An Integrated Machine Learning Model for Predicting Customer Churn in a Telecommunications Application

Authors: Rahul Roy Devarakonda

Country: India

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Abstract: Predicting customer churn is a crucial problem in the telecommunications sector, as retaining existing clients is more economical than acquiring new ones. To accurately anticipate customer turnover, this research proposes an integrated machine-learning model that leverages sophisticated data mining techniques. To improve prediction performance, the suggested strategy integrates deep learning, ensemble learning, and feature engineering. The model identifies the primary factors contributing to churn by analyzing historical customer data, including call logs, billing records, and service usage patterns. The study compares the efficacy of several machine learning methods in churn prediction, such as artificial neural networks, decision trees, and support vector machines. Industry-standard metrics, including accuracy, precision, recall, and F1-score, are used to assess the suggested model. The findings demonstrate that utilizing ensemble learning techniques significantly enhances prediction accuracy, reduces false positives, and strengthens client retention strategies. The study also highlights the importance of pricing tactics, customer relationship management, and client lifetime value in mitigating churn risks. Furthermore, big data analytics and hybrid machine learning approaches are crucial for enhancing decision-making and refining predictive models in telecommunications companies. The results indicate that the robustness of the model can be further enhanced by adding fuzzy data mining and sentiment analysis of customer comments. For telecom firms seeking data-driven strategies to enhance client retention and service customisation, this study provides valuable insights.

Keywords: Customer Churn Prediction, Telecommunications Analytics, Machine Learning in Telecom, Predictive Modeling, Churn Detection

Paper Id: 232295

Published On: 2015-01-07

Published In: Volume 3, Issue 1, January-February 2015

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