Statistical Model Comparison in Marketing Research

Abstract

Analysis for marketing decisions often involves the consideration of several alternative statistical models. As an intermediate step prior to making policy decisions, a single model is typically selected and used to guide subsequent decisions. In this paper, Bayesian model comparison methods are shown to lead to a predictive distribution for the decision problem without the intermediate step of model selection. This approach utilizes all available information bearing on the validity of the alternative models, as well as information concerning model parameters, in order to draw inferences regarding the criterion of interest relative to the decision to be made. The procedure is illustrated in the context of an advertising budget decision in which the functional form of sales response to advertising is unknown.