Applying Data Mining to Customer Churn Prediction in an Internet Service Provider
نویسندگان
چکیده
A business incurs much higher charges when attempting to win new customers than to retain existing ones. As a result, much research has been invested into new ways of identifying those customers who have a high risk of churning. However, customer retention efforts have also been costing organizations large amounts of resources. Same is the situation in ISP industry in I.R.Iran. Exploiting the use of demographic, billing and usage data, this study tends to identify the best churn predictors on the one hand and evaluates the accuracy of different data mining techniques on the other. Clustering users as per their usage features and incorporating that cluster membership information in classification models is another aspect which has been addressed in this study
منابع مشابه
Hierarchical Alpha-cut Fuzzy C-means, Fuzzy ARTMAP and Cox Regression Model for Customer Churn Prediction
As customers are the main asset of any organization, customer churn management is becoming a major task for organizations to retain their valuable customers. In the previous studies, the applicability and efficiency of hierarchical data mining techniques for churn prediction by combining two or more techniques have been proved to provide better performances than many single techniques over a nu...
متن کاملTurning telecommunications call details to churn prediction: a data mining approach
As deregulation, stew technologies, and new competitors open up the mobile telecommunications industry, churn prediction and management has become of great concern to mobile service providers: A mobile service provider wishing to retain its subscribers needs to be able to predict which of them may be at-risk of changing services and will make those subscribers the focus of customer retention ef...
متن کاملApplying Data Mining to Telecom Churn Management
Taiwan deregulated its wireless telecommunication services in 1997. Fierce competition followed, and churn management becomes a major focus of mobile operators to retain subscribers via satisfying their needs under resource constraints. One of the challenges is churner prediction. Through empirical evaluation, this study compares various data mining techniques that can assign a “propensity-to-c...
متن کاملCustomer Behavior Mining Framework (CBMF) using clustering and classification techniques
The present study proposes a Customer Behavior Mining Framework on the basis of data mining techniques in a telecom company. This framework takes into account the customers’ behavior patterns and predicts the way they may act in the future. Firstly, clustering technique is used to implement portfolio analysis and previous customers are divided based on socio-demographic features using k</em...
متن کاملCluster & Rough Set Theory Based Approach to Find the Reason for Customer Churn
Data mining is the nontrivial process of extraction of interesting, implicit, potentially and previously unknown knowledge from large databases. There are many techniques used in data mining like: Statistical Analysis, Decision Tree, Neural Network, Clustering, Association Rule, Genetic Algorithms, Fuzzy Logic, and Rough Sets. Rough Set theory (RST), is a technique for dealing with uncertainty ...
متن کامل