This presentation describes use of big data analytics for contextual marketing and data monetization.

1. © 2015 IBM Corporation
Session 2183: Creating and
Monetizing a Customer Profile Hub –
The Etisalat Story
Mohamed Hashem, Director Analytics, Etisalat
Ken Kralick, IBM Global Solution Executive - Big Data & Analytics Leader
Dr. Sambit Sahu, IBM Research
Dr. Arvind Sathi, IBM WW Analytics Architect - Communications Sector

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Performance is based on measurements and projections using standard IBM benchmarks in a
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Therefore, no assurance can be given that an individual user will achieve results similar to those stated
here.
Please Note:
2

3. Overview
• Introduction on Etisalat Engagement
• Monetization opportunities for Telco
• Research FOAK and Etisalat
• The Profile Hub Pilot
• Results and Current Work
2

4. Real Time Actions in the Cognitive Era
DECISIONSINSIGHTS OUTCOMES
Measure
Results
Historical
Data
SUBSCRIBER
PROFILING &
ENRICHMENT
•Hangout
•Location
•Trends
•Behavior
•Lifestyle
Gotham
City
Night
Owls
PREDICTIVE ANALYTICS
(SCORES)
Sports
Fans
Lunch Crowd
KPI-DRIVEN
ACTIONABLE
INSIGHTS
•NPS
•Churn
•Upsell
•Cross-
Sell
BUSINESS DECISIONS
Upgrade
Phone
Bad Device
Low
NPS
Wrong Plan
DATA SOURCE
COLLECTION &
EXTRACTION
DATA / VALUE
SOCIAL
NETWORK
TROUBLE
TICKETSBILLING
DEVICES
APPs
OPERATIONS
TRANSFORMATION
•Proactive Care
•Enhanced Sales &
Marketing
•Fraud & Security
•Revenue Assurance
•Customer Data Location
Monetization
•New Business Models
BUSINESS OUTCOMES
Business Maturity
INDIVIDUAL
SUBSCRIBER
EXPERIENCE
•Device
•Usage
•Customer type
•Network
•Service
Experience
CUSTOMER PROFILE
(INSIGHTS)
iPhone 5C
Congested
3G Cell
Heavy
Netflix
Users

5. Background
Interest in monetizing Telco data has increased with Telcos’ setting up
organizations dedicated for this. Examples include:
• Etisalat
• StarHub
• Qrious
• PinSight Media+
The questions these Telcos’ are trying to address internally:
• Who are my potential monetization customers and how many are there?
• What will they be interested in?
• How do I get the insights into something of value to these customer?
• How will they get access to the data and how can I respect the privacy of my
consumer customer and yet provide value to the enterprise customer?
• How do I price this?
• How much do I need to invest in this and what sort of returns am I getting?
• How do I start small and test waters?
Passenger Movement DataTourismLocal Government
http://www.qrious.co.nz

6. What can CSPs monetize?
• Dara
• Location data and derived
attributes
• App and content usage data
• Social networks
• IoT data
• Engagement services
• Advertising
• Apps
• SMS
• Orchestration / Business Services
• Fraud detection
• Marketing

7. 6
• IgnitionOne’s Q3 Digital Marketing Report said that Google’s display business
dropped 19 percent from last year’s third quarter to this year’s, while Facebook’s
share of display advertising spend increased by 40 percent.
• With Facebook’s Custom Audiences product, advertisers upload a list of their
existing customers from their CRM, then Facebook goes out and finds people
with the same demographic and social characteristics. Facebook then delivers
ads for the relevant products to these users’ desktop or mobile devices.
Monetization Business Models – Custom
Audiences from Facebook
http://venturebeat.com/2015/10/22/why-google-is-getting-schooled-by-facebook-in-display-ads-and-what-its-doing-about-it/
https://www.facebook.com/business/success/little-passports
https://www.facebook.com/business/a/custom-audiences

