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Handbook of Marketing Decision Models.

By: Contributor(s): Material type: TextTextSeries: International Series in Operations Research and Management Science SeriesPublisher: Cham : Springer International Publishing AG, 2017Copyright date: ©2017Edition: 2nd edDescription: 1 online resource (598 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319569413
Subject(s): Genre/Form: Additional physical formats: Print version:: Handbook of Marketing Decision ModelsDDC classification:
  • 658.802
LOC classification:
  • T57.6-.97
Online resources:
Contents:
Intro -- Preface -- Appendix: Reviewers of the chapters for the Handbook of Marketing Decision Models (Second Edition) -- Contents -- About the Editors -- 1 Marketing Decision Models: Progress and Perspectives -- 1.1 Dimensions of Marketing Decision Models -- 1.2 The Chapters in This Handbook -- 1.2.1 Accretion -- 1.2.2 Sophistication -- 1.2.3 Exploration -- 1.2.4 Excavation -- 1.3 Perspectives for Marketing Decision Models -- 1.3.1 Information Technology as the Main Driver -- 1.3.2 Shifts in Marketing Models -- 1.3.2.1 From Marketing Mix Instruments to Very Specific Marketing Actions -- 1.3.2.2 From Markets to Individual Customers -- 1.3.2.3 From Purchase to Customer Journey -- References -- 2 Sales Promotion Models -- 2.1 Promotions to the Consumer-Introduction -- 2.2 Customer-Level Models-Incidence, Choice, and Quantity -- 2.2.1 Category Incidence Models -- 2.2.2 Brand Choice Model -- 2.2.3 Purchase Quantity Model -- 2.2.4 Estimation -- 2.2.5 Heterogeneity -- 2.2.6 Integrated Incidence, Choice, Quantity Models -- 2.2.7 Dynamic Structural Models -- 2.3 Customer-Level Models-Extensions -- 2.3.1 Extension 1: Store Switching -- 2.3.2 Extension 2: Cross-Category Effects -- 2.3.3 Extension 3: SKU-Level Models -- 2.3.4 Extension 4: Deceleration -- 2.3.5 Discussion -- 2.4 Store-Level Models of Sales Promotion Effects -- 2.4.1 Scan*Pro Model -- 2.4.2 Models for Pre- and Postpromotion Dips -- 2.4.3 Store-Level Decomposition Model -- 2.4.4 Heterogeneity Across Stores -- 2.4.5 Aggregate Logit Model -- 2.5 Generalizations About the Decomposition -- 2.6 Long-Term Impact-Beyond the Immediate Sales Bump -- 2.6.1 Purchase-Event Feedback -- 2.6.2 Reference Prices -- 2.6.3 Consumer Learning -- 2.6.4 Long-Term Effects -- 2.6.5 Competitive Reactions -- 2.7 Endogeneity -- 2.7.1 What Is Endogeneity? -- 2.7.2 Addressing Endogeneity Through Control Variables.
