Soul and machine (learning)

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.

[1]  Matt Taddy,et al.  The Technological Elements of Artificial Intelligence , 2018, The Economics of Artificial Intelligence.

[2]  John R. Hauser,et al.  Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph , 2014, Manag. Sci..

[3]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[4]  Hema Yoganarasimhan,et al.  Targeting and Privacy in Mobile Advertising , 2020, Mark. Sci..

[5]  J. Robins,et al.  Double/Debiased Machine Learning for Treatment and Structural Parameters , 2017 .

[6]  J. Harrington DEVELOPING COMPETITION LAW FOR COLLUSION BY AUTONOMOUS ARTIFICIAL AGENTS† , 2018, Journal of Competition Law & Economics.

[7]  Abhishek Pani,et al.  Design and Evaluation of Personalized Free Trials , 2020, SSRN Electronic Journal.

[8]  Mallesh M. Pai,et al.  Algorithmic Collusion: Supra-Competitive Prices via Independent Algorithms , 2020 .

[9]  John R. Hauser,et al.  Identifying Customer Needs from User-Generated Content , 2019, Mark. Sci..

[10]  Eric T. Bradlow,et al.  Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments , 2016 .

[11]  Cynthia Rudin,et al.  This Looks Like That: Deep Learning for Interpretable Image Recognition , 2018 .

[12]  Olivier Toubia,et al.  Letting Logos Speak: Leveraging Multiview Representation Learning for Data-Driven Logo Design , 2019, SSRN Electronic Journal.

[13]  Eric M. Schwartz,et al.  Dynamic Online Pricing with Incomplete Information Using Multi-Armed Bandit Experiments , 2018, Mark. Sci..

[14]  Greg M. Allenby,et al.  Sentence-Based Text Analysis for Customer Reviews , 2016, Mark. Sci..

[15]  S. Athey,et al.  Generalized random forests , 2016, The Annals of Statistics.

[16]  Vincenzo Denicolò,et al.  Artificial Intelligence, Algorithmic Pricing, and Collusion , 2020 .

[17]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[18]  Dokyun Lee,et al.  Focused Concept Miner (FCM): Interpretable Deep Learning for Text Exploration , 2018 .

[19]  David Danks,et al.  Good Explanation for Algorithmic Transparency , 2020, AIES.

[20]  Ramesh Raskar,et al.  Streetscore -- Predicting the Perceived Safety of One Million Streetscapes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[21]  Scott Counts,et al.  The psychology of job loss: using social media data to characterize and predict unemployment , 2016, WebSci.

[22]  Xiao Liu,et al.  Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning , 2017, AAAI Workshops.

[23]  Alex Burnap,et al.  Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach , 2019, SSRN Electronic Journal.