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The Value of Personalized Pricing

Author

Listed:
  • Adam N. Elmachtoub

    (Department of Industrial Engineering and Operations Research and Data Science Institute, Columbia University, New York, New York 10027)

  • Vishal Gupta

    (Data Science and Operations, Marshall School of Business, University of Southern California, Los Angeles, California 90089)

  • Michael L. Hamilton

    (Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, Pennsylvania 15260)

Abstract

Increased availability of high-quality customer information has fueled interest in personalized pricing strategies, that is, strategies that predict an individual customer’s valuation for a product and then offer a price tailored to that customer. Although the appeal of personalized pricing is clear, it may also incur large costs in the forms of market research, investment in information technology and analytics expertise, and branding risks. In light of these trade-offs, our work studies the value of personalized pricing strategies over a simple single-price strategy. We first provide closed-form lower and upper bounds on the ratio between the profits of an idealized personalized pricing strategy (first-degree price discrimination) and a single-price strategy. Our bounds depend on simple statistics of the valuation distribution and shed light on the types of markets for which personalized pricing has little or significant potential value. Second, we consider a feature-based pricing model where customer valuations can be estimated from observed features. We show how to transform our aforementioned bounds into lower and upper bounds on the value of feature-based pricing over single pricing depending on the degree to which the features are informative for the valuation. Finally, we demonstrate how to obtain sharper bounds by incorporating additional information about the valuation distribution (moments or shape constraints) by solving tractable linear optimization problems.

Suggested Citation

  • Adam N. Elmachtoub & Vishal Gupta & Michael L. Hamilton, 2021. "The Value of Personalized Pricing," Management Science, INFORMS, vol. 67(10), pages 6055-6070, October.
  • Handle: RePEc:inm:ormnsc:v:67:y:2021:i:10:p:6055-6070
    DOI: 10.1287/mnsc.2020.3821
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    References listed on IDEAS

