Modeling Brand Choice Using Boosted and Stacked Neural Networks

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Author(s): Rob Potharst (Erasmus University Rotterdam, The Netherlands), Michiel V. Rijthoven (Oracle Nederland BV, The Netherlands)and Michiel C.V. Wezel (Erasmus University Rotterdam, The Netherlands)
Copyright: 2006
Pages: 20
Source title: Business Applications and Computational Intelligence
Source Author(s)/Editor(s): Kevin Voges (University of Canterbury, New Zealand)and Nigel Pope (Griffith University, Australia)
DOI: 10.4018/978-1-59140-702-7.ch005

Keywords: Business & Management / Business Information Systems / Business Intelligence / Business Science Reference

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Abstract

Starting with a review of some classical quantitative methods for modeling customer behavior in the brand choice situation, some new methods are explained which are based on recently developed techniques from data mining and artificial intelligence: boosting and/or stacking neural network models. The main advantage of these new methods is the gain in predictive performance that is often achieved, which in a marketing setting directly translates into increased reliability of expected market share estimates. The new models are applied to a well-known data set containing scanner data on liquid detergent purchases. The performance of the new models on this data set is compared with results from the marketing literature. Finally, the developed models are applied to some practical marketing issues such as predicting the effect of different pricing schemes upon market share.

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Modeling Brand Choice Using Boosted and Stacked Neural Networks

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