Enhanced Prediction Model for Customer Churn in Telecommunication Using EMOTE
Customer churn is the term that refers to the customers who are in threat to leave the company. A growing number of such customers are becoming critical for the telecommunication sector, and the telecom sector is also in situation to retain them to avoid
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Abstract Customer churn is the term that refers to the customers who are in threat to leave the company. A growing number of such customers are becoming critical for the telecommunication sector, and the telecom sector is also in situation to retain them to avoid the revenue loss. Prediction of such behavior is very essential for the telecom sector, and classifiers proved to be the most effective one for the same. A well-balanced data set is a vital resource for the classifiers to yield the best prediction. All existing classifiers tend to perform poorly on imbalanced data set. An imbalanced data set is the one, where the classification attribute is not evenly distributed. Like the other real-time applications, the telecommunication churn application also has the class imbalance problem. So it is extremely vital to go for fine-balanced data set for classification. In this paper, an empirical method enhanced classifier for telecommunication churn analysis model (EC_for_TELECAM) using enhanced minority oversampling technique (EMOTE) has been proposed to improve the performance of the classifier for customer churn analysis in telecom data set. To evaluate the proposed method, experiments were done with various data sets. The experimental study shows that the proposed method is able to produce well-balanced data set to improve the performance of the classifier and to produce the best prediction model.
Keywords Telecommunication Customer churn Imbalanced data set Oversampling
Classifier
1 Introduction The telecom services are accepted all over the world as an important source of socioeconomic growth for a nation. Particularly, the Indian telecom has attained a phenomenal growth during the last few years and is expected to take a positive growth in the future also. This rapid growth is possible due to the different positive S. Babu (&) N.R. Ananthanarayanan SCSVMV University, Kanchipuram, Tamil Nadu, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2018 S.S. Dash et al. (eds.), International Conference on Intelligent Computing and Applications, Advances in Intelligent Systems and Computing 632, https://doi.org/10.1007/978-981-10-5520-1_43
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and proactive decisions of the government and the contribution of both by the private sector and the public. Due to the increasing competition, telecom sectors are facing the issue of customer churn. Customer churn is the term that refers to the customers who are in risk of leaving the company. Churn is a very critical issue in telecom because of its association with loss of revenue and the high cost of attracting the new customers. Prediction of such behavior is very essential for the telecom sector, and classifiers proved to be the effective one for the same. Classification is widely accepted data mining technique for mining the data and predict about the future. By building the pertinent classifier, it is able to predict well about which class the new instance is [1]. In general, classifiers presume that the data se
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