Personalization Is About the Person
Guess what happened after that purchase? I started to receive a new series of emails from them, this time featuring nothing but more mice! What do they think, I love this mouse so much that I would start a mouse farm? Do they want me to find a better mouse “after” I purchased one already? The “last” thing I would buy for the rest of this fiscal year is another mouse (and another garden hose nozzle, if I might add). I cannot forgive their oversight, because I bought the first mouse with the same merchant, only about 16 months ago. Don’t they have my personal transaction history? For heaven’s sake, I can just log onto Amazon.com and check out what I have been buying from it going back almost 10 years! Why don’t they use such rich data? Isn’t Amazon supposed to be one of the leading database marketers?
How did this all happen? I have two words for you: “Collaborative Filtering,” though I have no idea what they are really collaborating in cases like these. That term has been around for some time now, actually. It basically means, “Oh, you bought this item? You may like these other products, too”-type marketing through some algorithm. Now I know that when we use terms like “algorithmic solution,” we may feel a little smarter about ourselves — as in, “Yeah! I am not afraid of math!” But let’s forget about how the math works, and let’s think about how the consumers feel about it.
If I may share my blunt sentiment about this type of suggestion engine, my language would have to be more colorful than this fine publication allows me to be. Let’s just say that I am pretty far from impressed. And marketers should not even think about calling this barrage of emails “personalized.” They are much closer to spam than personalized emails, because I know this type of personalization is based on products, not people. And this so-called “machine learning” becomes nothing more than a nuisance, if the all-important human touch is missing from the equation.
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.