Market segmentation : conceptual and methodological foundations /

Market segmentation : conceptual and methodological foundations /

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Bibliographic Details
Author / Creator:Wedel, Michel.
Imprint:Boston : Kluwer Academic, c1998.
Description:xxii, 378 p. : ill. ; 24 cm.
Language:English
Series:International series in quantitative marketing
Subject:
Market segmentation.
Market segmentation -- Statistical methods.
Market segmentation.
Market segmentation -- Statistical methods.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/2921756
Hidden Bibliographic Details
Other authors / contributors:Kamakura, Wagner A. (Wagner Antonio)
ISBN:0792380711 (alk. paper)
Notes:Includes bibliographical references (p. [339]-365) and index.
Table of Contents:
  • Part 1. Introduction
  • 1. The Historical Development of the Market Segmentation Concept
  • 2. Segmentation Bases
  • Observable General Bases
  • Observable Product-Specific Base
  • Unobservable General Bases
  • Unobservable Product-Specific Bases
  • Conclusion
  • 3. Segmentation Methods
  • A-Priori Descriptive Methods
  • Post-Hoc Descriptive Methods
  • A-Priori Predictive Methods
  • Post-Hoc Predictive Methods
  • Normative Segmentation Methods
  • Conclusion
  • 4. Tools for Market Segmentation
  • Part 2. Segmentation Methodology
  • 5. Clustering Methods
  • Example of the Clustering Approach to Market Segmentation
  • Nonoverlapping Hierarchical Methods
  • Similarity Measures
  • Agglomerative Cluster Algorithms
  • Divisive Cluster Algorithms
  • Ultrametric and Additive Trees
  • Hierarchical Clusterwise Regression
  • Nonoverlapping Nonhierarchical Methods
  • Nonhierarchical Algorithms
  • Determining the number of Clusters
  • Nonhierarchical Clusterwise Regression
  • Miscellaneous Issues in Nonoverlapping Clustering
  • Variable Weighting, Standardization and Selection
  • Outliers and Missing Values
  • Non-uniqueness and Inversions
  • Cluster Validation
  • Cluster Analysis Under Various Sampling Strategies
  • Stratified samples
  • Cluster samples
  • Two-stage samples
  • Overlapping and Fuzzy Methods
  • Overlapping Clustering
  • Overlapping Clusterwise Regression
  • Fuzzy Clustering
  • Market Segmentation Applications of Clustering
  • 6. Mixture Models
  • Mixture Model Examples
  • Example 1. Purchase Frequency of Candy
  • Example 2. Adoption of Innovation
  • Mixture Distributions (MIX)
  • Maximum Likelihood Estimation
  • The EM Algorithm
  • EM Example
  • Limitations of the EM Algorithm
  • Local maxima
  • Standard errors
  • Identification
  • Determining the Number of Segments
  • Some Consequences of Complex Sampling Strategies for the Mixture Approach
  • Marketing Applications of Mixtures
  • Conclusion
  • 7. Mixture Regression Models
  • Examples of the Mixture Regression Approach
  • Example 1. Trade Show Performance
  • Example 2. Nested Logit Analysis of Scanner Data
  • A Generalized Mixture Regression Model (GLIMMIX)
  • EM Estimation
  • EM Example
  • Standard Errors and Residuals
  • Identification
  • Monte Carlo Study of the GLIMMIX Algorithm
  • Study Design
  • Results
  • Marketing Applications of Mixture Regression Models
  • Normal Data
  • Binary Data
  • Multichotomous Choice Data
  • Count Data
  • Choice and Count Data
  • Response-Time Data
  • Conjoint Analysis
  • Conclusion
  • Appendix A1. The EM Algorithm for the GLIMMIX Model
  • The EM Algorithm
  • The E-Step
  • The M-Step
  • 8. Mixture Unfolding Models
  • Examples of Stochastic Mixture Unfolding Models
  • Example 1. Television Viewing
  • Example 2. Mobile Telephone Judgements
  • A General Family of Stochastic Mixture Unfolding Models
  • EM Estimation
  • Some Limitations
  • Issues in Identification
  • Model Selection
  • Synthetic Data Analysis
  • Marketing Applications
  • Normal Data
  • Binomial Data
  • Poisson, Multinomial and Dirichlet Data
  • Conclusion
  • Appendix A2. The EM Algorithm for the STUNMIX Model
  • The E-Step
  • The M-step
  • 9. Profiling Segments
  • Profiling Segments with Demographic Variables
  • Examples of Concomitant Variable Mixture Models
  • Example 1. Paired Comparisons of Food Preferences
  • Example 2. Consumer Choice Behavior with Respect to Ketchup
  • The Concomitant Variable Mixture Model
  • Estimation
  • Model Selection and Identification
  • Monte Carlo Study
  • Alternative Mixture Models with Concomitant Variables
  • Marketing Applications
  • Conclusions
  • 10. Dynamic Segmentation
  • Models for Manifest Change
  • Example 1. The Mixed Markov Model for Brand Switching
  • Example 2. Mixture Hazard Model for Segment Change
  • Models for Latent Change
  • Dynamic Concomitant Variable Mixture Regression Models
