|Liang Zheng||Princeton University, USA|
|Carlee Joewong||Carnegie Mellon University, USA|
|Matthew Andrews||Nokia Bell Labs, USA|
|Mung Chiang||Purdue University, USA|
As the U.S. mobile data market matures, Internet service providers (ISPs) generally charge their users with some variation on a quota-based data plan with overage charges. Common variants include unlimited, prepaid, and usage-based data plans. However, despite a recent flurry of research on optimizing mobile data pricing, few works have considered how these data plans affect users' consumption behavior. In particular, while users with such plans have a strong incentive to plan their usage over the month, they also face uncertainty in their future data usage needs that would make such planning difficult. In this work, we develop a dynamic programming model of users' consumption decisions over the month that takes this uncertainty into account. We use this model to quantify which types of users would benefit from different types of data plans, using these conditions to extrapolate the optimal types of data plans that ISPs should offer. Our theoretical findings are complemented by numerical simulations on a dataset of user usage from a large U.S. ISP. The results help mobile users to choose data plans that maximize their utilities and ISPs to gain profit by understanding their user behavior while choosing what data plans to offer.