Optimal sampling of paid content

Abstract

This paper analyzes optimal sampling and pricing of paid content for publishers of news websites. Publishers offer free content samples both to disclose journalistic quality to consumers and to generate online advertising revenues. We examine sampling where the publisher sets the number of free sample articles and consumers select the articles of their choice. Consumerslearn from the free samples in a Bayesian fashion and base their subscription decisions on posterior quality expectations. We show thatsampling enhances subscription demand only if consumers have low quality expectations in relation to actual quality. Taking advertising and subscription revenues into account, we find that the publisher should employ either a paid-only or a free content strategy when consumers have high quality expectations. When consumers have low quality expectations, employing a sampling strategy may be optimal for the publisher.