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.
Published Version
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