Artificial intelligence in marketing /

Review of Marketing Researchpushes the boundaries of marketing--broadening the marketing concept to make the world a better place. Here, leading scholars explore how marketing is currently shaping, and being shaped by, the evolution of Artificial Intelligence (AI).

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Sudhir, K. (Editor ), Toubia, Olivier (Editor ), Malhotra, Naresh K. (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Bingley, UK : Emerald Publishing Limited, 2023.
Edición:First edition.
Colección:Review of marketing research ; v. 20.
Temas:
Artificial intelligence > Marketing applications.
Sales & marketing.
Business & Economics > Marketing > General.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • ARTIFICIAL INTELLIGENCE IN MARKETING
  • REVIEW OF MARKETING RESEARCH
  • EDITORIAL ADVISORY BOARD
  • ARTIFICIAL INTELLIGENCE IN MARKETING
  • Copyright
  • CONTENTS
  • ABOUT THE EDITOR-IN-CHIEF
  • ABOUT THE VOLUME EDITORS
  • ABOUT THE CONTRIBUTORS
  • INTRODUCTION
  • The State of AI Research in Marketing: Active, Fertile, and Ready for Explosive Growth
  • 1 Introduction
  • 2 The Marketing-AI Ecosystem
  • 2.1 Organizing Existing Research on AI in Marketing
  • 3 The Chapters
  • 3.1 Marketing Purpose of AI
  • 3.1.1 Aiding Marketing Decisions
  • 3.1.2 Market Research
  • 3.2 Data
  • 3.3 Algorithms and Methods
  • 3.4 AI's Impact on Consumers and Society and Vice Versa
  • 4 Opportunity Identification for AI Research
  • 5 Conclusion
  • References
  • The Economics of Artificial Intelligence: A Marketing Perspective
  • Abstract
  • 1 Introduction
  • 2 Economic Framework of AI
  • 3 Level of Impact of AI
  • 3.1 Prediction
  • 3.2 Decisions
  • 3.3 Tools
  • 3.4 Strategy
  • 3.5 Society
  • 4 Agenda for Future Work
  • 5 Conclusion
  • References
  • AI and Personalization
  • Abstract
  • 1 Introduction
  • 2 Problem Definition
  • 3 Methodological Approaches to Personalization
  • 3.1 Scalability
  • 3.2 Generalizability and Counterfactual Validity
  • 3.3 Online and Interactive Methods
  • 3.4 Dynamic Methods
  • 4 Evaluation
  • 4.1 Direct Method
  • 4.2 Inverse Propensity Score Estimator
  • 4.3 Doubly Robust Method
  • 4.4 Extensions to Special Settings
  • 4.5 Alternative Approaches
  • 5 Returns to Personalization
  • 6 Personalization and Welfare
  • 6.1 Search Cost
  • 6.2 Privacy
  • 6.3 Fairness
  • 6.4 Polarization
  • 7 Conclusion and Directions for Future Research
  • 7.1 Signal-To-Noise Ratio
  • 7.2 Multiple Objectives and Long-Term Outcomes
  • 7.3 Time Drifts
  • 7.4 Strategic Behavior and Equilibrium Analysis
  • Notes
  • References
  • Artificial Intelligence and Pricing
  • Abstract
  • 1 Introduction
  • 2 Firms Implementing AI for Pricing
  • 2.1 Dynamic Pricing: Real-Time Swings in Demand and Supply
  • 2.2 Personalized Pricing: Price Discrimination
  • 2.3 Price Experimentation: Demand Learning
  • 3 Consequences of AI for Pricing
  • 3.1 Dynamic Pricing
  • 3.2 Personalized Pricing
  • 3.3 Algorithmic Collusion
  • 4 Summary
  • Notes
  • References
  • Leveraging AI for Content Generation: A Customer Equity Perspective
  • Abstract
  • 1 The Potential for AI Throughout the Customer Journey
  • 2 The Potential for Content Generation
  • 2.1 Generating Textual Content With Language Models
  • 2.2 Generating Synthetic Images
  • 3 Supporting Customer Equity Management With Content Generation
  • 3.1 Customer Acquisition
  • 3.2 Relationship Development
  • 3.3 Customer Retention
  • 4 Considerations for the Use of AI-Supported Content Generation
  • 4.1 Consumer Reactions
  • 4.2 Potential Abuse and the Need for Regulation
  • 4.3 Workforce Implications
  • 5 Conclusion
  • Notes
  • References
  • Artificial Intelligence and User-Generated Data Are Transforming How Firms Come to Understand Customer Needs

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