We characterize a selling mechanism that is robust to the seller's uncertainty about the buyer's signal structure . We show that by offering a generous refund policy the seller can significantly reduce this type of uncertainty and regain market power. A simple mechanism that utilizes a generous refund policy and random discountsachieves the best guaranteed-profit among all possible mechanisms .
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 may 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 .
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We characterize a selling mechanism that is robust to the seller's uncertainty about the buyer's signal structure
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
R
by offering a generous refund policy the seller can significantly reduce this type of uncertainty and regain market power. A simple mechanism that utilizes
the seller may 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
R
policy and random discountsachieves the best guaranteed-profit among all possible mechanisms
, which performs well when the buyer is relatively informed, with non-refundable random discounts, which work well when the buyer is relatively uninformed