  • Latent Markov Mixture Regression Models
  • Estimation
  • Examples of the Latent Change Approach
  • Example 1. The Latent Markov Model for Brand Switching
  • Example 2. Evolutionary Segmentation of Brand Switching
  • Example 3. Latent Change in Recurrent Choice
  • Marketing Applications
  • Conclusion
  • Appendix A3. Computer Software for Mixture models
  • Panmark
  • Lem
  • Glimmix
  • Part 3. Special Topics in Market Segmentation
  • 11. Joint Segmentation
  • Joint Segmentation
  • The Joint Segmentation Model
  • Synthetic Data Illustration
  • Banking Services
  • Conclusion
  • 12. Market Segmentation with Tailored Interviewing
  • Tailored Interviewing
  • Tailored Interviewing for Market Segmentation
  • Model Calibration
  • Prior Membership Probabilities
  • Revising the Segment Membership Probabilities
  • Item Selection
  • Stopping Rule
  • Application to Life-Style Segmentation
  • Life-Style Segmentation
  • Data Description
  • Model Calibration
  • Profile of the Segments
  • The Tailored Interviewing Procedure
  • Characteristics of the Tailored Interview
  • Quality of the Classification
  • Conclusion
  • 13. Model-Based Segmentation Using Structural Equation Models
  • Introduction to Structural Equation Models
  • A-Priori Segmentation Approach
  • Post Hoc Segmentation Approach
  • Application to Customer Satisfaction
  • The Mixture of Structural Equations Model
  • Special Cases of the Model
  • Analysis of Synthetic Data
  • Conclusion
  • 14. Segmentation Based on Product Dissimilarity Judgements
  • Spatial Models
  • Tree Models
  • Mixtures of Spaces and Mixtures of Trees
  • Mixture of Spaces and Trees
  • Conclusion
  • Part 4. Applied Market Segmentation
  • 15. General Observable Bases: Geo-demographics
  • Applications of Geo-demographic Segmentation
  • Commercial Geo-demographic Systems
  • PRIZM (Potential Rating Index for ZIP Markets)
  • ACORN (A Classification of Residential Neighborhoods)
  • The Geo-demographic System of Geo-Marktprofiel
  • Methodology
  • Linkages and Datafusion
  • Conclusion
  • 16. General Unobservable Bases: Values and Lifestyles
  • Activities, Interests and Opinions
  • Values and Lifestyles
  • Rokeach's Value Survey
  • The List of Values (LOV) Scale
  • The Values and Lifestyles (VALS) Survey
  • Applications of Lifestyle Segmentation
  • Conclusion
  • 17. Product-specific observable Bases: Response-based Segmentation
  • The Information Revolution and Marketing Research
  • Diffusion of Information Technology
  • Early Approaches to Heterogeneity
  • Household-Level Single-Source Data
  • Consumer Heterogeneity in Response to Marketing Stimuli
  • Models with Exogenous Indicators of Preferences
  • Fixed-Effects Models
  • Random-Intercepts and Random Coefficients Models
  • Response-Based Segmentation
  • Example of Response-Based Segmentation with Single Source Scanner Data
  • Extensions
  • Conclusion
  • 18. Product-Specific Unobservable Bases: Conjoint Analysis
  • Conjoint Analysis in Marketing
  • Choice of the Attributes and Levels
  • Types of Attributes
  • Number of Attributes
  • Attribute Levels
  • Stimulus Set Construction
  • Stimulus Presentation
  • Data Collection and Measurement Scales
  • Preference Models and Estimation Methods
  • Choice Simulations
  • Market Segmentation with Conjoint Analysis
  • Application of Conjoint Segmentation with Constant Sum Response Data
  • Market Segmentation with Metric Conjoint Analysis
  • A-Priori and Post-Hoc Methods Based on Demographics
  • Componential Segmentation
  • Two-Stage Procedures
  • Hagerty's Method
  • Hierarchical and Non-Hierarchical Clusterwise Regression
  • Mixture Regression Approach
  • A Monte Carlo Comparison of Metric Conjoint Segmentation Approaches
  • The Monte Carlo Study
  • Results
  • Predictive Accuracy
  • Segmentation for Rank-Order and Choice Data
  • A-Priori and Post-Hoc Approaches to Segmentation
  • Two-Stage Procedures
  • Hierarchical and Non-hierarchical Clusterwise Regression
  • The Mixture Regression Approach for Rank-Order and Choice Data
  • Application of Mixture Logit Regression to Conjoint Segmentation
  • Results
  • Conclusion
  • Part 5. Conclusions and Directions for Future Research
  • 19. Conclusions: Representations of Heterogeneity
  • Continuous Distribution of Heterogeneity versus Market Segments
  • Continuous or Discrete
  • ML or MCMC
  • Managerial relevance
  • Individual Level versus Segment Level Analysis
  • 20. Directions for Future Research
  • The Past
  • Segmentation Strategy
  • Agenda for Future Research
  • References
  • Index
Purchased through the Fairfax M. Cone Book Fund Established by his Friends at Foote, Cone & Belding bookplate

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