Beyond prediction

A failure to distinguish between the goal of regression-based segmentation modeling as being “predictive” rather than “explanatory” is viewed as the prime catalyst behind the unwarranted model intervention that occurs in the direct response industry today. Moreover, such “model meddling” is not merely an innocuous indulgence, but can result in opportunity costs or real-dollar losses in terms of the utilization of less-than-optimal models. The inability to interpret correctly the meaning of the regression coefficient values is another factor which can lead to the unjustified modification of demonstrably valid segmentation models, as is made evident with an example. Finally, the premature deletion of potentially viable predictors due to certain misconceptions surrounding the harmful effects of “multicollinearity” is also cited as one more example of inappropriate model intervention.