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Artificial Intelligence and Marketing: Pitfalls and Opportunities

Author

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  • De Bruyn, Arnaud
  • Viswanathan, Vijay
  • Beh, Yean Shan
  • Brock, Jürgen Kai-Uwe
  • von Wangenheim, Florian

Abstract

This article discusses the pitfalls and opportunities of AI in marketing through the lenses of knowledge creation and knowledge transfer. First, we discuss the notion of “higher-order learning” that distinguishes AI applications from traditional modeling approaches, and while focusing on recent advances in deep neural networks, we cover its underlying methodologies (multilayer perceptron, convolutional, and recurrent neural networks) and learning paradigms (supervised, unsupervised, and reinforcement learning). Second, we discuss the technological pitfalls and dangers marketing managers need to be aware of when implementing AI in their organizations, including the concepts of badly defined objective functions, unsafe or unrealistic learning environments, biased AI, explainable AI, and controllable AI. Third, AI will have a deep impact on predictive tasks that can be automated and require little explainability, we predict that AI will fall short of its promises in many marketing domains if we do not solve the challenges of tacit knowledge transfer between AI models and marketing organizations.

Suggested Citation

  • De Bruyn, Arnaud & Viswanathan, Vijay & Beh, Yean Shan & Brock, Jürgen Kai-Uwe & von Wangenheim, Florian, 2020. "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 91-105.
    • Handle: RePEc:eee:joinma:v:51:y:2020:i:c:p:91-105
    DOI: 10.1016/j.intmar.2020.04.007
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    1. Hoyer, Wayne D. & Kroschke, Mirja & Schmitt, Bernd & Kraume, Karsten & Shankar, Venkatesh, 2020. "Transforming the Customer Experience Through New Technologies," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 57-71.
    2. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2017. "Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments," Marketing Science, INFORMS, vol. 36(4), pages 500-522, July.
    3. Chiara Longoni & Andrea Bonezzi & Carey K Morewedge, 2019. "Resistance to Medical Artificial Intelligence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 46(4), pages 629-650.
    4. Ayres, Ian & Siegelman, Peter, 1995. "Race and Gender Discrimination in Bargaining for a New Car," American Economic Review, American Economic Association, vol. 85(3), pages 304-321, June.
    5. Peter E. Rossi, 2014. "Invited Paper —Even the Rich Can Make Themselves Poor: A Critical Examination of IV Methods in Marketing Applications," Marketing Science, INFORMS, vol. 33(5), pages 655-672, September.
    6. John R. Hauser & Guilherme (Gui) Liberali & Glen L. Urban, 2014. "Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph," Management Science, INFORMS, vol. 60(6), pages 1594-1616, June.
    7. Ikujiro Nonaka, 1994. "A Dynamic Theory of Organizational Knowledge Creation," Organization Science, INFORMS, vol. 5(1), pages 14-37, February.
    8. Füsun F. Gönül & Frenkel Ter Hofstede, 2006. "How to Compute Optimal Catalog Mailing Decisions," Marketing Science, INFORMS, vol. 25(1), pages 65-74, 01-02.
    9. Oliver J. Rutz & George F. Watson, 2019. "Endogeneity and marketing strategy research: an overview," Journal of the Academy of Marketing Science, Springer, vol. 47(3), pages 479-498, May.
    10. Xueming Luo & Siliang Tong & Zheng Fang & Zhe Qu, 2019. "Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases," Marketing Science, INFORMS, vol. 38(6), pages 937-947, November.
    11. Anja Lambrecht & Catherine Tucker, 2019. "Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads," Management Science, INFORMS, vol. 65(7), pages 2966-2981, July.
    12. Sande, Jon Bingen & Ghosh, Mrinal, 2018. "Endogeneity in survey research," International Journal of Research in Marketing, Elsevier, vol. 35(2), pages 185-204.
    13. Agrawal, Ajay & Gans, Joshua & Goldfarb, Avi (ed.), 2019. "The Economics of Artificial Intelligence," National Bureau of Economic Research Books, University of Chicago Press, number 9780226613338, August.
    14. Glen L. Urban & Guilherme (Gui) Liberali & Erin MacDonald & Robert Bordley & John R. Hauser, 2014. "Morphing Banner Advertising," Marketing Science, INFORMS, vol. 33(1), pages 27-46, January.
    15. Libai, Barak & Bart, Yakov & Gensler, Sonja & Hofacker, Charles F. & Kaplan, Andreas & Kötterheinrich, Kim & Kroll, Eike Benjamin, 2020. "Brave New World? On AI and the Management of Customer Relationships," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 44-56.
    16. Gupta, Shaphali & Leszkiewicz, Agata & Kumar, V. & Bijmolt, Tammo & Potapov, Dmitriy, 2020. "Digital Analytics: Modeling for Insights and New Methods," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 26-43.
    17. Grewal, Dhruv & Kroschke, Mirja & Mende, Martin & Roggeveen, Anne L. & Scott, Maura L., 2020. "Frontline Cyborgs at Your Service: How Human Enhancement Technologies Affect Customer Experiences in Retail, Sales, and Service Settings," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 9-25.
    18. Savica Dimitrieska & Aleksandra Stankovska & Tanja Efremova, 2018. "Artificial Intelligence And Marketing," Entrepreneurship, Faculty of Economics, SOUTH-WEST UNIVERSITY "NEOFIT RILSKI", BLAGOEVGRAD, vol. 6(2), pages 298-304.
    19. Martin Natter & Thomas Reutterer & Andreas Mild & Alfred Taudes, 2007. "—An Assortmentwide Decision-Support System for Dynamic Pricing and Promotion Planning in DIY Retailing," Marketing Science, INFORMS, vol. 26(4), pages 576-583, 07-08.
    20. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    21. Tat Y. Chan & Jia Li & Lamar Pierce, 2014. "Learning from Peers: Knowledge Transfer and Sales Force Productivity Growth," Marketing Science, INFORMS, vol. 33(4), pages 463-484, July.
    22. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    23. Ralf Elsner & Manfred Krafft & Arnd Huchzermeier, 2004. "The 2003 ISMS Practice Prize Winner: Optimizing Rhenania's Direct Marketing Business Through Dynamic Multilevel Modeling (DMLM) in a Multicatalog-Brand Environment," Marketing Science, INFORMS, vol. 23(2), pages 192-206, June.
    24. Felbermayr, Armin & Nanopoulos, Alexandros, 2016. "The Role of Emotions for the Perceived Usefulness in Online Customer Reviews," Journal of Interactive Marketing, Elsevier, vol. 36(C), pages 60-76.
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