Data Mining for Churn Prediction: Multiple Regressions Approach

Awang, Mohd Khalid and Abd Rahman, Mohd Nordin and Ismail, Mohammad Ridwan (2012) Data Mining for Churn Prediction: Multiple Regressions Approach. In: Computer Applications for Database, Education, and Ubiquitous Computing. Communications in Computer and Information Science, 352 . Springer Verlag, Germany, pp. 318-324. ISBN 1865-0929

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The rapid development in the telecommunications industry contributed to the increased rivalry among the competitors. Customers switch to competitors or move out from the service provider become critical concerns for companies to retain customer loyalty. Churn prevention through churn prediction is one of the methods to ensure customer loyalty with the service provider. Detect and analyze early churn is a proactive step to ensure that existing customers did not move out or subscribe to the product from competitors. Selection of customer characteristics is one of the core issues to forecast customer churn in the telecommunications industry. This paper proposes multiple regressions analysis to predict the customers churn in the telecommunications industry based on recommended features. The results have shown that the performance of multiple regressions for predicting customer churn is acceptably good.

Item Type: Book Section
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Institute: Faculty of Informatics & Computing
Depositing User: Dr Mohd Nordin Abdul Rahman
Date Deposited: 13 Jan 2015 08:21
Last Modified: 13 Jan 2015 08:21

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