Measuring consumer, nonlinear brand choice response to price

Abstract Nonlinear price responses by shoppers in brand choice decisions have important managerial and theoretical implications for designing pricing and price promotions. However, the study of these phenomena by traditional parametric techniques is not only tedious, but may lead to an incorrect confirmation of theory. To avoid these problems I demonstrate a simplified means for detecting the possible presence of nonlinearities in consumer response by means of a unique, nonparametric method. This method relaxes the usual linear-in-parameters character of the multinomial logit (MNL) model by an additive sum of one-dimensional nonparametric functions of explanatory variables. In an application of this model to aseptic and red drink databases, I document the presence of these nonlinearities in the form of price threshold effects and a saturation effect with respect to price reductions. I further apply the model to orange juice and ground coffee scanner data by employing the framework of a previous parametric study of Kalyanaram and Little (1994). The model easily found the expected existence of weak price sensitivity around the reference price and a greater consumer negative reaction to price increases than positive reactions to price decreases. Researchers may find this nonparametric technique a useful addition to their kit of statistical tools.