Do Mobile Data Plans Affect Usage? Results from a Pricing Trial with ISP Customers

The growing amount of traffic in mobile data networks is causing concern for Internet service providers (ISPs), especially smaller ISPs that need to lease expensive links to Tier 1 networks. Large amounts of traffic in “peak” hours are of especial concern, since network capacity must be provisioned to accommodate these peaks. In response, many ISPs have begun trying to influence user behavior with pricing. Time-dependent pricing (TDP) can help reduce peaks, since it allows ISPs to charge higher prices during peak periods. We present results from the first TDP trial with a commercial ISP. In addition to analyzing application-specific mobile and WiFi traffic, we compare changes in user behavior due to monthly data caps and time-dependent prices. We find that monthly data caps tend to reduce usage, while TDP can increase usage as users consume more data during discounted times. Moreover, unlike data caps, TDP reduces the network’s peak-to-average usage ratio, lessening the need for network over-provisioning and increasing ISP profit.

[1]  Paul Barford,et al.  Cell vs. WiFi: on the performance of metro area mobile connections , 2012, Internet Measurement Conference.

[2]  Deborah Estrin,et al.  Diversity in smartphone usage , 2010, MobiSys '10.

[3]  Sangtae Ha,et al.  Smart Data Pricing , 2014 .

[4]  Sangtae Ha,et al.  When the price is right: enabling time-dependent pricing of broadband data , 2013, CHI.

[5]  Sangtae Ha,et al.  TUBE: time-dependent pricing for mobile data , 2012, SIGCOMM '12.

[6]  Rittwik Jana,et al.  Managing cellular congestion using incentives , 2012, IEEE Communications Magazine.

[7]  Qiang Xu,et al.  Identifying diverse usage behaviors of smartphone apps , 2011, IMC '11.

[8]  M. Chiang,et al.  Smart Data Pricing (SDP): Economic Solutions to Network Congestion , 2013 .

[9]  Petri Mähönen,et al.  Riding the data tsunami in the cloud: myths and challenges in future wireless access , 2013, IEEE Communications Magazine.

[10]  K. K. Ramakrishnan,et al.  Understanding the super-sized traffic of the super bowl , 2013, Internet Measurement Conference.

[11]  Admela Jukan,et al.  The Evolution of Cellular Backhaul Technologies: Current Issues and Future Trends , 2011, IEEE Communications Surveys & Tutorials.

[12]  Sangtae Ha,et al.  A survey of smart data pricing , 2012, ACM Comput. Surv..

[13]  Victor Glass,et al.  Customer Price Sensitivity to Broadband Service Speed: What are the Implications for Public Policy? , 2014 .

[14]  Ted Taekyoung Kwon,et al.  AMUSE: Empowering users for cost-aware offloading with throughput-delay tradeoffs , 2013, 2013 Proceedings IEEE INFOCOM.

[15]  Feng Qian,et al.  An in-depth study of LTE: effect of network protocol and application behavior on performance , 2013, SIGCOMM.

[16]  Amit Mukhopadhyay,et al.  Mobile data explosion: Monetizing the opportunity through dynamic policies and QoS pipes , 2011, Bell Labs Technical Journal.

[17]  Paramvir Bahl,et al.  Anatomizing application performance differences on smartphones , 2010, MobiSys '10.

[18]  Deborah Estrin,et al.  A first look at traffic on smartphones , 2010, IMC '10.

[19]  Feng Qian,et al.  Screen-off traffic characterization and optimization in 3G/4G networks , 2012, Internet Measurement Conference.

[20]  Sangtae Ha,et al.  Incentivizing time-shifting of data: a survey of time-dependent pricing for internet access , 2012, IEEE Communications Magazine.

[21]  Clayton Shepard,et al.  Exploring iPhone usage: the influence of socioeconomic differences on smartphone adoption, usage and usability , 2012, Mobile HCI.

[22]  Anja Feldmann,et al.  A First Look at Mobile Hand-Held Device Traffic , 2010, PAM.