Market segmentation: conceptual and methodological foundations
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Boston [u.a.]
Kluwer
2000
|
| Ausgabe: | 2. ed. |
| Schriftenreihe: | International series in quantitative marketing
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=019835777&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XVII, 382 S. |
| ISBN: | 0792386353 |
Internformat
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Datensatz im Suchindex
| _version_ | 1819254953069248512 |
|---|---|
| adam_text | Contents
PART 1 - INTRODUCTION 1
1 The Historical Development of the Market Segmentation Concept 3
2 Segmentation Bases 7
Observable General Bases 8
Observable Product-Specific Base 10
Unobservable General Bases 11
Unobservable Product-Specific Bases 14
Conclusion 16
3 Segmentation Methods 17
A-Priori Descriptive Methods 18 ¦*
Post-Hoc Descriptive Methods 19 /
A-Priori Predictive Methods 22 /
Post-Hoc Predictive Methods 23/
Normative Segmentation Methods 26
Conclusion 28
4 Tools for Market Segmentation 31
PART 2 - SEGMENTATION METHODOLOGY 37
5 Clustering Methods 39
Example of the Clustering Approach to Market Segmentation 42
Nonoverlapping Hierarchical Methods 43
Similarity Measures 44
Agglomerative Cluster Algorithms 48 v
Divisive Cluster Algorithms 50X
Ultrametric and Additive Trees 50
Hierarchical Clusterwise Regression 51 v
Nonoverlapping Nonhierarchical Methods 52
Nonhierarchical Algorithms 53 /
Determining the number of Clusters 54Y
Nonhierarchical Clusterwise Regression 55
Miscellaneous Issues in Nonoverlapping Clustering 56
Variable Weighting, Standardization and Selection 56
Outliers and Missing Values 58
Contents Non-uniqueness and Inversions 59
Cluster Validation 59
Cluster Analysis Under Various Sampling Strategies 60
Stratified samples 60
Cluster samples 62
Two-stage samples 63
Overlapping and Fuzzy Methods 64
Overlapping Clustering 64
Overlapping Clusterwise Regression 65
Fuzzy Clustering 65 V
Market Segmentation Applications of Clustering 69
6 Mixture Models 75
Mixture Model Examples 75
Example 1: Purchase Frequency of Candy 75
Example 2: Adoption of Innovation 76
Mixture Distributions (MIX) 77
Maximum Likelihood Estimation 80
The EM Algorithm 84 K
EM Example 86 *
Limitations of the EM Algorithm 88 *
Local maxima 88 v
Standard errors 88
Identification 90
Determining the Number of Segments 91
Some Consequences of Complex Sampling Strategies for the Mixture
Approach 94
Marketing Applications of Mixtures 96
Conclusion 99
7 Mixture Regression Models 101
Examples of the Mixture Regression Approach 102
Example 1: Trade Show Performance 102
Example 2: Nested Logit Analysis of Scanner Data 103
A Generalized Mixture Regression Model (GLIMMIX) 106 ^
EM Estimation 1°8
EM Example 108
Standard Errors and Residuals 109
Identification 09
Monte Carlo Study of the GLIMMIX Algorithm 110
Study Design 11°
Results H2
Marketing Applications of Mixture Regression Models 112
Normal Data H3
Binary Data H3
VIII
Contents
Multichotomous Choice Data 115
Count Data 116
Choice and Count Data 116
Response-Time Data 117
Conjoint Analysis 117
Conclusion 119
Appendix A1 The EM Algorithm for the GLIMMIX Model 120
The EM Algorithm 120
TheE-Step 12 hi
TheM-Step 12lX
8 Mixture Unfolding Models 125
Examples of Stochastic Mixture Unfolding Models 127
Example 1: Television Viewing 127
Example 2: Mobile Telephone Judgements 128
A General Family of Stochastic Mixture Unfolding Models 131
EM Estimation 133
Some Limitations 133
Issues in Identification 134
Model Selection 134
Synthetic Data Analysis 136
Marketing Applications 138
Normal Data 138
Binomial Data 140
Poisson, Multinomial and Dirichlet Data 140
Conclusion 140
Appendix A2 The EM Algorithm for the STUNMIX Model 142
The E-Step 142
The M-step 142
9 Profiling Segments 145
Profiling Segments with Demographic Variables 145
Examples of Concomitant Variable Mixture Models 146
Example 1: Paired Comparisons of Food Preferences 146
Example 2: Consumer Choice Behavior with Respect to
Ketchup 147
The Concomitant Variable Mixture Model 150
Estimation 152
Model Selection and Identification 152
Monte Carlo Study 152
Alternative Mixture Models with Concomitant Variables 153
Marketing Applications 156
Conclusions 156
—
