Soul and machine (learning)

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2020-12-01

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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.

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Published Version (Please cite this version)

10.1007/s11002-020-09538-4

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Proserpio, D, JR Hauser, X Liu, T Amano, A Burnap, T Guo, D Lee, R Lewis, et al. (2020). Soul and machine (learning). Marketing Letters, 31(4). pp. 393–404. 10.1007/s11002-020-09538-4 Retrieved from https://hdl.handle.net/10161/23378.

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Guo

Tong Guo

Associate Professor of Business Administration

Tong Guo is an Associate Professor of Marketing at Duke University’s Fuqua School of Business and at the Department of Economics (by courtesy). Tong Guo studies the causal role of information in marketing and its policy implications, especially in the domain of healthcare, new technology and consumer protection. To do so, she uses a collection of methodologies from econometrics, machine learning, quasi-experiments, and online experiments. Her research explores the heterogeneous effects in healthcare marketing under the mandated information disclosure, consumer responses to misinformation in ads, moral hazard in airline loyalty programs, biotech adoptions, marijuana legalization and opioid prescriptions. She serves on the Editorial Board of Marketing Science.

Tong Guo is a faculty fellow at 2022 AMA Sheth Foundation Doctoral Consortium, 2022 ISMS Early Career Scholars Camp Fellow, the finalist of the 2018 UM ProQuest Distinguished Dissertation Awards, the 2017 AMA Sheth Foundation Doctoral Consortium Fellow, and the 2016 INFORMS Marketing Science Doctoral Consortium Fellow. 

Tong received her BS and BA at Peking University, her MA in Economics at Duke University, and her PhD in Marketing at the University of Michigan, Ann Arbor. At Fuqua, Tong teaches Strategic Brand Management in the Daytime MBA and Executive MBA programs. She also taught Marketing Core in the MMS programs and was recognized as the DKU Runner-Up for Excellence in Teaching in 2021-22.


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