"How can big data help us accelerate external monetization?" A presentation by Hezi Zelevski, VP Corporate Development at cVidya Presented in the " Monetizing Big Data in Telecoms World Summit 2015" conference in Singapore on April 20-21, 2015

1. © 2014 – PROPRIETARY AND CONFIDENTIAL INFORMATION OF CVIDYA April 21st , 2015
Your Success
is Our Business
Big Data
Monetization
The Path from
Internal to External
Hezi Zelevski
VP Corporate Development
hezi.zelevski@cvidya.com

2. 22
Everybody is Talking about Big Data…
“Top Technology Trends Impacting Information Infrastructure in 2013”
However…
“Processing large volumes or wide varieties of data,
remains merely a technological solution, unless it is tied to
business goals and objectives”

3. 3
Reduce operational costs Increase revenues, launch
Decline in traditional service revenues (Voice, SMS)
Unlimited Price Plans
Increasing competitionConsolidations & mergersGlobal financial recession
Real-time Self-Service
Data
Monetization
New services /products:
“ Internet of Things ”
Data
Explosion
New
Billing Schemes
Telecom Market Trends
3

4. 4
Data Monetization Opportunities
 Internal
− Effective customer proposition
− Effective campaigns execution
− Greater value and differentiation versus
− ……
 External
− Resell aggregated data to third party partners in the
form of trends
− Profiles
− Location
− usage patterns
− Movement
− ......

5. 5
External Monetization is Still at Early Maturity Stages
 60% of operators believe that “it is important for Telcos to harness the
power of Big Data to drive new revenue streams externally...”
 Only 10% of respondents claimed they are currently focusing on an
external monetization program for their subscriber Big Data
5

6. 6
External Monetization - Push or Pull ?
End Subscriber
 Added value
Third Party
 Use cases
 Customer engagement
Operator
 Data
 Platform

7. 7
Accelerating Business Breakthroughs
The right Use Cases
Location
Advertisement
Financial
…….
External Monetization
Bid Data Solution
External web portal
Rich GUI with analytical and
reporting capabilities
Control over the data
3rd Party Engagement
3rd party value
Partnership
Market knowledge
Privacy &
Regulation
Customer data
Complete
Accurate
Enriched
Online

8. 8
Big Data
Analytics
Platform
Data
Analytics
Use Cases
Big Data
The Analytics Workflow
Big Data
CRM
Usage
DPI
Location
ERP
DWH
Billing
Switch
…….
Data
Analytics
Collection
Verification
Enrichment
Aggregation
Use Cases
Value
Solution
Analysis
Simulation
Action
Monitoring
Big Data
Analytics
Platform

9. Use Case Example
9

10. 10
Examples for External Monetization Use Cases
Targeted Advertising
Micro-segment the base into behavioral, demographic and
geographic segments, offering advertisers the possibility of
targeting those segments directly via the operator
 FMCG
 Large Retailers
Description Potential Customers
Location Trend Reports
Track trends in customers’ location and
movements, and send period reports to clients
 Real estate companies
 Public transport agencies
 Large retailers
Market Research
Leveraging the customer base, as a proxy for the
market to support customized market studies
 Travel Agencies
 Banks
 Municipalities
Financial Fraud Detecting real-time CC and ATM fraud
 Banks
 Credit Card Companies
10

11. 11
Examples for External Monetization Use Cases

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Targeting the Right Use Case
 Geography
 Regulation
 Maturity
 Market
 Need
 Value
12

13. Happening in the Industry
13

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AT&T Credit Card / ATM Fraud Detection
 When a CC (or debit card) is either stolen or
“duplicated” and used by another person in
another location to purchase a good or
withdraw cash
 Identify in real time (when transaction is
submitted) that the use of it is not performed by
the card owner
 Block the card from additional use and/or block
the transaction
 Solution is based on physical location of mobile
device
14

15. 15
Verizon Location and Profiling

16. 16
Orange France Application for Business

17. Use Case Definition and Execution Example

18. 18
Defining Use Cases
 Need
 Customer
 Value
 Maturity
 Privacy & Regulatory

19. 19
Transportation Example
Key Success Factors
 Understand potential partners’ business needs
 Translate needs to relevant insights
 Accurate and reliable data
 Intuitive environment for data exploration
 Establish a business model to accommodate
partner’s maturity
 Accompany your partners – key to a long-
term success

20. 20
 Analyze location data by providing
statistics for predefined hotspots at any
time range, enriched with subscribers'
profile and usage data
 Answer questions such as:
– Where are the most crowded hotspots?
– What are the potential locations for new
hotspots?
– What are popular roaming visitors'
locations?
– First timers vs. repeated visitors in different
locations?
General Geographical Traffic Analysis

21. 21
Telco Added Value
 Origin and destination definitions – based on
commuter movements and behavior
 Origin/destination predictions - Given
origin/destination location and a certain time,
date and events, predict destination/origin in a
predefined time.
 Commuter profile
 Public vs. private journey
 Real-time congestion

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Business Attributes
Enriched Commuter
Profile
 Home Location
 Work/School
 Location
 PT Digital Habits
 Age
 Gender
 Interests
 Families and Social
Circles
Destination Prediction
Algorithms
Waiting Time
Calculations
 SWT
 AWT
 EWT
Origin & Destination
Analysis
 Transfer Time Public
vs. Private
Density/Congestion
 By Station
 By route (Shape)
 By Location
Origin & Destination
Analysis
 Journey Analysis
 Public Transpiration
Users

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Operators Data Sources:
 Subscribers Location
̶ Location Based System
̶ Access points
 Subscribers Profile
̶ CRM
̶ Advanced models
 Subscribers mobile usage behavior
̶ Voice, Text, Data
Accessible Transportation Data Sources – Optional
 Real-Time data from GPS devices on vehicles
 SWT and other internal data sources
Data Sources

24. 24
3rd Party Portal
 Intuitive UI
 Analysis Capabilities
 Required attributes
 Reporting capabilities
 APIs

25. 25
Home – Work/School Journey Pattern Identification
machine
learning
algorithms
machine
learning
algorithms
LBS
Data
AP +
LBS
Data
LBS
Data
AP +
LBS
Data
AP +
LBS
Data
LBS
Data
LBS
Data
 Journey Analysis
̶ Duration
̶ Distance
̶ Cost
̶ Congestion level
̶ Dwell time
̶ Walk time
̶ Number of connections in journey
̶ Etc.
 Transfer Time: Public vs. Private

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Home – Work/School Journey Pattern Identification

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Congestion heat maps

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Possible Routs

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Rout Analysis

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Rout Analysis

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Rout Analysis

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Rout Analysis

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Rout Analysis

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Summary
 Operators have huge amounts of data
 The challenge is to monetize it
 The Push strategy
− Learn the market needs
− Define and build the right solution
− Treat 3rd party as another customer we need to
understand and propose the right solution
− Accompany your partners – key to a long-term
success

35. Website
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www.cvidya.com
Your
Success
is Our
Business