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Personalized Pricing and Consumer Welfare

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  • Jean-Pierre Dubé
  • Sanjog Misra

Abstract

We study the welfare implications of personalized pricing implemented with machine learning. We use data from a randomized controlled pricing field experiment to construct personalized prices and validate these in the field. We find that unexercised market power increases profit by 55%. Personalization improves expected profits by an additional 19% and by 86% relative to the nonoptimized price. While total consumer surplus declines under personalized pricing, over 60% of consumers benefit from personalization. Under some inequity-averse welfare functions, consumer welfare may even increase. Simulations reveal a nonmonotonic relationship between the granularity of data and consumer surplus under personalization.

Suggested Citation

  • Jean-Pierre Dubé & Sanjog Misra, 2023. "Personalized Pricing and Consumer Welfare," Journal of Political Economy, University of Chicago Press, vol. 131(1), pages 131-189.
    • Handle: RePEc:ucp:jpolec:doi:10.1086/720793
    DOI: 10.1086/720793
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    Cited by:

    1. Adam N. Smith & Stephan Seiler & Ishant Aggarwal, 2023. "Optimal Price Targeting," Marketing Science, INFORMS, vol. 42(3), pages 476-499, May.
    2. Günter J. Hitsch & Sanjog Misra & Walter W. Zhang, 2024. "Heterogeneous treatment effects and optimal targeting policy evaluation," Quantitative Marketing and Economics (QME), Springer, vol. 22(2), pages 115-168, June.
    3. Diego Aparicio & Zachary Metzman & Roberto Rigobon, 2024. "The pricing strategies of online grocery retailers," Quantitative Marketing and Economics (QME), Springer, vol. 22(1), pages 1-21, March.
    4. Robert Evan Sanders, 2024. "Dynamic Pricing and Organic Waste Bans: A Study of Grocery Retailers’ Incentives to Reduce Food Waste," Marketing Science, INFORMS, vol. 43(2), pages 289-316, March.
    5. Michael Allan Ribers & Hannes Ullrich, 2023. "Machine learning and physician prescribing: a path to reduced antibiotic use," Berlin School of Economics Discussion Papers 0019, Berlin School of Economics.
    6. Robert Donnelly & Ayush Kanodia & Ilya Morozov, 2024. "Welfare Effects of Personalized Rankings," Marketing Science, INFORMS, vol. 43(1), pages 92-113, January.
    7. Tesary Lin & Avner Strulov-Shlain, 2023. "Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data," Papers 2308.13496, arXiv.org.
    8. Alexander Erlei & Mattheus Brenig & Nils Engelbrecht, 2024. "Consumer Behavior under Benevolent Price Discrimination," Papers 2404.03581, arXiv.org.
    9. Qiuyu Lu & Noriaki Matsushima & Shiva Shekhar, 2024. "Welfare Implications of Personalized Pricing in Competitive Platform Markets: The Role of Network Effects," CESifo Working Paper Series 10994, CESifo.
    10. Wenjie Bi & Bing Wang & Haiying Liu, 2024. "Personalized Dynamic Pricing Based on Improved Thompson Sampling," Mathematics, MDPI, vol. 12(8), pages 1-14, April.
    11. Esteves, Rosa-Branca & Carballo-Cruz, Francisco, 2023. "Can data openness unlock competition when an incumbent has exclusive data access for personalized pricing?," Information Economics and Policy, Elsevier, vol. 64(C).

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