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Market Segmentation : Conceptual and Methodological Foundations - Michel Wedel

Market Segmentation

Conceptual and Methodological Foundations

Hardcover Published: 31st December 1999
ISBN: 9780792386353
Number Of Pages: 382

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Modern marketing techniques in industrialized countries cannot be implemented without segmentation of the potential market. Goods are no longer produced and sold without a significant consideration of customer needs combined with a recognition that these needs are heterogeneous. Since first emerging in the late 1950s, the concept of segmentation has been one of the most researched topics in the marketing literature. Segmentation has become a central topic to both the theory and practice of marketing, particularly in the recent development of finite mixture models to better identify market segments.
This second edition of Market Segmentation updates and extends the integrated examination of segmentation theory and methodology begun in the first edition. A chapter on mixture model analysis of paired comparison data has been added, together with a new chapter on the pros and cons of the mixture model. The book starts with a framework for considering the various bases and methods available for conducting segmentation studies. The second section contains a more detailed discussion of the methodology for market segmentation, from traditional clustering algorithms to more recent developments in finite mixtures and latent class models. Three types of finite mixture models are discussed in this second section: simple mixtures, mixtures of regressions and mixtures of unfolding models. The third main section is devoted to special topics in market segmentation such as joint segmentation, segmentation using tailored interviewing and segmentation with structural equation models. The fourth part covers four major approaches to applied market segmentation: geo-demographic, lifestyle, response-based, and conjoint analysis. The final concluding section discusses directions for further research.

