No One Is One-Dimensional

If anyone says to your face “You’re one-dimensional,” you would be rightfully offended by such statement. It would almost sound like “You are so simple that I just figured you out.”

If anyone says to your face “You’re one-dimensional,” you would be rightfully offended by such statement. It would almost sound like “You are so simple that I just figured you out.” Along with that line of thinking, you should be mad at most marketers, as they treat consumers as one-dimensional subjects. Even advanced marketers who claim that they pursue personalized marketing routinely treat customers as if they belong to “1” segment along with millions of other people. Sort of like drones with similar characteristics. Some may title such segments with other names, like “clusters” or “cohorts.” But no matter. That is how personalization works most times, and that is why most consumers are not impressed with so-called personalized messages.

Here is how segments are built through cluster analysis. Unlike regression models, clusters are built without clear “target” (or dependent) variables (refer to “Data Deep Dive: The Art of Targeting”). Considering all available variables, statisticians group the universe with commonly shared characteristics. A common analogy is that they throw spaghetti noodles on the wall, and see which ones stick together. Analysts can control the number of segments and closeness (or “stickiness”) of resultant groups. I have seen major banks grouping their customers into six to seven major segments. Most commercial clustering products by data compilers maintain 50 to 60 segments or cohorts (I am not going to name names here, but I am sure you have heard of most of them). I was personally involved in a project where we divided every town in the U.S. into 108 distinctive clusters using consumer, business and geo-demographic variables. The number of segments may vary greatly, depending on the purpose.

Once distinctive segments are created through a mathematical process, then the real fun begins. The creators get to describe characteristics of each segment in plain English, and group smaller segments into higher-level “super” clusters. Some creative companies name each cluster with whimsical titles or dominant first names of each cluster (for copyright reasons, I wouldn’t use actual names, but again, I’m sure marketers have heard about them). To identify dominating characteristics of people within each cluster, analysts use various measurements to compare them against the whole universe. For instance, if a cluster shows an above-average index of post-college graduates, then they may call it “highly educated.” If analysts see a high index-value of luxury car owners, then they may label the whole cluster with some luxurious-sounding name.

Segmentation is an age-old technique and, of course, it still has its place in marketing. Let me make it clear that using segments for target marketing is much better than not using anything at all. It also provides a common language among various players in marketing, binding clients and vendors together. Marketing agencies, who cannot realistically create an unlimited number of copies, may prepare a set number of creatives for major segments that their clients are targeting. With descriptions of segments in front of them, copywriters may write as if they are talking to the target directly. Surely, writing copy for a “Family-oriented young couple with dual income” would be easier than doing so for some anonymous target.

However, the trouble begins when marketers start using such a “descriptive” tool for targeting purposes. Just because there is a higher-than-average index value of a certain characteristic in a segment, is it justified to treat thousands, or sometimes millions, of people in the target group the same way? Surely, not everyone in the “luxury” segment is about luxury automobiles or vacations. It is just that the cluster that someone happened to have belonged through some statistical process has a higher-than-average concentration of such folks.

Then how do we overcome such shortcomings of a popular method? I suggest we reverse the way we look at the behavioral indices completely. The traditional method defines the clusters first, and then the analysts put descriptions looking at various behavioral and demographic indices. For promotions for specific products or services, they may examine more than 50, sometimes more than a few hundred index values. Only to label everyone in a segment the same way.

Instead, for targeting and personalization, marketers should commission independent models for every type of behavioral or demographic characteristic that may matter for their campaigns. So, instead of using one “luxury segment,” we should build multiple models. For example, for a travel industry like airlines or cruise lines, we may consider the following series of model-based “personas”:

  • Foreign vacationers
  • Luxury vacationers
  • Frequent business travelers
  • Frequent flyers
  • Budget-conscious travelers
  • Family vacationers
  • Travelers with young children
  • Frequent theme park visitors
  • Bargain-seekers
  • Adventure-seekers
  • Wine enthusiasts
  • Gourmets
  • Brand-loyal travelers
  • Point collectors
  • etc.

This way, we can describe “everyone” in the target universe in a multi-dimensional way. Surely, not everyone is about everything. That is why we need a system under which one person may score high in multiple categories at the same time. We all have tendencies to be bargain seekers, but everyone has a different threshold for it (i.e., what length of trouble would you go through for a 10 percent discount?). If you have multiple descriptors for everybody, you can find the most dominant characteristics for one person at a time. Yes, one may have high scores in “luxury vacationers,” “frequent flyers” and “frequent business travelers” models, but which characteristic has the highest score for “him”?

Imagine having assigned scores for these “personas” for everyone. I may score nine out of nine in “frequent flyer” (and that is for certain, as I am writing this on a plane again), score six out of nine in “luxury vacation,” and score two out of nine in “family vacationers” (as my kids are not young anymore). If you have one chance to show me something that resonates with me this second, what would be the offer? Even a machine can decide the outcome with a scoring system like this. Now imagine doing it for millions of people, all customized.

Last month, I wrote that personalization is not an option anymore, and further, marketers should aspire to personalize their messages for most people, most times, through all channels, instead of personalizing only for some people sometimes through some channels (refer to “Road to Personalization”). Because “personas” based on statistical models will not have any missing values, we can achieve that ambitious goal with this technique.

With new modeling techniques and software, this is just a matter of commitment now. We are not operating in the 80s anymore, and it is time to move ahead from simple segmentation methods. Yes, using segments would be much better than no targeting at all. But with a few more tweaks, we can build more than 20 personas in the same time that we would spend for developing segments using a clustering technique, which isn’t exactly cheap even nowadays.

Another downside of a clustering technique is that, once the statistical work is done, it is very difficult to update the formula without changing existing marketing schemas. By nature, segments are very static. It is no secret that even some data compilers chose to stay with old models, as they are afraid of creating inconsistencies with newly updated ones. Some are more than a decade old.

Conversely, it is very easy to update personas, as it is not much different from refitting the models one at a time. And we don’t have to update the whole series every time, either. Just watch out for the ones that do not validate very well over time. With real machine learning techniques around the corner, we can even consider automating the whole process, from model update to deployment of messages through every channel.

The hard part would be imagining the categories of personas, but I suggest starting small with essential categories, and then keep building upon them. Surely, teenage apparel companies would have a very different list than business service companies that sell their services to other businesses. Start with obvious ones, like bargain seekers, high-value customers and specific key product targets.

Connecting personas to actual creatives will require some work in the beginning, too. However, if you plan the categories with set creatives in mind from the get-go, it won’t be so difficult. Again, start small and see how it goes, along with some A/B testing. Ten categories will be plenty for many businesses. But having more than 100 personas won’t take up much space in supporting databases, either. Once the system gets stable, marketers can automate much of the process, as most commercial software can take these personas like any other raw variable.

So, if your marketing team is committed enough to have purchased personalization engines for various channels, get out of the old segmentation method and consider building model-based personas. After all, no one is one-dimensional, and everyone deserves personalized offers and messages in this day of abundant data and machine power. This is not 1984 anymore.

Author: Stephen H. Yu

Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at

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