List Price and Discount in A Stochastic Selling Process

Abstract

From B2B sales to AI-powered ecommerce, one common pricing mechanism is “list price - discount”: the seller first publishes a (committed) list price, then during interactions with a buyer, offers (non-committed) discounts off of the list price. Some B2B sellers never sell at their list prices. In such cases, what role does a list price play and how to choose the optimal list price and discounts? In this paper, I study a stochastic sales process in which a buyer and a seller discover their match value sequentially. The seller can adjust its price offers over time, and the buyer decides whether to accept each offer. I show that this discovery process creates a hold-up problem for the buyer that results in inefficient no-trades. The seller alleviates this problem by committing to an upper bound in the form of a list price. But in equilibrium players always reach agreement at a discount. I show that the seller prefers this mechanism to having no list price or committing not to discount. When the cost of selling is high, the seller’s ability to offer discount is necessary for trade to happen. There exists reverse price discrimination when buyers are heterogeneous, and list price can serve as a signaling device when sellers are heterogeneous. Extensions with alternative pricing or matching mechanisms are discussed.

Keywords: dynamic pricing, information acquisition, sales process, bargaining, price discrimination, matching, continuous-time game, optimal stopping