Pricing for Collaboration between Online Apps and Offline Venues

Abstract

An increasing number of mobile applications (abbrev. apps), like Pokemon Go and Snapchat, reward the users who physically visit some locations tagged as POIs (places-of-interest) by the apps. We study the novel POI-based collaboration between apps and venues (e.g., restaurants). On the one hand, an app charges a venue and tags the venue as a POI. The POI tag motivates users to visit the venue, which potentially increases the venue's sales. On the other hand, the venue can invest in the app-related infrastructure, which enables more users to use the app and further benefits the app's business. The apps' existing POI tariffs cannot fully incentivize the venue's infrastructure investment, and hence cannot lead to the most effective app-venue collaboration. We design an optimal two-part tariff, which charges the venue for becoming a POI, and subsidizes the venue every time a user interacts with the POI. The subsidy design efficiently incentivizes the venue's infrastructure investment, and we prove that our tariff achieves the highest app's revenue among a general class of tariffs. Furthermore, we derive some counter-intuitive guidelines for the POI-based collaboration. For example, a bandwidth-consuming app should collaborate with a low-quality venue (users have low utilities when consuming the venue's products).

Monetizing Mobile Data via Data Rewards

“…. Similar assumptions about the range of users' attributes have been made in [35]- [37]. As shown in Sections III and IV, this assumption implies that the highvaluation users may both subscribe to the data plan and watch ads under a small reward ω.…”

“…In Stage I, the operator decides the unit data reward ω and ad price p. In Stage II, each type-θ user chooses the subscription decision r and the number of watched ads x, and each advertiser decides the number of purchased ad slots m. 9 We assume that the users' maximum valuation θ max satisfies θ max > u (0)F u (Q)u(Q) . Similar assumptions about the range of users' attributes have been made in [35]- [37]. As shown in Sections III and IV, this assumption implies that the highvaluation users may both subscribe to the data plan and watch ads under a small reward ω.…”