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    1. Dirk Bergemann & Benjamin Brooks & Stephen Morris, 2015. "The Limits of Price Discrimination," American Economic Review, American Economic Association, vol. 105(3), pages 921-957, March.
    2. Yuxin Chen & Sridhar Moorthy & Z. John Zhang, 2005. "Research Note---Price Discrimination After the Purchase: Rebates as State-Dependent Discounts," Management Science, INFORMS, vol. 51(7), pages 1131-1140, July.
    3. Schmalensee, Richard, 1981. "Output and Welfare Implications of Monopolistic Third-Degree Price Discrimination," American Economic Review, American Economic Association, vol. 71(1), pages 242-247, March.
    4. Goker Aydin & Serhan Ziya, 2009. "Technical Note---Personalized Dynamic Pricing of Limited Inventories," Operations Research, INFORMS, vol. 57(6), pages 1523-1531, December.
    5. Simon Cowan, 2016. "Welfare-increasing third-degree price discrimination," RAND Journal of Economics, RAND Corporation, vol. 47(2), pages 326-340, May.
    6. Dirk Bergemann & Karl Schlag, 2012. "Robust Monopoly Pricing," World Scientific Book Chapters, in: Robust Mechanism Design The Role of Private Information and Higher Order Beliefs, chapter 13, pages 417-441, World Scientific Publishing Co. Pte. Ltd..
    7. Omar Besbes & Ilan Lobel, 2015. "Intertemporal Price Discrimination: Structure and Computation of Optimal Policies," Management Science, INFORMS, vol. 61(1), pages 92-110, January.
    8. Fernando Bernstein & A. Gürhan Kök & Lei Xie, 2015. "Dynamic Assortment Customization with Limited Inventories," Manufacturing & Service Operations Management, INFORMS, vol. 17(4), pages 538-553, October.
    9. Chakravarthi Narasimhan, 1984. "A Price Discrimination Theory of Coupons," Marketing Science, INFORMS, vol. 3(2), pages 128-147.
    10. Ioana Popescu, 2005. "A Semidefinite Programming Approach to Optimal-Moment Bounds for Convex Classes of Distributions," Mathematics of Operations Research, INFORMS, vol. 30(3), pages 632-657, August.
    11. Varian, Hal R, 1985. "Price Discrimination and Social Welfare," American Economic Review, American Economic Association, vol. 75(4), pages 870-875, September.
    12. Omar Besbes & Assaf Zeevi, 2015. "On the (Surprising) Sufficiency of Linear Models for Dynamic Pricing with Demand Learning," Management Science, INFORMS, vol. 61(4), pages 723-739, April.
    13. Juanjuan Zhang, 2011. "The Perils of Behavior-Based Personalization," Marketing Science, INFORMS, vol. 30(1), pages 170-186, 01-02.
    14. Vidyanand Choudhary & Anindya Ghose & Tridas Mukhopadhyay & Uday Rajan, 2005. "Personalized Pricing and Quality Differentiation," Management Science, INFORMS, vol. 51(7), pages 1120-1130, July.
    15. Yunchuan Liu & Z. John Zhang, 2006. "Research Note—The Benefits of Personalized Pricing in a Channel," Marketing Science, INFORMS, vol. 25(1), pages 97-105, 01-02.
    16. Felipe Caro & Jérémie Gallien, 2012. "Clearance Pricing Optimization for a Fast-Fashion Retailer," Operations Research, INFORMS, vol. 60(6), pages 1404-1422, December.
    17. Xuanming Su, 2007. "Intertemporal Pricing with Strategic Customer Behavior," Management Science, INFORMS, vol. 53(5), pages 726-741, May.
    18. Kinshuk Jerath & Serguei Netessine & Senthil K. Veeraraghavan, 2010. "Revenue Management with Strategic Customers: Last-Minute Selling and Opaque Selling," Management Science, INFORMS, vol. 56(3), pages 430-448, March.
    19. L. Elisa Celis & Gregory Lewis & Markus Mobius & Hamid Nazerzadeh, 2014. "Buy-It-Now or Take-a-Chance: Price Discrimination Through Randomized Auctions," Management Science, INFORMS, vol. 60(12), pages 2927-2948, December.
    20. Adam N. Elmachtoub & Michael L. Hamilton, 2021. "The Power of Opaque Products in Pricing," Management Science, INFORMS, vol. 67(8), pages 4686-4702, August.
    21. Omar Besbes & Robert Phillips & Assaf Zeevi, 2010. "Testing the Validity of a Demand Model: An Operations Perspective," Manufacturing & Service Operations Management, INFORMS, vol. 12(1), pages 162-183, June.
    22. K. Sridhar Moorthy, 1984. "Market Segmentation, Self-Selection, and Product Line Design," Marketing Science, INFORMS, vol. 3(4), pages 288-307.
    23. Shih, Jun-ji & Mai, Chao-cheng & Liu, Jung-chao, 1988. "A General Analysis of the Output Effect under Third-Degree Price Discrimination," Economic Journal, Royal Economic Society, vol. 98(389), pages 149-158, March.
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    2. van Eekelen, Wouter, 2023. "Distributionally robust views on queues and related stochastic models," Other publications TiSEM 9b99fc05-9d68-48eb-ae8c-9, Tilburg University, School of Economics and Management.
    3. Guillermo Gallego & Gerardo Berbeglia, 2024. "Bounds and Heuristics for Multiproduct Pricing," Management Science, INFORMS, vol. 70(6), pages 4132-4144, June.
    4. Maxime C. Cohen & Adam N. Elmachtoub & Xiao Lei, 2022. "Price Discrimination with Fairness Constraints," Management Science, INFORMS, vol. 68(12), pages 8536-8552, December.
    5. Jialie Chen, 2023. "Evaluating the ending‐9 pricing strategy along the online shopping funnel," Production and Operations Management, Production and Operations Management Society, vol. 32(11), pages 3469-3483, November.
    6. Shixin Wang, 2023. "The Power of Simple Menus in Robust Selling Mechanisms," Papers 2310.17392, arXiv.org, revised Sep 2024.
    7. Xi Li & Xin (Shane) Wang & Barrie R. Nault, 2024. "Is Personalized Pricing Profitable When Firms Can Differentiate?," Management Science, INFORMS, vol. 70(7), pages 4184-4199, July.
    8. Shixin Wang, 2024. "Semi-Separable Mechanisms in Multi-Item Robust Screening," Papers 2408.13580, arXiv.org.

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