A Hybrid Data Mining Method for Customer Churn Prediction

Data Mining Techniques in Customer Churn Prediction

Customer churn prediction is one of the most important problems in customer relationship management (CRM). Its aim is to retain valuable customers to maximize the profit of a company. To predict whether a customer will be a churner or non-churner, there are a number of data mining techniques applied for churn prediction, such as artificial neural networks, decision trees, and support vector mac...

Customer churn prediction by hybrid neural networks

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 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...

Modelling Customer Attraction Prediction in Customer Relation Management using Decision Tree: A Data Mining Approach

In Today’s quality- based competitive world, known as knowledge age, customer attraction is of ultimate importance. In respect to the slogan “customer is always right”, customer relation management is the core of an organizational strategy playing an important role in four aspects of customer identification, customer attraction, customer retaining, and customer satisfaction. Commercial organiza...