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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Detalhes bibliográficos
Autor: Marín Díaz, Gabriel
Formato: artículo
Fecha de publicación:2025
País:España
Recursos:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/129648
Acesso em linha:https://hdl.handle.net/20.500.14352/129648
Access Level:acceso abierto
Palavra-chave:658.8
004.8
311
510.6
164
519.226
RFM model
fuzzy C-Means clustering
2-tuple linguistic representation
analytic hierarchy process (AHP)
customer segmentation
explainable AI (SHAP, LIME)
churn prediction
Marketing
Inteligencia artificial (Informática)
Estadística matemática (Estadística)
Lógica simbólica y matemática (Matemáticas)
Teoría de la decisión
5311.05 Marketing (Comercialización)
1203.04 Inteligencia Artificial
1209.01 Estadística Analítica
1209.04 Teoría y Proceso de decisión
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oai_identifier_str oai:docta.ucm.es:20.500.14352/129648
network_acronym_str ES
network_name_str España
repository_id_str
spelling A fuzzy-XAI framework for customer segmentation and risk detection: integrating RFM, 2-tuple modeling, and strategic scoringMarín Díaz, Gabriel658.8004.8311510.6164519.226RFM modelfuzzy C-Means clustering2-tuple linguistic representationanalytic hierarchy process (AHP)customer segmentationexplainable AI (SHAP, LIME)churn predictionMarketingInteligencia artificial (Informática)Estadística matemática (Estadística)Lógica simbólica y matemática (Matemáticas)Teoría de la decisión5311.05 Marketing (Comercialización)1203.04 Inteligencia Artificial1209.01 Estadística Analítica1209.04 Teoría y Proceso de decisiónThis 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. These scores were also translated into the 2-tuple domain, reinforcing interpretability. The model then identified customers at risk of disengagement, defined by a combination of low Recency, high Frequency and Monetary values, and a low AHP score. Based on Recency thresholds, customers are classified as Active, Latent, or Probable Churn. A second XGBoost model was applied to predict this risk level, with SHAP used to explain its predictive behavior. Overall, the proposed framework integrated fuzzy logic, semantic representation, and explainable AI to support actionable, transparent, and human-centered customer analytics.MDPIUniversidad Complutense de Madrid20252025-06-3020252025-06-30journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/129648reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1296482026-06-02T12:44:21Z
dc.title.none.fl_str_mv A fuzzy-XAI framework for customer segmentation and risk detection: integrating RFM, 2-tuple modeling, and strategic scoring
title A fuzzy-XAI framework for customer segmentation and risk detection: integrating RFM, 2-tuple modeling, and strategic scoring
spellingShingle A fuzzy-XAI framework for customer segmentation and risk detection: integrating RFM, 2-tuple modeling, and strategic scoring
Marín Díaz, Gabriel
658.8
004.8
311
510.6
164
519.226
RFM model
fuzzy C-Means clustering
2-tuple linguistic representation
analytic hierarchy process (AHP)
customer segmentation
explainable AI (SHAP, LIME)
churn prediction
Marketing
Inteligencia artificial (Informática)
Estadística matemática (Estadística)
Lógica simbólica y matemática (Matemáticas)
Teoría de la decisión
5311.05 Marketing (Comercialización)
1203.04 Inteligencia Artificial
1209.01 Estadística Analítica
1209.04 Teoría y Proceso de decisión
title_short A fuzzy-XAI framework for customer segmentation and risk detection: integrating RFM, 2-tuple modeling, and strategic scoring
title_full A fuzzy-XAI framework for customer segmentation and risk detection: integrating RFM, 2-tuple modeling, and strategic scoring
title_fullStr A fuzzy-XAI framework for customer segmentation and risk detection: integrating RFM, 2-tuple modeling, and strategic scoring
title_full_unstemmed A fuzzy-XAI framework for customer segmentation and risk detection: integrating RFM, 2-tuple modeling, and strategic scoring
title_sort A fuzzy-XAI framework for customer segmentation and risk detection: integrating RFM, 2-tuple modeling, and strategic scoring
dc.creator.none.fl_str_mv Marín Díaz, Gabriel
author Marín Díaz, Gabriel
author_facet Marín Díaz, Gabriel
author_role author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 658.8
004.8
311
510.6
164
519.226
RFM model
fuzzy C-Means clustering
2-tuple linguistic representation
analytic hierarchy process (AHP)
customer segmentation
explainable AI (SHAP, LIME)
churn prediction
Marketing
Inteligencia artificial (Informática)
Estadística matemática (Estadística)
Lógica simbólica y matemática (Matemáticas)
Teoría de la decisión
5311.05 Marketing (Comercialización)
1203.04 Inteligencia Artificial
1209.01 Estadística Analítica
1209.04 Teoría y Proceso de decisión
topic 658.8
004.8
311
510.6
164
519.226
RFM model
fuzzy C-Means clustering
2-tuple linguistic representation
analytic hierarchy process (AHP)
customer segmentation
explainable AI (SHAP, LIME)
churn prediction
Marketing
Inteligencia artificial (Informática)
Estadística matemática (Estadística)
Lógica simbólica y matemática (Matemáticas)
Teoría de la decisión
5311.05 Marketing (Comercialización)
1203.04 Inteligencia Artificial
1209.01 Estadística Analítica
1209.04 Teoría y Proceso de decisión
description 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. These scores were also translated into the 2-tuple domain, reinforcing interpretability. The model then identified customers at risk of disengagement, defined by a combination of low Recency, high Frequency and Monetary values, and a low AHP score. Based on Recency thresholds, customers are classified as Active, Latent, or Probable Churn. A second XGBoost model was applied to predict this risk level, with SHAP used to explain its predictive behavior. Overall, the proposed framework integrated fuzzy logic, semantic representation, and explainable AI to support actionable, transparent, and human-centered customer analytics.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-06-30
2025
2025-06-30
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/129648
url https://hdl.handle.net/20.500.14352/129648
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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