2.7.3 Addressing Endogeneity Through Instrumental Variables -- 2.7.4 Problems in Addressing Endogeneity -- 2.7.5 IV-Free Methods to Address Endogeneity -- 2.7.6 VAR Models and Endogeneity -- 2.8 Promotions to the Trade-Introduction -- 2.9 Forward Buying -- 2.10 Pass-Through -- 2.11 Decision Models for Promotions to the Consumer -- 2.11.1 Retailer Promotion Optimization -- 2.11.2 Targeting Promotions to Consumers -- 2.12 Manufacturer Decision Models for Trade Promotions -- 2.13 Retailer Decision Models for Forward Buying and Pass-Through -- 2.14 Summary -- 2.15 Practical Modeling Guidelines -- 2.16 Future Research -- Appendix: Variable Definition for the Decomposition in Sect. 2.4.3 -- References -- 3 Innovation and New Products Research: A State-of-the-Art Review, Models for Managerial Decision Making, and Future Research Directions -- 3.1 Introduction -- 3.2 Organizing Research on Innovation and New Products -- 3.2.1 Opportunity Identification -- 3.2.1.1 Modeling Opportunity Identification -- 3.2.2 Product Design and Development -- 3.2.2.1 Models for Product Design -- 3.2.3 Sales Forecasting -- 3.2.3.1 Sales Forecasting Models -- 3.2.4 Commercialization -- 3.2.4.1 Modeling Commercialization -- 3.3 Future Research Opportunities -- 3.3.1 Opportunity Identification -- 3.3.2 Product Design and Development -- 3.3.3 Sales Forecasting -- 3.3.4 Commercialization -- 3.4 Conclusion -- References -- 4 Models for the Financial-Performance Effects of Marketing -- 4.1 Introduction -- 4.2 Marketing and Cash Flows -- 4.3 Criteria for Good Performance Metrics -- 4.4 The Process Perspective -- 4.4.1 The Core Sales-Response Model -- 4.4.2 Cash-Flow Generation -- 4.4.3 Flow and Stock Metrics -- 4.4.3.1 Time-Varying Baseline Models -- 4.4.3.2 Generalized Diffusion Models -- 4.4.3.3 Brand Equity -- 4.4.3.4 Customer Equity.
4.4.3.5 Intermediate Performance Variables and Marketing Dashboards -- 4.5 The Investor Perspective -- 4.5.1 Single-Equation Approach -- 4.5.2 Vector-Autoregressive Approach -- 4.5.3 Key Findings on Marketing and Firm Value -- 4.6 Conclusion -- References -- 5 Loyalty Programs: Current Insights, Research Challenges, and Emerging Trends -- 5.1 Introduction -- 5.2 Loyalty Program Phases -- 5.3 Effect Mechanisms of Loyalty Programs -- 5.3.1 Customer Responses to LPs -- 5.3.2 Pre-rewarding Effects: Direct Discounts and Points Pressure -- 5.3.3 Post-rewarding Effects: Rewarded Behavior -- 5.3.4 LP-Based Personalization -- 5.4 LP Design -- 5.4.1 The General LP Design -- 5.4.2 Short-Term Programs -- 5.4.3 Multi-vendor Programs -- 5.4.4 Hierarchical Loyalty Programs -- 5.5 Modeling LP Effects -- 5.6 Emerging Trends -- 5.6.1 Digitalization of LPs -- 5.6.2 Aligning LPs with the Customer Experience -- 5.6.3 Reviving Existing LPs -- 5.7 Conclusion -- References -- 6 Structural Models in Marketing: Consumer Demand and Search -- 6.1 What Is a Structural Model? -- 6.2 Structural Models: A Simple Illustration -- 6.2.1 The First Unobservable: An Aspect of Quality Known to the Consumer, But Not Observed by the Researcher -- 6.2.2 The Second Unobservable: Consumers Are Heterogeneous in Their Preferences and How They Respond to Marketing Activities -- 6.3 A Detour: Discrete-Choice Demand Models for Aggregate Data -- 6.4 Unobserved Demand Factors at the Aggregate Level (I.e., Common Across Consumers) -- 6.5 Back to the Consumer Demand Model -- 6.5.1 A Third Unobservable: Consumption (and Other) Signals Received by Consumers As They Seek to Learn About the Quality of a Product -- 6.5.2 A Fourth Unobservable: The Consumer is Uncertain About the Value of an Attribute (Say Price) and Engages in Costly Search to Resolve This Uncertainty -- 6.6 Simultaneous Search.
