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    Soul and Machine (Learning)
    04 Oct 2019Working Paper Summaries

    Soul and Machine (Learning)

    by Davide Proserpio, John R. Hauser, Xiao Liu, Tomomichi Amano et al.
    This paper argues with examples and predictions that while marketing science theory, engineering, and machine learning capabilities are changing the way we think about marketing, true advances will come when marketing managers know when to trust the machine and when to trust their instincts.
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    Author Abstract

    Machine learning is bringing us self-driving cars, improved medical diagnostics, and machine translation, but can it improve marketing decisions? It can. Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to rich media such as text, images, audio, and video. Examples include identification of customer needs from online data, accurate prediction of consumer response to advertising, personalized pricing, and product recommendations. But without a soul, the applications of machine learning are limited. Consumer behavior and competitive strategies are nuanced and richly described by formal theory. To learn across applications, to be accurate for “what-if” and “but-for” applications, and to advance knowledge, machine learning needs theory and 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.

    Paper Information

    • Full Working Paper Text
    • Working Paper Publication Date: September 2019
    • HBS Working Paper Number: HBS Working Paper #20-036
    • Faculty Unit(s): Marketing
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