Application of multiple adaptive regression splines (MARS) in direct response modeling

Abstract

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.

Visible-near infrared reflectance spectroscopy for assessment of soil properties in a semi-arid area of Turkey

“…It is capable of modeling both linear and nonlinear relationships between response and predictor variables by fitting local regression curves to spectral subregions and including higher order interactions among predictors. It has been successfully applied in various fields (Deichmann et al, 2002;Luoto and Hjort, 2005;Shepherd and Walsh, 2002;Yang et al, 2003) and generally provides better results in modeling compared to other linear and non-parametric regression techniques like Generalized Linear Models (GLM), Artificial Neutral Networks (ANN), and Classification and Regression Trees (CART).…”

Data Mining CEO Compensation

“…This approach is adopted in Deichman, et al (2002) where MARS is used in the context of direct response modeling; the authors find that response models which use MARS Basis Functions perform better than alternatives on independent validation samples. Munoz & Felicisimo contrast a MARS methodology with several alternatives and reach two interesting conclusions: one is that MARS yields the best predictive power, and the other is that an independent validation sample is truly needed (cross-validation is not sufficient).…”

Improving direct mail targeting through customer response modeling

“…However, we complement academic literature by presenting and integrating the most popular classifiers into one predictive benchmark study over multiple response datasets, while summarizing the managerial implications for managers. Several statistical classification methods to predict customer responses have been proposed and utilized, such as logistic regression, discriminant analysis and naïve Bayes (Baesens et al 2002, Berger and Magliozzi 1992, Coussement et al 2014, Cui et al 2010, Deichmann et al 2002, Kang et al 2012, Lee et al 2010. These techniques can be very powerful, but each algorithm also make several stringent, but different, assumptions on the underlying distribution between the independent variables and the dependent variable.…”