Markdown Policies for Demand Learning with Forward-looking Customers

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 an 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: markdown pricing, model uncertainty, Bayesian learning, exploration-exploitation, forward-looking customer behavior