Contents 10 Dynamic Segmentation 159
Models for Manifest Change 160
Example 1: The Mixed Markov Model for Brand Switching 161
Example 2: Mixture Hazard Model for Segment Change 162
Models for Latent Change 167
Dynamic Concomitant Variable Mixture Regression Models 167
Latent Markov Mixture Regression Models 168
Estimation 169
Examples of the Latent Change Approach 170
Example 1: The Latent Markov Model for Brand Switching 170
Example 2: Evolutionary Segmentation of Brand Switching 171
Example 3: Latent Change in Recurrent Choice 175
Marketing Applications 176
Conclusion 176
Appendix A3 Computer Software for Mixture models 178
PANMARK 178
LEM 179
GLIMMIX 181
** PART 3 - SPECIAL TOPICS IN MARKET
SEGMENTATION 187
11 Joint Segmentation 189
Joint Segmentation 189
The Joint Segmentation Model 189
Synthetic Data Illustration 191
Banking Services 192
Conclusion 194
12 Market Segmentation with Tailored Interviewing 195
Tailored Interviewing 195
Tailored Interviewing for Market Segmentation 198
Model Calibration 199
Prior Membership Probabilities 200
Revising the Segment Membership Probabilities 201
Item Selection 202
Stopping Rule 202
Application to Life-Style Segmentation 203
Life-Style Segmentation 203
Data Description 203
Model Calibration 203
Profile of the Segments 204
The Tailored Interviewing Procedure 209
Characteristics of the Tailored Interview 209
Quality of the Classification 211
_ - . -
Contents
Conclusion 214
13 Model-Based Segmentation Using Structural Equation Models 217
Introduction to Structural Equation Models 217
A-Priori Segmentation Approach 222
Post Hoc Segmentation Approach 223
Application to Customer Satisfaction 223
The Mixture of Structural Equations Model 225
Special Cases of the Model 226
Analysis of Synthetic Data 227
Conclusion 229
14 Segmentation Based on Product Dissimilarity Judgements 231
Spatial Models 231
Tree Models 232
Mixtures of Spaces and Mixtures of Trees 235
Mixture of Spaces and Trees 238
Conclusion 238
PART 4 - APPLIED MARKET SEGMENTATION 239
15 General Observable Bases: Geo-demographics 241
Applications of Geo-demographic Segmentation 242
Commercial Geo-demographic Systems 244
PRIZM™ (Potential Rating Index for ZIP Markets) 244
ACORN™ (A Classification of Residential Neighborhoods) 247
The Geo-demographic System of Geo-Marktprofiel 248
Methodology 254
Linkages and Datafusion 256
Conclusion 257
16 General Unobservable Bases: Values and Lifestyles 259
Activities, Interests and Opinions 260
Values and Lifestyles 261
Rokeach s Value Survey 261
The List of Values (LOV) Scale 265
The Values and Lifestyles (VALS™) Survey 266
Applications of Lifestyle Segmentation 268
Conclusion 276
17 Product-specific observable Bases: Response-based Segmentation 277
The Information Revolution and Marketing Research 277
Diffusion of Information Technology 277
Early Approaches to Heterogeneity 278
Household-Level Single-Source Data 279
—
Contents Consumer Heterogeneity in Response to Marketing Stimuli 282 V
Models with Exogenous Indicators of Preferences 283
Fixed-Effects Models 283
Random-Intercepts and Random Coefficients Models 284
Response-Based Segmentation 285
Example of Response-Based Segmentation with Single
Source Scanner Data 286
Extensions 288
Conclusion 292
18 Product-Specific Unobservable Bases: Conjoint Analysis 295
Conjoint Analysis in Marketing 295
Choice of the Attributes and Levels 296
Types of Attributes 296
Number of Attributes 297
Attribute Levels 298
Stimulus Set Construction 298
Stimulus Presentation 299
Data Collection and Measurement Scales 300
Preference Models and Estimation Methods 301
Choice Simulations 302
Market Segmentation with Conjoint Analysis 303
Application of Conjoint Segmentation with Constant Sum
Response Data 303
Market Segmentation with Metric Conjoint Analysis 305
A-Priori and Post-Hoc Methods Based on Demographics 306
Componential Segmentation 306
Two-Stage Procedures 306
Hagerty s Method 307
Hierarchical and Non-Hierarchical Clusterwise Regression 307
Mixture Regression Approach 308
A Monte Carlo Comparison of Metric Conjoint Segmentation
Approaches 310
The Monte Carlo Study 310
Results 312
Predictive Accuracy 313
Segmentation for Rank-Order and Choice Data 314
A-Priori and Post-Hoc Approaches to Segmentation 315
