Personalization Is About the Person
Without a doubt, proper personalization starts with a personalized data view, which is skipped over all too often. Some may use terms like “360-degree customer view,” “single customer view” or my favorite, “customer-centric portrait.” No matter. All the transactional, behavioral, demographic and environmental data must be realigned around “each” customer or prospect. Some may say that they already have some fancy ID system that connects all those data points (many don’t). Great, but that is just a good beginning. We still need to convert such “event”-level data into “descriptors” of individuals. Transaction-level data may tell you what happened on a certain date, for how much money and for what product. Descriptors of individuals display their personal spending patterns, such as personal compositions of categorical purchases and browsing history, frequency of purchases and spending levels in each category or channel, and sets of times series variables nicely lined up around the person (refer to “Beyond RFM Data”). This is quite different from stacks of transaction or event-level data sitting in data platforms designed for mass storage and rapid retrieval.
When we line up information around people, we often find out that we really do not know much about our customers. All those fancy variables created around the target individuals have many holes in them, for various reasons. Maybe they are new customers, or they just browsed a few items but never bought anything yet. Some customers may have shopped only in certain categories, but stayed away from others. Some customers may have been very diligent in deleting their online trails. To do the personalization properly and consistently, we need to fill in such gaps.
Most of personalization engines, unfortunately, are designed to act only on available (largely, “known”) data. When marketers go too far with what’s known to them, the customers who casually let some parts of their lives known to marketers get bombarded with the same messages until they get completely sick of them. That is a sad situation as, categorically speaking, people with known behaviors often account for less than – at times far less than – 5 percent of the approachable universe. So, in that scenario, 5 percent get to be stalked, while 95 percent are ignored. Not ideal at all.
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 email@example.com.