Soul and machine (learning)

dc.contributor.author

Proserpio, D

dc.contributor.author

Hauser, JR

dc.contributor.author

Liu, X

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Amano, T

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Burnap, A

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Guo, T

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Lee, D

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Lewis, R

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Misra, K

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Schwarz, E

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Timoshenko, A

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Xu, L

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Yoganarasimhan, H

dc.date.accessioned

2021-06-11T15:06:26Z

dc.date.available

2021-06-11T15:06:26Z

dc.date.issued

2020-12-01

dc.date.updated

2021-06-11T15:06:24Z

dc.description.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.

dc.identifier.issn

0923-0645

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1573-059X

dc.identifier.uri

https://hdl.handle.net/10161/23378

dc.language

en

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Springer Science and Business Media LLC

dc.relation.ispartof

Marketing Letters

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10.1007/s11002-020-09538-4

dc.subject

Machine learning

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marketing

dc.title

Soul and machine (learning)

dc.type

Journal article

duke.contributor.orcid

Guo, T|0000-0002-3171-2890

pubs.begin-page

393

pubs.end-page

404

pubs.issue

4

pubs.organisational-group

Fuqua School of Business

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Economics

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Duke

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Trinity College of Arts & Sciences

pubs.publication-status

Published

pubs.volume

31

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