You’ve Got Me All Wrong

Dec 01, 2017


Several weeks ago, my husband and I received matching direct mailers inviting us to join an exclusive membership program that celebrates people of a certain age. The fact that we both received an identical mailing would have sent many marketers into a swirling debate about the merits and drawbacks of householding to eliminate direct mail redundancies. The fact that our last names were spelled wrong (Niemieo, in case you’re wondering), was also pretty disappointing. However, the biggest, most glaring failure on the part of this organization (or their delegates) was the fact that neither of us was eligible to join this exclusive group of individuals—and we won’t be for more than a decade.

So what did we do? We sent in an application, of course! At this point, we filled in our correct birthdates, assuming we’d get a polite notification that we don’t meet their admission criteria. Instead, we received a lovely pair of fanny packs to tuck away until such a time as we don’t care anymore about what the cool kids will say about us.

You might be tempted to toss this aside as a random accident—an edge case that slipped through the cracks. I was too, until later that month our Chief Operating Officer, Chuck, received a sample shaving kit with the phrase “Happy 18th Birthday, Charles!” on the box. Name wrong? Check. Birthday month wrong? Check. Age wrong? Big check.

How does this happen in the age of big data? Theoretically, machine learning and artificial intelligence are supposed to be smart enough to run a series of checks to validate the data that goes out. However, these types of mistakes can happen regardless when customer data gets too far away from the hands of the analysts, technologists, and strategists entrusted with its care.

Let’s take our “senior citizens club” example. Perhaps we visited a conference and entered our names to win a new car. Then, a careless administrator mistyped our names into their database. Because of the fine print on the bottom of the awards entry form that we clearly did not read, that company was authorized to sell our contact information to a varied and diverse set of third parties for marketing purposes. That next company decided to append more data to our profiles, matching loosely on last name and first initial. They then passed along our information again to a direct mail marketing partner, and voila! We are now the youngest members of a senior citizens program.

Some level of error may be inevitable, but the regularity with which marketing gets it wrong is not. Companies need to keep their most valuable asset—customer data—close to them. They need to set data matching rules that are fairly concrete and have high standards associated with them. And they need to create some form of a feedback loop so that these types of mistakes make their way back to the source. Only then can companies act with the confidence that what they think they know about customers is actually true.