Customer Churn Analysis in the Telecommunication Industry Essay

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Abstract— Customer churn is a business term used to describe the loss of customer. It describes those customers or clients who leave or switch to competitors. In the telecommunication industry, customers have multiple choices of services and they frequently switch from one service to another. In this competitive market, customers demand best products and services at low prices, while service providers constantly focus on getting hold of as their business goals. So that’s why there is very higher rate of customer churn in telecommunications industry experiences an average of 30-35% annual churn rate. The purpose of this paper is to propose an efficient Customer Churn Prediction Model based on classification techniques, which will help the telecommunication company to predict the customer churn rate to know about which customers are loyal to them.
Data mining is very famous technique for churn prediction and it is used in many fields. Data mining refers to the process of analyzing data in order to determine patterns and their relationships. It is an advanced technique which goes deep into data and uses machine learning algorithms to automatically shift through each record and variable to uncover the patterns and information that may have been hidden. There is a lot of work done in data mining for churn prediction in different fields. It is used to solve the customer churn problem by identifying the customer behavior from large number of customer data.

Problem Statement:
Customer churn refers to the periodic loss of customers in an organization. Customer churn is a very common problem of every organization all over the world. In the competitive market it’s a very big challenge for any organization to retai...

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...iningMart: Proceedings of the workshop on data mining and …, 2005 – Citeseer
[16] Mozer M. C., Wolniewicz R., Grimes D.B., Johnson E., Kaushansky H. Predicting Subscriber Dissatisfaction and Improving Retention in the Wireless Telecommunication Industry. IEEE Transactions on Neural Networks, Special issue on Data Mining and Knowledge Representation (2000).
[17] Mutanen,Teemu. Customer churn analysis- a case study, Research Report VTTR0118406, March 15, 2006.
[18] De Oliveira, J.V., Pedrycz W. (editors) (2007) Advances in Fuzzy Clustering and its Applications, John Wiley & Sons Ltd.
[19] J. Hadden, A. Tiwari, R. Roy, and D. Ruta. Churn Prediction using Complaints Data. International Journal of Intelligent Technology, 13:158{163, May 2006.
[20] H. Van Khuu, H.-KieLee, and J.-Liang Tsai. “Machine learning with neural networks and support vector machines”, 2005.

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