6.7 Sequential Search -- 6.8 Why Do We Need Structural Models? -- 6.9 Looking Back and Looking Ahead -- Appendix: Deriving the Indirect Utility Function (Equation (6.15)) -- References -- 7 Economic Models of Choice -- 7.1 Introduction -- 7.2 A Simple Model of Discrete Choice -- 7.2.1 Applications and Extensions -- 7.3 A General Model for Choice -- 7.3.1 Statistical Specification -- 7.3.2 Non-linear Outside Good -- 7.3.3 Applications and Extensions -- 7.4 Constraints -- 7.4.1 Multiple Constraints -- 7.4.2 Non-linear Constraints -- 7.5 Error Specification -- 7.5.1 Correlated Errors -- 7.5.2 Indivisible Demand -- 7.6 Indirect Utility Models -- 7.7 Conclusion -- References -- 8 Empirical Models of Learning Dynamics: A Survey of Recent Developments -- 8.1 Introduction -- 8.2 The Basic Bayesian Learning Model -- 8.3 Learning from Others (Social Learning) -- 8.4 Learning and Strategic Interaction -- 8.5 Information Spillovers and Correlated Learning -- 8.6 Models Incorporating Both Learning and Search -- 8.7 Heuristic and Approximation Approaches to Study Consumer Incentives to Explore -- 8.8 Using Exogenous Events and Policy Changes to Study Learning -- 8.9 Future Research Directions -- 8.10 Conclusion -- Acknowledgements -- References -- 9 Measurement Models for Marketing Constructs -- 9.1 Introduction -- 9.2 The Congeneric Measurement Model -- 9.2.1 Conceptual Development -- 9.2.2 Empirical Example -- 9.3 Multi-sample Congeneric Measurement Models with Mean Structures -- 9.3.1 Conceptual Development -- 9.3.2 Empirical Example -- 9.4 The Formative Measurement Model -- 9.4.1 Conceptual Development -- 9.4.2 Empirical Example -- 9.5 Extension 1: Relaxing the Assumption of Zero Non-target Loadings -- 9.5.1 Conceptual Development -- 9.5.2 Empirical Example -- 9.6 Extension 2: Relaxing the Assumption of Uncorrelated Unique Factors.
9.6.1 Conceptual Development -- 9.6.1.1 Sources of Shared Method Variance Among the Indicators -- 9.6.1.2 Models for Method Effects -- 9.6.2 Empirical Example -- 9.7 Extension 3: Relaxing the Assumption of Continuous, Normally Distributed Observed Measures -- 9.7.1 Conceptual Development -- 9.7.2 Empirical Example -- 9.8 Conclusion -- References -- 10 Marketing Models for the Customer-Centric Firm -- 10.1 Introduction -- 10.2 Customer Acquisition -- 10.2.1 Direct Approaches to Customer Acquisition -- 10.2.2 Beyond Classic Direct Marketing -- 10.3 Managing Acquired Customers -- 10.3.1 Computing Customer Lifetime Value -- 10.3.2 Churn Management -- 10.3.3 Contact Response Models -- 10.3.4 Contact Customization -- 10.4 Coordinating Acquisition and Retention -- 10.5 Discussion -- References -- 11 Eye Movements During Search and Choice -- 11.1 Introduction -- 11.2 The Eye and Visual Processes -- 11.2.1 Fixations and Saccades -- 11.2.2 The Perceptual Field Around a Fixation Is Small -- 11.2.3 Eye Movements Are Indicators of Visual Attention -- 11.2.4 Bottom-up and Top-Down Process Guide Eye Movements -- 11.3 Eye-Movement Recording -- 11.4 Analyzing Eye-Movement Data -- 11.4.1 Fixation Algorithms and the 80% Rule -- 11.4.2 AOIs and Eye-Movement Metrics -- 11.5 Eye Movements During Search -- 11.5.1 Brand Search Theory -- 11.5.2 Brand Search Model -- 11.6 Eye Movements During Choice -- 11.6.1 Choice Models with Eye-Tracking Metrics as Explanatory Variables -- 11.6.2 Integrated Models of Choice and Visual Attention -- 11.7 Conclusion -- Acknowledgements -- References -- 12 Business-Cycle Research in Marketing -- 12.1 Introduction -- 12.2 Main Insights from Business-Cycle Research in Marketing -- 12.3 How Is the Business Cycle Inferred? -- 12.3.1 Proxied by Official Economic Indicator(S) -- 12.3.2 Discrete Categorization by Official Economic Instances.
