Hybrid Models Using Unsupervised Clustering for Prediction of Customer Churn

Hybrid Models Using Unsupervised Clustering for Prediction of Customer Churn

In this paper, we use two-stage hybrid models consisting of unsupervised clustering techniques and decision trees with boosting on two different data sets and evaluate the models in terms of top decile lift. We examine two different approaches for hybridization of the models for utilizing the results of clustering based on various attributes related to service usage and revenue contribution of ...

Customer churn prediction by hybrid neural networks

Customer churn prediction using a hybrid genetic programming approach

A churn consumer can be defined as a customer who transfers from one service provider to another service provider. Recently, business operators have investigated many techniques that identify the customer churn since churn rates leads to serious business loss. In this paper, a hybrid technique has been used which combines K-means clustering with Genetic Programming to predict churners in teleco...

ADTreesLogit model for customer churn prediction

In this paper, we propose ADTreesLogit, a model that integrates the advantage of ADTrees model and the logistic regression model, to improve the predictive accuracy and interpretability of existing churn prediction models. We show that the overall predictive accuracy of ADTreesLogit model compares favorably with that of TreeNet®, a model which won the Gold Prize in the 2003 mobile customer chur...

Review on Prediction of Churn Customer Behavior