Modeling Markets (eBook)
XIV, 417 Seiten
Springer New York (Verlag)
978-1-4939-2086-0 (ISBN)
This book is about how models can be developed to represent demand and supply on markets, where the emphasis is on demand models. Its primary focus is on models that can be used by managers to support marketing decisions.Modeling Markets presents a comprehensive overview of the tools and methodologies that managers can use in decision making. It has long been known that even simple models outperform judgments in predicting outcomes in a wide variety of contexts. More complex models potentially provide insights about structural relations not available from casual observations. In this book, the authors present a wealth of insights developed at the forefront of the field, covering all key aspects of specification, estimation, validation and use of models. The most current insights and innovations in quantitative marketing are presented, including in-depth discussion of Bayesian estimation methods. Throughout the book, the authors provide examples and illustrations. This book will be of interest to researchers, analysts, managers and students who want to understand, develop or use models of marketing phenomena.
Preface 8
Contents 10
1 Building Models for Markets 16
1.1 Introduction 16
1.2 Verhouten Case 17
1.3 Typologies of Marketing Models 19
1.3.1 Introduction 19
1.3.2 Decision Models Versus Models That Advance Marketing Knowledge 19
1.3.3 Degree of Explicitness 22
1.3.3.1 Implicit Models 22
1.3.3.2 Verbal Models 23
1.3.3.3 Formalized Models 24
1.3.3.4 Numerically Specified Models 26
1.3.4 Intended Use: Descriptive, Predictive and Normative Models 28
1.3.5 Level of Demand 29
1.4 Benefits from Using Marketing Decision Models 30
1.4.1 Direct Benefits 30
1.4.2 Indirect Benefits 31
1.5 The Model Building Process 33
1.6 Outline 36
References 37
2 Model Specification 40
2.1 Introduction 40
2.2 Model Criteria 41
2.2.1 Implementation Criteriasubject]Implementation Criteria Related to Model Structure 41
2.2.2 Models Should Be Simple 41
2.2.3 Models Should Be Built in an Evolutionary Way 44
2.2.4 Models Should Be Complete on Important Issues 45
2.2.5 Models Should Be Adaptive 47
2.2.6 Models Should Be Robust 48
2.3 Model Elements 49
2.4 Specification of the Functional Form 52
2.4.1 Models Linear in Parameters and Variables 52
2.4.2 Models Linear in Parameters But Not in Variables 53
2.4.3 Models That Are Nonlinear in Parameters, But Linearizable 56
2.4.4 Models That Are Nonlinear in Parameters and Not Linearizable 58
2.5 Moderation and Mediation Effects 59
2.6 Formalized Models for the Verhouten Case 61
2.7 Including Heterogeneity 63
2.8 Marketing Dynamics 65
2.8.1 Introduction 65
2.8.2 Modeling Lagged Effects: One Explanatory Variable 67
2.8.3 Modeling Lagged Effects: Several Explanatory Variables 73
2.8.4 Lead Effects 75
References 76
3 Data 79
3.1 Introduction 79
3.2 Data Structures 80
3.3 ``Good Data'' 80
3.3.1 Availability 81
3.3.2 Quality 82
3.3.3 Variability 82
3.3.4 Quantity 83
3.4 Data Characteristics and Model Choice 84
3.5 Data Sources 85
3.5.1 Introduction 85
3.5.2 Classification 87
3.5.3 Internal Data 88
3.5.4 External Data 89
3.5.4.1 Store (Retail) Level 90
3.5.4.2 Manufacturer Level 90
3.5.4.3 Evaluation 90
3.5.4.4 Scanner Data 91
3.5.4.5 HandScan Panels 93
3.5.4.6 Causal Data 93
3.5.4.7 Other Data Inputs 93
3.5.5 Household Data and/or Store Level Data? 94
3.5.6 Big Data 95
3.5.7 Subjective Data 97
3.5.7.1 Justification 97
3.5.7.2 Obtaining Subjective Estimates 98
References 106
4 Estimation and Testing 109
4.1 Introduction 109
4.2 The General Linear Model 110
4.2.1 One Explanatory Variable 110
