Discrete choice models have proven to be good methods for predicting market shares for new products based on consumers ' expressed preferences between choice alternatives. However, the standard aggregate model fails to take into account the fact that preferences (utilities) differ from one respondent to another (or at least from one segment to another). This failure often yields poor share predictions. The most popular remedy for this problem has been to use a mixture model. In this paper, we provide insights into this problem and illustrate the solution posed by latent class (LC) finite mixture models. We also describe several recent advances in the development of LC models for choice which have been implemented in a new computer program (Vermunt and Magidson, 2003a). We conclude with a comparison of the LC finite mixture approach with the Hierarchical Bayes (HB) continuous mixture approach to choice modeling in a case study involving boots. We find that while both models provide comparable predictions, the LC models take much less time to estimate. In addition, the discrete nature of the LC model makes it more useful for identifying market segments and providing within-segment share predictions.

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www.elsevier.com/locate/ijresmar Recovering and profiling the true segmentation structure in markets: an empirical investigation

Because consumers are limited information processors seeking to conserve cognitive energy, it is likely that at least some use identical decision heuristics across product categories. This study develops a finite mixture logit model that identifies segments of households with identical behaviors across product categories. The proposed model is shown to fit in-sample choices and forecast out-of-sample choices at least as well as an unrestricted model in which all choice behaviors are independent across product categories. The results show that about 32 % of the sample households have choice behaviors that are identical across at least two of the three product categories studied, while the remaining households have choice behaviors that are independent across all three categories. The empirical results show that the segment with identical behaviors is quite price sensitive, not at all sensitive to store feature advertising, and not very brand- or size-loyal. These households are more likely to have larger families and marginally lower incomes and to shop less frequently and spend less per shopping trip. They are also lighter users in two of the three product categories investigated. Implications of the model for manufacturers and retailers are discussed. D 2002 Published by Elsevier Science B.V.

Marketing and engineering design decisions are typically treated as separate tasks both in the academic literature and in industrial practice, and their interdisciplinary interactions are not welldefined. In this article, analytical target cascading (ATC), a hierarchical optimization methodology, is used to frame a formal optimization model that links marketing and engineering design decision-making models by defining and coordinating interactions between the two. For complex products, engineering constraints typically restrict the ability to achieve some desirable combinations of product characteristic targets, and the ATC process acts to guide marketing in setting achievable targets while designing feasible products that meet those targets. The model is demonstrated with a case study on the design of household scales.