Soul and machine (learning)

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.

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Notes

  1. 1.

    Examples of machine learning algorithms include neural networks, gradient-boosted trees, variational autoencoders, probabilistic graphical models, and reinforcement learning.

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Correspondence to Davide Proserpio.

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Our title pays homage to an earlier era of rapid computational advancement, The Soul and the New Machine, by Tracy Kidder published in 1981 by Little, Brown, and Company, New York. All opinions are our own or as cited.

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Proserpio, D., Hauser, J.R., Liu, X. et al. Soul and machine (learning). Mark Lett 31, 393–404 (2020). https://doi.org/10.1007/s11002-020-09538-4

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Keywords

  • Machine learning
  • Marketing applications
  • Knowledge