Soul and machine (learning)

Davide Proserpio (), John R. Hauser, Xiao Liu, Tomomichi Amano, Alex Burnap, Tong Guo, Dokyun (DK) Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu and Hema Yoganarasimhan
Additional contact information
Davide Proserpio: USC Marshall School of Business
John R. Hauser: MIT Sloan School of Management
Xiao Liu: NYU Stern School of Business
Tomomichi Amano: Harvard Business School
Alex Burnap: Yale School of Management
Tong Guo: Duke Fuqua School of Business
Dokyun (DK) Lee: CMU Tepper School of Business
Randall Lewis: Independent
Kanishka Misra: UCSD Rady School of Management
Eric Schwarz: University of Michigan Ross School of Business
Artem Timoshenko: Northwestern University Kellogg School of Management
Lilei Xu: Airbnb
Hema Yoganarasimhan: UW Foster School of Business

Marketing Letters, 2020, vol. 31, issue 4, No 10, 393-404

Abstract: Abstract Machine learning is bringing us self-driving cars, medical diagnoses, and language translation, but how can machine learning help marketers improve marketing decisions? Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to analyze rich media content, such as text, images, audio, and video. Examples of current marketing applications include identification of customer needs from online data, accurate prediction of consumer response to advertising, personalized pricing, and product recommendations. But without the human input and insight—the soul—the applications of machine learning are limited. To create competitive or cooperative strategies, to generate creative product designs, to be accurate for “what-if” and “but-for” applications, to devise dynamic policies, to advance knowledge, to protect consumer privacy, and avoid algorithm bias, machine learning needs a soul. The brightest future is based on the synergy of what the machine can do well and what humans do well. We provide examples and predictions for the future.

Keywords: Machine learning; Marketing applications; Knowledge (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11002-020-09538-4

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