An Artificial Neural Net Attraction Model (ANNAM) to Analyze Market Share Effects of Marketing Instruments

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 can achieve greater exibility by using the neural net based approach introduced 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.