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Erschienen in: Marketing Letters 4/2020

27.08.2020

Soul and machine (learning)

verfasst von: 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

Erschienen in: Marketing Letters | Ausgabe 4/2020

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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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Fußnoten
1
Examples of machine learning algorithms include neural networks, gradient-boosted trees, variational autoencoders, probabilistic graphical models, and reinforcement learning.
 
Literatur
Zurück zum Zitat Athey, S., Tibshirani, J., & Wager, S. (2019). Generalized random forests. The Annals of Statistics, 47(2), 1148–1178.CrossRef Athey, S., Tibshirani, J., & Wager, S. (2019). Generalized random forests. The Annals of Statistics, 47(2), 1148–1178.CrossRef
Zurück zum Zitat Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Zurück zum Zitat Büschken, J., & Allenby, G. M. (2016). Sentence-based text analysis for customer reviews. Marketing Science 35(6), 953–975. Büschken, J., & Allenby, G. M. (2016). Sentence-based text analysis for customer reviews. Marketing Science 35(6), 953–975.
Zurück zum Zitat Burnap, A., Hauser, J. R., & Timoshenko, A. (2019). Design and evaluation of product aesthetics: A human-machine hybrid approach. Rochester, NY: Social Science Research Network. Burnap, A., Hauser, J. R., & Timoshenko, A. (2019). Design and evaluation of product aesthetics: A human-machine hybrid approach. Rochester, NY: Social Science Research Network.
Zurück zum Zitat Emilio Calvano, Giacomo Calzolari, Vincenzo Denicolò, and Sergio Pastorello. (2018). Artificial intelligence, algorithmic pricing and collusion. (2018). Emilio Calvano, Giacomo Calzolari, Vincenzo Denicolò, and Sergio Pastorello. (2018). Artificial intelligence, algorithmic pricing and collusion. (2018).
Zurück zum Zitat Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., & Su, J. K. (2019). This looks like that: Deep learning for interpretable image recognition. In Advances in NIPS (pp. 8930-8941). Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., & Su, J. K. (2019). This looks like that: Deep learning for interpretable image recognition. In Advances in NIPS (pp. 8930-8941).
Zurück zum Zitat Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1–C68.CrossRef Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1–C68.CrossRef
Zurück zum Zitat Dew, R., Ansari, A., & Toubia, O. (2019). Letting logos speak: Leveraging multiview representation learning for data-driven logo design. Rochester, NY: Social Science Research Network. Dew, R., Ansari, A., & Toubia, O. (2019). Letting logos speak: Leveraging multiview representation learning for data-driven logo design. Rochester, NY: Social Science Research Network.
Zurück zum Zitat Hansen, K., Misra, K. & Pai, M., (2020). Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms. Forthcoming, Marketing Science Hansen, K., Misra, K. & Pai, M., (2020). Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms. Forthcoming, Marketing Science
Zurück zum Zitat Harrington, J. E. (2018). Developing competition law for collusion by autonomous artificial agents. Journal of Competition Law and Economics, 14(3), 331–363.CrossRef Harrington, J. E. (2018). Developing competition law for collusion by autonomous artificial agents. Journal of Competition Law and Economics, 14(3), 331–363.CrossRef
Zurück zum Zitat Hauser, J. R., Liberali, G., & Urban, G. L. (2014). Website morphing 2.0: Switching costs, partial exposure, random exit, and when to morph. Management Science, 60(6), 1594–1616.CrossRef Hauser, J. R., Liberali, G., & Urban, G. L. (2014). Website morphing 2.0: Switching costs, partial exposure, random exit, and when to morph. Management Science, 60(6), 1594–1616.CrossRef
Zurück zum Zitat Lee, Dokyun (DK) and Manzoor, Emaad and Cheng, Zhaoqi, Focused concept miner (FCM): Interpretable deep learning for text exploration (written May 20, 2018, Revised may 19, 2020). Available at SSRN: https://ssrn.com/abstract=3304756. Lee, Dokyun (DK) and Manzoor, Emaad and Cheng, Zhaoqi, Focused concept miner (FCM): Interpretable deep learning for text exploration (written May 20, 2018, Revised may 19, 2020). Available at SSRN: https://​ssrn.​com/​abstract=​3304756.
Zurück zum Zitat Little, J. D. C. (1970). Managers and models: The concept of a decision calculus. Management Science, 16(8), B466–B485.CrossRef Little, J. D. C. (1970). Managers and models: The concept of a decision calculus. Management Science, 16(8), B466–B485.CrossRef
Zurück zum Zitat Liu, X. (2020). Dynamic personalized pricing using batch deep reinforcement learning: An application to LiveStream shopping. New York University. Working paper. Liu, X. (2020). Dynamic personalized pricing using batch deep reinforcement learning: An application to LiveStream shopping. New York University. Working paper.
