Generational Segmentation has Become a Marketing Industry Joke

Customers are all unique individuals.

However, marketing professionals tend to resist the notion that they should engage each customer uniquely. And that’s for a simple reason: you can’t truly mass-market anything if you don’t, on some level, approach the potential market as a mass, or at least as distinct segments. If you can segment your customer base effectively, you can optimize your productization, pricing, distribution, messaging, and merchandising to those groups. Just as important, it gives you a battleplan for amassing disparate sources of customer data into big-data repositories to support segmentation into as many micro-categories as you wish.

Customer segmentation is the core approach that drives modern target marketing. And when you’re talking segmentation, it’s not long before marketing people start to lump potential customers into various generational, demographic, psychographic, lifestyle, and other oversimplified breakdowns. But you dare not tell the customers to their face how you’re grouping and targeting them.

They may not recognize or appreciate the stereotypes you’ve assigned to them.

But, from your point of view as a marketing pro, is there any meaningful alternative? Is there any approach other than traditional segmentation practices that helps you identify the specific clusters of product features preferred by distinct customer groupings?

In a recent article ( ), Judy Bayer and Marie Taillard propose what they call a “new framework” for segmentation: one that enables targeting based on the outcomes or experiences that customers wish to realize with products. Bayer and Taillard use the clunky term “jobs” to refer to the intangible cloud of value that customers wish to realize from their engagement with your company, brand, and products. Per their discussion: “Examples of such jobs in the mobile telco realm might include: ‘being in touch with family and friends while roaming,’ ‘choosing the best entertainment and dining opportunities on the go over the weekend’ and ‘becoming more confident and secure in the use of a smartphone.’”

Bayer and Taillard believe that their approach will help marketing professionals avoid jamming customers into “new, irrelevant buckets” (demographic, psychographic, lifestyle, etc.). That strikes a responsive chord in my heart. Like you, I’m painfully aware when marketing people seem to be putting me into a bucket that feels out of touch with the “real me.”

For example, I’m a professional man in my mid-50s, but my musical tastes are often more in line with people of a younger generation. If you were marketing recordings, concerts, and the like to me, you’d probably assume I’m into “classic rock,” but that would be a miscalculation on your part (personally, I stopped caring about that stuff long ago). Likewise, I’m obviously technically savvy on all the social, mobile, and other new techs, but if you were marketing any and all of that to me, you’d probably assume that somebody of my age is stuck in old tech and old ways of using it. Boy, would you be wrong!

What I like about their approach is that, in theory, it could enable marketeers to better identify those product-engagement contexts where they should be clustering 50-something me alongside my 20-something college-educated children.  From a big-data standpoint, it would require that you collect and analyze more data relevant to what individuals truly want from their engagement with your brands. Satisfaction survey data, social sentiment intelligence, call-center logs, and other sources of attititudinal data would help you get deeper into what we sometimes call the “data of desire.”

Some CRM professionals call this “customer journey” data.

However, who’s to say that these journey/desire/attitude buckets don’t distort your customer segmentation efforts as much as the demographic and lifestyle approaches? Many older people such as yours truly often pretend we’re still young at heart, when our actual buying patterns are often in line with others our age. We might be deceiving ourselves when responding to surveys that ask about what truly moves us to whip out our wallets.

Yes, I’m not a young man anymore. But what gets my goat are “generational” approaches that concoct cutesy segmentations such as –”Baby Boomer,” “Generation X,” “Generation Y,” “Millennial,” and “Yuppie.” Generational segmentations such as these are hallmarks of the terminally trend-besotted marketing pro. Like you, I’ve been subjected to this socio-babble for so long that I cynically backtalk it under my breath. I regard such phrases as a sort of never-ending practical joke perpetrated by the shallowest purveyors of the marketing arts.

Granted, that’s just one evangelist’s admittedly non-humble opinion. But I recently came across an article ( ) that confirms my longtime feeling on the matter. The marketing profession’s generation-labeling mania has gotten completely out of hand. This article discusses yet another attempt to concoct a customer segment: so-called “Generation C” (for “connected”). I was initially receptive to the idea, until I read further and discovered that it’s the most absurd example of this practice that’s not technically coming from our friends at The Onion.

For starters, so-called “Generation C” is self-contradictory by definition. It has nothing to do with generations in the demographic sense. Instead, it has everything to do with attitudes and lifestyles. Or, as the author (who didn’t actually coin the phrase but simply explains what the perpetrator hath wrought) states: “[T]he connected generation, transcends age groups. It’s a psychographic and attitudinal segmentation versus an age-based demographic segmentation. transcends age groups. It’s a psychographic and attitudinal segmentation versus an age-based demographic segmentation.”

Got that? Wait, there’s more! Throwing an apparent synonym–”digital natives”–into the mix, the purveyor of “Generation C” construes it thusly: “Members are most commonly found in the 18- to 34-year-old age group…grew up with technology … have a love of content creation and mashing….tend to form active communities rather than remain passive…gravitate toward social media sites to participate in discussions about different ideas and involve themselves in cultural conversations….desire to be in control of their lives and be content with complexity….desire to work in more creative industries and be less restricted by rigid social structures.”

Ummmm, OK. If you’re a data scientist trying to build a segmentation model to distinguish this amorphous “generation” from everybody else, you have to ask: Apart from that first criterion (which is sort of “generational”), how can we objectively measure any of this? Doesn’t it seem to apply to almost everybody these days, if not in their overt behaviors at least in their aspirations? If we conduct a survey, aren’t the vast majority of respondents going to check off most of those attributes as applying to themselves? Or if we ask marketing professionals to code existing customer records as “Generation C” vs. “non-Generation C,” what are the chances that different coders will be consistent in how they place people into this amorphous category? Not great. Given all of these issues, the quality of your customer data will be so poor that any segmentation models you build from that data will be next to useless.

The only measurable criteria discussed in the article concern the intensity of people’s adoption of social media. However, those are behavioral variables, not generational. If you want to segment the world into “social media devotees” vs. “everybody else,” that’s fine and dandy, but that’s not a generation. And it probably doesn’t map directly to the demographic, attitudinal, lifestyle, and other categories that characterize your customer base.

Any time you see a too-clever-by-half made-up name attached to a supposed human generation, you’re seeing shoddy social science at work. The bottom line is that data scientists can’t work miracles if your marketing organization doesn’t have strong subject-matter experts in the social sciences–demographics, sociology, economics, psychology, etc.–to assist them in crafting segmentation models.

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