• Van-Sang Ha
• Hien Nguyen Thi Bao

The rapid evolution of digital banking has generated massive and diverse datasets, providing unprecedented opportunities to optimize customer service. In this enhanced study, we extend our previous framework by incorporating additional analytical models and evaluation metrics. In addition to customer segmentation via K-means clustering and predictive modeling using logistic regression, we introduce ensemble methods (Random Forest and Gradient Boosting) and advanced text processing via TF-IDF weighting for sentiment analysis. The improved methodology leverages new formulas and sensitivity analyses to provide deeper insights and robust performance comparisons, ultimately enabling banks to implement more effective and targeted service strategies.

big data analytics, customer service, banking, predictive modeling, sentiment analysis

"Enhanced Big Data Analytics to Optimize Customer Service in Banking: An Integrated and Comparative Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 8, page no.f166-f170, August-2025, Available :http://www.jetir.org/papers/JETIR2508523.pdf

"Enhanced Big Data Analytics to Optimize Customer Service in Banking: An Integrated and Comparative Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 8, page no. ppf166-f170, August-2025, Available at : http://www.jetir.org/papers/JETIR2508523.pdf