Two-Stage Procedures 315
Hierarchical and Non-hierarchical Clusterwise Regression 316
The Mixture Regression Approach for Rank-Order and
Choice Data 316
Application of Mixture Logit Regression to Conjoint Segmentation 318
Results 319
Conclusion 320
XII
Contents
PART 5 - CONCLUSIONS AND DIRECTIONS FOR
FUTURE RESEARCH 323
19 Conclusions: Representations of Heterogeneity 325 ;
Continuous Distribution of Heterogeneity versus Market Segments 325
Continuous or Discrete 326
ML or MCMC 327
Managerial relevance 329
Individual Level versus Segment Level Analysis 331
20 Directions for Future Research 335
The Past 335
Segmentation Strategy 336
Agenda for Future Research 341
References 345
Index 371
List of Tables and Figures
Table 2.1: Evaluation of Segmentation Bases 16
Table 3.1: Evaluation of Segmentation Methods 29
Table 5.1: The Most Important Similarity Coefficients 46
Table 5.2: Definitions of Cluster Distance for several Types of
Hierarchical Algorithms 49
Table 5.3: Optimization Criteria in Nonhierarchical Clustering 52
Table 5.4: Fuzzy Clustering Algorithm Estimating Equations 70
Table 5.5: Cluster Analysis Applications to Segmentation 72
Table 6.1: Results of the Green etal. Three-Segment Model 78
Table 6.2: Some Distributions from the Univariate Exponential Family 82
Table 6.3: Application of the EM Algorithm to Synthetic Mixture
Poisson Data 87
Table 6.4: Mixture Model Applications in Marketing 97
Table 6.5: Special Cases of the Bockenholt (1993) Mixture Model
Family 98
Table 7.1: Aggregate and Segment-Level Results of the Trade Show
Performance Study 103
Table 7.2: Average Price Elasticities within Three Segments 105
Table 7.3: Application of the EM Algorithm to Synthetic Mixture
Regression Data !1 *
Table 7.4: Results of the Monte Carlo Study on GLIMMIX Performance 114
Table 7.5: GLIMMIX Applications in Marketing 118
Table 8.1: Numbers of Parameters for STUNMIX Models 135
Table 8.2: Results of STUNMIX Synthetic Data Analyses 137
Table 8 3 • STUNMIX Results for Normal and Log-Normal Synthetic
Data I3*
Table 8.4: STUNMIX Marketing Applications 141
Table 9.1: Concomitant Variable Mixture Model results for Food
Concern Data 148
Table 9.2: Concomitant Variable Mixture Model Results for Ketchup
Choice Data . |^j
Table 9 3- Estimates from Synthetic Datasets Under Two Conditions 157
Table 9.4: Marketing Applications of Concomitant Variable Models 158
Table 10 1- Mixed Markov Results for MRCA Data 162
Table 10.2: Segment Level S=2 Nonproportional Model Estimates 65
Table 10.3: Latent Markov Results for MRCA Data 171
Table 104- Conditional Segment Transition Matrix Between Periods 1
, - 174
and/
List of tables and figures
Table 10.5: Preference Structure in Three and Four Switching Segments
in Two Periods 174
Table 10.6: Marketing Applications of Dynamic Segmentation 176 :
Table 11.1: True and Estimated Joint Segment Probabilities 191
Table 11.2: Segmentation Structure of Binary Joint Segmentation Model
for Banking Services 193
Table 11.3: Estimated Joint Segment Probabilities for Banking Services
Application 194
Table 12.1: Demographics and Activities by Segment 205
Table 12.2: Activities, Interests and Opinions Toward Fashion by
Segment 207
Table 12.3: Percentage of Cases Correctly Classified 213
Table 13.1: Latent Satisfaction Variables and Their Indicators 218
Table 13.2: Mean Factor Scores in Three Segments 224
Table 13.3: Estimates of Structural Parameters in Three Segments 225
Table 13.4: Performance Measures in First Monte Carlo Study 228
Table 13.5: Performance Measures in Second Monte Carlo Study 229
Table 15.1: PRIZM ™ Classification by Broad Social Groups 246
Table 15.2: ACORN™ Cluster Classification 250
Table 15.3: Description of the Dimensions of the GMP System 253
Table 15.4: Media and Marketing Databases Linked to Geo-demographic ,
Systems 258
Table 16.1: Rokeach s Terminal and Instrumental Values 263
Table 16.2: Motivational Domains of Rekeach s Values Scale 264
Table 16.3: Double-Centered Values 271
Table 17.1: Estimated Price Elasticities 289
Table 18.1: Estimated Allocations for Three Profiles in Two Segments 305
Table 18.2: Comparison of Conjoint Segmentation Procedures 309
Table 18.3: Mean Performance Measures of the Nine Conjoint
Segmentation Methods 313