4.2.2 The K-Variable Case 112
4.2.3 Model Assumptions 114
4.3 Statistical Inference 116
4.3.1 Goodness of Fit 116
4.3.2 Assessing Statistical Significance 120
4.4 Numerically Specified Models for the Verhouten Case 125
4.5 Estimating Pooled Models 129
4.5.1 Introduction 129
4.5.2 Estimating Unit-by-Unit Models 130
4.5.3 Estimating Fully Pooled Modelssubject]Fully pooled models 130
4.5.4 Estimating Partially Pooled Models 131
References 133
5 Validation and Testing 135
5.1 Introduction 135
5.2 Testing the Six Basic Assumptions of the GeneralLinear Model 136
5.2.1 Nonzero Expectation 138
5.2.2 Heteroscedasticity 140
5.2.3 Correlated Disturbances 143
5.2.4 Nonnormal Errors 147
5.2.5 Endogenous Predictor Variables 150
5.2.6 Multicollinearity 152
5.2.6.1 Solutions to Multicollinearity 154
5.3 Mediation Tests 157
5.4 Joint Tests, Pooling Tests and Causality Tests 158
5.4.1 Joint Tests 158
5.4.2 Pooling Tests 161
5.4.3 Causality Tests 162
5.5 Face Validity 166
5.6 Model Selection 167
5.6.1 Introduction 167
5.6.2 Nested Models 167
5.6.3 Non-nested Models 171
5.7 Predictive Validity 173
5.8 Model Validation for the Verhouten Case 179
5.8.1 Testing the Six Assumptions for the Verhouten Case 180
5.8.2 Assessing Predictive Validity for the Verhouten Case 184
References 185
6 Re-estimation: Introduction to More AdvancedEstimation Methods 189
6.1 Introduction 189
6.2 Generalized Least Squares 190
6.2.1 Introduction 190
6.2.2 GLS and Heteroscedasticity 191
6.2.3 GLS and Autocorrelation 193
6.2.4 Using Generalized Least Squares with Panel Data 194
6.3 The Verhouten Case Revisited 198
6.3.1 Multicollinearity 199
6.3.2 Autocorrelation 201
6.3.3 Heteroscedasticity 201
6.4 Maximum Likelihood Estimation 202
6.4.1 Maximizing the Likelihood 202
6.4.2 Large Sample Properties of the MLE 205
6.4.3 MLE with Explanatory Variables 207
6.4.4 Statistical Tests 210
6.4.5 MLE with Explanatory Variables: An Example 212
6.5 Simultaneous Systems of Equations 214
6.6 Instrumental Variables Estimation 219
6.7 Tests for Endogeneity 223
6.8 Bayesian Estimation 225
6.8.1 Subjective Data 225
6.8.2 Combining Objective and Subjective Data: Bayes' Theorem 227
6.8.3 Likelihood, Prior and Posteriors 229
6.8.4 Conjugate Priors 230
6.8.5 Markov Chain Monte Carlo (MCMC) Estimation 230
6.8.6 Bayesian Analysis in Marketing 231
6.8.7 Example: Bayesian Analysis of the SCAN*PRO Model 232
References 235
7 Examples of Models for Aggregate Demand 237
7.1 Introduction 237
7.2 An Introduction to Individual and Aggregate Demand 238
7.3 Example of Descriptive/Predictive Models 241
7.3.1 Product Class Sales Models 241
7.3.2 Brand Sales Models 244
7.3.2.1 Introduction 244
7.3.2.2 Modeling Brand Sales Directly: The SCAN*PRO Modelsubject]SCAN*PRO model 245
7.3.2.3 Modeling Brand Sales Directly: Models for Pharmaceutical Markets 246
7.3.2.4 Modeling Brand Sales Indirectly 250
7.3.3 Market Share Models 251
7.3.3.1 Attraction Models 251
7.3.3.2 Own-Brand Elasticities 252
7.3.3.3 Cross-Brand Elasticities 253
7.4 Examples of Normative/Prescriptive Models 257
7.4.1 Introduction and Illustrations 257
7.4.1.1 Basic Model 257
7.4.1.2 Determination of the Short-Term Advertising Budget 259
7.4.1.3 Determination of the Long-Term Advertising Budget 261
7.4.2 Other Normative Models 263
7.4.3 Allocation Models 264
Appendix: The Dorfman–Steiner Theorem 266
References 268
8 Individual Demand Models 274
8.1 Introduction 274
8.2 Choice Models 275
8.2.1 Introduction 275
8.2.2 Binary Choice Models Specification 277
8.2.2.1 Basic Model 277
8.2.2.2 Estimation 279
8.2.2.3 Numerical Examples 280
8.2.2.4 Validation 281
8.2.2.5 Empirical Example 282
8.2.3 Multinomial Choice Models 283
8.2.3.1 Structure 283
