B, or segment creation, stage has been established, further fallacies, such as creating reliable marketing, operational and experiential strategies around these supposed propensities, can be built. It’s a classic situation, where correlation is thought to be the same as causation. As your economics or stat professors may have told you, correlation and causation are far from being identical concepts.

As a consultant and analyst, I’ve seen this result of this application of research and analytics play out on a firsthand basis on multiple occasions. Here’s a recent one. A client in the retail office products market had been using an attitudinally derived element importance question framework for small business market segmentation purposes. The segment assumptions went unquestioned until followup qualitative research was conducted to better shape and target their planned marketing and operational initiatives. Importance of certain products and reliable service were identified in the research as key areas of focus and opportunity for the office products retailer; but, in the qualitative research, power of both focus areas appeared, anecdotally, to be consistent across all segments. And, even though implied supplier roles were suggested to build purchases, this was much more “leap of faith”-based on the established quantitative research segment personas than actual qualitative research findings.

There are related issues with what we can describe as quasi-behavioral measures, such as single question metrics (likelihood to recommend to a friend or colleague or the amount of service effort required on the part of a consumer); or traditional customer loyalty indices (where future purchase intent is included, but also attitudinal questions such as overall satisfaction). It’s not that they don’t offer some segmentation guidance. They do—on a macro or global level; but they tend to be less effective on a granular level, especially where elements of customer touchpoint experience are involved.

And, they tend to have limitations as predictors of segment behavior, a key business outcome for marketers and operations management. When compared to research and analysis techniques, such as customer advocacy and customer brand-bonding, which are contemporary, real-world frameworks built on actual customer experience—high satisfaction scores, high index scores and high net recommendation scores produced likely future purchase results (in studies across multiple industries) which were often 50 percent to 75 percent lower than advocacy or brand bonding frameworks. I’d be happy to provide proof for anyone interested in reviewing the findings.

So, that’s the scenario. The challenge, and potential danger, for marketers and those responsible for optimizing customer experience is that these attitudinal and quasi-behavioral questions are just that—attitudes and quasi-behaviors. Attitudes are fairly superficial feelings, and tend to be both tactical and reactive. And, because they are so transitory, their predictive value is often unstable and unreliable. Quasi-behaviors are also open to many similar challenges. More importantly, attitudes and quasi-behaviors are not behaviors, such as high probability downstream purchase intent based on actual previous purchase, evidence of positive and negative word-of-mouth about a brand based on prior personal experience, and brand favorability level based on experience. These are especially valuable in understanding competitive set, and they have real, and very stable, predictive and analytical value for marketers.

As Jaggers, the lawyer, said to Pip in Charles Dickens’, “Great Expectations,” take nothing on its looks; take everything on evidence. There’s no better rule.” For marketers, that’s excellent shorthand for taking everything on behavior, and perceptions based on documented personal experience, rather than attitudes and quasi-behaviors.

Author: Michael Lowenstein

Michael Lowenstein, PhD, CMC, is thought leadership principal for Beyond Philosophy, a U.S.-based international customer management experience consultancy. He's an international conference keynoter and speaker, workshop facilitator and trainer, author and a contributor to two customer loyalty newsletters and portals. He has more than 30 years of management and consulting experience with expertise in customer and employee loyalty research, CEM, loyalty program and product/service development, customer win-back, service and channel quality, customer-driven corporate culture, human resource development, and strategic marketing and planning.

"Marketing Nuggets" will include observations regarding trends, and often study results, representing current, real-world issues of high importance to direct marketers. Those issues include omnichannel communication usage, mobile marketing, content, informal offline and online social communication, consumer behavior, message personalization, internal customer-centric processes and organization, strategic customer life cycle planning, proactive employee contribution, etc.

One thought on “Bad Thing! Or Why Segmentation by Consumer Attitudes May Be Dangerous”

  1. One thing we can all agree on is that analytics can’t be 100% accurate. I believe the purpose of business analytics is to help optimize our performance by comparing it to our past performance or with that of our competitors. Even Google will admit that the data they collect isn’t 100% accurate. With that said, attitudinal segmentation can be very useful if applied correctly. I’ll go as far as saying attitudinal data helps better understand behavioral data. For example, Google Analytics does a good job in presenting behavioral data. We are able to view where our visitors are coming from as well as how long they stayed on our site. Imagine if we collected attitudinal data on visitors who stayed on our site for over one minute compared to those that stayed less than one minute. Even if we used only the net promoter questionnaire with an additional text field to explain why, we would learn more about our visitors and can even segment them based on a positive, neutral or negative attitude. Depending on the goal of our website, it may be a good or bad thing that people stayed longer. If it is a good thing people stayed longer on the site, we may discover that people who stayed longer than one minute had a positive attitude towards the site because it was friendly, intelligent and organized; whereas, those who stayed less than one minute found the webpage unattractive, and boring. Equipped with this knowledge we can then choose the following course of action: optimize our webpage to be more attractive, focus on the type of people (e.g. middle aged men) that are likely to have a positive attitude towards our site or both.

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