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A strategic framework for artificial intelligence in marketing

Author

Listed:
  • Ming-Hui Huang

    (National Taiwan University)

  • Roland T. Rust

    (University of Maryland)

Abstract

The authors develop a three-stage framework for strategic marketing planning, incorporating multiple artificial intelligence (AI) benefits: mechanical AI for automating repetitive marketing functions and activities, thinking AI for processing data to arrive at decisions, and feeling AI for analyzing interactions and human emotions. This framework lays out the ways that AI can be used for marketing research, strategy (segmentation, targeting, and positioning, STP), and actions. At the marketing research stage, mechanical AI can be used for data collection, thinking AI for market analysis, and feeling AI for customer understanding. At the marketing strategy (STP) stage, mechanical AI can be used for segmentation (segment recognition), thinking AI for targeting (segment recommendation), and feeling AI for positioning (segment resonance). At the marketing action stage, mechanical AI can be used for standardization, thinking AI for personalization, and feeling AI for relationalization. We apply this framework to various areas of marketing, organized by marketing 4Ps/4Cs, to illustrate the strategic use of AI.

Suggested Citation

  • Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    • Handle: RePEc:spr:joamsc:v:49:y:2021:i:1:d:10.1007_s11747-020-00749-9
    DOI: 10.1007/s11747-020-00749-9
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    as
    1. Artem Timoshenko & John R. Hauser, 2019. "Identifying Customer Needs from User-Generated Content," Marketing Science, INFORMS, vol. 38(1), pages 1-20, January.
    2. Rodrigo Montes & Wilfried Sand-Zantman & Tommaso Valletti, 2019. "The Value of Personal Information in Online Markets with Endogenous Privacy," Management Science, INFORMS, vol. 65(3), pages 1342-1362, March.
    3. Nico Neumann & Catherine E. Tucker & Timothy Whitfield, 2019. "Frontiers: How Effective Is Third-Party Consumer Profiling? Evidence from Field Studies," Marketing Science, INFORMS, vol. 38(6), pages 918-926, November.
    4. Ajayeb S. Abu Daabes & Faten F. Kharbat, 2017. "Customer-based perceptual map as a marketing intelligence source," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 13(4), pages 360-379.
    5. Shuba Srinivasan & Oliver J. Rutz & Koen Pauwels, 2016. "Paths to and off purchase: quantifying the impact of traditional marketing and online consumer activity," Journal of the Academy of Marketing Science, Springer, vol. 44(4), pages 440-453, July.
    6. 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.
    7. Schneider, Matthew J. & Jagpal, Sharan & Gupta, Sachin & Li, Shaobo & Yu, Yan, 2017. "Protecting customer privacy when marketing with second-party data," International Journal of Research in Marketing, Elsevier, vol. 34(3), pages 593-603.
    8. David H. Autor & David Dorn, 2013. "The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market," American Economic Review, American Economic Association, vol. 103(5), pages 1553-1597, August.
    9. Ashlee Humphreys & Rebecca Jen-Hui Wang & Eileen FischerEditor & Linda PriceAssociate Editor, 2018. "Automated Text Analysis for Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(6), pages 1274-1306.
    10. Ritu Agarwal & Michelle Dugas & Guodong (Gordon) Gao & P. K. Kannan, 2020. "Emerging technologies and analytics for a new era of value-centered marketing in healthcare," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 9-23, January.
    11. Lehmann, Donald R., 2020. "The evolving world of research in marketing and the blending of theory and data," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 27-42.
    12. Tuck Siong Chung & Roland T. Rust & Michel Wedel, 2009. "My Mobile Music: An Adaptive Personalization System for Digital Audio Players," Marketing Science, INFORMS, vol. 28(1), pages 52-68, 01-02.
    13. 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.
    14. Daria Dzyabura & John R. Hauser, 2011. "Active Machine Learning for Consideration Heuristics," Marketing Science, INFORMS, vol. 30(5), pages 801-819, September.
    15. Dokyun Lee & Kartik Hosanagar & Harikesh S. Nair, 2018. "Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook," Management Science, INFORMS, vol. 64(11), pages 5105-5131, November.
    16. Thomas P. Novak & Donna L. Hoffman, 2019. "Relationship journeys in the internet of things: a new framework for understanding interactions between consumers and smart objects," Journal of the Academy of Marketing Science, Springer, vol. 47(2), pages 216-237, March.
    17. Naveen Donthu & Roland T. Rust, 1989. "Note—Estimating Geographic Customer Densities Using Kernel Density Estimation," Marketing Science, INFORMS, vol. 8(2), pages 191-203.
    18. Ming-Hui Huang & Roland T. Rust, 2017. "Technology-driven service strategy," Journal of the Academy of Marketing Science, Springer, vol. 45(6), pages 906-924, November.
    19. Frey, Carl Benedikt & Osborne, Michael A., 2017. "The future of employment: How susceptible are jobs to computerisation?," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 254-280.
    20. Gah-Yi Ban & Noureddine El Karoui & Andrew E. B. Lim, 2018. "Machine Learning and Portfolio Optimization," Management Science, INFORMS, vol. 64(3), pages 1136-1154, March.
