Data Mining: Churn Management and Client Retention in Telecommunications. Lori Caton Clearnet, Business Intelligence

Transcription

1 Data Mining: Churn Management and Client Retention in Telecommunications Lori Caton Clearnet, Business Intelligence

2 Agenda Company Business Issue: Churn Addressing the Issue Moving Forward

3 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

4 Our Wireless Scope

5 Future Friendly Innovations 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!

6 Churn Switching from one carrier to another Completely discontinuing wireless service Both represent a lost subscriber which is costly to carrier

7 What we suspect... Cost Reception Need Equipment Client Service Source: Strategis Group Inc, 1999 & Clearnet Market Research

8 Create Value A multi-stage process to facilitate decision making while developing new products, services, and processes at Clearnet Gate 1 Gate 2 Gate 3 Gate 4 Gate 5 Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 CONCEPT (PROPOSAL) ASSESSMENT (Business case) DESIGN (Integrated Specification Document) DEVELOP & IMPLEMENT (Launch Checklist) 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 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 Pilot Project - 2 vendors On site Knowledge Transfer Evaluate Scored list against churned clients to measure success What is the idea? Why should we do it? How do we do it? Build it. How did we do?

9 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?

10 Concept Assessment Design Develop & Implement Rollout Assessment REQUIREMENT Weight 1. Telco Data Mining Experience Vendor Viability Financials 2.2 Maturity 2.3 Employee Turnover 2.4 Installed Base 2.5 Market Share 3. Service and Support 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 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 Platform Availability 5.2 Architecture 5.3 Maturity of Design and Implementation 5.4 Scalability Vendor 1 RFP Vendor 2 Vendor 3 Vendor 4 Pilot Project Vendor 1 Pilot Project Vendor 2 Vendor 5

11 Concept Assessment Design Develop & Implement Rollout Working together Who? What? How? Corporate Competitive Advantage (External View) Technology Perspective (Internal) Business Unit (Internal)

12 Concept Assessment Design Develop & Implement Rollout 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

13 Concept Assessment Design Develop & Implement Rollout Process Overview Define Business Problem Evaluate Environment Make Data Available Review Mine Data in Cycles Explore Sample Modify Implement in Production Assess Model

14 Concept Assessment Design Develop & Implement Rollout 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

15 Decision Trees provide a convenient method for checking the validity of the data mining question being examined Results Concept Assessment Design Develop & Implement Rollout Churn Proportion Exceeded pilot benchmarks Good lift Actionable scored list Further refinement needed for production Further modeling to add data from wish list Captured Lift Cutoff Probability

16 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

17 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)

18 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)

19 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

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