Presented during the WKWSCI Symposium 2014 21 March 2014 Marina Bay Sands Expo and Convention Centre Organized by the Wee Kim Wee School of Communication and Information at Nanyang Technological University

1. © 2014 IBM Corporation
A Big Data Telco Solution
Laura Wynter
Director, IBM Research Singapore Collaboratory
IBM Master Inventor
Research Scientist, Watson Research Center, New York
WKWSCI
SYMPOSIUM
2014
Big Data, Big Ideas for Smarter Communities

2. The Global Reach of IBM Research
IBM Research Labs
IBM Research – Openings in 2011
IBM Research – Openings in 2012
China
WatsonAlmaden
Austin
Tokyo
Zurich
India
Dublin
Australia
Brazil
Africa
 Next Gen Public Sector
 Water & transportation
 Human Capacity Development
 Natural Resources
 Disaster management
 Healthcare/Life Sciences
 Natural Resources
 Smarter Devices
 Human Systems/Events
 Analytics & Intelligence
 Systems &Software
 Industry research
 Internet of Things
 Big Data / Analytics
 Enterprise Cloud Services
 Energy, Commerce, Traffic
 Big Data Analytics
 HW & SW Quality
 Cloud
 Mobile
Haifa
 Smarter Cities
 Analytics
 Services
 Big Data Analytics
 Front Office Digitization
 Semiconductors
 Systems
 Software
 Services
 Analytics
 Semiconductors
 Processors
 Analytics
 Storage
 Nanotech
 Healthcare
 Nanotech
 Security
 Business
Analytics
 Systems
Industry Solution Lab
Singapore
•Analytics

3. IBM Research Singapore Collaboratory: who we are
3
Statistics
Transportation science
Data mining & management
Computer science
Optimization
Computational
science
A team of research scientists and research software engineers with expertise in mathematical &
computer sciences, a branch of our global IBM Research presence
IBM Confidential

4. Telco Data Monetization
• Telcos have lot of data about their customers from daily operations –
especially location and movement data.
• Our objective is to build an asset for Telcos to leverage these data about
their customers to enable emerging new market opportunities.
• Key to such data monetization is the ability to connect different data pieces
to better understand customers, their preferences, life style, intent etc.

5. Two Scenarios: Aggregate and Individual
• Aggregate Anonymized Analytics: Sensing
City-scale people movement from Telco data
and leveraging for Transit Optimization
• Enriched Consumer Profile: Customer
Analytics with Mobility Profiles from Telco
Data

6. Let’s review the potential areas of Business Benefit of Big Data for Vodafone
GPS
External Data
Customer Service
Representatives
... could offer
personalized price
promotions to different
customer segments in
real-time
Business Development
... could find new mechanisms
to monetize network traffic and
partner with upstream content
providers
Network Operations
... could identify network bottlenecks in
real-time for faster resolution
Executive Leaders
... could get real-time reports and
analysis based on data inside as well as
outside the enterprise (web, social
media etc.)
Business Analysts
... Could analyze social
media buzz for the new
services/offerings to gauge
initial success and any
course correction needed
Finance
... could analyze all Call Detail
Records (CDRs) to identify and
reduce revenue leakage due to
unbilled / underbilled CDRs
Marketing
... could analyze subscriber usage pattern
in real-time and combine that with the
profile for delivering promotional or
retention offers
What if …

7. A data sharing platform should capture and structure location, time and content
about the consumer from multiple industries to drive profitable consumer
actions
Structured
Repeatable
Linear
Monthly sales reports
Profitability analysis
Customer surveys
Other
Industries
Other
Data
Industry Reports
Retail
Social
Media Data
Customer
• Segment
• Social Network
• Demographics
• Sex, Age Group, etc
• Tenure
• Rate plan
• Credit Rating, ARPU
Group
Device
•Class
•Manufacturer
•Model
•OS
•Media Capability
•Keyboard Type
Transactions
• Voice, SMS, MMS
• Data & Web Sessions
• Click Streams
• Purchases
• Downloads
• Signaling,
Authentication
• Probe/DPI
Network
• Availability
• Throughput/Speed
• Latency
• Location
• Facilities Interface
• Discovery
• Navigation
• Recommendations
Product/Service
• Subscriptions
• Rate Plans
• Media Type
• Category/Classification
• Price
Starts, Stops
Success Rates
Errors
Throughput
Setup Time
Connection Time
Usage
Recency
Frequency
Monetary
Latency
Telco Data Cross Industry Data

8. Enriched Consumer Profiles for
Enabling Telco Data Monetization
• We develop enriched consumer profiles by
deriving insights about consumer preferences,
life style, and intent from location, mobility and
call data joined with use case appropriate data
sources.
• Enriched consumer profiles are utilized to
enable new services and effective campaign
through targeted segmentation.

