Customer Segmentation for Credit Card Customers : A Comprehensive Guide

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.

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Publication Details

Journal
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Published
2024-10-06
DOI
https://doi.org/10.32628/cseit241051015
Citations
1
Primary Topic
Customer churn and segmentation
Type
article
Field-Weighted Citation Impact
0.44
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article

Customer Segmentation for Credit Card Customers : A Comprehensive Guide

Gopi Unni Krishnan
1 citations
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Customer churn and segmentation
0.44
article

Customer Segmentation for Credit Card Customers : A Comprehensive Guide

Gopi Unni Krishnan
article en
1 citations

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.

International Journal of Scientific Research in Computer Science Engineering and Information TechnologyVol. 10(5)
Wilmington University (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 27%
Customer churn and segmentation
0.44
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