Digital Content Strategies

Abstract:     
This paper studies content strategies for online publishers of digital information goods. It examines sampling strategies and compares their performance to paid content and free content strategies. A sampling strategy, where some of the content is offered for free and consumers are charged for access to the rest, is known as a "metered model" in the newspaper industry. We analyze optimal decisions concerning the size of the sample and the price of the paid content 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. We assume that consumers learn about content quality from the free samples in a Bayesian fashion. Surprisingly, we find that it can be optimal for the publisher to generate advertising revenue by offering free samples even when sampling reduces both prior quality 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.

Number of Pages in PDF File: 34

Keywords: Sampling, Pricing, Product Quality, Bayesian Learning, News Websites

JEL Classification: L11, L15, L21, M21, M30

working papers series