Introductionp. 1
The Historical Development of the Market Segmentation Conceptp. 3
Segmentation Basesp. 7
Observable General Basesp. 8
Observable Product-Specific Basep. 10
Unobservable General Basesp. 11
Unobservable Product-Specific Basesp. 14
Conclusionp. 16
Segmentation Methodsp. 17
A-Priori Descriptive Methodsp. 18
Post-Hoc Descriptive Methodsp. 19
A-Priori Predictive Methodsp. 22
Post-Hoc Predictive Methodsp. 23
Normative Segmentation Methodsp. 26
Conclusionp. 28
Tools for Market Segmentationp. 31
Segmentation Methodologyp. 37
Clustering Methodsp. 39
Example of the Clustering Approach to Market Segmentationp. 42
Nonoverlapping Hierarchical Methodsp. 43
Similarity Measuresp. 44
Agglomerative Cluster Algorithmsp. 48
Divisive Cluster Algorithmsp. 50
Ultrametric and Additive Treesp. 50
Hierarchical Clusterwise Regressionp. 51
Nonoverlapping Nonhierarchical Methodsp. 52
Nonhierarchical Algorithmsp. 53
Determining the number of Clustersp. 54
Nonhierarchical Clusterwise Regressionp. 55
Miscellaneous Issues in Nonoverlapping Clusteringp. 56
Variable Weighting, Standardization and Selectionp. 56
Outliers and Missing Valuesp. 58
Non-uniqueness and Inversionsp. 59
Cluster Validationp. 59
Cluster Analysis Under Various Sampling Strategiesp. 60
Stratified samplesp. 60
Cluster samplesp. 62
Two-stage samplesp. 63
Overlapping and Fuzzy Methodsp. 64
Overlapping Clusteringp. 64
Overlapping Clusterwise Regressionp. 65
Fuzzy Clusteringp. 65
Market Segmentation Applications of Clusteringp. 69
Mixture Modelsp. 75
Mixture Model Examplesp. 75
Purchase Frequency of Candyp. 75
Adoption of Innovationp. 76
Mixture Distributions (MIX)p. 77
Maximum Likelihood Estimationp. 80
The EM Algorithmp. 84
EM Examplep. 86
Limitations of the EM Algorithmp. 88
Local maximap. 88
Standard errorsp. 88
Identificationp. 90
Determining the Number of Segmentsp. 91
Some Consequences of Complex Sampling Strategies for the Mixture Approachp. 94
Marketing Applications of Mixturesp. 96
Conclusionp. 99
Mixture Regression Modelsp. 101
Examples of the Mixture Regression Approachp. 102
Trade Show Performancep. 102
Nested Logit Analysis of Scanner Datap. 103
A Generalized Mixture Regression Model (GLIMMIX)p. 106
EM Estimationp. 108
EM Examplep. 108
Standard Errors and Residualsp. 109
Identificationp. 109
Monte Carlo Study of the GLIMMIX Algorithmp. 110
Study Designp. 110
Resultsp. 112
Marketing Applications of Mixture Regression Modelsp. 112
Normal Datap. 113
Binary Datap. 113
Multichotomous Choice Datap. 115
Count Datap. 116
Choice and Count Datap. 116
Response-Time Datap. 117
Conjoint Analysisp. 117
Conclusionp. 119
The EM Algorithm for the GLIMMIX Modelp. 120
The EM Algorithmp. 120
The E-Stepp. 121
The M-Stepp. 121
Mixture Unfolding Modelsp. 125
Examples of Stochastic Mixture Unfolding Modelsp. 127
Television Viewingp. 127
Mobile Telephone Judgementsp. 128
A General Family of Stochastic Mixture Unfolding Modelsp. 131
EM Estimationp. 133
Some Limitationsp. 133
Issues in Identificationp. 134
Model Selectionp. 134
Synthetic Data Analysisp. 136
Marketing Applicationsp. 138
Normal Datap. 138
Binomial Datap. 140
Poisson, Multinomial and Dirichlet Datap. 140
Conclusionp. 140
The EM Algorithm for the STUNMIX Modelp. 142
The E-Stepp. 142
The M-stepp. 142
Profiling Segmentsp. 145
Profiling Segments with Demographic Variablesp. 145
Examples of Concomitant Variable Mixture Modelsp. 146
Paired Comparisons of Food Preferencesp. 146
Consumer Choice Behavior with Respect to Ketchupp. 147
The Concomitant Variable Mixture Modelp. 150
Estimationp. 152
Model Selection and Identificationp. 152
Monte Carlo Studyp. 152
Alternative Mixture Models with Concomitant Variablesp. 153
Marketing Applicationsp. 156
Conclusionsp. 156
Dynamic Segmentationp. 159
Models for Manifest Changep. 160
The Mixed Markov Model for Brand Switchingp. 161
Mixture Hazard Model for Segment Changep. 162
Models for Latent Changep. 167
Dynamic Concomitant Variable Mixture Regression Modelsp. 167
Latent Markov Mixture Regression Modelsp. 168
Estimationp. 169
Examples of the Latent Change Approachp. 170
The Latent Markov Model for Brand Switchingp. 170
Evolutionary Segmentation of Brand Switchingp. 171
Latent Change in Recurrent Choicep. 175
Marketing Applicationsp. 176
Conclusionp. 176
Computer Software for Mixture modelsp. 178
Panmarkp. 178
Lemp. 179
Glimmixp. 181
Special Topics in Market Segmentationp. 187
Joint Segmentationp. 189
Joint Segmentationp. 189
The Joint Segmentation Modelp. 189
Synthetic Data Illustrationp. 191
Banking Servicesp. 192
Conclusionp. 194
Market Segmentation with Tailored Interviewingp. 195
