Engagement Maximization

Benjamin H\'ebert and Weijie Zhong
Authors registered in the RePEc Author Service: Benjamin M. Hebert

Papers from arXiv.org

Abstract: We investigate the management of information provision to maximize user engagement. A principal sequentially reveals signals to an agent who has a limited amount of information processing capacity and can choose to exit at any time. We identify a ``dilution'' strategy -- sending rare but highly informative signals -- that maximizes user engagement. The platform's engagement metric shapes the direction and magnitude of biases in provided information relative to a user-optimal benchmark. Even without intertemporal commitment, the platform replicates full-commitment revenue by inducing the user's belief to remain ``as uncertain as'' the prior until the rare, decisive signal arrives and induces stopping. We apply our results to two contexts: an ad-supported internet media platform and a teacher attempting to engage test-motivated students.

Date: 2022-07, Revised 2025-03
New Economics Papers: this item is included in nep-mic
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http://arxiv.org/pdf/2207.00685 Latest version (application/pdf)

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Working Paper: Engagement Maximization (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2207.00685

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