Optimal Refund Mechanism with Consumer Learning

TL;DR

This paper analyzes how optimal refund policies can influence consumer learning and valuation in markets, revealing that simple, deterministic refund strategies are optimal and depend on the buyer's prior beliefs.

Contribution

It characterizes the structure of optimal refund mechanisms in the presence of consumer learning, showing they are deterministic and depend on prior beliefs.

Findings

Optimal refund mechanisms are deterministic.

Mechanisms are either low-price non-return or high-price with free returns.

The form of the optimal mechanism is non-monotone in prior beliefs.

Abstract

This paper studies the optimal refund mechanism when an uninformed buyer can privately acquire information about his valuation of a product over time. We consider a class of refund mechanisms based on stochastic return policies: if the buyer requests a return, the seller will issue a (partial) refund while allowing the buyer to keep the product with some probability. Such return policies can affect the buyer's learning process and thereby influence the return rate. Nevertheless, we show that the optimal refund mechanism is deterministic and takes a simple form: either the seller offers a sufficiently low price and disallows returns to deter buyer learning, or she offers a sufficiently high price with free returns to implement maximal buyer learning. The form of the optimal refund mechanism is non-monotone in the buyer's prior belief regarding his valuation.