Market Segmentation

Name: Market Segmentation
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ISBN: 9780792386353

Conceptual and Methodological Foundations

By: Michel Wedel, Wagner A. Kamakura

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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.

Introduction	p. 1
The Historical Development of the Market Segmentation Concept	p. 3
Segmentation Bases	p. 7
Observable General Bases	p. 8
Observable Product-Specific Base	p. 10
Unobservable General Bases	p. 11
Unobservable Product-Specific Bases	p. 14
Conclusion	p. 16
Segmentation Methods	p. 17
A-Priori Descriptive Methods	p. 18
Post-Hoc Descriptive Methods	p. 19
A-Priori Predictive Methods	p. 22
Post-Hoc Predictive Methods	p. 23
Normative Segmentation Methods	p. 26
Conclusion	p. 28
Tools for Market Segmentation	p. 31
Segmentation Methodology	p. 37
Clustering Methods	p. 39
Example of the Clustering Approach to Market Segmentation	p. 42
Nonoverlapping Hierarchical Methods	p. 43
Similarity Measures	p. 44
Agglomerative Cluster Algorithms	p. 48
Divisive Cluster Algorithms	p. 50
Ultrametric and Additive Trees	p. 50
Hierarchical Clusterwise Regression	p. 51
Nonoverlapping Nonhierarchical Methods	p. 52
Nonhierarchical Algorithms	p. 53
Determining the number of Clusters	p. 54
Nonhierarchical Clusterwise Regression	p. 55
Miscellaneous Issues in Nonoverlapping Clustering	p. 56
Variable Weighting, Standardization and Selection	p. 56
Outliers and Missing Values	p. 58
Non-uniqueness and Inversions	p. 59
Cluster Validation	p. 59
Cluster Analysis Under Various Sampling Strategies	p. 60
Stratified samples	p. 60
Cluster samples	p. 62
Two-stage samples	p. 63
Overlapping and Fuzzy Methods	p. 64
Overlapping Clustering	p. 64
Overlapping Clusterwise Regression	p. 65
Fuzzy Clustering	p. 65
Market Segmentation Applications of Clustering	p. 69
Mixture Models	p. 75
Mixture Model Examples	p. 75
Purchase Frequency of Candy	p. 75
Adoption of Innovation	p. 76
Mixture Distributions (MIX)	p. 77
Maximum Likelihood Estimation	p. 80
The EM Algorithm	p. 84
EM Example	p. 86
Limitations of the EM Algorithm	p. 88
Local maxima	p. 88
Standard errors	p. 88
Identification	p. 90
Determining the Number of Segments	p. 91
Some Consequences of Complex Sampling Strategies for the Mixture Approach	p. 94
Marketing Applications of Mixtures	p. 96
Conclusion	p. 99
Mixture Regression Models	p. 101
Examples of the Mixture Regression Approach	p. 102
Trade Show Performance	p. 102
Nested Logit Analysis of Scanner Data	p. 103
A Generalized Mixture Regression Model (GLIMMIX)	p. 106
EM Estimation	p. 108
EM Example	p. 108
Standard Errors and Residuals	p. 109
Identification	p. 109
Monte Carlo Study of the GLIMMIX Algorithm	p. 110
Study Design	p. 110
Results	p. 112
Marketing Applications of Mixture Regression Models	p. 112
Normal Data	p. 113
Binary Data	p. 113
Multichotomous Choice Data	p. 115
Count Data	p. 116
Choice and Count Data	p. 116
Response-Time Data	p. 117
Conjoint Analysis	p. 117
Conclusion	p. 119
The EM Algorithm for the GLIMMIX Model	p. 120
The EM Algorithm	p. 120
The E-Step	p. 121
The M-Step	p. 121
Mixture Unfolding Models	p. 125
Examples of Stochastic Mixture Unfolding Models	p. 127
Television Viewing	p. 127
Mobile Telephone Judgements	p. 128
A General Family of Stochastic Mixture Unfolding Models	p. 131
EM Estimation	p. 133
Some Limitations	p. 133
Issues in Identification	p. 134
Model Selection	p. 134
Synthetic Data Analysis	p. 136
Marketing Applications	p. 138
Normal Data	p. 138
Binomial Data	p. 140
Poisson, Multinomial and Dirichlet Data	p. 140
Conclusion	p. 140
The EM Algorithm for the STUNMIX Model	p. 142
The E-Step	p. 142
The M-step	p. 142
Profiling Segments	p. 145
Profiling Segments with Demographic Variables	p. 145
Examples of Concomitant Variable Mixture Models	p. 146
Paired Comparisons of Food Preferences	p. 146
Consumer Choice Behavior with Respect to Ketchup	p. 147
The Concomitant Variable Mixture Model	p. 150
Estimation	p. 152
Model Selection and Identification	p. 152
Monte Carlo Study	p. 152
Alternative Mixture Models with Concomitant Variables	p. 153
Marketing Applications	p. 156
Conclusions	p. 156
Dynamic Segmentation	p. 159
Models for Manifest Change	p. 160
The Mixed Markov Model for Brand Switching	p. 161
Mixture Hazard Model for Segment Change	p. 162
Models for Latent Change	p. 167
Dynamic Concomitant Variable Mixture Regression Models	p. 167
Latent Markov Mixture Regression Models	p. 168
Estimation	p. 169
Examples of the Latent Change Approach	p. 170
The Latent Markov Model for Brand Switching	p. 170
Evolutionary Segmentation of Brand Switching	p. 171
Latent Change in Recurrent Choice	p. 175
Marketing Applications	p. 176
Conclusion	p. 176
Computer Software for Mixture models	p. 178
