Neural networks and the multinomial logit for brand choice modelling: A hybrid approach

The study of brand choice decisions with multiple alternatives has been successfully modelled for more than a decade using the Multinomial Logit model. Recently, neural network modelling has received increasing attention and has been applied to an array of marketing problems such as market response or segmentation. We show that a Feedforward Neural Network with Softmax output units and shared weights can be viewed as a generalization of the Multinomial Logit model. The main difference between the two approaches lies in the ability of neural networks to model non-linear preferences with few (if any) a priori assumptions about the nature of the underlying utility function, while the Multinomial Logit can suffer from a specification bias. Being complementary, these approaches are combined into a single framework. The neural network is used as a diagnostic and specification tool for the Logit model, which will provide interpretable coefficients and significance statistics. The method is illustrated on an artificial dataset where the market is heterogeneous. We then apply the approach to panel scanner data of purchase records, using the Logit to analyse the non-linearities detected by the neural network.