9. Two Scenarios: Aggregate and Individual
• Aggregate Anonymized Analytics: Sensing
City-scale people movement from Telco data
and leveraging for Transit Optimization
• Enriched Consumer Profile: Customer
Analytics with Mobility Profiles from Telco
Data

10. Sensing City Scale People Movement from Telco Data
Cities Demonstrated: Istanbul (Turkey), Dubuque (USA) for Transit Optimization
and a series of subsequent client pipeline
Challenge Cities have very little real understanding of where citizens, goods and
transportation move during the day. Without this information it is difficult to
accurately plan and manage the usage of roads and infrastructure.
Solution Using a variety of real time data from “smart phones”, GPS devices, terminals,
traffic cameras, public transportation schedules and transit data, develop models
of zonal density, flow of goods and origin / destination pairs. From these models,
drive processes to manage this flow against a specific objective.
Benefits Evaluates the efficacy of existing transit system and transportation infrastructure;
provides the structure for design incentive strategies to win new riders –
information, incentives, services; optimize fleet operations in situations where
demand outpaces supply; manage revenue through better zoning and permits.
comprehensive solution that will address the management of congestion, fleet
management, people attending events, and multimodal transit
10

11. Sensing People Movement from Telco Data

12. Example Challenges
Objective: Derive people movement model from tower level information
(communication between cell phone and tower)
Key Challenges
• CDR data is typically sparse
– Uncertainty both in space and time domain
– Location/movement from sparse and often incorrect (tower information) information
• Tower oscillation is very common in cellular network
• Typically only short term (e.g. one week) data is available due to various privacy regulations
Figure: Example for CDR and GPS. Left: CDR with tower oscillation; Right: GPS points

13. 13
Meaningful Location Detection and O/D Estimation
• Meaningful locations are the locations
where people spend a significant
amount of time, e.g. home, work, mall.
• Duration of stay (dos) is used to
measure how meaningful each cluster is.
– i.e. Given a threshold (e.g. 30 min), if the
duration of stay (dos) in a cluster is more than
the threshold, then the location of the cluster
locates is a meaningful location.
• Home and work can be identified by
selecting the locations with the largest
accumulated dos in the night time and
day time of week days.
• After meaningful locations detection,
users’ traces are described in a set of
meaningful locations.
• Trips and O/D pair can be segmented
from users’ trace on these meaningful
locations.
• For example:

14. Identifying Meaningful Locations
Where People Live Where People Work
Istanbul Movement Analysis
- 4.7 million phones w. 3B+ events/week
- Accurate detection of home, work & meaningful
locations

15. Traffic Monitoring
Uses basic analytics building blocks already seen to display time based traffic flow levels
mapped to city road system. A snapshot at 8:30am:
15

16. Time of Day Density Maps

17. Prominent Trip Attractors and
Producers
Major attractor of trips in Dubuque

18. Commuter Pain Index

19. Feeder Bus Route Optimization for M4 Metro Line on
Anatolian side of Istanbul
Feeder bus routes based on demand to 4 metro stations on Kadikoy-Kartal metro line

20. Optimal Bus Stop Location Design
• Stops are added by considering
the greatest potential demand
for transit and accessibility at
origin and destination
• Some stops are added to far
places in which demand to the
area already served by existing
stops is potentially large
20

21. 21
Clean sheet Optimization of Bus
Routes based on Demand Models
Clean sheet optimization to
minimize opex, unmet demand
and travel time
Constraints include fleet size,
max transfers, duration, etc.
Optimal routes can
• reduce OPEX cost up to
40%
• reduce unmet demand by
37%
• reduce avg. travel time from
37 minute average to 10-22
minute average

22. Two Scenarios: Aggregate and Individual
• Aggregate Anonymized Analytics: Sensing
City-scale people movement from Telco data
and leveraging for Transit Optimization
• Enriched Consumer Profile: Customer
Analytics with Mobility Profiles from Telco
Data

