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Data Mining:Churn Managementand Client Retention
in Telecommunications
Lori CatonClearnet, Business Intelligence
Agenda
Company
Business Issue: Churn
Addressing the Issue
Moving Forward
Clearnet Background
Largest wireless phone spectrum position in Canada45 MHz of mobile wireless spectrumOnly national CDMA carrier, only national iDEN carrierOnly company with both Mike and Clearnet PCS synergies612K total subscribers at Dec-99C$1.7 billion in total assetsLeads wireless industry in network revenue growth
Our Wireless Scope
Future Friendly Innovations
è Clearnet was the first company to:Do away with long-term agreementsIntroduce per-second billingInclude the extras: voice mail, caller id and call
waitingOffer Free Local Calling on your Birthday -
FOREVER!
Churn
Switching from one carrier to another
Completely discontinuing wireless service
Both represent a lost subscriberwhich is costly to carrier
What we suspect...
CostReceptionNeedEquipmentClient Service
Source: Strategis Group Inc, 1999 & Clearnet Market Research
Create ValueA multi-stage process to facilitate decision making while developing new products, services, andprocesses at Clearnet
Gate 1
Steps• Data Mining Tools
will allowexploration ofcorporate data toidentify key patterns
• Analyzing churn andits drivers throughdata mining
What is the idea?
Phase 1
CONCEPT(PROPOSAL)
Gate 2
Cash flow analysisPredict x% of churn by
targeting y% ofdatabase - NetPresent Value
Why should we do it?
Phase 2
ASSESSMENT(Business case)
Gate 3
Data mining toolBusiness IssueData definition &
PreparationPredictive ModelingModel EvaluationAnalysisAction
How do we do it?
Phase 3
DESIGN(Integrated Specification
Document)
Gate 4
Pilot Project - 2 vendorsOn site Knowledge
Transfer
Build it.
Phase 4
DEVELOP &IMPLEMENT
(Launch Checklist)
Gate 5
Evaluate Scored listagainst churnedclients to measuresuccess
How did we do?
Phase 5
ROLLOUT(Post LaunchEvaluation)
Concept
To investigate and determine an optimal churnsolution for Clearnet
Advances in software and computing power have made itpossible to tackle this issue in an analytic and proactivemanner
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?
Design RolloutConcept Develop &ImplementAssessment
REQUIREMENT Weight1. Telco Data Mining Experience 352. Vendor Viability 5
2.1 Financials2.2 Maturity2.3 Employee Turnover2.4 Installed Base2.5 Market Share
3. Service and Support 203.1 Training3.2 Hotline and On-site support3.3 Professional Services3.4 Partnerships3.5 Canadian Presence3.6 Reference Accounts
4. Features & Functionality 154.1 Usability4.2 Analysis and Visualization4.3 Flexibility and Breadth and Depth4.4 Ease of Integration4.5 Customization and Imbedded Applications
5. Technology 255.1 Platform Availability5.2 Architecture5.3 Maturity of Design and Implementation5.4 Scalability
Assessment
Vendor 1
RFP
Vendor 2 Vendor 3 Vendor 4
Pilot Project
Vendor 1
Pilot Project
Vendor 2
Vendor 5
Design RolloutConcept Develop &ImplementAssessment
Working together
Business Unit(Internal)
TechnologyPerspective
(Internal)
ü Who?ü What?ü How?
Design RolloutConcept Develop &ImplementAssessment
CorporateCompetitiveAdvantage
(External View)
ObjectivesDesign RolloutConcept Develop &
ImplementAssessment
To build a foundation for the development of a churn datamining environment
A predictive model for Churn that identifies x% of churn bytargeting y% of database
To understand the key drivers of churnActionable scored list of subscribers with associated
probability of churningData mining knowledge transferGenerate opportunities for additional modelingArm decision makers with the knowledge needed to create
programs & fine tune services
Process Overview
Define BusinessProblem
EvaluateEnvironment
Implement inProduction
Review
Mine Data in Cycles
Assess
Sample Modify
Explore
Model
Make DataAvailable
Design RolloutConcept Develop &ImplementAssessment
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 behaviorPredictive Power of derived variables
Design RolloutConcept Develop &ImplementAssessment
Exceeded pilot benchmarks Good lift Actionable scored list Further refinement needed for production Further modeling to add data from wish list
Results
Churn Proportion
Captured Lift
Decision Trees provide aconvenient method forchecking the validity of the datamining question being examined
Cutoff Probability
Design RolloutConcept Develop &ImplementAssessment
Key Learnings
Churn is a very complex issueCritical nature of appropriate data structures for data miningResults of analysis make business senseThe 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 CanadianWireless Industry?
Wireless Internet/IntranetLandline substitution - a way of lifeLocal number portability / calling party paysLocation servicesMultitude 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 KoreaOne # Discounts (Omnitel Italy)
Moving Forward
Profile of Corporate Data warehouseModify corporate data warehouse and develop
datamart with appropriate framework forchurn modeling
Expanded opportunities for data mining
Continuously driving towards intelligent CRM
Organizational changes to support outcomes
www.clearnet.com
