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| 020 | ▼a 0792377729 (hbk. : alk. paper) | |
| 020 | ▼a 9780792377726 (hbk. : alk. paper) | |
| 024 | 3 1 | ▼a 9780792377726 |
| 035 | ▼a (OCoLC)ocm43370380 | |
| 035 | ▼a (OCoLC)43370380 | |
| 035 | ▼a (KERIS)REF000005918484 | |
| 040 | ▼a DLC ▼c DLC ▼d DLC ▼d 211009 | |
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| 050 | 0 0 | ▼a HF5415.135 ▼b .B85 2000 |
| 082 | 0 4 | ▼a 658.8/02 ▼2 22 |
| 090 | ▼a 658.802 ▼b B932 | |
| 245 | 0 0 | ▼a Building models for marketing decisions / ▼c Peter S.H. Leeflang ... [et al.]. |
| 260 | ▼a Boston : ▼b Kluwer , ▼c c2000. | |
| 300 | ▼a xvi, 645 p. : ▼b ill. ; ▼c 25 cm. | |
| 440 | 0 | ▼a International series in quantitative marketing ; ▼v 9 |
| 504 | ▼a Includes bibliographical references (p. 579-615) and indexes. | |
| 650 | 0 | ▼a Marketing ▼x Management ▼x Decision making ▼x Mathematical models. |
| 650 | 0 | ▼a Marketing ▼x Mathematical models. |
| 700 | 1 | ▼a Leeflang, P. S. H. , ▼d 1946- |
| 945 | ▼a KINS |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/서고7층/ | 청구기호 658.802 B932 | 등록번호 111426048 (22회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
The market environment is changing rapidly. Prior to scanner data, ACNielsen, the major supplier of information on brand performances, said its business was to provide the score but not to explain or predict it. Now, model-based insights are not only demanded by managers, but can also be meaningfully provided. It is common for managers in many countries to receive market feedback frequently, quickly and in great detail due to the use of scanners and computers. With advances in information technology and expertise in modeling, IRI introduced model-based services in the US that explain and predict essential parts of the marketplace. ACNielsen followed, and marketing researchers have been developing increasingly valid, useful and relevant models of marketplace behavior ever since. Models that provide information about the sensitivity of market behavior to marketing activities such as advertising, pricing, promotions and distribution are now routinely used by managers for the identification of changes in marketing programs that can improve brand performances. Building Models for Marketing Decisions describes marketing models that managers can use as an aid 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. Although marketing models are now widely accepted, the quality of the marketing decisions is critically dependent upon the quality of the models on which those decisions are based. In this book, which is a revision and expansion of Naert and Leeflang's Building Implementable Marketing Models (1978), the authors discuss in detail the model-building process. They distinguish four parts in this process: specification, estimation, validation and use of models. 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.
정보제공 :
목차
CONTENTS Preface = xiii 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 Clssifying 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 specified models = 18 3 Benefits from using marketing models = 21 3.1 Are marketing problems quantifiable? = 21 3.2 Benefits 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 TWO Specification = 47 5 Elements of model building = 49 5.1 The model-building process = 49 5.2 Some basic model-building terminology = 55 5.3 Specification 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 profit 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 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-Inflated 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 specification 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 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 fit 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 Justification = 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 specification 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 17.1.5 Confirmatory 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 19.2.2 Evolutionary model building = 535 19.2.3 Model scope = 538 19.2.4 Ease of use = 543 20 Cost-benefit considerations in model building and use = 545 20.1 Tradeoffs = 546 20.2 The cost of building models = 547 20.3 Measuring benefits = 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 Index = 617 Subject Index = 637