CONNECTIVITY CRISIS: TACKLING TELECOM CHURN WITH MACHINE LEARNING

Abstract: Customer churn is when customers stop using a particular telecom service and switch to a competitor or cancel their contract altogether. This presents a major challenge in the telecommunication industry, as acquiring new customers is typically more expensive than retaining existing ones. To reduce the churn rate, businesses can analyze large volumes of customer data to gain insightful knowledge about customer behavior, preferences, and potential churn tendencies using machine learning algorithms. By utilizing machine learning models, telecom companies can gain an understanding of their customers' preferences and implement retention strategies that can increase customer satisfaction. In this study, we aim to illustrate the effectiveness of Random Forest, Cat Boost, and XG Boost models in accurately predicting customer attrition.

Keywords: Customer churn, Telecommunication Industries, Machine Learning algorithms, Retention Strategies,  Insightful Knowledge. Cite: V. Chandana, S. Manohari, Y. Lakshmi Prasanna, SK. Feroze Moinuddee, DR. K.Pavan Kumar, "CONNECTIVITY CRISIS: TACKLING TELECOM CHURN WITH MACHINE LEARNING", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.133142.