8. IBM Research FOAK Engagement with Etisalat
• ProfileHUB: Enriched Consumer Profiles
• Monetization Examples
• Sample Demos
• Business Opportunities and Current Ongoing Work

9. 8
Global Telco Operator Etisalat and IBM Research
Join Forces to Deliver Personalized Mobile and
IPTV Service
• First-of-a-kind Project uses Cognitive Computing and Big Data
Technologies to Transform the Customer Experience
• ProfileHUB: First-Of-A-Kind (FOAK) project demonstrates
– Creation of enriched customer segments using attributes derived from
Mobile, IPTV, Browsing data, Social Media and Billing Data
– Flexible business rule based approach towards customer
segmentation
• Data monetization demonstration and client pilots
– Targeted advertisement delivery on OTT clients for VOD
– Predicted location based campaigns
• Data driven Audience measurement capability with enriched
consumer profiles
– Derived from location, web browsing, social and IPTV channel viewing
data
– Real-time Audience insight for Video streaming services
Egyptian Nationality, Regular 9am – 5 pm worker.
Interested in sports & entertainment, speaks
Arabic/English. Home near Dubai Marina, shops in
Dubai Marina Mall during weekdays. Goes to Dubai
Mall during Weekends & spends more than 100 mins
on average. He uses WhatsApp.

10. 9
From Raw Data to Inferred Attributes to Derived
Customer Segments
Inferred attribute
Location Analytics
URL AnalyticsIPTV Analytics
Call Analytics
Luxury
Football Fan
Travelers
Youth
Business
Home, Work location, Nationality,
Preferred Language, Income,
Interests, Weekday &
Weekend visits and time spent, travel
frequency, top TV channels viewed, etc.
70-90% accuracy
Billing Analytics
Segmentation Rules
for Marketing

11. 3. Location Based Real-Time Targeted Campaigns
in Egypt
Pilot: Send real-time promotions to people near a
sports store if he/she is a sports lover.
2. Targeted Advertisement on IPTV based on location,
web access and viewership data in UAE
Pilot: Real-time targeted advertisement on IPTV based on
consumer profile
Profile HUB: From Raw Data to Inferred Attributes to
Derived Customer Segments for Service Personalization
1. Sensing Country-scale People Movement
from Telco Data and Application to Transit
Pilot: Engaged with 6+ transit authorities and
validated results with extreme accuracy
360 degree consumer portraits
Example Pilot Engagements

12. 1. Sensing UAE Scale People Movement from Telco
Data and Application to Smarter Transit and Planning
• Derived from Anonymized and Aggregate tower level location
data
• Location Analytics for deriving UAE scale people movement
models
• Example models include
 Origin-Destination (O-D) matrices
 Time-of-day analysis
 Footfall analytics by segments
 Meaningful locations and Point of Interests

13. City in Motion – Understanding People Movement &
Optimizing Services
Network Data (millions of
events/day)
Transit System & GIS
Data
Census &
Demographics Data
Analytics &
Models
Information Sources Business
Services
Outcomes
Time of
Day
Density
Maps
Origin-Destination
Traffic Flow
Planning Large Scale Events,
Emergency Response
Transit Planning
Location-based Services, Traffic Alerts
Reduce
Congestion
Reduce
Journey Time
Reduce
Carbon
footprint
Reduce
Emergency
Response
Time

14. O-D Spatio-temporal Heatmap
Select
Origin or
Destination
Select
Time-of-the-day
Heat map of origin and destinations
A 2nd side-by-side
map to compare

15. O-D Pair Arc Visualization at different hour-of-
the-day
Select
“hour-of-the-day”
Enter the threshold (say, n)
for number of trajectories –
Only OD pairs which have
greater than ‘n’ are displayed
OD Pairs displayed as arcs
• An arc represent an O-D pair
• Begins with green flat slope
• Ends as red with sharp turn
• Thickness is proportional to
number of trajectories
Select
the date
Select the type
of the trips. Currently
we support analyzing
“all trips” only
Select
the week of
Analysis
(Ramadan or
School Holidays)

