Nonparametric modeling of buying behavior in fast moving consumer goods markets

Abstract

"From the statistical point of view a nonparametric formulation of a brand choice model (NDE) is a powerful alternative to the logit model. But in the marketing context, researchers in general want to have parameter values to make predictions or to estimate market shares. This leads to a semiparametric model (GAM) formulation with two possible ways of using the results. One is to perform estimation of choice probabilities, but there one is confronted with the same problem as in the nonparametric approach, because no parameters are estimated for the nonparametric part of the model. The second possibility of a semiparametric model formulation overcomes this problem. In addition, with the estimation results a modified parametric model formulation can be estimated. This also gives the possibility to work with the parameter values to estimate market shares or make predictions. Especially for this use of modeling, the underlying data structure should be detected correctly. Therefore, two different estimation algorithms for a GAM were presented and the application of the semiparametric model to a real data set was reported. The estimations were made by the two common algorithms, backfitting and marginal integration, and are compared to each other. An interaction effect in the variable price in the data set was discovered, which leads to the need of additional studies of the data set." (author's abstract)