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An informed seller designs a dynamic mechanism to sell an experience good.The seller has partial information about the product match, which affects thebuyer's private consumption experience. We characterize equilibrium mechanismsof this dynamic informed principal problem. The belief gap between the informedseller and the uninformed buyer, coupled with the buyer's learning, gives riseto mechanisms that provide the skeptical buyer with limited access to theproduct and an option to upgrade if the buyer is swayed by a good experience.Depending on the seller's screening technology, this takes the form offree/discounted trials or tiered pricing, which are prevalent in digitalmarkets. In contrast to static environments, having consumer data can reducesellers' revenue in equilibrium, as they fine-tune the dynamic design withtheir data forecasting the buyer's learning process.

From Doubt to Devotion: Trials and Learning-Based Pricing