12.3.3 Infer Cyclical Information Using Business-Cycle Filters.
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Intro -- Preface -- Appendix: Reviewers of the chapters for the Handbook of Marketing Decision Models (Second Edition) -- Contents -- About the Editors -- 1 Marketing Decision Models: Progress and Perspectives -- 1.1 Dimensions of Marketing Decision Models -- 1.2 The Chapters in This Handbook -- 1.2.1 Accretion -- 1.2.2 Sophistication -- 1.2.3 Exploration -- 1.2.4 Excavation -- 1.3 Perspectives for Marketing Decision Models -- 1.3.1 Information Technology as the Main Driver -- 1.3.2 Shifts in Marketing Models -- 1.3.2.1 From Marketing Mix Instruments to Very Specific Marketing Actions -- 1.3.2.2 From Markets to Individual Customers -- 1.3.2.3 From Purchase to Customer Journey -- References -- 2 Sales Promotion Models -- 2.1 Promotions to the Consumer-Introduction -- 2.2 Customer-Level Models-Incidence, Choice, and Quantity -- 2.2.1 Category Incidence Models -- 2.2.2 Brand Choice Model -- 2.2.3 Purchase Quantity Model -- 2.2.4 Estimation -- 2.2.5 Heterogeneity -- 2.2.6 Integrated Incidence, Choice, Quantity Models -- 2.2.7 Dynamic Structural Models -- 2.3 Customer-Level Models-Extensions -- 2.3.1 Extension 1: Store Switching -- 2.3.2 Extension 2: Cross-Category Effects -- 2.3.3 Extension 3: SKU-Level Models -- 2.3.4 Extension 4: Deceleration -- 2.3.5 Discussion -- 2.4 Store-Level Models of Sales Promotion Effects -- 2.4.1 Scan*Pro Model -- 2.4.2 Models for Pre- and Postpromotion Dips -- 2.4.3 Store-Level Decomposition Model -- 2.4.4 Heterogeneity Across Stores -- 2.4.5 Aggregate Logit Model -- 2.5 Generalizations About the Decomposition -- 2.6 Long-Term Impact-Beyond the Immediate Sales Bump -- 2.6.1 Purchase-Event Feedback -- 2.6.2 Reference Prices -- 2.6.3 Consumer Learning -- 2.6.4 Long-Term Effects -- 2.6.5 Competitive Reactions -- 2.7 Endogeneity -- 2.7.1 What Is Endogeneity? -- 2.7.2 Addressing Endogeneity Through Control Variables.

2.7.3 Addressing Endogeneity Through Instrumental Variables -- 2.7.4 Problems in Addressing Endogeneity -- 2.7.5 IV-Free Methods to Address Endogeneity -- 2.7.6 VAR Models and Endogeneity -- 2.8 Promotions to the Trade-Introduction -- 2.9 Forward Buying -- 2.10 Pass-Through -- 2.11 Decision Models for Promotions to the Consumer -- 2.11.1 Retailer Promotion Optimization -- 2.11.2 Targeting Promotions to Consumers -- 2.12 Manufacturer Decision Models for Trade Promotions -- 2.13 Retailer Decision Models for Forward Buying and Pass-Through -- 2.14 Summary -- 2.15 Practical Modeling Guidelines -- 2.16 Future Research -- Appendix: Variable Definition for the Decomposition in Sect. 2.4.3 -- References -- 3 Innovation and New Products Research: A State-of-the-Art Review, Models for Managerial Decision Making, and Future Research Directions -- 3.1 Introduction -- 3.2 Organizing Research on Innovation and New Products -- 3.2.1 Opportunity Identification -- 3.2.1.1 Modeling Opportunity Identification -- 3.2.2 Product Design and Development -- 3.2.2.1 Models for Product Design -- 3.2.3 Sales Forecasting -- 3.2.3.1 Sales Forecasting Models -- 3.2.4 Commercialization -- 3.2.4.1 Modeling Commercialization -- 3.3 Future Research Opportunities -- 3.3.1 Opportunity Identification -- 3.3.2 Product Design and Development -- 3.3.3 Sales Forecasting -- 3.3.4 Commercialization -- 3.4 Conclusion -- References -- 4 Models for the Financial-Performance Effects of Marketing -- 4.1 Introduction -- 4.2 Marketing and Cash Flows -- 4.3 Criteria for Good Performance Metrics -- 4.4 The Process Perspective -- 4.4.1 The Core Sales-Response Model -- 4.4.2 Cash-Flow Generation -- 4.4.3 Flow and Stock Metrics -- 4.4.3.1 Time-Varying Baseline Models -- 4.4.3.2 Generalized Diffusion Models -- 4.4.3.3 Brand Equity -- 4.4.3.4 Customer Equity.

