Abstract
Customer churn prediction is a critical challenge for businesses aiming to retain valuable customers. This study employs a rough neuro-fuzzy classifier with CA defuzzification to analyze churn behavior, with a particular focus on gender disparities. Thanks to rough set theory, our approach effectively handles incomplete or missing data by utilizing lower and upper approximations, ensuring robust predictions even when feature values are absent. We evaluate feature importance through two distinct methods: directly from the data and via the classifier, to uncover gender-specific patterns in churn behavior. Moreover, we introduce the notion of conditional significance. Our findings reveal notable gender-based differences in the significance of predictive features. Experimental results, validated through ten-fold cross-validation, demonstrate the classifier's ability to manage missing data without imputation, while also underscoring the heightened sensitivity of female customers to feature availability. This research contributes to the growing body of knowledge on gender-driven consumer behavior, offering practical implications for businesses to refine customer relationship management and reduce churn through gender-specific interventions.
Paper Type
Poster
DOI
10.62036/ISD.2025.55
Gender Disparities in Customer Churn Rates: A Rough Neuro-Fuzzy Classifier-based Analysis
Customer churn prediction is a critical challenge for businesses aiming to retain valuable customers. This study employs a rough neuro-fuzzy classifier with CA defuzzification to analyze churn behavior, with a particular focus on gender disparities. Thanks to rough set theory, our approach effectively handles incomplete or missing data by utilizing lower and upper approximations, ensuring robust predictions even when feature values are absent. We evaluate feature importance through two distinct methods: directly from the data and via the classifier, to uncover gender-specific patterns in churn behavior. Moreover, we introduce the notion of conditional significance. Our findings reveal notable gender-based differences in the significance of predictive features. Experimental results, validated through ten-fold cross-validation, demonstrate the classifier's ability to manage missing data without imputation, while also underscoring the heightened sensitivity of female customers to feature availability. This research contributes to the growing body of knowledge on gender-driven consumer behavior, offering practical implications for businesses to refine customer relationship management and reduce churn through gender-specific interventions.
Recommended Citation
Scherer, M. & Nowicki, R. (2025). Gender Disparities in Customer Churn Rates: A Rough Neuro-Fuzzy Classifier-based AnalysisIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.55