Privacy-Preserving Personalized Revenue Management

43 Pages Posted: 15 Oct 2020 Last revised: 23 Mar 2021

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

Northwestern University - Kellogg School of Management

Date Written: October 3, 2020

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 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. 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 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, we also investigate the problem of privacy-preserving personalized assortment optimization and derive results parallel to those in the pricing setting.

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: https://ssrn.com/abstract=3704446 or http://dx.doi.org/10.2139/ssrn.3704446

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
Canada

Sentao Miao

McGill University - Desautels Faculty of Management ( email )

1001 Sherbrooke St W
Montreal, Quebec h3A 1G5

Ruslan Momot (Contact Author)

Northwestern University - Kellogg School of Management ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

HOME PAGE: http://www.ruslanmomot.info

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