Pricing the last mile: Data capping for residential broadband

Abstract

In recent years, ISPs in mature residential broadband markets have been moving away from flat-rate pricing schemes to ones that involve usage limits (or ?data caps). These changes have led to a lively debate about the merits of data capping. While previous work has studied the theoretical benefits of data capped pricing models, there is relatively little work that provides insights into exactly how to design data capped tariffs, and their impact on the parties involved, i.e., ISPs and their broadband subscribers. In this paper, we formulate the problem of selecting optimal data capped tariffs based on a simple, extensible model of ISP costs and revenues, and a novel model that captures how users evaluate a set of offered tariffs. We show that the problem is NP-Hard and describe two heuristics that provide reasonable solutions. We also describe an approximation algorithm that yields bounded solutions. We apply this framework on a novel broadband usage dataset collected from real-users and examine the cost-benefit impact of data capping for various cost regimes that reflect different regional markets around the world. We find that in highly competitive markets with relatively high traffic costs, data capping can indeed help the ISP to manage costs while resulting in a more equitable per unit data cost across the users. However, in the absence of meaningful competition or when traffic costs are low, data capping largely benefits the ISP and is to the detriment of subscribers. Copyright 2014 ACM.