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Engagement Maximization

Author

Listed:
  • Hebert, Benjamin

    (Stanford U)

  • Zhong, Weijie

    (Stanford U)

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.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Hebert, Benjamin & Zhong, Weijie, 2022. "Engagement Maximization," Research Papers 4035, Stanford University, Graduate School of Business.
    • Handle: RePEc:ecl:stabus:4035
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    File URL: https://www.gsb.stanford.edu/faculty-research/working-papers/engagement-maximization
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    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D86 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Economics of Contract Law

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