Automated Cellular Modeling and Prediction on a Large Scale

Abstract

We describe CHAMP (CHurn Analysis, Modeling, and Prediction), an automated system for modeling cellular subscriber churn that is predicting which customers will discontinue cellular phone service. We describe various issues related to developing and deploying this system including automating data access from a remote data warehouse, preprocessing, feature selection, model validation, and optimization to reflect business tradeoffs. Using data from GTE's data warehouse for cellular phone customers, CHAMP is capable of developing churn models customized by region for over one hundred GTE cellular phone markets totaling over 5 million customers. Every month churn factors are identified for each geographic region and models are updated to generate churn scores predicting who is likely to churn in the short term. Learning methods such as decision trees and genetic algorithms are used for feature selection and a cascade neural network is used for predicting churn scores. In addition to producing churn scores, CHAMP also produces qualitative results in the form of rules and comparison of market trends that are disseminated through a web based interface.