Telecom Industry Customer Attrition Prediction with Machine Learning Approach

In order to keep current customers, the organization in the telecommunications sector must focus on one of the very important research areas: customer churn identification. Customer attrition known as churn occurs when rivals discontinue selling certain products or services, or possibly when network issues arise.

Deepika Bansal, Bhoomi Gupta and Sachin Gupta

Department of Information Technology

Department of Computer Science Engineering

Maharaja Agrasen Institute of Technology

New Delhi 110086, India

Abstract. In order to keep current customers, the organization in the telecommunications sector must focus on one of the very important research areas: customer churn identification. Customer attrition known as churn occurs when rivals discontinue selling certain products or services, or possibly when network issues arise.

Customers often tend to abandon their service subscriptions in these kinds of circumstances. The churn rate significantly influences the client's lifetime value since it influences both the company's potential income and the length of service. The corporations are searching for a model that can forecast client attrition as the revenues are directly impacted. Machine learning techniques are used in the proposed model. For a more sustainable business environment, companies need to understand and effectively manage customer attrition. Reducing network issues, resource allocation optimization and waste reduction can improve customer satisfaction and thus decreasing churn rates. Optimizing service provisioning and resource allocation helps in promoting sustainable decision making and enhances revenue generation by development of predictive models utilizing machine learning concepts and methods for artificial intelligence. It empowers organization to build long lasting customer relationships and minimizing environmental impact of their operations.

Keywords: Customer Churn, Telecom, Predictive Analysis, Optimization, Client Retention

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