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Optimal Refund Mechanism with Consumer Learning

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  • Qianjun Lyu

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.

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  • Qianjun Lyu, 2024. "Optimal Refund Mechanism with Consumer Learning," Papers 2404.14927, arXiv.org.
    • Handle: RePEc:arx:papers:2404.14927
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    References listed on IDEAS

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    1. Luciano Pomatto & Philipp Strack & Omer Tamuz, 2018. "The Cost of Information: The Case of Constant Marginal Costs," Papers 1812.04211, arXiv.org, revised Feb 2023.
    2. Godfrey Keller & Sven Rady & Martin Cripps, 2005. "Strategic Experimentation with Exponential Bandits," Econometrica, Econometric Society, vol. 73(1), pages 39-68, January.
    3. Luciano Pomatto & Philipp Strack & Omer Tamuz, 2023. "The Cost of Information: The Case of Constant Marginal Costs," American Economic Review, American Economic Association, vol. 113(5), pages 1360-1393, May.
    4. Steven A. Matthews & Nicola Persico, 2007. "Information Acquisition and Refunds for Returns," Carlo Alberto Notebooks 54, Collegio Carlo Alberto.
    5. Lang, Ruitian, 2019. "Try before you buy: A theory of dynamic information acquisition," Journal of Economic Theory, Elsevier, vol. 183(C), pages 1057-1093.
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