23. Consumer Analytics with Enhanced Consumer Profiles
• Derive advanced location/mobility attributes and patterns
from Telco data to enrich consumer profiles with mobility
context
• Derive predictive model about consumers location and
mobility patterns
• Leverage enriched consumer profiles for data monetization
opportunities by correlating and joining other data sources
• Build an operational asset on IBM Big Data platform to enable
Telco to extract mobility attributes and patterns efficiently

24. Set of example mobility attributes
• Base set of example mobility attributes
–Home and work location
–Weekday top locations
–Weekend top locations
–Meaningful location detection
–Classification of where and when time spent
–Detecting tourism pattern
–Detecting specified habits related to mobility
– Trip purpose
–Anomaly in mobility from baseline patterns
–Detecting who’s who in the household based on mobility pattern
• Advanced predictive models (Next Best Location)
–Likely place a person would be at a future time
–Likelihood of a person going to a Mall during this weekend
–When this person is likely to be a tourist

25. Determining Buddies, Hangouts, Life Style
Example Lifestyle Attributes for marketing demonstration
 Subscriber Lifestyles
 Popular Locations
 Subscriber Pairings
Who Are You?
Homebody
Daily Grinder
Delivering the Goods
Globetrotter
Nomad
10 Top Hangouts
Best Buddies
Next Steps
• Given the lifestyles, popular locations, and best buddy data => predict where individuals or
groups of similar individuals will be and when.
• Use time series modeling and clustering we can create time/location based marketing
campaigns targeted at homogenous groups in specific locales.

26. 26
Enhanced Micro-segmentation with Mobility Model
Mobility
Patterns
Mobility Model
•Location and movement pattern
(space, time)
•Meaningful location detection
•Meaningful location
classification
•Trip purpose
•Estimated Duration of stay
•Estimated Duration of travel
•Mode of travel
•Calling patterns
•Detecting tourist patterns
•Detecting student patterns
•Estimated demographic profile
of user of phone
•Anomalies in regular patterns
Example Data Monetization Use Cases
Telcos cannot assume that
person who buys phone
is the user. Discovering profile
of actual user is helpful in
retail & marketing
Smarter LBS would take movement
patterns (i.e, likely to be in a shopping complex on
Saturday afternoon etc.) into account instead of merely
using momentary location
Telcos can find out inter-city travel
patterns which are helpful to T&T
Banks can correlate ATM usage with
Movement patterns for better mgmt
Life style and brand preference
determination from mobility data
for targeted segmentation

27. 27
Buying
Patterns
Social Patterns
Demographics
•Gender
•Age group
•Address
•Income
•Historical buying
patterns
•Buying preferences
•…..
•Social network
influencers
• friends choices
• friends activities
Attributes for Customer Segmentation

28. 28
Enhanced Micro-segmentation with Mobility Model
Mobility
Patterns
Buying
Patterns
Social Patterns
Demographics
•Gender
•Age group
•Address
•Income
Historical buying patterns
Social network
influencers
Mobility Model
•Location and movement pattern
(space, time)
•Meaningful location detection
•Meaningful location
classification
•Trip purpose
•Estimated Duration of stay
•Estimated Duration of travel
•Mode of travel
•Calling patterns
•Detecting tourist patterns
•Detecting student patterns
•Estimated demographic profile
of user of phone
•Anomalies in regular patterns
Enhanced Attributes for Customer Segmentation

29. Building Context and Intent from
Location data• Deriving location: location information may be derived using multi-modal
information
– CDR data, tower data, device data, Wi-fi etc.
– Accuracy of location information depends on data fidelity etc.
• Building context: making sense of the location information
– Correlate location information with business data
– Various other correlation rules may be used to build a rich context
• Inferring intent: infer consumer level intents by leveraging location and
mobility patterns
Deriving Location Inferring IntentBuilding Context

