Learning from refund patterns


In the past few weeks, I’ve had to request a larger number of refunds than normal for my food purchases. These refunds fall into two categories:

– The price at the register is higher than the price on the shelf, so the store ends up refunding the difference.

– The item is damaged in shipping and isn’t safe to eat, so the retailer has to provide a credit for the damaged product.

Each time, I’ve been careful to explain the source of the problem so that the store can correct it. Yet, the same issues keep cropping up, forcing me to submit the same type of refund request over and over again.

What’s wrong with this picture? Clearly, the retailer has the data to show that there’s a pattern in the refund requests that their customers are submitting. But maybe they don’t consider shipping damage or pricing errors to be worth correcting unless something like 100 people report the same problem.

However, I’m guessing that very few customers actually take the time to pursue a refund, especially for less costly items or small pricing disparities. This means that even a small pattern can convey valuable data. By paying more attention to these refund trends, a savvy retailer should be able to deliver more consistent pricing and reduce the number of products damaged in transit — before the problem becomes so widespread that customers have already started to shop elsewhere.