Tailored Interviewingp. 195
Tailored Interviewing for Market Segmentationp. 198
Model Calibrationp. 199
Prior Membership Probabilitiesp. 200
Revising the Segment Membership Probabilitiesp. 201
Item Selectionp. 202
Stopping Rulep. 202
Application to Life-Style Segmentationp. 203
Life-Style Segmentationp. 203
Data Descriptionp. 203
Model Calibrationp. 203
Profile of the Segmentsp. 204
The Tailored Interviewing Procedurep. 209
Characteristics of the Tailored Interviewp. 209
Quality of the Classificationp. 211
Conclusionp. 214
Model-Based Segmentation Using Structural Equation Modelsp. 217
Introduction to Structural Equation Modelsp. 217
A-Priori Segmentation Approachp. 222
Post Hoc Segmentation Approachp. 223
Application to Customer Satisfactionp. 223
The Mixture of Structural Equations Modelp. 225
Special Cases of the Modelp. 226
Analysis of Synthetic Datap. 227
Conclusionp. 229
Segmentation Based on Product Dissimilarity Judgementsp. 231
Spatial Modelsp. 231
Tree Modelsp. 232
Mixtures of Spaces and Mixtures of Treesp. 235
Mixture of Spaces and Treesp. 238
Conclusionp. 238
Applied Market Segmentationp. 239
General Observable Bases: Geo-demographicsp. 241
Applications of Geo-demographic Segmentationp. 242
Commercial Geo-demographic Systemsp. 244
PRIZM (Potential Rating Index for ZIP Markets)p. 244
ACORN (A Classification of Residential Neighborhoods)p. 247
The Geo-demographic System of Geo-Marktprofielp. 248
Methodologyp. 254
Linkages and Datafusionp. 256
Conclusionp. 257
General Unobservable Bases: Values and Lifestylesp. 259
Activities, Interests and Opinionsp. 260
Values and Lifestylesp. 261
Rokeach's Value Surveyp. 261
The List of Values (LOV) Scalep. 265
The Values and Lifestyles (VALS) Surveyp. 266
Applications of Lifestyle Segmentationp. 268
Conclusionp. 276
Product-specific observable Bases: Response-based Segmentationp. 277
The Information Revolution and Marketing Researchp. 277
Diffusion of Information Technologyp. 277
Early Approaches to Heterogeneityp. 278
Household-Level Single-Source Datap. 279
Consumer Heterogeneity in Response to Marketing Stimulip. 282
Models with Exogenous Indicators of Preferencesp. 283
Fixed-Effects Modelsp. 283
Random-Intercepts and Random Coefficients Modelsp. 284
Response-Based Segmentationp. 285
Example of Response-Based Segmentation with Single Source Scanner Datap. 286
Extensionsp. 288
Conclusionp. 292
Product-Specific Unobservable Bases: Conjoint Analysisp. 295
Conjoint Analysis in Marketingp. 295
Choice of the Attributes and Levelsp. 296
Types of Attributesp. 296
Number of Attributesp. 297
Attribute Levelsp. 298
Stimulus Set Constructionp. 298
Stimulus Presentationp. 299
Data Collection and Measurement Scalesp. 300
Preference Models and Estimation Methodsp. 301
Choice Simulationsp. 302
Market Segmentation with Conjoint Analysisp. 303
Application of Conjoint Segmentation with Constant Sum Response Datap. 303
Market Segmentation with Metric Conjoint Analysisp. 305
A-Priori and Post-Hoc Methods Based on Demographicsp. 306
Componential Segmentationp. 306
Two-Stage Proceduresp. 306
Hagerty's Methodp. 307
Hierarchical and Non-Hierarchical Clusterwise Regressionp. 307
Mixture Regression Approachp. 308
A Monte Carlo Comparison of Metric Conjoint Segmentation Approachesp. 310
The Monte Carlo Studyp. 310
Resultsp. 312
Predictive Accuracyp. 313
Segmentation for Rank-Order and Choice Datap. 314
A-Priori and Post-Hoc Approaches to Segmentationp. 315
Two-Stage Proceduresp. 315
Hierarchical and Non-hierarchical Clusterwise Regressionp. 316
The Mixture Regression Approach for Rank-Order and Choice Datap. 316
Application of Mixture Logit Regression to Conjoint Segmentationp. 318
Resultsp. 319
Conclusionp. 320
Conclusions and Directions for Future Researchp. 323
Conclusions: Representations of Heterogeneityp. 325
Continuous Distribution of Heterogeneity versus Market Segmentsp. 325
Continuous or Discretep. 326
ML or MCMCp. 327
Managerial relevancep. 329
Individual Level versus Segment Level Analysisp. 331
Directions for Future Researchp. 335
The Pastp. 335
Segmentation Strategyp. 336
Agenda for Future Researchp. 341
Referencesp. 345
Indexp. 371
Table of Contents provided by Syndetics. All Rights Reserved.

ISBN: 9780792386353
ISBN-10: 0792386353
Series: International Quantitative Marketing
Audience: General
Format: Hardcover
Language: English
Number Of Pages: 382
Published: 31st December 1999
Country of Publication: US
Dimensions (cm): 24.23 x 16.2  x 2.74
Weight (kg): 0.8
Edition Number: 2
Edition Type: Revised

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