Behavior-Based Telecommunication Churn Prediction with Neural Network Approach

Abstract

A behavior-based telecom customer churn prediction system is presented in this paper. Unlike conventional churn prediction methods, which use customer demographics, contractual data, customer service logs, call-details, complaint data, bill and payment as inputs and churn as target output, only customer service usage information is included in this system to predict customer churn using a clustering algorithm. It can solve the problems which traditional methods have to face, such as missing or non-reliable data and the correlation among inputs. This study provides a new way to solve traditional churn prediction problems.