big data monetization - the path from internal to external
TRANSCRIPT
© 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 [email protected]
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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 ”
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Reduce operational costs Increase revenues, launch
Decline in traditional service revenues (Voice, SMS)
Unlimited Price Plans
Increasing competition Consolidations & mergers Global financial recession
Real-time Self-Service Data
Monetization
New services /products: “ Internet of Things ”
Data Explosion
New Billing Schemes
Telecom Market Trends
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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
− ......
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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
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External Monetization - Push or Pull ?
End Subscriber Added value
Third Party Use cases Customer engagement
Operator Data Platform
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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
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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
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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
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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
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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
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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
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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
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3rd Party Portal
Intuitive UI
Analysis Capabilities
Required attributes
Reporting capabilities
APIs
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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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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
Website RA Blog www.ra-blog.org
www.cvidya.com
Your Success is Our Business