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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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期刊全称Building Models for Marketing Decisions
影响因子2023Peter S. H. Leeflang,Dick R. Wittink,Philippe A. N
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学科分类International Series in Quantitative Marketing
图书封面Titlebook: Building Models for Marketing Decisions;  Peter S. H. Leeflang,Dick R. Wittink,Philippe A. N Book 2000 Springer Science+Business Media Dord
影响因子This book is about marketing models and the process of model building. Our primary focus is on models that can be used by managers to support marketing decisions. It has long been known that simple models usually outperform judgments in predicting outcomes in a wide variety of contexts. For example, models of judgments tend to provide better forecasts of the outcomes than the judgments themselves (because the model eliminates the noise in judgments). And since judgments never fully reflect the complexities of the many forces that influence outcomes, it is easy to see why models of actual outcomes should be very attractive to (marketing) decision makers. Thus, appropriately constructed models can provide insights about structural relations between marketing variables. Since models explicate the relations, both the process of model building and the model that ultimately results can improve the quality of marketing decisions. Managers often use rules of thumb for decisions. For example, a brand manager will have defined a specific set of alternative brands as the competitive set within a product category. Usually this set is based on perceived similarities in brand characteristics, ad
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https://doi.org/10.1007/3-540-31224-2mber 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.
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Sustainable Development: An Overview,The classic approach to model building consists of three major parts:
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Mathematical Background to Decision MakingThus, stochastic models take the probabilistic nature of consumers’ behavior into account. By contrast, econometric models include an error component that may capture uncertainty due to model misspeciflcation (omitted variables, functional form) or measurement error.
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Case Study: Reduction of Domestic WasteIn this chapter we discuss specification issues that transcend the intended use of a model (Chapter 8), the specific level of demand (Chapter 9), the amount of behavioral detail (Chapter 10) and the specific models discussed in Chapters 11–13.
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