Zurück zum Zitat Liu, X., Lee, D., & Srinivasan, K. (2019). Large-scale cross-category analysis of consumer review content on sales conversion leveraging deep learning. Journal of Marketing Research, 56(6), 918–943.CrossRef Liu, X., Lee, D., & Srinivasan, K. (2019). Large-scale cross-category analysis of consumer review content on sales conversion leveraging deep learning. Journal of Marketing Research, 56(6), 918–943.CrossRef
Zurück zum Zitat Lu, Joy, Dokyun (DK) Lee, Tae Wan Kim, and David Danks (2020). “Good explanation for algorithmic transparency.” Proceedings of the 2020 AAAI/ACM conference on AI, Ethics, and Society. Lu, Joy, Dokyun (DK) Lee, Tae Wan Kim, and David Danks (2020). “Good explanation for algorithmic transparency.” Proceedings of the 2020 AAAI/ACM conference on AI, Ethics, and Society.
Zurück zum Zitat Misra, K., Schwartz, E. M., & Abernethy, J. D. (2019). Dynamic online pricing with incomplete information using multi-armed bandit experiments. Marketing Science, 38(2), 226–252.CrossRef Misra, K., Schwartz, E. M., & Abernethy, J. D. (2019). Dynamic online pricing with incomplete information using multi-armed bandit experiments. Marketing Science, 38(2), 226–252.CrossRef
Zurück zum Zitat Naik, Nikhil, Jade Philipoom, Ramesh Raskar, and César Hidalgo (2014). "Streetscore-predicting the perceived safety of one million streetscapes." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 779–785. Naik, Nikhil, Jade Philipoom, Ramesh Raskar, and César Hidalgo (2014). "Streetscore-predicting the perceived safety of one million streetscapes." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 779–785.
Zurück zum Zitat Proserpio, Davide, Scott Counts, and Apurv Jain (2016). “The psychology of job loss: using social media data to characterize and predict unemployment.” In Proceedings of the 8th ACM Conference on Web Science, pp. 223–232. Proserpio, Davide, Scott Counts, and Apurv Jain (2016). “The psychology of job loss: using social media data to characterize and predict unemployment.” In Proceedings of the 8th ACM Conference on Web Science, pp. 223–232.
Zurück zum Zitat Rafieian, O. (2020a). Optimizing user engagement through adaptive ad sequencing. Working paper: Cornell University. Rafieian, O. (2020a). Optimizing user engagement through adaptive ad sequencing. Working paper: Cornell University.
Zurück zum Zitat Rafieian, O. (2020b). Revenue-optimal dynamic auctions for adaptive ad sequencing. Working paper: Cornell University. Rafieian, O. (2020b). Revenue-optimal dynamic auctions for adaptive ad sequencing. Working paper: Cornell University.
Zurück zum Zitat Rafieian, O., & Yoganarasimhan, H. (2020). Targeting and privacy in mobile advertising. Marketing Science: Forthcoming. Rafieian, O., & Yoganarasimhan, H. (2020). Targeting and privacy in mobile advertising. Marketing Science: Forthcoming.
Zurück zum Zitat Ribeiro MT, Singh S, Guestrin C (2016). Why should I trust you?” explaining the predictions of a classifier, arXiv:1602.04938v3. Ribeiro MT, Singh S, Guestrin C (2016). Why should I trust you?” explaining the predictions of a classifier, arXiv:1602.04938v3.
Zurück zum Zitat Schwartz, E. M., Bradlow, E. T., & Fader, P. S. (2017). Customer acquisition via display advertising using multi-armed bandit experiments. Marketing Science, 36(4), 500–522.CrossRef Schwartz, E. M., Bradlow, E. T., & Fader, P. S. (2017). Customer acquisition via display advertising using multi-armed bandit experiments. Marketing Science, 36(4), 500–522.CrossRef
Zurück zum Zitat Taddy, M. (2018). The technological elements of artificial intelligence. National Bureau of Economic Research: Tech. rep.CrossRef Taddy, M. (2018). The technological elements of artificial intelligence. National Bureau of Economic Research: Tech. rep.CrossRef
Zurück zum Zitat Timoshenko, A., & Hauser, J. R. (2019). Identifying customer needs from user-generated content. Marketing Science, 38(1), 1–20.CrossRef Timoshenko, A., & Hauser, J. R. (2019). Identifying customer needs from user-generated content. Marketing Science, 38(1), 1–20.CrossRef
Zurück zum Zitat Yoganarasimhan, H., Barzegary, E., and Pani, A. (2020) “Design and evaluation of personalized free trials.” University of Washington, Working Paper. Yoganarasimhan, H., Barzegary, E., and Pani, A. (2020) “Design and evaluation of personalized free trials.” University of Washington, Working Paper.
Metadaten
Titel
Soul and machine (learning)
verfasst von
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
Publikationsdatum
27.08.2020
Verlag
Springer US
Erschienen in
Marketing Letters / Ausgabe 4/2020
Print ISSN: 0923-0645
Elektronische ISSN: 1573-059X
DOI
https://doi.org/10.1007/s11002-020-09538-4

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