Watching Ads for Free Mobile Data: A Game-Theoretic Analysis of Sponsored Data with Reward Task

Sponsored data with reward task is an emerging monetization mechanism in which consumers are subsidized with free megabytes by content providers (CPs, e.g., Netflix) in exchange for engagement with advertisers by performing various forms of reward task. Consumers are endowed with the option of whether or not to participate in reward tasks, which is different from traditional push advertising that consumers have no control of. Although it is an emerging phenomenon, to the best of our knowledge, this has not yet been analyzed rigorously. In order to fill this gap in literature, we provide an economic analysis of this mechanism. Our results show that, interestingly, CP’s optimal subsidization rate increases in its marginal revenue of traditional advertising, but decreases in that of reward task. We also find that the amount of reward tasks performed by consumers actually sometimes decreases with these revenue rates. Further, while the profit of both the CP and the mobile network operator (e.g., AT&T) increases with the marginal revenue of traditional advertising, the effect of the marginal revenue of reward task on their profit is not straightforward. Specifically, when the marginal revenue of reward tasks is relatively high, it affects the CP and the mobile network operator’s profit positively; otherwise, the effect is reversed. We further find that, interestingly, the introduction of sponsored data might not necessarily increase consumer surplus. Similarly, although vertical integration of the mobile network operator and the CP reduces double marginalization by aligning incentives and reducing strategic information asymmetry, we find that it could sometimes hurt consumer surplus. Our results provide important insights for both the mobile network operator and the CP. In addition, we also provide useful guidance to policymakers.