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dc.contributor.authorProserpio, Davide
dc.contributor.authorHauser, John R
dc.contributor.authorLiu, Xiao
dc.contributor.authorAmano, Tomomichi
dc.contributor.authorBurnap, Alex
dc.contributor.authorGuo, Tong
dc.contributor.authorLee, Dokyun (
dc.contributor.authorLewis, Randall
dc.contributor.authorMisra, Kanishka
dc.contributor.authorSchwarz, Eric
dc.contributor.authorTimoshenko, Artem
dc.contributor.authorXu, Lilei
dc.contributor.authorYoganarasimhan, Hema
dc.date.accessioned2021-09-20T17:31:01Z
dc.date.available2021-09-20T17:31:01Z
dc.date.issued2020-08-27
dc.identifier.urihttps://hdl.handle.net/1721.1/131935
dc.description.abstractAbstract 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.en_US
dc.publisherSpringer USen_US
dc.relation.isversionofhttps://doi.org/10.1007/s11002-020-09538-4en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceSpringer USen_US
dc.titleSoul and machine (learning)en_US
dc.typeArticleen_US
dc.contributor.departmentSloan School of Management
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-11-20T04:59:20Z
dc.language.rfc3066en
dc.rights.holderSpringer Science+Business Media, LLC, part of Springer Nature
dspace.embargo.termsY
dspace.date.submission2020-11-20T04:59:20Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Needed


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