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.

Create a Bucket List

Whether you’re new to database marketing or a seasoned pro looking for some new idea to get your creative juices flowing, one of the most useful, and impactful, activities you can embark upon is to create what is called a “Bucket List.”

Whether you’re new to database marketing or a seasoned pro looking for some new idea to get your creative juices flowing, one of the most useful, and impactful, activities you can embark upon is to create what is called a “Bucket List.”

No, I’m not talking about a building a list of activities that you and a middle-aged companion wish to complete before you shed this mortal coil. I’m talking about taking a long, hard, and close look at your customers or prospects and getting to know them—really getting to know them—well enough to create broad classifications about who they are, what they do, what they like, and what affinities they share.

Remember, at the end of the day, database marketing is about sending out the right message to the right people at the right time—and, hopefully, achieving the desired response from the customer or prospect as a result. And without proper customer segmentation, this task simply cannot be done cost effectively, if at all.

Now, of course, there are many great customer segmentation models out there you can use. In a great article titled “Selecting a Customer Segmentation Approach” by Andrew Banasiewicz, Director of Analytic Services at Epsilon, four groups are identified: Predictive, Descriptive, Behavioral and Attitudinal.

Out of these four, the Predictive and Attitudinal models are arguably the most popular and widely used. Predictive is a model that uses value segments driven by customer purchase behaviors, extrapolating past behavior into future actions. An Attitudinal model, on the other hand, identifies affinity segments based on respondents’ expressed attitudes toward a company’s brand or products.

Now of course this list isn’t exhaustive and there are other models you can use. One popular alternative is Psychographic Profiling, which is used widely in the B-to-C space. In this model, consumers are assigned into groups according to their lifestyle, personality, attitude, interests and values.

Many B-to-B marketers, on the other hand, may prefer to use a segmentation model based on Firmographic variables, such as industry, number of locations, annual sales, job function and so on. Many software companies, not surprisingly, trend toward usage-based profiling, which includes variables, such as type of device used (desktop, tablet, mobile device), Operating System and so on.

One important fact that’s routinely overlooked is that successful customer segmentation requires taking a holistic approach. This includes aligning a firm’s segmentation goals to its marketing objectives and data acquisition investments. In other words, the data you have will determine not only which model you use, but also what marketing campaigns you’re able to run.

Now of course both data inputs and needs are in flux throughout the firm’s lifecycle. As Banasiewicz points out, for a firm in high-growth customer acquisition mode an Attitudinal model might work effectively for demand generation initiatives among qualified and segmented pools of prospects. Marketing campaigns in this scenario, we can assume, would speak to customer desires and affinities, with purpose of lead generation/nurture.

On the other hand, once the firm has acquired a large pool of customers, it’s not unrealistic to think that transitioning to a Behavioral model using inputs from past purchases will be more effective for running what are now CRM campaigns, focusing on driving lifetime value and repeat purchases.

Different groups not only have different attributes and attitudes, but consume different types of media. As such, they will respond to different types of offers, communicated in different ways and in different places. Where should a firm spend its marketing budget? Online display, email, direct mail, social media, print? … The choices are dizzying in today’s multichannel environment. Having a robust customer segmentation model can definitely help in the decision-making process.

Another important feature of customer segmentation is the realization that different customer groups can not only have wildly different demographic and psychographic identities, but very often will have strikingly varying lifetime values. To the surprise of some, a customer segment with a with younger average age will very often have a higher lifetime value than a group far senior to them, despite having far less disposable income to spend today. This may be based solely on the fact that the younger customers have, simply by being younger, many more years of being a loyal customer ahead of them. Taking this into account, many brands’ obsession with successfully penetrating the youth market should come as no surprise.

Now of course it’s easy to miss the forest for the trees, as customer segmentation is simply a means to an end, not an end in itself. Once you have broken your customers or prospects down into segments, the trickier (and for those who are not data geeks) more fun part of the equation involves devising incentive and reward strategies for each segment, and creating compelling marketing messages and collateral that can be used to get the message out across the various marketing channels. Knowing your customers, this part is a lot easier, which brings me full circle back to my point from the top: Create a bucket list.