Soumya Sen, WITE 2012 1

Smart Data Pricing (SDP)

Soumya Sen

Joint Work with: Sangtae Ha, Carlee Joe-Wong, Mung Chiang

Innovating Data Plans

Soumya Sen, WITE 2012 2

?

Challenges to the Internet

1

10

100

feasible

economicallyviable

Is it

year

/GB$

%technologically

sustainable

to keep the Internet

&

?

?

Soumya Sen, WITE 2012 3

Challenges to the Internet

feasible

economicallyviable

Is it

1

10

100

year

/GB$

%technologically

sustainable

to keep the Internet

&

?

Technologicalfactors

Humanfactors

Economicfactors

Soumya Sen, WITE 2012 4

feasible

economicallyviable

Is it

technologicallysustainable

to keep the Internet

&

?

Technologicalfactors

Humanfactors

Economicfactors

Network Economics

NetEcon as a SolutionChallenges to the Internet

HCI,Engineering

ConsumerBehavior

Economics

Systems

UserTrials

Theory

Tools Methods

Soumya Sen, WITE 2012 5

Network Economics

NetEcon: A Holistic Agenda

Technology

HCI & Analytics

Soumya Sen, WITE 2012 6

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

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NetEcon as a SolutionThe Driving Forces

Soumya Sen, WITE 2012 8

Evolution of Access PricingThe Driving Forces

Soumya Sen, WITE 2012 9

Time Elasticity

Time Elasticity

Volu

me

Streamingvideos,Gaming

Texting,Weather, Finance

Email,Social

Network updates

Cloud

SoftwareDownloads

Movies & Multimedia downloads,

P2P

Opportunities

Large Peak-Valley Differential

Opportunities forExploiting time-elasticity

of demand

Soumya Sen, WITE 2012 10

Stakeholder Perspectives

• Consumers• Policy feasibility• Industry moves: US, Europe, India, Africa

Soumya Sen, WITE 2012 11

Time Dependent Pricing (TDP)

ISP cost optimization, taking user reaction into account

Soumya Sen, WITE 2012 12

ISP’s Optimization ProblemCost of

overshooting capacity

Cost of rewards

Soumya Sen, WITE 2012 13

Estimating Waiting FunctionEconomic modeling

reward

patience indexdelay

waiting function

Soumya Sen, WITE 2012 14

TDP Architecture

Soumya Sen, WITE 2012 15

Princeton Trial

• TDP for 3G data – Feasibility study

• Prototype development

• Trial • 50 volunteers

Data Flow

Money Flow

Soumya Sen, WITE 2012 16

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 17

Price Sensitivity

• Do users wait to use mobile data in return for a monetary discount?– Average usage decrease in 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 18

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 19

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 20

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 21

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 22

Viability

• Will you be able to decide on “when” to use?– “I think it's a great idea, ..the iPads would 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 23

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 24

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 sale

I 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 25

Prototypes and Trials

Soumya Sen, WITE 2012 26

Soumya Sen, WITE 2012 27

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.