Providing Data Samples for Free
38 Pages Posted: 29 Jun 2020
Date Written: May 26, 2020
We consider the problem of a data provider (Seller of information) who sells information to a firm (Buyer of information) regarding an unknown (to both parties) state of the world. Traditionally, the literature explores one-round strategies for selling information due to the Seller’s hold-up problem: once a portion of the dataset is released, the Buyer’s estimate improves and as a result the value of the remaining dataset drops. In this paper we show that this intuition is true when the Buyer’s objective is to improve the precision of her estimate. On the other hand, we establish that when the Buyer’s objective is to improve operational decisions (e.g. better pricing decisions on a market with unknown elasticity) and when the Buyer’s initial estimate is misspecified, one-round strategies are outperformed by free-sample selling strategies and dynamic pricing. In particular, we provide conditions under which free sample strategies generate strictly higher revenues than static strategies (with and without strategic Buyers) and provide examples that illustrate the benefit of providing data samples for free.
Keywords: Selling Data Samples, Selling Information, Pricing, Bayesian Learning, Brownian Motion
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