No One Is One-Dimensional
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
- Wine enthusiasts
- Brand-loyal travelers
- Point collectors
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”?
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.