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α: calibrated so average coauthorship-adjusted count equals average raw count
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 take best 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.