This paper analyzes optimal decisions concerning the size of the sample and the price of the paid content for online publishers of digital information goods when sampling serves the dual purpose of disclosing content quality and generating advertising revenue. We show in a reduced-form model how the publisher?s optimal ratio of advertising revenue to sales revenue is linked to characteristics of both the content market and the advertising market. Assuming that consumers learn about content quality from the free samples in a Bayesian fashion, we find that it can be optimal for the publisher to generate advertising revenue by offering free samples even when sampling reduces high prior expectations and content demand. In addition, we show that it can be optimal for the publisher to refrain from revealing quality through free samples when advertising effectiveness is low and content quality is high.
Halbheer, Daniel, Florian Stahl, Oded Koenigsberg, and Donald Lehmann. "Sampling Strategies for Information Goods." Working paper, Columbia Business School, June 2012.
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