Panmark	p. 178
Lem	p. 179
Glimmix	p. 181
Special Topics in Market Segmentation	p. 187
Joint Segmentation	p. 189
Joint Segmentation	p. 189
The Joint Segmentation Model	p. 189
Synthetic Data Illustration	p. 191
Banking Services	p. 192
Conclusion	p. 194
Market Segmentation with Tailored Interviewing	p. 195
Tailored Interviewing	p. 195
Tailored Interviewing for Market Segmentation	p. 198
Model Calibration	p. 199
Prior Membership Probabilities	p. 200
Revising the Segment Membership Probabilities	p. 201
Item Selection	p. 202
Stopping Rule	p. 202
Application to Life-Style Segmentation	p. 203
Life-Style Segmentation	p. 203
Data Description	p. 203
Model Calibration	p. 203
Profile of the Segments	p. 204
The Tailored Interviewing Procedure	p. 209
Characteristics of the Tailored Interview	p. 209
Quality of the Classification	p. 211
Conclusion	p. 214
Model-Based Segmentation Using Structural Equation Models	p. 217
Introduction to Structural Equation Models	p. 217
A-Priori Segmentation Approach	p. 222
Post Hoc Segmentation Approach	p. 223
Application to Customer Satisfaction	p. 223
The Mixture of Structural Equations Model	p. 225
Special Cases of the Model	p. 226
Analysis of Synthetic Data	p. 227
Conclusion	p. 229
Segmentation Based on Product Dissimilarity Judgements	p. 231
Spatial Models	p. 231
Tree Models	p. 232
Mixtures of Spaces and Mixtures of Trees	p. 235
Mixture of Spaces and Trees	p. 238
Conclusion	p. 238
Applied Market Segmentation	p. 239
General Observable Bases: Geo-demographics	p. 241
Applications of Geo-demographic Segmentation	p. 242
Commercial Geo-demographic Systems	p. 244
PRIZM (Potential Rating Index for ZIP Markets)	p. 244
ACORN (A Classification of Residential Neighborhoods)	p. 247
The Geo-demographic System of Geo-Marktprofiel	p. 248
Methodology	p. 254
Linkages and Datafusion	p. 256
Conclusion	p. 257
General Unobservable Bases: Values and Lifestyles	p. 259
Activities, Interests and Opinions	p. 260
Values and Lifestyles	p. 261
Rokeach's Value Survey	p. 261
The List of Values (LOV) Scale	p. 265
The Values and Lifestyles (VALS) Survey	p. 266
Applications of Lifestyle Segmentation	p. 268
Conclusion	p. 276
Product-specific observable Bases: Response-based Segmentation	p. 277
The Information Revolution and Marketing Research	p. 277
Diffusion of Information Technology	p. 277
Early Approaches to Heterogeneity	p. 278
Household-Level Single-Source Data	p. 279
Consumer Heterogeneity in Response to Marketing Stimuli	p. 282
Models with Exogenous Indicators of Preferences	p. 283
Fixed-Effects Models	p. 283
Random-Intercepts and Random Coefficients Models	p. 284
Response-Based Segmentation	p. 285
Example of Response-Based Segmentation with Single Source Scanner Data	p. 286
Extensions	p. 288
Conclusion	p. 292
Product-Specific Unobservable Bases: Conjoint Analysis	p. 295
Conjoint Analysis in Marketing	p. 295
Choice of the Attributes and Levels	p. 296
Types of Attributes	p. 296
Number of Attributes	p. 297
Attribute Levels	p. 298
Stimulus Set Construction	p. 298
Stimulus Presentation	p. 299
Data Collection and Measurement Scales	p. 300
Preference Models and Estimation Methods	p. 301
Choice Simulations	p. 302
Market Segmentation with Conjoint Analysis	p. 303
Application of Conjoint Segmentation with Constant Sum Response Data	p. 303
Market Segmentation with Metric Conjoint Analysis	p. 305
A-Priori and Post-Hoc Methods Based on Demographics	p. 306
Componential Segmentation	p. 306
Two-Stage Procedures	p. 306
Hagerty's Method	p. 307
Hierarchical and Non-Hierarchical Clusterwise Regression	p. 307
Mixture Regression Approach	p. 308
A Monte Carlo Comparison of Metric Conjoint Segmentation Approaches	p. 310
The Monte Carlo Study	p. 310
Results	p. 312
Predictive Accuracy	p. 313
Segmentation for Rank-Order and Choice Data	p. 314
A-Priori and Post-Hoc Approaches to Segmentation	p. 315
Two-Stage Procedures	p. 315
Hierarchical and Non-hierarchical Clusterwise Regression	p. 316
The Mixture Regression Approach for Rank-Order and Choice Data	p. 316
Application of Mixture Logit Regression to Conjoint Segmentation	p. 318
Results	p. 319
Conclusion	p. 320
Conclusions and Directions for Future Research	p. 323
Conclusions: Representations of Heterogeneity	p. 325
Continuous Distribution of Heterogeneity versus Market Segments	p. 325
Continuous or Discrete	p. 326
ML or MCMC	p. 327
Managerial relevance	p. 329
Individual Level versus Segment Level Analysis	p. 331
Directions for Future Research	p. 335
The Past	p. 335
Segmentation Strategy	p. 336
Agenda for Future Research	p. 341
References	p. 345
Index	p. 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
Publisher: SPRINGER VERLAG GMBH
Country of Publication: US
Dimensions (cm): 24.23 x 16.2 x 2.74
Weight (kg): 0.8
Edition Number: 2
Edition Type: Revised