A Better Mousetrap
A “Different” Model
What modeling does is turn no difference into real difference, where an entire customer base is segmented in terms of significant attributes. The direct mail segment in Table 2 (shown on page 110) exhibits a typical segmentation procedure starting with the first entry (age). Similarly, more variables can be selected into a multivariate model like ANN (artificial neural network), CHAID (chi-square automatic interaction detection) or regression analysis.
Many times, a modeling procedure only sticks to the treated segment while ignoring the benchmark, or holdout, segment. Unfortunately, the model developed in lack of the holdout information often describes an overall likelihood of responding to multiple campaigns rather than a single one. A differentiation model solves this puzzle.
According to the direct mail segment in this example (see Table 2), older customers seem more likely to respond to your promotion. By further investigating the holdout segment, you realize those senior sectors virtually did better without receiving this mailing. A comparison between the treated and holdout groups alters the previous conclusion based on the direct mail segment results alone and indicates a revised outcome and a new approach: targeting those groups with consumers between the ages of 36 and 65.
As a basic rule, a differentiation model divides an entire population into two categories—one with significant gain and another with significant loss. An overall response can be enhanced because you keep the positive impact and drop the negative impact. The downward side sometimes could be hard for a company to accept: How can a costly direct marketing effort hurt sales?
One answer: It has been proven that wrong messages are worse than no message to some customers. That is to say, once customers are interrupted or misled by any unwelcome marketing touches, they either change their mind or simply switch to your competitors. To this point, eliminating older and younger sectors has nothing to do with evaluating your customers for response likelihood, since they could be very profitable groups but just don’t like the way in which you approached them.