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Developing Churn Models Using Data Mining Techniques and Social Network Analysis

Book Description

Churn prediction, recognition, and mitigation have become essential topics in various industries. As a means for forecasting and managing risk, further research in this field can greatly assist companies in making informed decisions based on future possible scenarios. Developing Churn Models Using Data Mining Techniques and Social Network Analysis provides an in-depth analysis of attrition modeling relevant to business planning and management. Through its insightful and detailed explanation of best practices, tools, and theory surrounding churn prediction and the integration of analytics tools, this publication is especially relevant to managers, data specialists, business analysts, academicians, and upper-level students.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Dedication
  5. Foreword by Gino Yu
  6. Foreword by Sachit Murthy
  7. Preface
    1. CHAPTER DESCRIPTIONS
  8. Introduction
  9. Chapter 1: Churn Problem in Everyday Business
    1. ABSTRACT
    2. 1.1 INTRODUCTION
    3. 1.2 WHAT IS CHURN AND HOW TO RECOGNIZE IT?
    4. 1.3 SOFT CHURN, SILENT BUSINESS KILLER
    5. 1.4 WHEN NUMBERS CAMOUFLAGE REAL BUSINESS PICTURE
    6. 1.5 HOW TO RECOGNIZE SPARKLE WHICH HAS POTENTIAL TO BECOME A FLAME?
    7. REFERENCES
    8. KEY TERMS AND DEFINITIONS
    9. ENDNOTES
  10. Chapter 2: Setting (Realistic) Business Aims
    1. ABSTRACT
    2. 2.1 INTRODUCTION
    3. 2.2 WHEN CHURN BECAME OBVIOUS IT IS TOO LATE FOR DATA MINING
    4. 2.3 100% CHURN REDUCTION IS IMPOSSIBLE TO ACHIEVE
    5. 2.4 WHEN IS THE APPROPRIATE TIME FOR APPLYING DATA MINING METHODS?
    6. 2.5 CHURN MONITORING AS A LONG TERM STRATEGY
    7. 2.6 SETTING EARLY WARNING CHURN SYSTEMS
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  11. Chapter 3: Data Mining Techniques for Churn Mitigation/Detection
    1. ABSTRACT
    2. 3.1 INTRODUCTION
    3. 3.2 WHICH IS THE BEST DATA MINING TECHNIQUE FOR CHURN DETECTION?
    4. 3.3 PUTTING IT ALL TOGETHER
    5. REFERENCES
    6. KEY TERMS AND DEFINITIONS
  12. Chapter 4: Social Network Analysis (SNA) for Churn Mitigation/Detection
    1. ABSTRACT
    2. 4.1 INTRODUCTION TO SOCIAL NETWORK ANALYSIS
    3. 4.2 SOCIAL NETWORK ANALYSIS (SNA) METHODS AND METRICS
    4. 4.4 APPLICATION OF SNA IN CHURN ANALYSIS
    5. 4.5 DATA MINING METHODS AND SNA: AN INTEGRATED APPROACH
    6. REFERENCES
    7. KEY TERMS AND DEFINITIONS
    8. ENDNOTES
  13. Chapter 5: Data Preparation and Churn Detection
    1. ABSTRACT
    2. 5.1 DATA PREPARATION FOR PREDICTIVE CHURN MODELING
    3. 5.2 DATA PREPARATION SPECIFICS FOR SURVIVAL ANALYSIS, FUZZY EXPERT SYSTEMS, AND OTHER METHODS RELATED TO CHURN MODELING
    4. REFERENCES
    5. KEY TERMS AND DEFINITIONS
  14. Chapter 6: Churn Analysis Using Selected Structured Analytic Techniques
    1. ABSTRACT
    2. 6.1 SIMPLE HYPOTHESIS
    3. 6.2 ACH (COMPETITIVE HYPOTHESES ANALYSIS)
    4. 6.3 METHOD 180 DEGREES
    5. 6.4 INDICATOR BASED METHODS
    6. 6.5 HOW TO USE STRUCTURED ANALYTIC TECHNIQUES IN CHURN ANALYSIS
    7. REFERENCES
    8. KEY TERMS AND DEFINITIONS
    9. ENDNOTES
  15. Chapter 7: Attribute Relevance Analysis
    1. ABSTRACT
    2. 7.1 INTRODUCTION AND BASIC CONCEPTS
    3. 7.2 BINOMIAL TARGET VARIABLE VERSUS MULTINOMIAL TARGET VARIABLE
    4. 7.3 ATTRIBUTE RELEVANCE ANALYSIS IN CASE OF BINOMIAL TARGET VARIABLE
    5. 7.4 MULTINOMIAL TARGET VARIABLE ATTRIBUTE RELEVANCE ANALYSIS
    6. 7.5 WHY DO WE NEED ATTRIBUTE RELEVANCE ANALYSIS?
    7. REFERENCES
    8. KEY TERMS AND DEFINITIONS
  16. Chapter 8: From Churn Models to Churn Solution
    1. ABSTRACT
    2. 8.1 INTRODUCTION
    3. 8.2 USING DATA MINING METHODS AS A PUZZLE FOR CHURN SOLUTION
    4. 8.3 FINDING OPTIMAL CHURN SOLUTION
    5. REFERENCES
    6. KEY TERMS AND DEFINITIONS
  17. Chapter 9: Measuring Predictive Power
    1. ABSTRACT
    2. 9.1 PRINCIPLE 80:20
    3. 9.2 ROC CHART
    4. 9.3 KOLMOGOROV-SMIRNOV CHART
    5. 9.4 STABILITY INDEX
    6. REFERENCES
    7. KEY TERMS AND DEFINITIONS
  18. Chapter 10: Churn Model Development, Monitoring, and Adjustment
    1. ABSTRACT
    2. 10.1 CHURN MODEL DEVELOPMENT
    3. 10.2 HOW MUCH MONITORING AND ADJUSTMENT IS ENOUGH?
    4. 10.3 “WHAT IF” ANALYSIS
    5. 10.4 HOW TO EFFICIENTLY FOLLOW BUSINESS STRATEGY
    6. REFERENCES
    7. KEY TERMS AND DEFINITIONS
  19. Chapter 11: Churn Case Studies
    1. ABSTRACT
    2. 11.1 INTRODUCTION
    3. 11.2 CASE STUDY 1: PREPARING FOR NEW COMPETITION IN THE RETAIL INDUSTRY
    4. 11.3 CASE STUDY 2: CHURN REDUCTION AND CUSTOM PRODUCT DEVELOPMENT IN TELECOMMUNICATION COMPANY
    5. REFERENCES
    6. KEY TERMS AND DEFINITIONS
  20. Conclusion
    1. REFERENCES
  21. Appendix
    1. SOFTWARE TOOLS AND APPLICATIONS FOR DATA MINING AND CHURN MANAGEMENT
    2. ENDNOTES
  22. Compilation of References
  23. About the Authors