Enhanced Customer Retention: Deep Learning- Based Churn Prediction for Telecom Industry

Abstract

The Telecommunications Industry (TCI) faces a significant problem with customer attrition since revenue generation depends on keeping current customers. A deep learning-based architecture makes more accurate predictions about customer attrition. This brings to light a lot of problems. Churn aids in prioritizing new features or services that have the best chance of increasing customer retention. This guarantees that resources are directed toward the areas that can reduce churn the most.In this research, a deep learning-based model for predicting customer attrition in the telecom sector is presented. In order to extract sequential patterns from consumer behaviour, the model uses a 1D Convolutional Neural Network (CNN). By identifying spatial links in customer data, a 2D CNN improves feature extraction.The telecom statistics available on the Kaggle website aid in the prediction of churn in the telecom sector. Class imbalance in datasets is addressed by using the SMOTE, SMOTEEN, and SMOTETomek approaches. The performance analysis assesses recall rate, accuracy, precision, and F1 score. This methodical approach improves prediction accuracy