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Big Data Monetization - The Path From Internal to External

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"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

Published in: Business

Big Data Monetization - The Path From Internal to External

  1. 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. 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. 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. 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. 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. 6 External Monetization - Push or Pull ? End Subscriber  Added value Third Party  Use cases  Customer engagement Operator  Data  Platform
  7. 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. 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. 9. Use Case Example 9
  10. 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. 11 Examples for External Monetization Use Cases
  12. 12. 12 Targeting the Right Use Case  Geography  Regulation  Maturity  Market  Need  Value 12
  13. 13. Happening in the Industry 13
  14. 14. 14 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. 15 Verizon Location and Profiling
  16. 16. 16 Orange France Application for Business
  17. 17. Use Case Definition and Execution Example
  18. 18. 18 Defining Use Cases  Need  Customer  Value  Maturity  Privacy & Regulatory
  19. 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. 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. 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
  22. 22. 22 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
  23. 23. 23 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. 24 3rd Party Portal  Intuitive UI  Analysis Capabilities  Required attributes  Reporting capabilities  APIs
  25. 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
  26. 26. 26 Home – Work/School Journey Pattern Identification
  27. 27. 27 Congestion heat maps
  28. 28. 28 Possible Routs
  29. 29. 29 Rout Analysis
  30. 30. 30 Rout Analysis
  31. 31. 31 Rout Analysis
  32. 32. 32 Rout Analysis
  33. 33. 33 Rout Analysis
  34. 34. 34 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. 35. Website RA Blog www.ra-blog.org www.cvidya.com Your Success is Our Business

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