Titlebook: Building Models for Marketing Decisions; Peter S. H. Leeflang,Dick R. Wittink,Philippe A. N Book 2000 Springer Science+Business Media Dord

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Multi-Criteria Decision Support Methodse should ideally satisfy in order to stand a good chance of being implemented. The likelihood of model acceptance depends also, as indicated in Chapter 4, on other criteria which have little or nothing to do with model structure. We consider those other criteria in Chapter 19.

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Mathematical Background to Decision Makingof the (early) 1980s and (early) 1990s was characterized by minimal growth in many markets. Further, a higher rate of new-product introductions and reactions to new entries2 may result from as well as contribute to a higher intensity of competition.

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State Space System Representationsands in the detergent product category. Each individual brand may at some time have been positioned to serve a specific consumer segment. However, if each brand manager maximizes the performance of a brand independently of other brands in the same category sold by the same firm, then the distinction

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International Series in Quantitative Marketinghttp://image.papertrans.cn/b/image/191698.jpg

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Classifying marketing models according to degree of explicitnessmber of ways of representing these elements, the differentiating dimension being the “degree of explicitness”. We distinguish implicit models, verbal models, formalized models, and numerically specified models, and illustrate different methods of representing systems with an example.