TY - JOUR
T1 - Soul and machine (learning)
AU - Proserpio, Davide
AU - Hauser, John R.
AU - Liu, Xiao
AU - Amano, Tomomichi
AU - Burnap, Alex
AU - Guo, Tong
AU - Lee, Dokyun (Dk)
AU - Lewis, Randall
AU - Misra, Kanishka
AU - Schwarz, Eric
AU - Timoshenko, Artem
AU - Xu, Lilei
AU - Yoganarasimhan, Hema
N1 - Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - Knowledge
KW - Machine learning
KW - Marketing applications
UR - http://www.scopus.com/inward/record.url?scp=85089894378&partnerID=8YFLogxK
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U2 - 10.1007/s11002-020-09538-4
DO - 10.1007/s11002-020-09538-4
M3 - Article
AN - SCOPUS:85089894378
SN - 0923-0645
VL - 31
SP - 393
EP - 404
JO - Marketing Letters
JF - Marketing Letters
IS - 4
ER -