An artificial neural net attraction model (ANNAM) to analyze market share effects of marketing instruments

Abstract

Attraction models are very popular in marketing research for studying the e ects of marketing instruments on market shares. However, so far the marketing literature only considers attraction models with certain functional forms that exclude threshold or saturation e ects on attraction values. We c a n a c hieve greater exibility b y using the neural net based approach i n troduced here. This approach assesses brands' attraction values by means of a perceptron with one hidden layer. The approach uses log-ratio transformed market shares as dependent variables. Stochastic gradient descent followed by a quasiNewton method estimates parameters. For store-level data, neural net models perform better and imply a price response that is qualitatively di erent from the well-known multinomial logit attraction model. Price elasticities of neural net attraction models also lead to speci c managerial implications in terms of optimal prices.

Flexible estimation of price response functions using retail scanner data

“…In his empirical study, the semiparametric model provided better fits according to the BIC criterion and error measures determined by bootstrapping compared to the strict parametric MNL and MCI attraction models. In another paper, Hruschka (2001) further shows that a neural net based market share attraction model can also achieve greater flexibility than a common parametric attraction model leading to different managerial implications. Moreover, the use of nonparametric or neural net based (also called seminonparametric) techniques has become increasingly popular for modeling brand choice of consumers using disaggregate data.…”

Neural Net Extensions of Stochastic Brand Choice Models

“…This weakness can be alleviated by multiple random restarts or by hybrid algorithms. The latter use a stochastic method (e. g., stochastic backpropagation or a genetic algorithm) as first step, and fast optimization techniques as second step (e. g., Hruschka 2001).…”

Data Analysis and Decision Support

“…The artificial neural net attraction (ANNAM) model (Hruschka (2001)) is similar to the NN-MNL model, but has brand-specific coefficients and brandspecific numbers of hidden units Qi:…”