The enhanced abilities of online retailers to learn about their customers’ shopping behaviors have increased fears of dynamic pricing, a practice in which a seller sets prices based on the estimated buyer’s willingness-to-pay. However, among online retailers, a deviation from a one-price-for-all policy is the exception. When price discrimination is observed, it is often in the context of customer outrage about unfair pricing.
One setting where pricing varies is the name-your-own-price (NYOP) mechanism. In contrast to a typical retail setting, in NYOP markets, it is the buyer who places an initial offer. This offer is accepted if it is above some threshold price set by the seller. If the initial offer is rejected, the buyer can update her offer in subsequent rounds. By design, the final purchase price is opaque to the public; the price paid depends on the individual buyer’s willingness-to-pay and offer strategy. Further, most forms of NYOP employ a fixed threshold price policy.
In this paper, we compare a fixed threshold price setting with an adaptive threshold price setting. A seller who considers an adaptive threshold price has to weigh potentially greater profits against customer objections about the perceived fairness of such a policy. We first derive the optimal strategy for the seller. We analyze the effectiveness of an adaptive threshold price vis-à-vis a fixed threshold price on seller profit and customer satisfaction. Further, we evaluate the moderating effect of revealing the threshold price policy (adaptive versus fixed) to buyers. We test our model in a series of laboratory experiments and in a large field experiment at a prominent NYOP seller involving real purchases. Our results show that revealing the usage of an adaptive mechanism yields higher profits and more transactions than not revealing this information. In the field experiment, we find that applying a revealed adaptive threshold price can increase profits by over 20 percent without lowering customer satisfaction.