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Smart Data Pricing (SDP). Innovating Data Plans. Soumya Sen Joint Work with: Sangtae Ha, Carlee Joe-Wong, Mung Chiang. Challenges to the Internet. Is it. 1. year. feasible. ?. t o keep the Internet. economically viable. 10. /GB. $. ?. &. technologically sustainable.
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Smart Data Pricing (SDP) Innovating Data Plans Soumya Sen Joint Work with: Sangtae Ha, Carlee Joe-Wong, Mung Chiang Soumya Sen, WITE 2012
Challenges to the Internet Is it 1 year feasible ? to keep the Internet economically viable 10 /GB $ ? & technologically sustainable 100 % ? Soumya Sen, WITE 2012
Challenges to the Internet Is it 1 Technological factors year feasible to keep the Internet Human factors economically viable 10 /GB $ & Economic factors technologically sustainable 100 % ? Soumya Sen, WITE 2012
Challenges to the Internet NetEcon as a Solution Is it Technological factors feasible to keep the Internet Consumer Behavior User Trials Network Economics HCI, Engineering Theory Systems Human factors Economics economically viable & Tools Methods Economic factors technologically sustainable ? Soumya Sen, WITE 2012
NetEcon: A Holistic Agenda Technology Network Economics HCI & Analytics Soumya Sen, WITE 2012
What is Smart Data Pricing (SDP)? A. Usage pricing/metering/throttling/capping B. Time/location/congestion-dependent pricing C. App based pricing D. Smart Markets, Sponsored content E. Paris metro pricing F. Quota-aware content distribution G. All of the above… Time-Dependent Pricing Soumya Sen, WITE 2012
NetEcon as a Solution The Driving Forces Soumya Sen, WITE 2012
Evolution of Access Pricing The Driving Forces Soumya Sen, WITE 2012
Time Elasticity Movies & Multimedia downloads, P2P Streaming videos, Gaming Large Peak-Valley Differential Software Downloads Opportunities Cloud Texting, Weather, Finance Email, Social Network updates Opportunities for Exploiting time-elasticity of demand Soumya Sen, WITE 2012
Stakeholder Perspectives • Consumers • Policy feasibility • Industry moves: US, Europe, India, Africa Soumya Sen, WITE 2012
Time Dependent Pricing (TDP) ISP cost optimization, taking user reaction into account Soumya Sen, WITE 2012
ISP’s Optimization Problem Cost of overshooting capacity Cost of rewards Soumya Sen, WITE 2012
Estimating Waiting Function Economic modeling reward waiting function patience index delay Soumya Sen, WITE 2012
TDP Architecture Soumya Sen, WITE 2012
Princeton Trial Money Flow • TDP for 3G data • Feasibility study • Prototype development • Trial • 50 volunteers Data Flow Soumya Sen, WITE 2012
Graphical User Interfaces (GUIs) • Price display • Day-ahead • Color coded: red (<10%), orange (10 ~19%), yellow (20 ~ 29%) and green (>= 30%) • Self-education • Top 5 Apps • User control • Autopilot mode Soumya Sen, WITE 2012
Price Sensitivity • Do users wait to use mobile data in return for a monetary discount? • Average usage decreasein high-price periods relative to the changes in low-price periods Usages changed by -10.1% in high-price and 15.7% in low-price periods Soumya Sen, WITE 2012
Notification Effectiveness • Do notifications impact usage? • About 60-80% of the active users decrease their usage in response to price notification pop-ups Soumya Sen, WITE 2012
UI Effectiveness • Do users respond more to the numerical values of TDP prices or to the color of the price indicator bar on the home screen? Soumya Sen, WITE 2012
Optimized TDP Impact • Does the peak usage decrease with time-dependent pricing? And does this decrease come at the expense of an overall decrease in usage? • Optimized TDP reduce the peak-to-average ratio • Overall usage significantly increase with TDP 30% PAR reduction Soumya Sen, WITE 2012
Impact on Ecosystem • Does the application usage distribution change due to TDP? • People are motivated to use more bandwidth during low-price periods, “valley filling”. Soumya Sen, WITE 2012
Viability • Will you be able to decide on “when” to use? • “I think it's a great idea, ..the iPadswould say, 'If you wait a half an hour, you can have...' I thought that was incredibly useful. And I would be able to make that decision.” • Are there apps for which you usually wait? • “[I]f I'm out in my car and I needed it for GPS, I wouldn't care how much money I'm spending… if I just wanted to be on a social network or check my email, I would certainly wait.” Soumya Sen, WITE 2012
Usefulness • What are your main concerns with TDP? • “If it's predictable, yes, I think so, because let's say I know that definitely everyday from 9 to 10 it's less, then I can plan a little bit.” • Was the color-coded notification bar useful to you? • “I group the colors I would see if it's a good color for me... because I couldn't always figure out what it meant in terms of the dollar amount and translate that into how much I was using” Soumya Sen, WITE 2012
Opinions • Were you tempted to use more data when the discounts were higher? • “[laughs] Kind of! But that also goes toward my personality of if it's on saleI must buy it!” • Will TDP adversely affect high-bandwidth app developers? • “I don't think this will result in those kinds of applications being developed less, and I think that's because you're giving users the option” Soumya Sen, WITE 2012
Prototypes and Trials Soumya Sen, WITE 2012
From $10/GB To SDP • Real progress feasible • http://www.datami.com • Collaboration will help • http://scenic.princeton.edu/SDP2012/ • Get to win-win for all • References: • S. Sen, C. Joe-Wong, S. Ha, M.Chiang, “Incentivizing Time-Shifting of Data: A Survey of Time-Dependent Pricing for Internet Access”,IEEE Communications Magazine, Nov. 2012. • S. Ha, S. Sen, C. Joe-Wong, Y. Im, M. Chiang, “TUBE: Time Dependent Pricing for Mobile Data”, ACM SIGCOMM 2012. • S. Sen, C. Joe-Wong, S. Ha, J. Bawa, “When the Price is Right: Enabling Time-Dependent Pricing of Broadband Data,”ACM SIGCHI 2013. Soumya Sen, WITE 2012