    21. Hartmann, Jochen & Huppertz, Juliana & Schamp, Christina & Heitmann, Mark, 2019. "Comparing automated text classification methods," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 20-38.
    22. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    23. Dhruv Grewal & Stephanie M. Noble & Anne L. Roggeveen & Jens Nordfalt, 2020. "The future of in-store technology," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 96-113, January.
    24. Jacky C. K. Chow, 2018. "Analysis of Financial Credit Risk Using Machine Learning," Papers 1802.05326, arXiv.org.
    25. Donna L Hoffman & Thomas P Novak & Eileen FischerEditor & Robert KozinetsAssociate Editor, 2018. "Consumer and Object Experience in the Internet of Things: An Assemblage Theory Approach," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(6), pages 1178-1204.
    26. Seo Young Kim & Bernd H. Schmitt & Nadia M. Thalmann, 2019. "Eliza in the uncanny valley: anthropomorphizing consumer robots increases their perceived warmth but decreases liking," Marketing Letters, Springer, vol. 30(1), pages 1-12, March.
    27. Juan Feng & Xin Li & Xiaoquan (Michael) Zhang, 2019. "Online Product Reviews-Triggered Dynamic Pricing: Theory and Evidence," Information Systems Research, INFORMS, vol. 30(4), pages 1107-1123, December.
    28. Xiao Liu & Param Vir Singh & Kannan Srinivasan, 2016. "A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing," Marketing Science, INFORMS, vol. 35(3), pages 363-388, May.
    29. Siliang Tong & Xueming Luo & Bo Xu, 2020. "Personalized mobile marketing strategies," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 64-78, January.
    30. Daria Dzyabura & John R. Hauser, 2019. "Recommending Products When Consumers Learn Their Preference Weights," Marketing Science, INFORMS, vol. 38(3), pages 417-441, May.
    31. Arun Rai, 2020. "Explainable AI: from black box to glass box," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 137-141, January.
    32. Felipe Thomaz & Carolina Salge & Elena Karahanna & John Hulland, 2020. "Learning from the Dark Web: leveraging conversational agents in the era of hyper-privacy to enhance marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 43-63, January.
    33. Matthew J. Schneider & Sharan Jagpal & Sachin Gupta & Shaobo Li & Yan Yu, 2018. "A Flexible Method for Protecting Marketing Data: An Application to Point-of-Sale Data," Marketing Science, INFORMS, vol. 37(1), pages 153-171, January.
    34. Tuck Siong Chung & Michel Wedel & Roland T. Rust, 2016. "Adaptive personalization using social networks," Journal of the Academy of Marketing Science, Springer, vol. 44(1), pages 66-87, January.
    35. Liu, Xingyi, 2020. "De-targeting to signal quality," International Journal of Research in Marketing, Elsevier, vol. 37(2), pages 386-404.
    36. Manjit S. Yadav & Paul A. Pavlou, 2020. "Technology-enabled interactions in digital environments:a conceptual foundation for current and future research," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 132-136, January.
    37. Duncan Simester & Artem Timoshenko & Spyros I. Zoumpoulis, 2020. "Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges," Management Science, INFORMS, vol. 66(6), pages 2495-2522, June.
    38. Kanishka Misra & Eric M. Schwartz & Jacob Abernethy, 2019. "Dynamic Online Pricing with Incomplete Information Using Multiarmed Bandit Experiments," Marketing Science, INFORMS, vol. 38(2), pages 226-252, March.
    39. Thomas Davenport & Abhijit Guha & Dhruv Grewal & Timna Bressgott, 2020. "How artificial intelligence will change the future of marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 24-42, January.
    40. Miremad Soleymanian & Charles B. Weinberg & Ting Zhu, 2019. "Sensor Data and Behavioral Tracking: Does Usage-Based Auto Insurance Benefit Drivers?," Marketing Science, INFORMS, vol. 38(1), pages 21-43, January.
    41. Ma, Liye & Sun, Baohong, 2020. "Machine learning and AI in marketing – Connecting computing power to human insights," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 481-504.
    42. Willcock, Simon & Martínez-López, Javier & Hooftman, Danny A.P. & Bagstad, Kenneth J. & Balbi, Stefano & Marzo, Alessia & Prato, Carlo & Sciandrello, Saverio & Signorello, Giovanni & Voigt, Brian & Vi, 2018. "Machine learning for ecosystem services," Ecosystem Services, Elsevier, vol. 33(PB), pages 165-174.
    43. Dekimpe, Marnik G., 2020. "Retailing and retailing research in the age of big data analytics," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 3-14.
    44. Alan D. J. Cooke & Peter P. Zubcsek, 2017. "The Connected Consumer: Connected Devices and the Evolution of Customer Intelligence," Journal of the Association for Consumer Research, University of Chicago Press, vol. 2(2), pages 164-178.
    45. Pradeep Chintagunta & Dominique M. Hanssens & John R. Hauser, 2016. "Editorial—Marketing Science and Big Data," Marketing Science, INFORMS, vol. 35(3), pages 341-342, May.
    46. Quan Wang & Beibei Li & Param Vir Singh, 2018. "Copycats vs. Original Mobile Apps: A Machine Learning Copycat-Detection Method and Empirical Analysis," Information Systems Research, INFORMS, vol. 29(2), pages 273-291, June.
    47. Rust, Roland T., 2020. "The future of marketing," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 15-26.
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