Due to market deregulation and globalisation, competitive environments in various sectors
continuously evolve, leading to increased customer churn. Effectively anticipating and …

Ensemble learning has become a cornerstone in various classification and regression tasks,
leveraging its robust learning capacity across disciplines. However, the computational time …

In the evolving landscape of targeted marketing, integrating deep learning (DL) and
explainable AI (XAI) offers a promising avenue for enhanced customer segmentation. This …

This study investigates customer churn, which is a challenge in the telecommunications
sector. Using a dataset of telecom customer churn, multiple classifiers were employed …

Customer churn can be defined as the phenomenon of customers who discontinue their
relationship with a company. This problem is transversal to many industries, including the …

In this research, a predictive model was developed using data mining techniques to analyze
customer behavior, in order to identify and classify customers with a higher risk of defection …

Although some machine learning methods have been widely applied to customer churn
prediction across various fields, few studies have focused on customer churn in card and …

This study examines the efficacy of Random Forest and XGBoost classifiers in conjunction
with three upsampling techniques—SMOTE, ADASYN, and Gaussian noise upsampling …

Customer reviews play a crucial role in a company's success, particularly by amplifying the
Electronic Word-of-Mouth (eWOM) effect. This study aims to harness ensemble learning …

Predicting customer behavior is crucial for businesses, including churn and purchasing
behavior. We propose a tailored model for this purpose, applied to two types of problems …