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Basics and future of telecom analytics
Citation preview
Vineeth Menon
Data Analytics in to Telecom
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
Agenda
Enterprise Performance
Revenue Optimization
Predictive Analytics
Call Center Analytics
Customer Experience Analytics
Intelligent Campaigns
• Master Data Management
• Information Rationalization
Lean predictive analysis
CustomerAnalytics
Service EnablementAnalytics
Data Analytics Opportunities
Vineeth Menon
Focus Areas
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
• 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 CSPs Focus Areas
Vineeth Menon
INDUSTRY AT A GLANCE
Scams
Loss of customers
Financial losses
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
Back ground
• 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
Current Situation
Vineeth Menon
GSM architecture
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
Vineeth Menon
KEY AREAS of present day Telecom analytics
Fraud Management
Churn Prediction
Service assurance
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
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
Vineeth Menon
Fraud Management and
Risk analysis
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
Vineeth Menon
Churn Prediction
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
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
Vineeth Menon
References:-
• IBM Telco BAE
• Churn Management : by Customer tele-care Series
• www.telecomanalytics.com
Vineeth Menon
Wishing U Luck
Thank You