30. Enriched Consumer Profile Hub
Customer Profile Hub
IPTV
- Subscription Billing
-VOD Billing & viewed
- channel viewing history
-- contents purchased
-Logs & Tuning Events
- package subscription
Mobile
- Location
- URL+App Transactions
- xDRs and inb. roaming
- RAN (incl. HLR/VLR)
- Top Up
- Pkgs
- Billing
- SMS, browing URLs
Other:
- Devices
- Dealer Network
- Contact Center
- Call Recordings
- Trouble Tickeing
- Campaign Results (Imagine)
- Loyalty
- Competition Website
- Retail Transactions
Fixed
- CDR
- URL (IP)
-Radius (IP-Cust)
- Pkgs
- Billing
Historical
Transactions/
Events
Partners/Retailers
Advertisers
Other/Internal
GIS
- Business map and numbers
- Point of Interest maps
ConsumersofnewInsights
Feedback
Social
Media Data

31. 1
2
3
Advanced Analytics Platform
End-use
Applications
Analytics
Visualization
Big Data Analytics
Warehouse
Predictive Analytics
Sens
e
Analyze Act
Search / Explore
KPIs
Dashboards
Drill-Downs
Reports
Marketing
Campaigns
Rules Engine
Behavioral
Analysis
Outcome
Optimization
Propensity
Scoring
Model
Creation
Structured /
Unstructured
Data
Data Governance
Data Integration
ETL/ELT
ChangeCapture
DataQuality/Validity/Security-Privacy
Format/UnitConversion
Consolidation/De-duplication
DataRepositories
Network
Data
Customer
Behavior Data
Customer
Data
ProductDataNetworkTopology
Data
ContinuousFeed
Sources
Usage Data
Reference
Data
Historical
Analysis Data
Demographics
Segmentation
Location
Past Actions
Propensity
Scores
Behaviors
Predictive Model
Deployment
Actionable
Insight
Stream Processing
Streaming Data
Operational
Systems
4
5
AAP Capabilities
High Performance Historical analysis (Big Data Platform)
Model Based Analytics - behavioral scoring, micro segmentation,
correlation detection analysis
Real-time scoring, classification, detection and action
Visualize, explore, investigate, search and report
Take action on analytics
IBM’s Advanced Analytics
Platform (AAP) Supports Use
Cases across the business with
New Era Capabilities
Create new Services and
Business Models Transform Operations
Build Smarter
Networks
Personalize Customer
Engagements
1
1
2
3
4
5
5

32. Retailer Customer Profile
Real Time Targeted Advertisement for IPTV
AAP
(Advanced
Analytics
Platform)
3- AAP catches the
new football interest
flag, his frequent
sports shopping, and
realtime matches
Tom’s profile with an
offer for 20% off
coupon to an Nike
store.
4- Tom is also an
existing SMS Opt-
In mobile cust.
5– Tom receives
targeted IPTV
advertisements based
on his IPTV, mobility
and social profiles
2- Tom is channel surfing,
mostly sports channels,
primarily football games where
Nike advertises a lot (AAP enhances
his customer profile, after 10 football
games viewed in 1st month,
with an interest flag as a “football fan”)
Enhanced Cust. Profile
Interest / Mobile # / Email
1- Tom activates IPTV service
with the America 50 package and
adds the ESPN sports ala carte
option (we have an initial
customer profile with his fixed #
and a mobile#)
A la carte option
Sports Packages
tom@gmail.com
212-201-1234
Language
Package

33. Location Based Real Time Offering on Mobile Phone
Lisa
4- AAP catches that
Lisa is entering a mall,
and matches her
“Fashion” interest flag
and “Perfume”
preference, realtime
with an offer for 20%
off coupon for Byonce
fragrance at Sephora
in that mall.
5 - Lisa receives
an SMS/email/App
notification that
her mobile app
account contains a
new offer for
Beyonce perfume.
Beyonce Fan Page
2- She follows a
friend’s post on FB and
clicks the Like button on
the Beyonce Fan Page.
3- Lisa’s IPTV viewing
& mobile clickstream
behaviors set her Interest
flag to “Fashion” and one
preference to “Perfume”.
6- Lisa uses
the mWallet
app on her
smartphone to
purchase some
perfume at POS
via NFC.
1- Lisa is a mobile subscriber
with Telco and downloads the
mobile app and agrees to receive
offers related to her interests.
AAP
(Advanced
Analytics
Platform)
Retailer Customer Profile
Enhanced Cust. Profile
Interest & Preference
IPTV a la carte option &
Mobile Features/Apps
IPTV Lang
Pkg &
Mobile Pkg

34. Thank You
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