JOURNAL ARTICLE

Designing Loot Boxes: Implications for Profits and Welfare.

  • Published In: Marketing Science (INFORMS), 2024, v. 43, n. 6. P. 1242 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Miao, Jin; Jain, Sanjay 3 of 3

Abstract

This article analyzes the optimal pricing and design of loot boxes—probabilistic virtual product bundles whose exact contents are revealed only after purchase—and their effects on firm profits, consumer welfare, and social welfare. Using a model with horizontally differentiated products and rational consumers, it finds that firms may profitably assign asymmetric allocation probabilities to symmetric products, enabling better price discrimination and market expansion compared to traditional fixed pricing. Contrary to common criticism, loot boxes can improve social welfare by serving consumers excluded under traditional pricing, though consumer welfare may sometimes decline, especially when firms promise guaranteed acquisition within a limited number of purchases. Extensions of the model show that loot boxes are more profitable for virtual goods with low production costs, can remain optimal even when consumers are addictive or budget-constrained, and that firms may optimally offer only loot boxes without deterministic product sales. The study cautions policymakers that banning loot boxes could reduce social welfare by limiting market coverage.

Additional Information

  • Source:Marketing Science (INFORMS). 2024/11, Vol. 43, Issue 6, p1242
  • Document Type:Article
  • Subject Area:Sports and Leisure
  • Publication Date:2024
  • ISSN:0732-2399
  • DOI:10.1287/mksc.2023.0007
  • Accession Number:180657671
  • Copyright Statement:Copyright of Marketing Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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