Personalization Is About the Person
Enter statistical modeling. I have been emphasizing the importance of statistical modeling even in the data-rich environment, because we will never know everything about everyone, and statistical modeling systematically converts “unknowns” to “potentials.” No, we may not know for sure that a particular target is indeed a “gardening enthusiast” (and no, buying just “one” garden hose nozzle may not be enough). But yes, we can say that she is “very likely to be” a gardening enthusiast, with statistical techniques effectively mining available data — such as what other products she purchased and browsed with varying frequencies and intervals. The results of the models are “scores” by which you can measure the degree of confidence, as in a nine out of a 10 scale. This is much simpler than having to worry about hundreds of variables with more holes Swiss cheese.
Building a customer-centric view and filling in the gaps with modeling techniques is far more superior to a personalization engine that would just ingest unrefined SKU-level data. For one, we don’t get to bother people just because we had a glimpse of certain behavior, as statistical modeling considers hundreds, if not thousands, of variables around the person. Secondly, having “potential” values for certain behavior enables marketers to act on most of the targets, not just fractions of them. Going further, we can even estimate channel and timing preference in addition to what we often call “personas” or propensity scores.
The result of modeling work will make the personalization engines run better, too. After all, those software solutions are designed to ingest any type of variable. And the model scores – which are summaries of hundreds of data points – look just like another set of variables, anyway. Consider such scores without any missing values as really tasty coffee beans that you can put into your shiny espresso machine.
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.