Once on a calibration call, I heard a customer service rep actually tell a customer who was doggedly asking for a better rate something like “You’re one of our low-segment customers. No matter how many times you ask, none of you guys can get that rate.”
Clearly, that rep was seriously off-script. But the segmentation score on her CRM screen told her that the company didn’t value this customer very much, so she didn’t either. I built the segmentation model that told her that. For me, it brought into stark relief how marketing segmentation can affect dynamics far down the road.
In many years in the field, I’ve seen the good that data-driven marketing can do. It makes online life more relevant. It helps businesses stock products their customers want to buy. But the intrinsic power of data analytics to segment a population can also be wielded to divide it.
The last twenty years in which database marketing has hit the big-time coincides with a period of increasing polarization between rich and poor. I’m not suggesting that segmentation causes this polarization. Rather, it’s that it helps drive the wedge, already in place due to a complex mix of social, economic and political factors, deeper.
The objective of segmentation is to enable businesses to target their marketing capital toward the acquisition and retention of those customers yielding the greatest profit. There isn’t anything wrong with this, per se. Making money is what businesses are supposed to do, and it is the responsibility of their marketing organizations to help make that happen.
Customers and prospects are identified by their potential to enhance the bottom line, and strategies are crafted to reward the more desirable segments for doing business with them and not reward less desirable groups for it (or even subtly discourage them from it). The most profitable customers are not always the wealthiest – but let’s face it, it’s often the way predictive models will tell you to bet.
Predictive and yield models tell builders how to market and build most profitably. A prospect who can only afford a $195K house is courted by no one and 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.
Profiling will tell businesses which customers are likely to have the wherewithal to pay on time and 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, the 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. And the other segments?
It costs them more to do business. They pay more for products. They have to wait in a longer phone queue for customer service. As for the service they do get when the phone 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 reps will (perhaps in a rush to minimize the Average-Handle-Time metrics they’re bonused on) 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, the assumption was that every customer had that potential. But in the data age, there is no more benefit of the doubt. When people complain that customer service doesn’t exist anymore, they’re wrong. It’s still alive and well – it’s just heavily up-market.
To reiterate, marketing segmentation and analytics did not cause the class divide. That has existed for millennia. Let’s at least be aware about how marketing analytics contributes to it in present day.