Building Models for Marketing Decisions英文原版

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<P>Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . xi
PART ONE: Introduction to marketing models . . . . . . . . . . . 1
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 3
1.1 Purpose . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Outline . . . . . . . . . . . . . . . . . . . . . . 7
1.3 The model concept . . . . . . . . . . . . . . . . . . 10
2 Classifying marketing models according to degree of explicitness . . . 13
2.1 Implicit models . . . . . . . . . . . . . . . . . . . 13
2.2 Verbal models . . . . . . . . . . . . . . . . . . . . 13
2.3 Formalized models . . . . . . . . . . . . . . . . . . 15
2.4 Numerically specied models . . . . . . . . . . . . . . 18
3 Benets from using marketing models . . . . . . . . . . . . . 21
3.1 Are marketing problems quantiable? . . . . . . . . . . . 21
3.2 Benets from marketing decision models . . . . . . . . . . 24
3.3 Building models to advance our knowledge of marketing . . . . 28
3.4 On the use of a marketing model: a case study . . . . . . . . 32
4 A typology of marketing models . . . . . . . . . . . . . . . 37
4.1 Intended use: descriptive, predictive, normative models . . . . . 37
4.2 Demand models: product class sales, brand sales, and market share
models . . . . . . . . . . . . . . . . . . . . . . 40
4.3 Behavioral detail . . . . . . . . . . . . . . . . . . . 41
4.4 Time series and causal models . . . . . . . . . . . . . . 44
4.5 Models of “single” versus “multiple” products . . . . . . . . 45
PART TW Specication . . . . . . . . . . . . . . . . . . . 47
5 Elements of model building . . . . . . . . . . . . . . . . . 49
5.1 The model-building process . . . . . . . . . . . . . . . 49
5.2 Some basic model-building terminology . . . . . . . . . . 55</P>
<P>5.3 Specication of behavioral equations: some simple examples . . . 66
5.3.1 Models linear in parameters and variables . . . . . . 66
5.3.2 Models linear in the parameters but not in the variables . . 67
5.3.3 Models non-linear in the parameters and not linearizable . 79
6 Marketing dynamics . . . . . . . . . . . . . . . . . . . . 85
6.1 Modeling lagged effects: one explanatory variable . . . . . . . 85
6.2 Modeling lagged effects: several explanatory variables . . . . . 96
6.3 Selection of (dynamic) models . . . . . . . . . . . . . . 97
6.4 Lead effects . . . . . . . . . . . . . . . . . . . . 98
7 Implementation criteria with respect to model structure . . . . . . . 101
7.1 Introduction . . . . . . . . . . . . . . . . . . . . 101
7.2 Implementation criteria . . . . . . . . . . . . . . . . 102
7.2.1 Models should be simple . . . . . . . . . . . . 102
7.2.2 Models should be built in an evolutionary way . . . . . 105
7.2.3 Models should be complete on important issues . . . . 105
7.2.4 Models should be adaptive . . . . . . . . . . . . 107
7.2.5 Models should be robust . . . . . . . . . . . . . 108
7.3 Can non-robust models be good models? . . . . . . . . . . 110
7.4 Robustness related to intended use . . . . . . . . . . . . 115
7.5 Robustness related to the problem situation . . . . . . . . . 120
8 Specifying models according to
intended use . . . . . . . . . . . . . . . . . . . . . . . 123
8.1 Descriptive models . . . . . . . . . . . . . . . . . . 123
8.2 Predictive models . . . . . . . . . . . . . . . . . . 130
8.3 Normative models . . . . . . . . . . . . . . . . . . 144
8.3.1 A prot maximization model . . . . . . . . . . . 144
8.3.2 Allocation models . . . . . . . . . . . . . . . 151
Appendix: The Dorfman-Steiner theorem . . . . . . . . . . . . 154
9 Specifying models according to level of demand . . . . . . . . . 157
9.1 An introduction to individual and aggregate demand . . . . . . 157
9.2 Product class sales models . . . . . . . . . . . . . . . 164
9.3 Brand sales models . . . . . . . . . . . . . . . . . . 167
9.4 Market share models . . . . . . . . . . . . . . . . . 171
10 Specifying models according to amount of behavioral detail . . . . . 179
10.1 Models with no behavioral detail . . . . . . . . . . . . . 180
10.2 Models with some behavioral detail . . . . . . . . . . . . 180
10.3 Models with a substantial amount of behavioral detail . . . . . 195</P>
<P>11 Modeling competition . . . . . . . . . . . . . . . . . . . 201
11.1 Competitor-centered approaches to diagnose competition . . . . 202
