Recent Papers

This review examines the use of machine learning (ML) techniques in predicting customer churn across various industries, including telecommunications, financial services, and e-commerce. The study explores the effectiveness of different models, such as decision trees, support vector machines (SVM), and neural networks, in identifying at-risk customers based on behavioral, transactional, and demographic data. Ensemble models and hybrid approaches, such as those combining decision trees and logistic regression, demonstrate superior accuracy in predicting churn, particularly in handling large and complex datasets. However, challenges like model interpretability and ethical concerns around data privacy and bias remain significant barriers to widespread adoption. To address these issues, the review highlights recent advancements in explainable artificial intelligence (XAI) and profitdriven machine learning models, which aim to balance accuracy with transparency. The analysis concludes that while ML models offer substantial promise in improving customer retention strategies, further research is needed to enhance their applicability across diverse sectors and ensure they operate ethically and transparently.

Copyright © 2024 Chhipi Abdul Karim. This is an open access article distributed under the Creative Commons Attribution License.