Basics and future of telecom analytics

1. Vineeth Menon

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.  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. 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. • 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. INDUSTRY AT A
GLANCE
Scams
Loss of customers
Financial losses

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. • 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. Vineeth Menon

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. Vineeth Menon
KEY AREAS of present
day Telecom analytics
Fraud Management
Churn Prediction
Service assurance

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.  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. Vineeth Menon

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. Vineeth Menon

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. 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. Vineeth Menon
References:-
• IBM Telco BAE
• Churn Management : by Customer tele-care Series
• www.telecomanalytics.com

20. Vineeth Menon