Building Implementable Marketing Models

I: Marketing Modelsl.- 1. Introduction.- 1.1. Purpose and outline.- 1.2. The model concept.- 2. Classifying Models According to Their Degree of Explicitness.- 2.1. Implicit models.- 2.2. Verbal models.- 2.3. Formalised models.- 2.4. Numerically specified models.- 3 Benefits From Using Marketing Models.- 3.1 Are marketing problems quantifiable?.- 3.2. Benefits from marketing decision models.- 3.3 Building models to advance our knowledge of marketing.- 3.4. On the use of a marketing model: A case study.- 4. A Typology of Marketing Models.- 4.1. Intended use: Descriptive, predictive, normative models.- 4.2. Demand models: Product class sales, brand sales, and market share models.- 4.3. Behavioural detail: None, some, a substantial amount.- II: Building Marketing Models.- 5. Elements of Model Building.- 5.1. The model building process.- 5.2. Some basic model building terminology.- 5.3. Specification of behaviour equations: Some simple examples.- 5.3.1. Mathematical forms.- 5.3.1.1. Models linear in parameters and variables.- 5.3.1.2. Models linear in the parameters but not in the variables.- 5.3.1.3. Models nonlinear in the parameters but linearizable.- 5.3.1.4. Models nonlinear in the parameters and not linearizable.- 5.3.2. Modelling marketing dynamics.- 5.3.2.1. One explanatory variable.- 5.3.2.2. Several explanatory variables.- 6. Implementation Criteria with Respect to Model Structure.- 6.1. Introduction.- 6.2. Implementation criteria.- 6.2.1. Models should be simple.- 6.2.2. Models should be built in an evolutionary way.- 6.2.3. Models should be complete on important issues.- 6.2.4. Models should be adaptive.- 6.2.5. Models should be robust.- 6.3. Can non-robust models be good models?.- 6.4. Robustness related to intended use.- 6.5. Robustness related to the problem situation.- 7. Specifying Models According to Intended Use.- 7.1. Descriptive models.- 7.2. Predictive models.- 7.3. Normative models.- 7.3.1. A profit maximization model.- 7.3.2. Allocation models.- Appendix The Dorfman-Steiner theorem.- 8. Specifying Models According to Different Levels of Demand.- 8.1. Product class sales models.- 8.2. Brand sales models.- 8.3. Market share models.- 9. Specifying Models According to Amount of Behavioural Detail.- 9.1. Models with no behavioural detail.- 9.2. Models with some behavioural detail.- 9.3. Models with a substantial amount of behavioural detail.- 10 Stochastic Consumer Behaviour Models.- 10.1. Brand choice and purchase incidence models.- 10.1.1. Brand choice models.- 10.1.1.1. Markov and Bernouilli models.- 10.1.1.2. Learning models.- 10.1.2. Purchase incidence models.- 10.2. Response models based on stochastic models of consumer behaviour.- 10.2.1. Response models based on Markov models.- 10.2.2. Response models based on learning models.- 10.2.3. Response models based on purchase incidence models.- 10.3. A normative model based on a stochastic consumer behaviour model.- 11. Parameterization.- 11.1. Organizing data.- 11.2. Estimating parameters in models with no behavioural detail.- 11.2.1. The linear model: Ordinary least squares.- 11.2.2. The linear model: Generalized least squares.- 11.2.3. Estimating parameters in simultaneous equation systems.- 11.2.4. Nonlinear estimation.- 11.3. Parameterization of models with some behavioural detail.- 11.3.1. Parameterization of models of intermediate market response.- 11.3.1.1. Descriptive brand choice models.- 11.3.1.2. Brand choice response models.- 11.3.2. Parameterization of aggregate flow models.- 11.4. Parameterization of models with a substantial amount of behavioural detail.- 11.5. Subjective estimation.- 11.5.1. A justification for subjective estimation.- 11.5.2. Obtaining subjective estimates.- 11.5.2.1. Point estimation.- 11.5.2.2. Probability assessment.- 11.5.3. Combining subjective estimates.- 11.5.3.1. The mathematical approach.- 11.5.3.2. The behavioural approach.- 11.5.4. Combining subjective and objective data.- 11.5.4.1. Formal analysis.- 11.5.4.2. Informal analysis.- 12. Validation.- 12.1. Measure reliability and validity.- 12.2. Face validity.- 12.3. Statistical validation criteria.- 12.3.1. Goodness of fit.- 12.3.2. Reliability of the estimates.- 12.3.3. Multicollinearity.- 12.3.4. Assumptions concerning the disturbance term.- 12.4. Validation criteria related to intended use.- 12.5. Validation of models with no behavioural detail.- 12.6. Validation of models with some behavioural detail.- 12.7. Validation of models with a substantial amount of behavioural detail.- 12.8. Validation of subjective estimates.- III: Building Implementable Marketing Models.- 13. Determinants of Model Implementation.- 13.1. Organization validity.- 13.1.1. Matching model and user.- 13.1.2. The model user-model builder interface.- 13.1.3. Contingencies specific to the organization.- 13.2. Elements of implementation strategy.- 13.2.1. Evolutionary model building.- 13.2.2. Model scope.- 13.2.3. Ease of use.- 14. Some Cost-Benefit Considerations in Marketing Model Building.- 14.1. The cost of building models.- 14.2. Measuring benefits.- 14.3. Some qualitative examples.- 14.4. General conclusion.- Author Index.