Soul and machine (learning)

Davide Proserpio*, John R. Hauser, Xiao Liu, Tomomichi Amano, Alex Burnap, Tong Guo, Dokyun (Dk) Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu, Hema Yoganarasimhan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)393-404
Number of pages12
JournalMarketing Letters
Issue number4
StatePublished - Dec 2020


  • Knowledge
  • Machine learning
  • Marketing applications

ASJC Scopus subject areas

  • Business and International Management
  • Economics and Econometrics
  • Marketing


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