Telcoflutura

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Basics and future of telecom analytics

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  • Why we had the second UAT. Impression of ICTEAS not being serious, cause we should be able to generate the right report, right accountability.
  • Why we had the second UAT. Impression of ICTEAS not being serious, cause we should be able to generate the right report, right accountability.
  • Why we had the second UAT. Impression of ICTEAS not being serious, cause we should be able to generate the right report, right accountability.
  • Why we had the second UAT. Impression of ICTEAS not being serious, cause we should be able to generate the right report, right accountability.
  • Telcoflutura

    1. 1. Vineeth Menon
    2. 2.  Opportunities  Focus Areas  Factors binding focus areas and opportunities  Background  Current Situation and system Architecture  Issues of Fraud  Churn the big question and its focus Vineeth Menon
    3. 3.  Enterprise Performance  Revenue  Optimization  Predictive Analytics  Call Center Analytics  Customer Experience Analytics  Intelligent Campaigns •Master Data Management •Information Rationalization Lean predictive analysis Customer Analytics Service Enablement Analytics Data Analytics Opportunities Vineeth Menon
    4. 4. What is the most appropriate network architecture? What is the network efficiency / cost of ownership / individual customer experience? How can I identify lost revenue / minimise cost of failure? How can I identify and effectively target customer segments? How can I reduce time-to-market of new promotions? How can I measure the efficiency of my campaigns? How are we doing? What should we be doing? How are we comparing with others? What should we measure? Who should view it and how often? How can I offer a consistent customer service across channels? How can I get a consolidated, consistent, accurate and updated view of my customers to understand their behaviours and profitability with trust? How customers am I losing in this quarter? How to retain customers? What were the behaviour and requirements of lost customers? Network analytics Enterprise Performance Management Single View of Customer Intelligent Campaigns Churn & Retention
    5. 5. • Advanced Analytics for Loyalty, Churn Management, and Social Network Analysis. • Single and Complete Customer View • Intelligent Campaigns provides the best marketing expenditure. • Enterprise Performance Management • Network Analytics formulates observations and derived insight from network traffic information and component utilisation • Manage churn and drive customer loyalty and Improve retention • Differentiate campaigns • Predict business outcomes and manage trends as they evolve. • Enhance your revenue • Optimise customer experience and consistent experience • Understand customer usage patterns and behavioural tendencies • Manage network resources and investment costs, insight to ROI on CAPEX,OPEX investment • Plan for the future to support & maintain subscriber services • Optimise service portfolio, service experience, network investment ,managing frauds Helps CSPsFocus Areas Vineeth Menon
    6. 6. INDUSTRY AT A GLANCE Scams Loss of customers Financial losses
    7. 7. Large scale data in Mobile Operator Firm  Subscribers: 500 million  Subscribers’ CDR(calling data record) data  5~8TB/day in CMCC  For a branch company (> 20 million subscribers)  Voice: 100million* 1KB = 100GB/day  SMS: 100~200 million * 1KB = 100~200GB/day  Network signaling data, for a branch company (> 20 million subscribers)  GPRS signaling data: 48GB/day for a branch companies  3G signaling data: 300GB/day for a branch companies  voice, SMS signaling data, …… Vineeth Menon
    8. 8. • Promotions based only on their network usage • Network management in day to day with lesser future analysis • Use only active call switch for triggering promotions • No way of analyzing and processing high volume CDR records • No efficient churn analyzing method • No access to historical data • Complex access rules not supportive Vineeth Menon
    9. 9. Vineeth Menon
    10. 10. Service Provider:- Knowledge, Experience, Capabilities System Components Clients & Vendors Prior Capabilities •Billing & Mediation •OSS •Prepaid IN •Core Networks: •2G/3G infrastructure. HLR, MSC, EIR, GGSN, SGSN •Messaging Platforms: •SMSC, VMSC •Signaling network •Interconnect •Radio Networks •Vodafone •Etisalat •Du Telecom •Nokia •Ericsson •Nortel •Comverse •Airtel •Idea •Systems Integration •Data Modeling •Project Management •Technology Delivery •Business Intelligence •Network Capacity Planning •Network Optimization •Network Management •Pricing •Finance (Budget planning) •Product Marketing •CRM •Network Operations •Call Centre tech. ops Vineeth Menon Service Provider Perspective
    11. 11. Vineeth Menon KEY AREAS of present day Telecom analytics Fraud Management Churn Prediction Service assurance
    12. 12. Detecting Subscriber Fraud . . .  High number of calls to Black Listed numbers  High Roaming charges  High Internet Usages  High number of VAS calls  Frequent Change of Address • Pre-Subscription Check: • Verify address and home number • Set Credit Limits • Check PAN number, UID against Credit Violations • Check IMEI against Black Listed IMEI • Check for matching names with black listed customers. • Check for matching PIN codes. • Check for addresses from notorious localities. • Match subscriber usage profile with black listed subscribers :  Called numbers  Matching tower locations  Calling patterns (short calls, long calls) Vineeth Menon
    13. 13.  Detecting Recharge Voucher Fraud . . . • Unusual top-ups • High number of recharges in a given time-period  Detecting Pre-paid Balance Fraud . . . • Track employees with high number of manual balance change • Subscribers with high balances Vineeth Menon
    14. 14. Vineeth Menon
    15. 15.  Detecting Unauthorized Service Fraud . . . • HLR vs. Postpaid subscriber profile reconciliation • HLR services vs Postpaid Subscriber services • Profile mismatch • Sudden change in Subscriber usages (??)  Detecting SIM Cloning . . . • Velocity Check • Call Collision Vineeth Menon
    16. 16. Vineeth Menon
    17. 17. Churn prediction In telecom analytics. . Case:- The CEO of Mobtel which is having 12 million customer base , has come to Analytics Inc. with a problem.  Over the last two years after Mobile number portability was introduced, about 20 million subscribers has become inactive or has left Mobtel( post-paid users initially ). Vineeth Menon
    18. 18. Churn prediction is currently a relevant subject in data mining and has been applied in the field of banking, mobile telecommunication , life insurances and others. In fact , all companies who are dealing with long term customers can take advantage of churn predict ion methods. Models such as:- Are common choices of data miners to tackle this churn prediction problem . Vineeth Menon Neural Networks Logical regression Decision trees Model
    19. 19. Vineeth Menon References:- • IBM Telco BAE • Churn Management : by Customer tele-care Series • www.telecomanalytics.com
    20. 20. Vineeth Menon

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