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Big Data and Personalized Pricing

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  • Steinberg, Etye

Abstract

Technological advances introduce the possibility that, in the future, firms will be able to use big-data analysis to discover and offer consumers their individual reservation price (i.e., the highest price each consumer would be willing to pay, given their preferences and available income). This can generate some interesting benefits, such as a better state of affairs in terms of equality of both welfare and resources, as well as increased social welfare. However, these benefits are countered by considerations of relational equality. This article takes up the market-failures approach as its basis to demonstrate what is wrong with using big data to personalize prices. The article offers an improvement to the market-failures approach and argues that what is wrong with using big data to personalize prices is that it unfairly undermines consumers’ ability to benefit from the market, which is the very point of having a market.

Suggested Citation

  • Steinberg, Etye, 2020. "Big Data and Personalized Pricing," Business Ethics Quarterly, Cambridge University Press, vol. 30(1), pages 97-117, January.
    • Handle: RePEc:cup:buetqu:v:30:y:2020:i:1:p:97-117_5
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    Citations

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    Cited by:

    1. Kirsten Martin & Ari Waldman, 2023. "Are Algorithmic Decisions Legitimate? The Effect of Process and Outcomes on Perceptions of Legitimacy of AI Decisions," Journal of Business Ethics, Springer, vol. 183(3), pages 653-670, March.
    2. Shuilin Liu & Xudong Lin & Xiaoli Huang & Hanyang Luo & Sumin Yu, 2023. "Research on Service-Driven Benign Market with Platform Subsidy Strategy," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
    3. Bruno F. Abrantes & Klaus Grue Ostergaard, 2022. "Digital footprint wrangling: are analytics used for better or worse? A concurrent mixed methods research on the commercial (ab)use of dataveillance," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 187-206, September.
    4. Pade, Robin & Feurer, Sven, 2022. "The mitigating role of nostalgia for consumer price unfairness perceptions in response to disadvantageous personalized pricing," Journal of Business Research, Elsevier, vol. 145(C), pages 277-287.
    5. Lee, Boram & Fraser, Ian & Fillis, Ian, 2022. "To sell or not to sell? Pricing strategies of newly-graduated artists," Journal of Business Research, Elsevier, vol. 145(C), pages 595-604.
    6. Jerod Coker & Jean-Manuel Izaret, 2021. "Progressive Pricing: The Ethical Case for Price Personalization," Journal of Business Ethics, Springer, vol. 173(2), pages 387-398, October.
    7. Etye Steinberg, 2022. "Run for Your Life: The Ethics of Behavioral Tracking in Insurance," Journal of Business Ethics, Springer, vol. 179(3), pages 665-682, September.
    8. Wenjie Bi & Bing Wang & Haiying Liu, 2024. "Personalized Dynamic Pricing Based on Improved Thompson Sampling," Mathematics, MDPI, vol. 12(8), pages 1-14, April.

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