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.” 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.
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 firstname.lastname@example.org.