Data Deep Dive: The Art of Targeting
Let us start by reviewing what statistical models are: A model is a mathematical expression of "differences" between dichotomous groups; which, in marketing, are often referred to as "targets" and "non-targets." Let's say a marketer wants to target "high-value customers." To build a model to describe such targets, we also need to define "non-high-value customers," as well. In marketing, popular targets are often expressed as "repeat buyers," "responders to certain campaigns," "big-time spenders," "long-term, high-value customers," "troubled customers," etc. for specific products and channels. Now, for all those targets, we also need to define "bizarro" or "anti-" versions of them. One may think that they are just the "remainders" of the target. But, unfortunately, it is not that simple; the definition of the whole universe should be set first to even bring up the concept of the remainders. In many cases, defining "non-buyers" is much more difficult than defining "buyers," because lack of purchase information does not guarantee that the individual in question is indeed a non-buyer. Maybe the data collection was never complete. Maybe he used a different channel to respond. Maybe his wife bought the item for him. Maybe you don't have access to the entire pool of names that represent the "universe."
Remember T, C, & M
That is why we need to examine the following three elements carefully when discussing statistical models with marketers who are not necessarily statisticians:
- Comparison Universe, and
I call them "TCM" in short, so that I don't leave out any element in exploratory conversations. Defining proper target is the obvious first step. Defining and obtaining data for the comparison universe is equally important, but it could be challenging. But without it, you'd have nothing against which you compare the target. Again, a model is an algorithm that expresses differences between two non-overlapping groups. So, yes, you need both Superman and Bizarro-Superman (who always seems more elusive than his counterpart). And that one important variable that differentiates the target and non-target is called "Dependent Variable" in modeling.
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.