Highly Accurate Customer Churn Prediction in the Telecommunications Industry Using MLP - Rabih, Rahimullah; Weifeng Sun; Majid Ayoubi; Khan Wahib Jamal (MultiTech Publisher, 2024)
Predicting customer Churn is of principal importance for many businesses, as it directly influences the continued utilization of company products by consumers and has a significant impact on company’s financial health and sustainability. However, accurately forecasting customer attrition within the telecommunications sector presents considerable challenges. In response to this need, we propose a novel and highly accurate churn prediction model utilizing a Multilayer Perceptron (MLP) architecture. Our methodology begins with thorough data preprocessing, followed by the application of MLP to predict customer Churn. By employing the Synthetic Minority Over-sampling Technique (SMOTE) and iterative balancing, our proposed model has demonstrated an accuracy improvement of at least 5%, alongside a minimum 5% increase in the F1-score, when compared to existing state-of-the-art algorithms on the widely recognized Telco-Customer-Churn dataset. This research not only expands the opportunities for enhancing the interpretability of predictive models but also fosters a deeper understanding of Churn prediction mechanisms. Ultimately, these advancements contribute to the commercial sustainability and financial stability of telecommunications companies by facilitating more informed decision-making processes regarding customer retention strategies.