A Fuzzy-XAI Framework for Customer Segmentation and Risk Detection: Integrating RFM, 2-Tuple Modeling, and Strategic Scoring
This article presents an interpretable framework for customer segmentation and churn risk detection, integrating fuzzy clustering, explainable AI (XAI), and strategic scoring. The process begins with Fuzzy C-Means (FCM) applied to normalized RFM indicators (Recency, Frequency, Monetary), which were...
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad Europea (UEM) |
| Repositorio: | ABACUS. Repositorio de Producción Científica |
| Idioma: | inglés |
| OAI Identifier: | oai:abacus.universidadeuropea.com:11268/16656 |
| Acceso en línea: | https://hdl.handle.net/11268/16656 |
| Access Level: | acceso abierto |
| Palabra clave: | Marketing Inteligencia artificial Estadística Goal 8: Promote inclusive and sustainable economic growth, employment and decent work for all |
| Sumario: | This article presents an interpretable framework for customer segmentation and churn risk detection, integrating fuzzy clustering, explainable AI (XAI), and strategic scoring. The process begins with Fuzzy C-Means (FCM) applied to normalized RFM indicators (Recency, Frequency, Monetary), which were then mapped to a 2-tuple linguistic scale to enhance semantic interpretability. Cluster memberships and centroids were analyzed to identify distinct behavioral patterns. An XGBoost classifier was trained to validate the coherence of the fuzzy segments, while SHAP and LIME provided global and local explanations for the classification decisions. Following segmentation, an AHP-based strategic score was computed for each customer, using weights derived from pairwise comparisons reflecting organizational priorities. |
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