Enhancing Customer Subscription Prediction in Bank Telemarketing Using Deep Learning and Ensemble Model

Predicting customer subscriptions is a crucial task in bank telemarketing campaigns that aim to enhance customer acquisition, decrease operating expenses, and optimize marketing strategies. To resolve this classification problem, traditional machine learning methods, including bagging, boosting, and stacking, are currently used extensively. Stacking has a 91.88% accuracy rate. While these ensemble methods have demonstrated promising performance, they often lack interpretability and struggle to capture temporal dependencies and nonlinear interactions inherent in customer effort data. To address these limitations, this study explores the effectiveness of deep learning models—specifically, the Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN)—for predicting customer subscription outcomes. The RNN model performs noticeably better than MLP in all important metrics, according to a comparison and contrast, with 94.55% accuracy, 89.54% precision, 98.95% recall, and 94.01% F1-score. In contrast, MLP achieves slightly lower scores across the board. The superior performance of the RNN model can be attributed to its ability to capture sequential patterns and complex dependencies within the customer interaction data. These findings highlight the potential of RNN-based architectures for enhancing the predictive capability of telemarketing systems, offering a more robust and scalable solution for customer targeting and campaign optimization.