16. O-D Temporal Trip-Duration Histogram
Select
Origin or
Destination
Select
Time-of-the-day A 2nd side-by-side
map to compare

17. Number and origin of daily visitors to top landmarks
and segments
• Number of people and origin of visitors to landmarks
• Example: where visitors coming to Dubai Mall
• Illustration

18. 17
Customer Profile
Profile Hub
3 – Profile Hub catches the
new football interest flag
and realtime matches
Walid’s profile with an offer
for 20% off coupon to an
Nike store.
4 - Walid is also an existing
Etisalat SMS Opt-In mobile
cust.
5 - Walid receives and SMS
with a promo code for offer
on his smartphone.
2 - Walid is channel surfing,
mostly sports channels,
primarily football games where
Nike advertises a lot (FAP 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- Walid activates eLife TV service with the
Arabic package and adds the Jazeera
sports ala carte option (we have an initial
customer profile with his fixed # and a
mobile#)
A la carte option
Language
Package
walid@eim.ae
6 - Walid uses promo code in Nike
Store to purchase a pair of Nike
football shoes.
2. Monetization Use Case: Targeted Ads over IPTV

19. 18
Enriched Consumer Profile from Location, Web and
IPTV data for Targeted Campaigns
-Real-time targeted advertisement on IPTV based on consumer profiles
-Potential of power of micro-segmentation

20. 19
19
4 – Profile Hub catches that
Mariam 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 – Mariam receives an
SMS/email/App notification that
her mWallet account contains a
new offer for Beyonce perfume.
2 - She follows a friend’s
post on FB and clicks the
Like button on the Beyonce
Fan Page.
6 – Mariam uses
Etisalat App on her
smartphone to
purchase some
perfume at POS via
NFC.
1- Mariam is a mobile subscriber, has
Etisalat app and agrees to receive offers
related to her interests.
Profile Hub
Customer
Profile
Enhanced Cust. Profile
Interest & Preference
IPTV a la carte option &
Mobile Features/AppsIPTV &
Mobile Pkg
3. Monetization Use Case: Real time & Targeted
offers over Mobile
Beyonce Fan Page
3 - Mariam IPTV viewing & mobile
clickstream
behaviors set her Interest flag to
“Fashion” and one preference to
“Perfume”.

21. 20
Location Based Real-Time Targeted Campaigns

22. ProfileHUB for M&E: Real-Time Audience Insights for
OTT/TVE Video Streaming (Follow-up ongoing Work)
• ProfileHUB for providing real-time Audience Insights capability at scale
for OTT/TVE Video streaming services to enable a wide variety of
service personalization and data monetization opportunities
 Real-time dashboard for measuring and understanding audience segments
 Recommendations and personalization to several systems in order to optimize
various business objectives (content recommendations, targeted ad insertions,
QOE/QOS optimization, etc).
• A data driven approach towards creation of user profiles and insights
based on
 User generated video viewing events
 Leveraging social media and other third party data source to support enriched
profiling of users

23. Real-Time Audience Insights Platform
• xx
• Reactive scalable platform for both batch and real-time
audience insights
• API based models for quicker integration with a wide variety of
OTT/TVE Video Streaming platforms

24. • Inferred nationality and ethnicity through Mobile and IPTV data
• Segmented top channels by Arabic and Hindi speaking populations.
• Uncovering Accurate Actionable Insights
• While Kids channel is highest viewership at an aggregate level, ethnicity based segments showed
that Kids channel is not the highest rating channel among Arabic and Hindi speaking population
segments.
• In a multi-ethnic environment, micro-segmentation could uncover accurate, and more actionable
insights for improved targeted advertisement.
23
Uncovering Accurate Insights with Audience
Micro-Segmentation
Kids channel
Kids channel

25. Overview
• Introduction on Etisalat Engagement
• Monetization opportunities for Telco
• Research FOAK and Etisalat
• The Profile Hub Pilot
• Results and Current Work
24

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29. © 2015 IBM Corporation
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