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Contribution Details
| Type | Conference Presentation |
| Scope | Discipline-based scholarship |
| Title | Sampling Paid Content |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Event Title | 2011 INFORMS Marketing Science Conference |
| Event Type | conference |
| Event Location | Houston, Texas |
| Event Start Date | July 5 - 2011 |
| Event End Date | July 7 - 2011 |
| Abstract Text | This paper studies profit-maximizing sampling and pricing of paid content for online news publishers. The key feature of the model is the twofold role of free samples which allows publishers to generate advertising revenues and simultaneously disclose editorial quality to potential subscribers. Taking customers' prior beliefs about article qualities into account and employing Bayesian updating, we derive the subscription demand and characterize the optimal number of articles offered for free as well as the subscription price for the content behind the paywall. Considering two cases where free sampling aims to persuade either all consumers to subscribe or only those with the highest willingness-to-pay, we find that is optimal for the publisher to offer a larger number of free samples when consumers underestimate product quality. |
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