Privacy-Preserving Personalized Revenue Management

31 Pages Posted: 15 Oct 2020 Last revised: 19 Oct 2020

See all articles by Yanzhe (Murray) Lei

Yanzhe (Murray) Lei

Queen's University - Smith School of Business

Sentao Miao

McGill University - Desautels Faculty of Management

Ruslan Momot

HEC Paris

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 this classical framework of personalized pricing 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. The two settings we consider are theoretically and practically relevant: central and local models of differential privacy, which differ in the strength of the privacy guarantees they provide. For both models, we develop privacy-preserving personalized pricing algorithms and derive the theoretical bounds on their performance as measured by the firm's revenue. Our analyses suggest that, if the firm possesses a sufficient amount of historical data, then it can achieve central differential privacy at a cost of the same order as the "classical" loss in revenue due to estimation error. Comparing the two models, we conclude that local differentially private personalized pricing yields better privacy guarantees but leads to much greater revenue loss by the firm. We confirm our theoretical findings in a series of numerical experiments based on synthetically generated and real-world On-line Auto Lending (CPRM-12-001) data sets. Finally, we also apply our theoretical framework to the setting of 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). HEC Paris Research Paper No. MOSI-2020-1391, 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

McGill University - Desautels Faculty of Management ( email )

1001 Sherbrooke St W
Montreal, Quebec h3A 1G5

Ruslan Momot (Contact Author)

HEC Paris ( email )

1, rue de la Liberation
Jouy en Josas, 78351


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