Soul and machine (learning)
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Soul and machine (learning) Davide Proserpio 1 & John R. Hauser 2 & Xiao Liu 3 & Tomomichi Amano 4 & Alex Burnap 5 & Tong Guo 6 & Dokyun (DK) Lee 7 & Randall Lewis 8 & Kanishka Misra 9 & Eric Schwarz 10 & Artem Timoshenko 11 & Lilei Xu 12 & Hema Yoganarasimhan 13
# Springer Science+Business Media, LLC, part of Springer Nature 2020
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 “whatif” 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. Keywords Machine learning . Marketing applications . Knowledge
1 Without a soul, machine learning is but a tool In today’s information-rich environment, firms use real-time data and sensor signals combined with predictions of consumer response to automate decisions. Accurately predicting consumer reactions and competitor responses to marketing strategies remains a fundamental challenge. If used judiciously, machine learning—a set of 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.
* Davide Proserpio [email protected] Extended author information available on the last page of the article
Marketing Letters
algorithms, both supervised and unsupervised, that apply to large data to inform decisions—can greatly improve actionable predictions.1 Machine learning has made significant advances in recent years. Today we see progress in areas such as self-driving cars, recommender systems, automated conversational agents, automated advertising allocation and auctions, machine translation, and financial fraud detection. Marketing practice has already benefited from many of these advances, and firms of all sizes employ production-level machine learning systems to improve targeted advertising campaigns, the products offered to individual consumers, prices, and promotions. Marketing has just begun to leverage machine learning approaches to create new powerful applications, offer new insights, and generate new theories. In this paper, we take a step back an
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