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, ……

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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

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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)

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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 ).

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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