11.2 Customer-focused assessments to diagnose competition . . . . . 208
11.3 Congruence between customer-focused and competitor-centered approaches
. . . . . . . . . . . . . . . . . . . . . . 211
11.4 Game-theoretic models of competition . . . . . . . . . . . 215
12 Stochastic consumer behavior models . . . . . . . . . . . . . 221
12.1 Purchase incidence . . . . . . . . . . . . . . . . . . 222
12.1.1 Introduction . . . . . . . . . . . . . . . . . 222
12.1.2 The Poisson purchase incidence model . . . . . . . 222
12.1.3 Heterogeneity and the Negative Binomial (NBD) purchase
incidence model . . . . . . . . . . . . . . . . 223
12.1.4 The Zero-Inated Poisson (ZIP) purchase incidence model 224
12.1.5 Adding marketing decision variables . . . . . . . . 225
12.2 Purchase timing . . . . . . . . . . . . . . . . . . . 226
12.2.1 Hazard models . . . . . . . . . . . . . . . . 226
12.2.2 Heterogeneity . . . . . . . . . . . . . . . . 229
12.2.3 Adding marketing decision variables . . . . . . . . 230
12.3 Brand choice models . . . . . . . . . . . . . . . . . 231
12.3.1 Markov and Bernouilli models . . . . . . . . . . 232
12.3.2 Learning models . . . . . . . . . . . . . . . 239
12.3.3 Brand choice models with marketing decision variables . 240
12.4 Integrated models of incidence, timing and choice . . . . . . . 246
13 Multiproduct models . . . . . . . . . . . . . . . . . . . 251
13.1 Interdependencies . . . . . . . . . . . . . . . . . . 252
13.2 An example of a resource allocation model . . . . . . . . . 256
13.3 Product line pricing . . . . . . . . . . . . . . . . . . 258
13.4 Shelf space allocation models . . . . . . . . . . . . . . 261
13.5 Multiproduct advertising budgeting . . . . . . . . . . . . 264
14 Model specication issues . . . . . . . . . . . . . . . . . 267
14.1 Specifying models at different levels of aggregation . . . . . . 267
14.1.1 Introduction . . . . . . . . . . . . . . . . . 267
14.1.2 Entity aggregation . . . . . . . . . . . . . . . 268
14.1.3 Time aggregation . . . . . . . . . . . . . . . 279
14.2 Pooling . . . . . . . . . . . . . . . . . . . . . . 281
14.3 Market boundaries . . . . . . . . . . . . . . . . . . 282
14.4 Modeling asymmetric competition . . . . . . . . . . . . 286
14.5 Hierarchical models . . . . . . . . . . . . . . . . . 291
14.6 A comparison of hierarchical and non-hierarchical asymmetric models 295</P>
<P>PART THREE: Parameterization and validation . . . . . . . . . . . 299
15 Organizing Data . . . . . . . . . . . . . . . . . . . . . 301
15.1 “Good” data . . . . . . . . . . . . . . . . . . . . 301
15.2 Marketing management support systems . . . . . . . . . . 305
15.3 Data sources . . . . . . . . . . . . . . . . . . . . 308
15.4 Data collection through model development: A case study . . . . 316
16 Estimation and testing . . . . . . . . . . . . . . . . . . . 323
16.1 The linear model . . . . . . . . . . . . . . . . . . . 324
16.1.1 The two-variable case . . . . . . . . . . . . . . 324
16.1.2 The L-variable case . . . . . . . . . . . . . . 325
16.1.3 Assumptions about disturbances . . . . . . . . . . 327
16.1.4 Violations of the assumptions . . . . . . . . . . . 330
16.1.5 Goodness of t and reliability . . . . . . . . . . . 348
16.2 Pooling methods . . . . . . . . . . . . . . . . . . . 361
16.3 Generalized Least Squares . . . . . . . . . . . . . . . 369
16.4 Simultaneous equations . . . . . . . . . . . . . . . . 376
16.5 Nonlinear estimation . . . . . . . . . . . . . . . . . 383
16.6 Maximum Likelihood Estimation . . . . . . . . . . . . . 389
16.6.1 Maximizing the likelihood . . . . . . . . . . . . 389
16.6.2 Example . . . . . . . . . . . . . . . . . . 391
16.6.3 Large sample properties of the ML-Estimator . . . . . 392
16.6.4 Statistical tests . . . . . . . . . . . . . . . . 395
16.7 Non- and semiparametric regression models . . . . . . . . . 396
16.7.1 Introduction . . . . . . . . . . . . . . . . . 396
16.7.2 Advantages and disadvantages of the parametric regression
model . . . . . . . . . . . . . . . . . . . 397
16.7.3 The nonparametric regression model . . . . . . . . 397
16.7.4 The semiparametric regression model . . . . . . . . 402
16.8 Illustration and discussion . . . . . . . . . . . . . . . 408
16.9 Subjective estimation . . . . . . . . . . . . . . . . . 413
16.9.1 Justication . . . . . . . . . . . . . . . . . 413
