Explainable AI-Based Deep Learning System for Predicting Customer Churn in Telecommunication Industry

Abstract

Customer churn is a major challenge for the telecommunication industry as the loss of customers impacts revenue and business growth. Early identification of customers who are likely to churn allows telecom providers to develop effective customer retention strategies and increase customer satisfaction. Traditional machine learning approaches often fail to capture complex customer behavior patterns and offer limited interpretability in their predictions. To overcome these challenges, this project proposes an Explainable AI-Based Deep Learning System for prediction of customer churn in telecommunication industry. The system uses IBM Telco customer churn dataset and uses exhaustive data preprocessing techniques such as data cleaning, label encoding, feature scaling and class balancing using SMOTEENN. It employs a hybrid deep learning architecture called ChurnNet comprising a 1D Convolutional Neural Network (1D-CNN), Residual Blocks, Channel Attention, and Spatial Attention mechanisms to learn complex customer behavioural patterns and accurately predict churn probability. In order to enhance credibility and clarity, the suggested system incorporates Explainable Artificial Intelligence (XAI) methods like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) to provide transparency. SHAP performs global feature importance analysis, and LIME provides local explanations for each prediction, allowing users to understand the crucial features influencing the churn decision. The experimental results show that the proposed system has high prediction accuracy, reliability and interpretability, and thus it is a valuable decision-support tool for telecom organizations that aim to reduce customer attrition and improve retention strategies