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Approximating Purchase Propensities And Reservation Prices From Broad Consumer Tracking

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  • Benjamin Reed Shiller

Abstract

A consumer's web‐browsing history, now readily available, may be much more useful than demographics for both targeting advertisements and personalizing prices. Using a method that combines economic modeling and machine learning methods, I find a striking difference. Personalizing prices based on web‐browsing histories increases profits by 12.99%. Using demographics alone to personalize prices raises profits by only 0.25%, suggesting the percent profit gain from personalized pricing has increased 50‐fold. I then investigate whether regulations intended to prevent price gouging increase aggregate consumer surplus. Two feasible regulations considered offer at best modest improvements.

Suggested Citation

  • Benjamin Reed Shiller, 2020. "Approximating Purchase Propensities And Reservation Prices From Broad Consumer Tracking," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(2), pages 847-870, May.
    • Handle: RePEc:wly:iecrev:v:61:y:2020:i:2:p:847-870
    DOI: 10.1111/iere.12442
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    References listed on IDEAS

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    Cited by:

    1. Dirk Bergemann & Marco Ottaviani, 2021. "Information Markets and Nonmarkets," Cowles Foundation Discussion Papers 2296, Cowles Foundation for Research in Economics, Yale University.
    2. Jakučionytė-Skodienė, Miglė & Liobikienė, Genovaitė, 2023. "Changes in energy consumption and CO2 emissions in the Lithuanian household sector caused by environmental awareness and climate change policy," Energy Policy, Elsevier, vol. 180(C).
    3. Bruno Jullien & Markus Reisinger & Patrick Rey, 2023. "Personalized Pricing and Distribution Strategies," Management Science, INFORMS, vol. 69(3), pages 1687-1702, March.
    4. Andrew Rhodes & Jidong Zhou, 2022. "Personalized Pricing and Competition," Cowles Foundation Discussion Papers 2329, Cowles Foundation for Research in Economics, Yale University.
    5. Benjamin R. Shiller, 2022. "Discreet Personalized Pricing," CESifo Working Paper Series 10025, CESifo.
    6. Qiuyu Lu & Noriaki Matsushima, 2024. "Personalized Pricing When Consumers Can Purchase Multiple Items," Journal of Industrial Economics, Wiley Blackwell, vol. 72(4), pages 1507-1524, December.
    7. Abraham, Ittai & Athey, Susan & Babaioff, Moshe & Grubb, Michael D., 2020. "Peaches, lemons, and cookies: Designing auction markets with dispersed information," Games and Economic Behavior, Elsevier, vol. 124(C), pages 454-477.
    8. In'acio B'o & Li Chen & Rustamdjan Hakimov, 2023. "Strategic Responses to Personalized Pricing and Demand for Privacy: An Experiment," Papers 2304.11415, arXiv.org, revised Nov 2024.
    9. Mohit Tyagi & Nomesh B. Bolia, 2024. "Optimal pricing of subscription services in the restaurant industry," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(3), pages 262-273, June.
    10. Eric K. Clemons & Ravi V. Waran & Sebastian Hermes & Maximilian Schreieck & Helmut Krcmar, 2022. "Computing and Social Welfare," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(2), pages 417-436, June.
    11. 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.

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