Markdown Policies for Demand Learning with Forward-Looking Customers
John R. Birge (),
Hongfan (Kevin) Chen () and
N. Bora Keskin ()
Additional contact information
John R. Birge: Booth School of Business, University of Chicago, Chicago, Illinois 60637
Hongfan (Kevin) Chen: Chinese University of Hong Kong Business School, Chinese University of Hong Kong, Hong Kong
N. Bora Keskin: Fuqua School of Business, Duke University, Durham, North Carolina 27708
Operations Research, 2025, vol. 73, issue 5, 2550-2566
Abstract:
We consider the markdown pricing problem of a firm that sells a product to a mixture of myopic and forward-looking customers. The firm faces uncertainty about the customers’ forward-looking behavior, arrival pattern, and valuations for the product, which we collectively refer to as the demand model. Over a multiperiod selling season, the firm sequentially marks down the product’s price and makes demand observations to learn about the underlying demand model. Because forward-looking customers create an intertemporal dependency, we identify that the keys to achieving good profit performance are (i) judiciously accumulating information on the demand model and (ii) preserving the market size in early sales periods. Based on these, we construct and analyze markdown policies that exhibit near-optimal performance under a wide variety of forward-looking customer behaviors.
Keywords: Market; Analytics; and; Revenue; Management; markdown pricing; model uncertainty; Bayesian learning; exploration-exploitation; forward-looking customer behavior (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://dx.doi.org/10.1287/opre.2019.0402 (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:5:p:2550-2566
Access Statistics for this article
More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().