Privacy-Preserving Personalized Revenue Management

Yanzhe (Murray) Lei (), Sentao Miao () and Ruslan Momot ()
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Yanzhe (Murray) Lei: Smith School of Business, Queen’s University, Kingston, Ontario K7L 3N6, Canada
Sentao Miao: Leeds School of Business, University of Colorado Boulder, Boulder, Colorado 80309
Ruslan Momot: Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109

Management Science, 2024, vol. 70, issue 7, 4875-4892

Abstract: This paper examines how data-driven personalized decisions can be made while preserving consumer privacy. Our setting is one in which the firm chooses a personalized price based on each new customer’s vector of individual features; the true set of individual demand-generating parameters is unknown to the firm and so must be estimated from historical data. We extend the existing personalized pricing framework by requiring also that the firm’s pricing policy preserve consumer privacy, or (formally) that it be differentially private : an industry standard for privacy preservation. We develop privacy-preserving personalized pricing algorithms and show that they achieve near-optimal revenue by deriving theoretical (upper and lower) performance bounds. Our analyses further suggest that, if the firm possesses a sufficient amount of historical data, then it can achieve a certain level of differential privacy almost “for free.” That is, the revenue loss due to privacy preservation is of smaller order than that due to estimation. We confirm our theoretical findings in a series of numerical experiments based on synthetically generated and online auto lending (CPRM-12-001) data sets. Finally, motivated by practical considerations, we also extend our algorithms and findings to a variety of alternative settings, including multiproduct pricing with substitution effect, discrete feasible price set, categorical sensitive features, and personalized assortment optimization.

Keywords: privacy; revenue management; data-driven decision making; personalized pricing; assortment optimization (search for similar items in EconPapers)
Date: 2024
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