The rise of data-driven marketing segmentation has coincided with a period of increasing polarization between rich and poor. I’m not suggesting that segmentation caused this polarization. Class divides have always been with us, due to a complex mix of social, economic, and political factors. I am suggesting that the wedge could be driven deeper by the way segmentation is used. The intrinsic power of data analytics to differentiate a population can also be wielded to divide it.
As segmentation has grown more robust, customers and prospects are often ranked by their potential to enhance the bottom line. Marketers craft strategies to reward the high-potential segments for doing business with them – and not reward less desirable groups, or sometimes subtly discourage them. And here’s where the divide comes into play. The most profitable customers are not always the wealthiest. But let’s face it, that’s often the way predictive models will tell you to bet.
For instance, predictive-yield models tell builders how to market and build houses most profitably. A person who wants a new house and can only afford to pay $150K is courted by practically no one – in fact, they probably can’t find a new house to buy. A prospect who can afford a $950K house is courted by everyone and has plenty of choices.
Analytics helps identify which customers are likely to pay on time or to upgrade to more profitable products. This insight will be incorporated into the firms’ CRM systems. Those segments will receive the best offers, the special concierge customer service phone lines, waived fees. There might also be “aspirational” or “elite-in-training” groups that get slightly better treatment in hopes that they will start behaving like the elite groups. So what happens to the other segments?
It costs them more to do business. Fees are not waived. They have to wait in a longer phone queue for customer service. When their call is answered, there is no scripting in the CRM system that explicitly says “you don’t have to go the extra mile to treat this customer well.” But it’s pretty much guaranteed that some harried customer service rep (perhaps in a rush to minimize the Average-Handle-Time metrics that drive their bonuses) will eventually interpret it that way.
Before analytics, businesses often had policies that every customer should be treated like they’re the best customer – because absent the data, it was assumed that every customer had that potential. But in the data age, there’s no more benefit of the doubt. When people complain that customer service doesn’t exist anymore, it might be because their segment doesn’t receive much of it.
In many years in the data field, I’ve seen the good that data-driven marketing can do. It makes a person’s online experience richer and more relevant. It helps businesses stock the products their customers want to buy. It helps get the right message in front of the right people at the right time. It helps meet the needs of both constituents – people who need something and the organizations that can provide it. It strengthens companies’ bottom lines.
At its essence, the objective of customer segmentation is to enable businesses to target their marketing capital toward the acquisition and retention of those customers yielding the greatest profit. There is nothing intrinsically wrong with this. Making money is what businesses are supposed to do, and it is the responsibility of their marketing organizations to help make that happen.
My point is that we need to be mindful that a segmentation score on a record eventually translates into the treatment of an actual person. And all people deserve respect, regardless of their place in the segmentation model. At least, that’s DMW’s philosophy.