Yuanhu Gu
ABSTRACT
Nowadays, telecommunication markets are becoming more and more competitive, and customer churn is becoming more and more serious. In the tough competitive mobile market, Customer Churn Management is becoming more and more critical. In developing countries, most customers switch service providers because of good promotional incentives and lower monthly costs offered by competitive service providers. How to predict customer churn quickly and accurately becomes very important. In this paper, the researchers successfully analyzed the customer churn using big data feature analysis and multi-feature analysis. User data were modeled by XGBoost algorithm. The model is optimized repeatedly with GridSearchCV as a parameter tool. The accuracy of the model on the test set is 85.1%. The researchers predicted about 11000 customer lists per month that may be about to churn. Using K-means clustering method, 11000 churn target customers per month were classified into three categories and telecom companies are suggested to take some solutions which are found by feature analysis to retain customers. This big data analysis can be used to retain customers for the telecom industry.
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Index Terms
- Using Big Data Analysis to Retain Customers for Telecom Industry
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