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Artificial Intelligence in Marketing [Kietas viršelis]

Editor-in-chief (Georgia Institute of Technology, USA), Edited by (Columbia University, USA), Edited by (Yale University, USA)
  • Formatas: Hardback, 344 pages, aukštis x plotis x storis: 229x152x23 mm, weight: 610 g
  • Serija: Review of Marketing Research
  • Išleidimo metai: 13-Mar-2023
  • Leidėjas: Emerald Publishing Limited
  • ISBN-10: 1802628762
  • ISBN-13: 9781802628760
Kitos knygos pagal šią temą:
  • Formatas: Hardback, 344 pages, aukštis x plotis x storis: 229x152x23 mm, weight: 610 g
  • Serija: Review of Marketing Research
  • Išleidimo metai: 13-Mar-2023
  • Leidėjas: Emerald Publishing Limited
  • ISBN-10: 1802628762
  • ISBN-13: 9781802628760
Kitos knygos pagal šią temą:

Review of Marketing Research pushes 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). Topics covered include the effects of AI on: economics; personalisation; pricing; content generation; the identification, structuring, and prioritization of customer needs; customer feedback; Natural Language Processing; image analytics; deep learning; and the anthropomorphism of AI, such as in virtual assistants and chatbots.

Each chapter provides thought provoking discussions which will be relevant to researchers, professionals, and students.



Review of Marketing Research pushes 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).



Review of Marketing Research pushes 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). Topics covered include the effects of AI on: economics; personalisation; pricing; content generation; the identification, structuring, and prioritization of customer needs; customer feedback; Natural Language Processing; image analytics; deep learning; and the anthropomorphism of AI, such as in virtual assistants and chatbots. Each chapter provides thought provoking discussions which will be relevant to researchers, professionals, and students.
About the Editor-in-Chief ix
About the Volume Editors xi
About the Contributors xiii
Introduction xxi
The State of AI Research in Marketing: Active, Fertile, and Ready for Explosive Growth
1(12)
K. Sudhir
Olivier Toubia
The Economics of Artificial Intelligence: A Marketing Perspective
13(64)
Meng Qi (Annie) Ding
Avi Goldfarb
AI and Personalization
77(26)
Omid Rafieian
Hema Yoganarasimhan
Artificial Intelligence and Pricing
103(22)
Diego Aparicio
Kanishka Misra
Leveraging AI for Content Generation: A Customer Equity Perspective
125(22)
David A. Schweidel
Martin Reisenbichler
Thomas Reutterer
Kunpeng Zhang
Artificial Intelligence and User-Generated Data Are Transforming How Firms Come to Understand Customer Needs
147(22)
John R. Hauser
Zelin Li
Chengfeng Mao
Artificial Intelligence Applications to Customer Feedback Research: A Review
169(22)
Peter S. Lee
Ishita Chakraborty
Shrabastee Banerjee
Natural Language Processing in Marketing
191(26)
Jochen Hartmann
Oded Netzer
Marketing Through the Machine's Eyes: Image Analytics and Interpretability
217(22)
Xiaohang (Flora) Feng
Shunyuan Zhang
Kannan Srinivasan
Deep Learning in Marketing: A Review and Research Agenda
239(34)
Xiao Liu
Anthropomorphism in Artificial Intelligence: A Review of Empirical Work Across Domains and Insights for Future Research
273(36)
Ertugrul Uysal
Sascha Alavi
Valery Bezencon
Index 309
Naresh K. Malhotra is Senior Fellow, Georgia Tech CIBER, and Regents Professor Emeritus, Scheller College of Business, Georgia Institute of Technology (Georgia Tech), USA.



K. Sudhir is James L. Frank 32 Professor of Marketing at the Yale School of Management. His recent research uses AI methods such as NLP, video and music analytics to address questions on advertising, salesforce hiring, online reviews, bias and privacy.



Olivier Toubia is the Glaubinger Professor of Business at Columbia Business School. His research combines methods from social sciences and data science, in order to study human processes such as motivation, choice, and creativity.