
TL;DR
This paper introduces a mechanism for sellers of experience goods to maximize guaranteed profit by combining refunds and random discounts, leveraging the buyer's post-purchase learning about product fit.
Contribution
It proposes a simple, effective mechanism that adapts to the buyer's level of information, optimizing guaranteed profit through refunds and discounts.
Findings
The mechanism guarantees profit by exploiting buyer learning.
Refunds are effective with informed buyers.
Random discounts work well with uninformed buyers.
Abstract
Before purchase, a buyer of an experience good learns about the product's fit using various information sources, including some of which the seller may be unaware of. The buyer, however, can conclusively learn the fit only after purchasing and trying out the product. We show that the seller can use a simple mechanism to best take advantage of the buyer's post-purchase learning to maximize his guaranteed-profit. We show that this mechanism combines a generous refund, which performs well when the buyer is relatively informed, with non-refundable random discounts, which work well when the buyer is relatively uninformed.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
