Asymptotically Tight Bounds on the Optimal Pricing Strategy with Patient Customers

Shuaijie Qian (), Xizhi Su () and Chao Zhou ()
Additional contact information
Shuaijie Qian: Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
Xizhi Su: Department of Mathematics, National University of Singapore, Singapore 119076
Chao Zhou: Department of Mathematics, National University of Singapore, Singapore 119076; and Risk Management Institute, National University of Singapore, Singapore 119076

Operations Research, 2025, vol. 73, issue 2, 664-671

Abstract: This work considers a monopolist seller facing both patient and impatient customers. Given the current price, the impatient customers will either purchase or leave immediately, depending on the relative magnitude between this price and their valuation of the product. In comparison, the patient customers will wait for some periods to see if the price will drop to their valuation, and if that occurs, they will purchase immediately. The monopolist designs the pricing strategy to maximize the long-run average revenue from them. We give tight bounds on both the optimal strategy’s cycle period and the optimal revenue when the patient customers possess a high patience level. This result answers the open question of the optimal cycle period raised by extant work. Later we also extend our theoretical result to the general case with multiple patience levels.

Keywords: Market; Analytics; and; Revenue; Management; revenue maximization; dynamic pricing; customer behavior; asymptotic property (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/opre.2021.0459 (application/pdf)

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:inm:oropre:v:73:y:2025:i:2:p:664-671

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().