Enhancing Customer Churn Prediction in Telecommunication with CNN-Gradient Boosting Machine

The telecommunications industry faces a constant challenge in retaining customers amidst fierce competition, making accurate churn prediction a critical aspect of business strategy. In this paper, we propose a novel hybrid model, implemented in Python, that combines Convolutional Neural Networks (CNNs) with Gradient Boosting Machines (GBMs) to enhance customer churn prediction accuracy. Leveraging a comprehensive dataset encompassing demographic details, service usage, payment information, and customer support interactions, our model extracts intricate patterns from the data using CNNs and integrates them with traditional features for robust prediction. Through extensive experimentation and evaluation, we demonstrate that our proposed CNN-GBM model achieves an outstanding accuracy of 99%, surpassing other existing approaches by $\mathbf{2.6 \%}$. The model’s performance is evaluated using standard metrics such as accuracy, precision, recall, and F1-Score, showcasing its superiority over traditional methods like Random Forest and Support Vector Machine. The CNN-GBM model’s remarkable accuracy and superior performance highlight its efficacy in accurately identifying churned customers while minimizing false positives, thus providing a valuable tool for telecom companies to implement targeted retention strategies. Moreover, our methodology offers scalability and generalizability, making it applicable across industries facing similar churn challenges. Overall, our proposed hybrid CNNGBM model presents a significant advancement in customer churn prediction, offering telecom companies a reliable solution to improve customer retention and competitiveness in the market.