Soul and Machine (Learning)

17 Pages Posted: 24 Sep 2019 Last revised: 26 Aug 2020

See all articles by Davide Proserpio

Davide Proserpio

Marshall School of Business, University of Southern California

John R. Hauser

MIT Sloan School of Management

Xiao Liu

New York University (NYU) - Leonard N. Stern School of Business

Tomomichi Amano

Harvard University - Business School (HBS)

Alex Burnap

Yale School of Management

Tong Guo

Duke University, Fuqua School of Business

Dokyun Lee

Boston University - Questrom School of Business

Randall A. Lewis

Nanigans

Kanishka Misra

University of Michigan, Stephen M. Ross School of Business; University of Michigan at Ann Arbor

Eric M. Schwartz

University of Michigan, Stephen M. Ross School of Business

Artem Timoshenko

Kellogg School of Management, Northwestern University

Lilei Xu

affiliation not provided to SSRN

Hema Yoganarasimhan

University of Washington

Date Written: September 16, 2019

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

Suggested Citation

Proserpio, Davide and Hauser, John R. and Liu, Xiao and Amano, Tomomichi and Burnap, Alex and Guo, Tong and Lee, Dokyun and Lewis, Randall A. and Misra, Kanishka and Schwartz, Eric M. and Timoshenko, Artem and Xu, Lilei and Yoganarasimhan, Hema, Soul and Machine (Learning) (September 16, 2019). NYU Stern School of Business, Available at SSRN: https://ssrn.com/abstract=3454294 or http://dx.doi.org/10.2139/ssrn.3454294

Davide Proserpio (Contact Author)

Marshall School of Business, University of Southern California ( email )

701 Exposition Blvd
Los Angeles, CA Los Angeles 90089
United States

HOME PAGE: http://faculty.marshall.usc.edu/Davide-Proserpio/

John R. Hauser

MIT Sloan School of Management ( email )

International Center for Research on the Mngmt Tech.
Cambridge, MA 02142
United States
617-253-2929 (Phone)
617-258-7597 (Fax)

Xiao Liu

New York University (NYU) - Leonard N. Stern School of Business ( email )

Suite 9-160
New York, NY
United States

Tomomichi Amano

Harvard University - Business School (HBS) ( email )

Soldiers Field Road
Boston, MA 02163
United States

Alex Burnap

Yale School of Management ( email )

165 Whitney Avenue
New Haven, CT 06511
United States

Tong Guo

Duke University, Fuqua School of Business ( email )

100 Fuqua Dr
Durham, NC 27708
United States

Dokyun Lee

Boston University - Questrom School of Business ( email )

595 Commonwealth Avenue
Boston, MA MA 02215
United States

Randall A. Lewis

Nanigans ( email )

100 Summers St.
Boston, MA 02110
312-RA-LEWIS (Phone)

Kanishka Misra

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States

University of Michigan at Ann Arbor ( email )

500 S. State Street

Eric M. Schwartz

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States

Artem Timoshenko

Kellogg School of Management, Northwestern University ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

Lilei Xu

affiliation not provided to SSRN

Hema Yoganarasimhan

University of Washington ( email )

481 Paccar Hall
Seattle, WA 98195
United States

HOME PAGE: http://faculty.washington.edu/hemay/

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