AI-Driven Customer Segmentation for Enhanced Loyalty Programs

Iman Squalli Houssaini () and Daoud Miloud
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Iman Squalli Houssaini: National School of Business and Management (ENCG-FEZ), Laboratory of Research and Studies in Management, Entrepreneurship, and Finance (LAREMEF)
Daoud Miloud: National School of Business and Management (ENCG-FEZ), Laboratory of Research and Studies in Management, Entrepreneurship, and Finance (LAREMEF)

A chapter in Technological Innovations for Sustainable Development, 2025, pp 209-221 from Springer

Abstract: Abstract Loyalty programs have evolved into essential tools for customer retention, yet traditional segmentation methods often fail to capture the complexity and dynamism of customer behaviors. This study leverages AI-driven clustering techniques to enhance loyalty program effectiveness in the banking sector. We identify actionable customer segments by applying Principal Component Analysis (PCA) for dimensionality reduction and comparing the performance of KMeans and Gaussian Mixture Models (GMM) on a large-scale transaction dataset. Our findings reveal that K-Means significantly outperforms GMM, achieving a Silhouette Score of 0.5500 versus 0.3750, indicating more clearly defined and well-separated clusters. The identified segments provide valuable insights for targeted marketing strategies, optimizing resource allocation, and improving personalized engagement. This research offers a robust framework for transitioning from static segmentation models to adaptive, data-driven customer relationship management approaches, ensuring higher retention and profitability in competitive banking environments.

Keywords: AI-driven segmentation; loyalty programs; machine learning; clustering algorithms; customer retention; PCA; K-Means; Gaussian Mixture Models; banking sector; personalized marketing (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-032-06725-8_18

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DOI: 10.1007/978-3-032-06725-8_18

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