Customer Segmentation for Credit Card Customers: A Comprehensive Guide

Gopi Unni Krishnan

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 5, 286-296

Abstract: This comprehensive article explores customer segmentation in the credit card industry, examining its theoretical foundations, methodologies, practical applications, and future trends. It discusses the evolution of segmentation techniques from traditional demographic-based approaches to advanced AI-driven models. The article highlights key segmentation criteria, data collection and preparation methods, and various segmentation techniques, including RFM analysis, clustering algorithms, and machine learning approaches. Case studies of successful implementations by American bankholding companies, multinational financial services corporations, and American bank-holding companies demonstrate the tangible benefits of effective segmentation strategies. The article also addresses common challenges in customer segmentation and provides recommendations for credit card companies to optimize their segmentation efforts. Finally, it explores future trends, including the increasing role of big data and AI in segmentation and the impact of evolving customer expectations on segmentation strategies.

Keywords: Customer Segmentation; Credit Card Industry; Data Analytics; Personalization; AI/Machine Learning (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT241051015
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/home/article/view/CSEIT241051015 Article URL (text/html)
https://ijsrcseit.com/home/article/download/CSEIT241051015/CSEIT241051015 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i5:id:314

DOI: 10.32628/CSEIT241051015

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().