Estimation of posterior probabilities of consumer situational choices with neural network classifiers

This study shows how neural networks can be used to estimate the posterior probabilities in a consumer choice situation. We provide the theoretical basis for its use and illustrate the entire neural network modeling procedure with a situational choice data set from AT&T. Our findings supported the appropriateness of this application and clearly illustrate the nonlinear modeling capability of neural networks. The posterior probability estimates clearly add to the usefulness of the technique for marketing research.