4.4.3.5 Intermediate Performance Variables and Marketing Dashboards -- 4.5 The Investor Perspective -- 4.5.1 Single-Equation Approach -- 4.5.2 Vector-Autoregressive Approach -- 4.5.3 Key Findings on Marketing and Firm Value -- 4.6 Conclusion -- References -- 5 Loyalty Programs: Current Insights, Research Challenges, and Emerging Trends -- 5.1 Introduction -- 5.2 Loyalty Program Phases -- 5.3 Effect Mechanisms of Loyalty Programs -- 5.3.1 Customer Responses to LPs -- 5.3.2 Pre-rewarding Effects: Direct Discounts and Points Pressure -- 5.3.3 Post-rewarding Effects: Rewarded Behavior -- 5.3.4 LP-Based Personalization -- 5.4 LP Design -- 5.4.1 The General LP Design -- 5.4.2 Short-Term Programs -- 5.4.3 Multi-vendor Programs -- 5.4.4 Hierarchical Loyalty Programs -- 5.5 Modeling LP Effects -- 5.6 Emerging Trends -- 5.6.1 Digitalization of LPs -- 5.6.2 Aligning LPs with the Customer Experience -- 5.6.3 Reviving Existing LPs -- 5.7 Conclusion -- References -- 6 Structural Models in Marketing: Consumer Demand and Search -- 6.1 What Is a Structural Model? -- 6.2 Structural Models: A Simple Illustration -- 6.2.1 The First Unobservable: An Aspect of Quality Known to the Consumer, But Not Observed by the Researcher -- 6.2.2 The Second Unobservable: Consumers Are Heterogeneous in Their Preferences and How They Respond to Marketing Activities -- 6.3 A Detour: Discrete-Choice Demand Models for Aggregate Data -- 6.4 Unobserved Demand Factors at the Aggregate Level (I.e., Common Across Consumers) -- 6.5 Back to the Consumer Demand Model -- 6.5.1 A Third Unobservable: Consumption (and Other) Signals Received by Consumers As They Seek to Learn About the Quality of a Product -- 6.5.2 A Fourth Unobservable: The Consumer is Uncertain About the Value of an Attribute (Say Price) and Engages in Costly Search to Resolve This Uncertainty -- 6.6 Simultaneous Search.

