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Markdown Policies for Demand Learning with Forward-Looking Customers

Author

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  • 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)

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.

Suggested Citation

  • John R. Birge & Hongfan (Kevin) Chen & N. Bora Keskin, 2025. "Markdown Policies for Demand Learning with Forward-Looking Customers," Operations Research, INFORMS, vol. 73(5), pages 2550-2566, September.
    • Handle: RePEc:inm:oropre:v:73:y:2025:i:5:p:2550-2566
    DOI: 10.1287/opre.2019.0402
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