Privacy-Preserving Personalized Revenue Management

Yanzhe (Murray) Lei, Sentao Miao and Ruslan Momot
Additional contact information
Yanzhe (Murray) Lei: Queen's University - Smith School of Business
Sentao Miao: University of Colorado at Boulder
Ruslan Momot: HEC Paris

No 1391, HEC Research Papers Series from HEC Paris

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 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 (search for similar items in EconPapers)
JEL-codes: A10 A12 C02 C13 C18 C44 D11 D18 D21 L51 M15 M20 M31 M37 (search for similar items in EconPapers)
Pages: 59 pages
Date: 2020-10-15
References: Add references at CitEc
Citations:

Downloads: (external link)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3704446 Full text (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ebg:heccah:1391

DOI: 10.2139/ssrn.3704446

Access Statistics for this paper

More papers in HEC Research Papers Series from HEC Paris HEC Paris, 78351 Jouy-en-Josas cedex, France. Contact information at EDIRC.
Bibliographic data for series maintained by Antoine Haldemann ().