Hybrid Deep Networks Based On Periodshift Cosine Annealing For Customer Retention Prediction In Telecom Industry - Victor, Johnson Olanrewaju (2024)
In the dynamic landscape of Customer Retention Prediction (CRP), the imperative to strategically direct marketing and promotion efforts towards targeted customers has never been more crucial. Identifying potential churn indicators and continually exploring innovative retention methods becomes paramount. However, a major challenge is customers terminating their services are rarely known among the loyal ones leading to an imbalance problem. Conventional Machine Learning (ML), with its prevalent reliance on feature extraction and data sampling methods, including cost-sensitive techniques, grapples with issues such as overfitting, computational complexity, and an undue emphasis on rare cases. Deep Learning (DL) techniques applied to CRP is promising for automatic feature extraction compared to the handcrafted method used in ML. However, non-cost-sensitive nature, appropriately chosen Learning Rate (LR) for better convergence, and quality feature learning in DL models still pose challenges. This thesis introduces a Class Imbalance Ratio Weight (CIRW) designed to tackle the imbalance problem in DL classifiers without incurring additional computational costs or loss of data symmetry. Additionally, it proposes a novel Period-Shift Cosine Annealing Learning Rate (ps-CALR) method to address LR dynamics during DL model training, thereby enhancing generalization. Finally, a hybrid DL model, combining an improved multilayer perceptron and a onedimensional convolutional neural network, is developed to learn improved features for customer retention analysis.