Privacy-Preserving Personalized Revenue Management

59 Pages Posted: 15 Oct 2020 Last revised: 6 Jan 2023

See all articles by Yanzhe (Murray) Lei

Yanzhe (Murray) Lei

Queen's University - Smith School of Business

Sentao Miao

University of Colorado at Boulder

Ruslan Momot

University of Michigan, Stephen M. Ross School of Business

Date Written: October 3, 2020


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 On-line 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 multi-product pricing with substitution effect, discrete feasible price set, categorical sensitive features, and personalized assortment optimization.

Keywords: privacy, data-driven decision making, personalized pricing, revenue management

JEL Classification: A10, A12, C02, C13, C18, C44, D11, D18, D21, L51, M15, M20, M31, M37

Suggested Citation

Lei, Yanzhe (Murray) and Miao, Sentao and Momot, Ruslan, Privacy-Preserving Personalized Revenue Management (October 3, 2020). Available at SSRN: or

Yanzhe (Murray) Lei

Queen's University - Smith School of Business ( email )

Smith School of Business - Queen's University
143 Union Street
Kingston, Ontario K7L 3N6

Sentao Miao

University of Colorado at Boulder ( email )

256 UCB
Boulder, CO CO 80300-0256
United States

Ruslan Momot (Contact Author)

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States


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