Cengage Learning

Developing Churn Models Using Data Mining Techniques and Social Network Analysis, 1st Edition

  • Goran Klepac
  • Published By:
  • ISBN-10: 1466662891
  • ISBN-13: 9781466662896
  • DDC: 658.8
  • Grade Level Range: College Freshman - College Senior
  • 361 Pages | eBook
  • Original Copyright 2015 | Published/Released December 2015
  • This publication's content originally published in print form: 2015

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About

Overview

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

Front Cover.
Title Page.
Copyright Page.
Dedication.
Table of Contents.
Foreword by Gino Yu.
Foreword by Sachit Murthy.
Preface.
Introduction.
1: Churn Problem in Everyday Business.
2: Setting (Realistic) Business Aims.
3: Data Mining Techniques for Churn Mitigation/Detection: Intrinsic Attributes Approach.
4: Social Network Analysis (SNA) for Churn Mitigation/Detection: Introduction and Metrics.
5: Data Preparation and Churn Detection.
6: Churn Analysis Using Selected Structured Analytic Techniques.
7: Attribute Relevance Analysis.
8: From Churn Models to Churn Solution.
9: Measuring Predictive Power.
10: Churn Model Development, Monitoring, and Adjustment.
11: Churn Case Studies.
Conclusion.
Appendix.
Compilation of References.
About the Authors.
Index.