Revenue-Sharing Allocation Strategies for Two-Sided Media Platforms: Pro-Rata vs. User-Centric

• 成果类型: Article
• 署名作者: Alaei, Saeed; Makhdoumi, Ali; Malekian, Azarakhsh; Pekec, Sasa
• 署名单位: Alphabet Inc.; Google Incorporated; Duke University; University of Toronto
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.4307
• 发表日期: 2022
• 页码: 8699-8721
• 关键词: two-sided media platforms; subscription pricing; pro-rata and user-centric; digital goods; Revenue sharing

摘要

We consider a two-sided streaming service platform that generates revenues by charging users a subscription fee for unlimited access to the content and compensates content providers (artists) through a revenue-sharing allocation rule. Platform users are heterogeneous in both their overall consumption and the distribution of their consumption over different artists. We study two primary revenue allocation rules used by market-leading music streaming platforms-pro-rata and user-centric. With pro-rata, artists are paid proportionally to their share of the overall streaming volume, whereas with user-centric, each user's subscription fee is divided proportionally among artists based on the consumption of that user. We characterize when these two allocation rules can sustain a set of artists on the platform and compare them from both the platform's and the artists' perspectives. In particular, we show that, despite the cross-subsidization between low- and high-streaming volume users, the pro-rata rule can be preferred by both the platform and the artists. Furthermore, the platform's problem of selecting an optimal portfolio of artists is NP-complete. However, by establishing connections to the knapsack problem, we develop a polynomial time approximation scheme (PTAS) for the optimal platform's profit. In addition to determining the platform's optimal revenue allocation rule in the class of pro-rata and user-centric rules, we consider the optimal revenue allocation rule in the class of arbitrary rules. Building on duality theory, we develop a polynomial time algorithm that outputs a set of artists so that the platform's profit is within a single artist's revenue from the optimal profit.