Download Data Mining: Churn Management and Client Retention in Telecommunications

Data Mining:
Churn Management
and Client Retention
in Telecommunications
Lori Caton
Clearnet, Business Intelligence
[email protected]
Agenda
Company
Business Issue: Churn
Addressing the Issue
Moving Forward
Clearnet Background
Largest wireless phone spectrum position in Canada
45 MHz of mobile wireless spectrum
Only national CDMA carrier, only national iDEN carrier
Only company with both Mike and Clearnet PCS synergies
612K total subscribers at Dec-99
C$1.7 billion in total assets
Leads wireless industry in network revenue growth
Our Wireless Scope
Future Friendly Innovations
è Clearnet was the first company to:
Do away with long-term agreements
Introduce per-second billing
Include the extras: voice mail, caller id and call
waiting
Offer Free Local Calling on your Birthday FOREVER!
Churn
Switching from one carrier to another
Completely discontinuing wireless service
Both represent a lost subscriber
which is costly to carrier
What we suspect...
Cost
Reception
Need
Equipment
Client Service
Source: Strategis Group Inc, 1999 & Clearnet Market Research
Create Value
A multi-stage process to facilitate decision making while developing new products, services, and
processes at Clearnet
Gate 1
Phase 1
Gate 2
Phase 2
CONCEPT
ASSESSMENT
(PROPOSAL)
(Business case)
Gate 3
Phase 3
DESIGN
(Integrated Specification
Document)
Gate 4
Phase 4
DEVELOP &
IMPLEMENT
(Launch Checklist)
Gate 5
Phase 5
ROLLOUT
(Post Launch
Evaluation)
Steps
• Data Mining Tools
will allow
exploration of
corporate data to
identify key patterns
• Analyzing churn and
its drivers through
data mining
What is the idea?
Cash flow analysis
Predict x% of churn by
targeting y% of
database - Net
Present Value
Data mining tool
Business Issue
Data definition &
Preparation
Predictive Modeling
Model Evaluation
Analysis
Action
Why should we do it? How do we do it?
Pilot Project 2 vendors
On site Knowledge
Transfer
Evaluate Scored list
against churned
clients to measure
success
Build it.
How did we do?
Concept Assessment
Design
Develop &
Implement
Rollout
Concept
To investigate and determine an optimal churn
solution for Clearnet
Advances in software and computing power have made it
possible to tackle this issue in an analytic and proactive
manner
To understand a core business issue - Churn
• How do we reduce churn?
• What are the factors that affect churn?
• Who is most likely to churn?
Concept Assessment
Design
Develop &
Implement
Assessment
REQUIREMENT
1. Telco Data Mining Experience
2. Vendor Viability
RFP
Weight
35
5
2.1 Financials
2.2 Maturity
2.3 Employee Turnover
2.4 Installed Base
2.5 Market Share
3. Service and Support
Vendor 1
Pilot
Project
Vendor 1
15
4.1 Usability
4.2 Analysis and Visualization
4.3 Flexibility and Breadth and Depth
4.4 Ease of Integration
4.5 Customization and Imbedded Applications
5. Technology
5.1 Platform Availability
5.2 Architecture
5.3 Maturity of Design and Implementation
5.4 Scalability
Vendor 3
Vendor 4
20
3.1 Training
3.2 Hotline and On-site support
3.3 Professional Services
3.4 Partnerships
3.5 Canadian Presence
3.6 Reference Accounts
4. Features & Functionality
Vendor 2
25
Pilot
Project
Vendor 2
Vendor 5
Rollout
Concept Assessment
Working together
ü Who?
ü What?
ü How?
Corporate
Competitive
Advantage
(External View)
Business Unit
(Internal)
Technology
Perspective
(Internal)
Design
Develop &
Implement
Rollout
Concept Assessment
Design
Develop &
Implement
Objectives
To build a foundation for the development of a churn data
mining environment
A predictive model for Churn that identifies x% of churn by
targeting y% of database
To understand the key drivers of churn
Actionable scored list of subscribers with associated
probability of churning
Data mining knowledge transfer
Generate opportunities for additional modeling
Arm decision makers with the knowledge needed to create
programs & fine tune services
Rollout
Concept Assessment
Process Overview
Define Business
Problem
Evaluate
Environment
Make Data
Available
Mine Data in Cycles
Review
Explore
Sample
Implement in
Production
Assess
Modify
Model
Design
Develop &
Implement
Rollout
Concept Assessment
Design
Develop &
Implement
Along the way
DATA! DATA! DATA!
Client Learning
•
•
•
•
•
Price sensitive relationship seekers
Features & Services
Equipment changes related to specific models
Tenure
Billing/Payments/Credits
Churn prediction resembles non-linear behavior
Predictive Power of derived variables
Rollout
Concept Assessment
Decision Trees provide a
convenient method for
checking the validity of the data
mining question being examined
Churn Proportion
Design
Develop &
Implement
Results
Exceeded pilot benchmarks
Good lift
Actionable scored list
Further refinement needed for production
Further modeling to add data from wish list
Cutoff Probability
Captured Lift
Rollout
Key Learnings
Churn is a very complex issue
Critical nature of appropriate data structures for data mining
Results of analysis make business sense
The Basics are key
•
•
•
•
•
provide value for money
end-to-end service
coverage and capacity
quality of the network (Voice Mail, features)
extensive use of analytics to understand patterns of client behavior
What’s Driving the Canadian
Wireless Industry?
Wireless Internet/Intranet
Landline substitution - a way of life
Local number portability / calling party pays
Location services
Multitude of devices concentrated in a single
device (voice/data/Direct Connect)
Pre-paid package pressure (revenue)
Price (no barrier)
International Churn Reduction Tactics
Monthly High Volume Usage Awards (Peoples China)
Pilot Handset Club (1010 Hong Kong)
Same network calling discounts (Cellnet UK, Telus Mobility Canada)
Call/Network analyzer software (Rogers AT&T Canada, Orange UK,
T-Mobile Germany, Telsim Turkey, Era GSM Poland)
Competitive Tariff Matching (Orange UK)
Customize your off peak (Omnitel Italy)
Network Performance Guarantee (UK carriers, 1010 Hong Kong)
VIP Club (Proximus Belgium, SK Telecom Korea
One # Discounts (Omnitel Italy)
Moving Forward
Profile of Corporate Data warehouse
Modify corporate data warehouse and develop
datamart with appropriate framework for
churn modeling
Expanded opportunities for data mining
Continuously driving towards intelligent CRM
Organizational changes to support outcomes
www.clearnet.com