CUSTOMER CHURN PREDICTION AT SCALE FOR TELECOM

This paper shows how machine learning can help effectively minimize the problem of customer attrition in the telecom sector. With the use of data collected about user behavior, services used, as well as interactions, the model is capable of predicting users' intentions to churn. It turned out that algorithms such as XGBoost and Random Forest are very efficient in recognizing hidden patterns which other approaches cannot detect. In addition to increased accuracy of prediction, the created system offers valuable insights and enables visualization of results through dashboards. Ultimately, such an approach can assist telecommunication firms in minimizing their losses due to customer attrition. The solution can be made more powerful by leveraging more sophisticated technologies and information resources. Thus, by including sentiment analysis based on customer opinions, emails, or conversations with representatives of the company, one can gain additional insight into the customers' discontent. The application of the deep learning approach may increase the accuracy of predictions by accounting for more complex dependencies in data. Moreover, the system can be supplemented with a feature to suggest customized methods of retaining each particular customer. Periodic updates of the model, integration of fresh data, and learning from experience will keep the solution efficient and up-to-date.  

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