Comparative Analysis Of Machine Learning Approaches In Predicting Telecom Customer Churn

Abstract

In the fast-paced telecommunications industry, businesses encounter challenges in retaining customers, underscoring the vital role of advanced predictive analytics. To overcome these obstacles, companies are increasingly utilizing machine learning (ML) to enhance the accuracy of customer churn predictions. ML leverages extensive datasets to reveal trends and factors that shape customer behaviour, allowing companies to proactively address churn. ML-driven models play a crucial role in identifying customers at risk of leaving, enabling personalized interventions that increase satisfaction and loyalty. By using ML, businesses gain insight into customer preferences and patterns, empowering them to tailor services and communications more effectively. This research explores the role of ML in forecasting telecom churn, examining the efficacy of various algorithms in identifying likely churners. The study assesses algorithms such as decision trees, logistic regression, neural networks and other, evaluating their performance in predicting churn and providing actionable insights. By analysing survey data on the telecom industry, how different ML models predict churn risk. The aim is to identify the most effective algorithms for accurate forecasts, enabling companies to strategically combat churn and enhance customer retention. Understanding the most successful models allows telecom companies to adopt ML-driven strategies that not only decrease churn rates but also improve customer experiences and loyalty. These insights support the creation of targeted, personalized approaches to customer engagement and retention. In summary, this research highlights the importance of ML in transforming churn prediction and management within the telecom sector. By harnessing ML's capabilities, businesses can stay competitive and agile, securing sustained success in an ever-evolving market.