Application of Multiple Adaptive Regression Splines (MARS) in Direct Response Modeling

Increasing costs of direct marketing campaigns coupled with declining response rates have prompted many direct marketers to turn to more sophisticated techniques to model response behavior. The underlying premise is that even a small improvement in response rate can have significant implications for the bottom line. This article investigates the use of a recently developed technique, Multiple Adaptive Regression Splines (MARS), together with logistic regression in the context of modeling direct response. Specifically, our goal is to assess the relative effectiveness of MARS models vis-à-vis logistic regression with original predictor variables in modeling direct response behavior. Our analysis shows that the MARS models outperforms the logistic model in general, leading us to conclude that MARS offers a number of advantages over a logistic model. Direct marketing strategy implications are also discussed.