16.9.2 Obtaining subjective estimates . . . . . . . . . . 416
16.9.3 Combining subjective estimates . . . . . . . . . . 428
16.9.4 Combining subjective and objective data . . . . . . . 433
16.9.5 Illustration . . . . . . . . . . . . . . . . . . 436
17 Special topics in model specication and estimation . . . . . . . . 441
17.1 Structural equation models with latent variables . . . . . . . 441
17.1.1 Outline of the model and path diagram . . . . . . . 441
17.1.2 Seemingly unrelated regression models . . . . . . . 449
17.1.3 Errors-in-variables models . . . . . . . . . . . . 449
17.1.4 Simultaneous equations . . . . . . . . . . . . . 450</P>
<P>17.1.5 Conrmatory factor analysis . . . . . . . . . . . 450
17.2 Mixture regression models for market segmentation . . . . . . 451
17.2.1 Introduction . . . . . . . . . . . . . . . . . 451
17.2.2 General mixture models . . . . . . . . . . . . . 452
17.2.3 Mixture regression models . . . . . . . . . . . . 453
17.2.4 Application . . . . . . . . . . . . . . . . . 455
17.2.5 Concomitant variable mixture regression models . . . . 456
17.2.6 Latent Markov mixture regression models . . . . . . 457
17.3 Time-series models . . . . . . . . . . . . . . . . . . 458
17.3.1 Introduction . . . . . . . . . . . . . . . . . 458
17.3.2 Autoregressive processes . . . . . . . . . . . . 459
17.3.3 Moving average processes . . . . . . . . . . . . 461
17.3.4 ARMA processes . . . . . . . . . . . . . . . 462
17.3.5 Stationarity and unit root testing . . . . . . . . . . 463
17.3.6 Integrated processes . . . . . . . . . . . . . . 465
17.3.7 Seasonal processes . . . . . . . . . . . . . . . 465
17.3.8 Transfer functions . . . . . . . . . . . . . . . 467
17.3.9 Intervention analysis . . . . . . . . . . . . . . 470
17.4 Varying parameter models . . . . . . . . . . . . . . . 473
18 Validation . . . . . . . . . . . . . . . . . . . . . . . 479
18.1 Validation criteria . . . . . . . . . . . . . . . . . . 480
18.2 Statistical tests and validation criteria . . . . . . . . . . . 482
18.3 Face validity . . . . . . . . . . . . . . . . . . . . 484
18.4 Model selection . . . . . . . . . . . . . . . . . . . 487
18.4.1 Introduction . . . . . . . . . . . . . . . . . 487
18.4.2 Nested models . . . . . . . . . . . . . . . . 488
18.4.3 Non-nested models . . . . . . . . . . . . . . . 492
18.4.4 Causality tests . . . . . . . . . . . . . . . . 495
18.5 Predictive validity . . . . . . . . . . . . . . . . . . 500
18.6 Illustrations . . . . . . . . . . . . . . . . . . . . 508
18.7 Validation of subjective estimates . . . . . . . . . . . . . 517
PART FOUR: Use / Implementation . . . . . . . . . . . . . . . 523
19 Determinants of model implementation . . . . . . . . . . . . . 525
19.1 Organizational validity . . . . . . . . . . . . . . . . 526
19.1.1 Personal factors . . . . . . . . . . . . . . . . 526
19.1.2 Interpersonal factors: the model user - model builder interface
. . . . . . . . . . . . . . . . . . . . 528
19.1.3 Organizational factors . . . . . . . . . . . . . . 532
19.2 Implementation strategy dimensions . . . . . . . . . . . . 534
19.2.1 Introduction . . . . . . . . . . . . . . . . . 534</P>
<P>19.2.2 Evolutionary model building . . . . . . . . . . . 535
19.2.3 Model scope . . . . . . . . . . . . . . . . . 538
19.2.4 Ease of use . . . . . . . . . . . . . . . . . 543
20 Cost-benet considerations in model building and use . . . . . . . 545
20.1 Tradeoffs . . . . . . . . . . . . . . . . . . . . . 546
20.2 The cost of building models . . . . . . . . . . . . . . . 547
20.3 Measuring benets . . . . . . . . . . . . . . . . . . 548
20.4 Some qualitative examples . . . . . . . . . . . . . . . 553
20.5 General observations . . . . . . . . . . . . . . . . . 556
21 Models for marketing decisions in
the future . . . . . . . . . . . . . . . . . . . . . . . . 565
21.1 Examples of recent developments in model building . . . . . . 565
21.2 The role of models in management decisions . . . . . . . . 568
21.3 A broader framework . . . . . . . . . . . . . . . . . 570
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . 579
Author's Index . . . . . . . . . . . . . . . . . . . . . . . 617
Subject Index . . . . . . . . . . . . . . . . . . . . . . . 637</P>
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关键词：Marketing Decisions Building Decision models 英文原版 marketing

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