A flexible brand choice model based on neural net methodology A comparison to the linear utility multinomial logit model and its latent class extension

Abstract.

Brand choice models as a rule have a linear (deterministic) utility function, i.e. they conceive utility as linear combination of predictors like price, sales promotion variables, brand name and other product attributes. To discover nonlinear effects on brands' utilities in a flexible way we specify deterministic utility by means of a certain type of neural net. This feedforward multilayer perceptron is able to approximate any continuous multivariate function and its derivatives with the desired level of precision. In an empirical study the neural net based choice model leads to better out-of-sample results than homogeneous and heterogeneous versions of linear utility MNL models. On the other hand the latent class variant of the linear utility MNL model attains better fit values for estimation data than the neural net model. The neural net approach implies different choice elasticities for most predictors and identifies nonlinear effects (like interaction effects, thresholds, saturation effects).