How to Earn Money in Live Streaming Platforms? — A Study of Donation-Based Markets

Donation-based markets are becoming increasingly popular in our daily life. One example is the online streaming platform Twitch, which attracts millions of users on a daily basis. On such platforms, firms provide services to customers without mandatory charge, and customers voluntarily donate money to the firms. The donations are split between the firms and the platform with a fixed pre-agreed fraction. To gain insights into the operation and optimization of such platforms, we formulate a two-stage game to study the platform’s and firms’ behaviors. In Stage I, the platform decides a donation-split-fraction (DSF), which corresponds to the fraction of donations kept by the firms. In Stage II, firms decide whether to participate in the platform and how to choose their service attributes considering the DSF as well as the preferences of firms and customers. Analyzing such a two-stage game directly is challenging, as the Stage II problem corresponds to the multi-firm extension of the Hotelling model and is still an open problem. To resolve this issue, we approximate the large number of firms as non-atomic decision makers, where a single firm’s strategy choice does not affect the payoffs of the firm population. Under such an approximation, we prove that the Stage II problem is a potential game. We further show that at the equilibrium, a larger DSF leads to more firm participations and a better match to the customers’ preferences. The stage I problem, nevertheless, is a non-convex optimization problem that does not render a closed-form solution. To gain insights, we derive the upper-bound and lower-bound of the optimal DSF solution. The bounds suggest that the platform should increase its DSF if the customers’ donation sensitivity to the number of firms increases or if the firms’ opportunity cost for participation increases. Finally, we collect data from Twitch and demonstrate the results of the two-stage model with a case study. Our simulation results suggest that under our data and model settings, there exists a significant potential for Twitch to improve its payoff, by setting the DSF to 0.38, instead of 0.71 as in Twitch’s current practice.