6.7 Sequential Search -- 6.8 Why Do We Need Structural Models? -- 6.9 Looking Back and Looking Ahead -- Appendix: Deriving the Indirect Utility Function (Equation (6.15)) -- References -- 7 Economic Models of Choice -- 7.1 Introduction -- 7.2 A Simple Model of Discrete Choice -- 7.2.1 Applications and Extensions -- 7.3 A General Model for Choice -- 7.3.1 Statistical Specification -- 7.3.2 Non-linear Outside Good -- 7.3.3 Applications and Extensions -- 7.4 Constraints -- 7.4.1 Multiple Constraints -- 7.4.2 Non-linear Constraints -- 7.5 Error Specification -- 7.5.1 Correlated Errors -- 7.5.2 Indivisible Demand -- 7.6 Indirect Utility Models -- 7.7 Conclusion -- References -- 8 Empirical Models of Learning Dynamics: A Survey of Recent Developments -- 8.1 Introduction -- 8.2 The Basic Bayesian Learning Model -- 8.3 Learning from Others (Social Learning) -- 8.4 Learning and Strategic Interaction -- 8.5 Information Spillovers and Correlated Learning -- 8.6 Models Incorporating Both Learning and Search -- 8.7 Heuristic and Approximation Approaches to Study Consumer Incentives to Explore -- 8.8 Using Exogenous Events and Policy Changes to Study Learning -- 8.9 Future Research Directions -- 8.10 Conclusion -- Acknowledgements -- References -- 9 Measurement Models for Marketing Constructs -- 9.1 Introduction -- 9.2 The Congeneric Measurement Model -- 9.2.1 Conceptual Development -- 9.2.2 Empirical Example -- 9.3 Multi-sample Congeneric Measurement Models with Mean Structures -- 9.3.1 Conceptual Development -- 9.3.2 Empirical Example -- 9.4 The Formative Measurement Model -- 9.4.1 Conceptual Development -- 9.4.2 Empirical Example -- 9.5 Extension 1: Relaxing the Assumption of Zero Non-target Loadings -- 9.5.1 Conceptual Development -- 9.5.2 Empirical Example -- 9.6 Extension 2: Relaxing the Assumption of Uncorrelated Unique Factors.

9.6.1 Conceptual Development -- 9.6.1.1 Sources of Shared Method Variance Among the Indicators -- 9.6.1.2 Models for Method Effects -- 9.6.2 Empirical Example -- 9.7 Extension 3: Relaxing the Assumption of Continuous, Normally Distributed Observed Measures -- 9.7.1 Conceptual Development -- 9.7.2 Empirical Example -- 9.8 Conclusion -- References -- 10 Marketing Models for the Customer-Centric Firm -- 10.1 Introduction -- 10.2 Customer Acquisition -- 10.2.1 Direct Approaches to Customer Acquisition -- 10.2.2 Beyond Classic Direct Marketing -- 10.3 Managing Acquired Customers -- 10.3.1 Computing Customer Lifetime Value -- 10.3.2 Churn Management -- 10.3.3 Contact Response Models -- 10.3.4 Contact Customization -- 10.4 Coordinating Acquisition and Retention -- 10.5 Discussion -- References -- 11 Eye Movements During Search and Choice -- 11.1 Introduction -- 11.2 The Eye and Visual Processes -- 11.2.1 Fixations and Saccades -- 11.2.2 The Perceptual Field Around a Fixation Is Small -- 11.2.3 Eye Movements Are Indicators of Visual Attention -- 11.2.4 Bottom-up and Top-Down Process Guide Eye Movements -- 11.3 Eye-Movement Recording -- 11.4 Analyzing Eye-Movement Data -- 11.4.1 Fixation Algorithms and the 80% Rule -- 11.4.2 AOIs and Eye-Movement Metrics -- 11.5 Eye Movements During Search -- 11.5.1 Brand Search Theory -- 11.5.2 Brand Search Model -- 11.6 Eye Movements During Choice -- 11.6.1 Choice Models with Eye-Tracking Metrics as Explanatory Variables -- 11.6.2 Integrated Models of Choice and Visual Attention -- 11.7 Conclusion -- Acknowledgements -- References -- 12 Business-Cycle Research in Marketing -- 12.1 Introduction -- 12.2 Main Insights from Business-Cycle Research in Marketing -- 12.3 How Is the Business Cycle Inferred? -- 12.3.1 Proxied by Official Economic Indicator(S) -- 12.3.2 Discrete Categorization by Official Economic Instances.

12.3.3 Infer Cyclical Information Using Business-Cycle Filters.

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