Table 18.4: Mean Performance Measures for Each of the Factors 314
Table 18.5: Parameter Estimates of the Rank-Order Conjoint
Segmentation Model 320
Table 19.1 Comparison of Discrete and Continuous Representations of
Heterogeneity 328
Table 19.2 Comparison of Segment Level and Individual-Level
Approaches 332
Figure 2.1: Classification of Segmentation Bases 7
Figure 3.1: Classification of Methods Used for Segmentation 17
Figure 5.1: Classification of Clustering Methods 42
Figure 5.2: Hypothetical Example of Hierarchical Cluster Analysis 44
Figure 5.3: Schematic Representation of Some Linkage Criteria 48
Figure 5.4: An Example of Cluster Structures Recovered with the FCV
XVI
List of tables and figures Family 67
Figure 6.1: Empirical and Fitted Distributions of Candy Purchases 76
Figure 6.2: Local and Global Maxima 89
Figure 8.1: TV-Viewing Solution 128
Figure 8.2: Spatial Map of the Telephone Brands 130
Figure 8.3: Synthetic Data and Results of Their Analyses with
STUNMIX 139
Figure 9.1: Directed Graph for the Standard Mixture 154
Figure 9.2: Directed Graph for the Concomitant Variable Mixture 154
Figure 10.1: Observed and Predicted Shares for the Mixed Markov
Model 163
Figure 10.2: Stepwise Hazard Functions in Two Segments 166
Figure 10.3: Observed and Predicted Shares for the Latent Markov
Model 172
Figure 12.1: Illustration of the Tailored Interview 197
Figure 12.2: Cost Versus Accuracy Tradeoff: Discontent and Alienation
Scales 198
Figure 12.3: Number of Items Selected in the Tailored Interview until
p 0.99 210
Figure 12.4: Number of Items Selected until p 0.99 with Random Item
Selection 210
Figure 12.5: Entropy of Classification at Each Stage of the Interview 212
Figure 12.6: Number of Respondents Correctly Classified at Each Stage 213
Figure 13.1: Path Diagram for latent Variables in the Satisfaction Study 219
Figure 14.1: T=2 dimensional Space for the Schiffman et al. Cola Data 233
Figure 14.2: Ultrametric Tree for the Schiffman et al. Cola Data 235
Figure 14.3: Mixture of Ultrametric Trees for the Schiffman et al. Cola
Data 237
Figure 16.1: Values Map: Dimension 2 vs. Dimension 1 272
Figure 16.2: Values Map: Dimension 3 vs. Dimension 1 273
Figure 16.3: Regional Positions: Dimension 2 vs. Dimension 1 274
Figure 16.4: Regional Positions: Dimension 3 vs. Dimension 1 275
—
|
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| dewey-sort | 3658.802 |
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| discipline | Wirtschaftswissenschaften |
| edition | 2. ed. |
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| genre_facet | Statistik |
| id | DE-604.BV025195072 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T14:23:20Z |
| institution | BVB |
| isbn | 0792386353 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-019835777 |
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| physical | XVII, 382 S. |
| publishDate | 2000 |
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| series2 | International series in quantitative marketing |
| spellingShingle | Wedel, Michel Kamakura, Wagner Antonio Market segmentation conceptual and methodological foundations Marktsegmentierung (DE-588)4037644-8 gnd |
| subject_GND | (DE-588)4037644-8 (DE-588)4056995-0 |
| title | Market segmentation conceptual and methodological foundations |
| title_auth | Market segmentation conceptual and methodological foundations |
| title_exact_search | Market segmentation conceptual and methodological foundations |
| title_full | Market segmentation conceptual and methodological foundations Michel Wedel ; Wagner A. Kamakura |
| title_fullStr | Market segmentation conceptual and methodological foundations Michel Wedel ; Wagner A. Kamakura |
| title_full_unstemmed | Market segmentation conceptual and methodological foundations Michel Wedel ; Wagner A. Kamakura |
| title_short | Market segmentation |
| title_sort | market segmentation conceptual and methodological foundations |
| title_sub | conceptual and methodological foundations |
| topic | Marktsegmentierung (DE-588)4037644-8 gnd |
| topic_facet | Marktsegmentierung Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=019835777&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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