8.2.3.2 Heterogeneity 290
8.2.4 Markov Models 291
8.2.4.1 Markov Models 291
8.2.4.2 Hidden Markov Models 296
8.3 Purchase Quantity Models 298
8.3.1 General Structure 298
8.3.2 Heterogeneity in Count Models 299
8.4 Purchase Timing: Duration Models 301
8.4.1 Introduction 301
8.4.2 Hazard Models 302
8.4.3 Heterogeneity in Duration Models 304
8.4.4 Estimation and Validation of Duration Models 306
8.5 Integrated Models 308
8.5.1 Integrate Incidence, Timing and Choice 308
8.5.2 Tobit Models 309
8.5.2.1 Introduction 309
8.5.2.2 Type-1 Tobit Model 310
8.5.2.3 Type-2 Tobit Model 311
References 314
9 Examples of Database Marketing Models 319
9.1 Introduction 319
9.2 Data for Database Marketing 320
9.3 Modeling Customer Life Time Value 322
9.4 Models for Customer Selection and Acquisition 325
9.4.1 Models for Customer Selection 325
9.4.2 Models for Customer Acquisition 327
9.5 Models for Customer Development 330
9.6 Models for Customer Retention 331
9.6.1 Models to Support Loyalty/Reward Programmes 331
9.6.2 Churn Prediction Models 333
9.6.2.1 Introduction 333
9.6.2.2 Aggregation 334
9.6.2.3 Validation Criteria 334
9.6.2.4 Application 336
9.7 Models for Customer Engagement 338
9.7.1 Customer Engagement and Customer Management 338
9.7.2 Customer Engagement and Acquisition/Selection 340
9.7.3 Customer Engagement and Customer Development 343
9.7.4 Customer Engagement and Retention 344
9.8 Summary of Database Marketing Models 344
References 344
10 Use: Implementation Issues 349
10.1 Introduction 349
10.2 Model Related Dimensions 350
10.2.1 Cost–Benefit Considerationssubject]Cost-benefit considerations 350
10.2.1.1 Benefits 351
10.2.1.2 Costssubject]Model costs 351
10.2.2 Supply and Demand of Marketing Response Models 352
10.2.2.1 Introduction 352
10.2.2.2 Supply Side 354
10.2.2.3 Demand Side 359
10.3 Organizational Validity 361
10.3.1 Personal Factors 361
10.3.2 Interpersonal Factors: The Model User–Model Builder Interface 362
10.3.3 Organizational Factors 364
10.4 Implementation Strategy Dimensions 365
10.4.1 Introduction 365
10.4.2 Evolutionary Model Building 366
10.4.3 Model Scopesubject]Model scope 367
10.4.3.1 Global Versus Local Modelssubject]Global model 368
10.4.3.2 General Versus Detailed Descriptors of Marketing Variables 368
10.4.4 Ease of Use 368
10.5 Marketing Management Support Systems (MMSS), Dashboards and Metrics 369
10.5.1 Introduction 369
10.5.2 Marketing Management Support Systems (MMSS)subject]Marketing Management Support Systems (MMSS) 370
10.5.3 Dashboardssubject]Dashboards 373
10.5.4 Metricssubject]Metrics 375
References 379
Appendix A: Matrix Algebra 384
A.1 Matrices and Simple Matrix Operations 384
A.2 Matrix Multiplication 386
A.3 Special Matrices 388
A.4 Matrix Inverse 390
A.5 Determinants 392
A.6 Eigenvalues and Eigenvectors 394
A.7 Definiteness of a Matrix 397
A.8 Matrix and Vector Differentiation 398
Author Index 401
Subject Index 412
| Erscheint lt. Verlag | 12.11.2014 |
|---|---|
| Reihe/Serie | International Series in Quantitative Marketing |
| Zusatzinfo | XIV, 408 p. 43 illus., 1 illus. in color. |
| Verlagsort | New York |
| Sprache | englisch |
| Themenwelt | Wirtschaft ► Betriebswirtschaft / Management ► Marketing / Vertrieb |
| Wirtschaft ► Betriebswirtschaft / Management ► Planung / Organisation | |
| Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management | |
| Schlagworte | Data Analysis • diffusion models • Marketing • Marketing Science • Quantitative marketing • time series models |
| ISBN-10 | 1-4939-2086-3 / 1493920863 |
| ISBN-13 | 978-1-4939-2086-0 / 9781493920860 |
| Haben Sie eine Frage zum Produkt? |
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