Szczegóły publikacji
Opis bibliograficzny
Gender disparities in customer churn rates: a rough neuro-fuzzy classifier-based analysis / Magdalena M. Scherer, Robert K. NOWICKI // W: ISD2025 [Dokument elektroniczny] : [33rd international conference on Information Systems Development] : September 3-5, 2025, Belgrade, Serbia] : empowering the interdisciplinary role of ISD in addressing contemporary issues in digital transformation: how data science and generative AI contributes to ISD? : proceedings / eds. I. Luković, [et al.]. — Wersja do Windows. — Dane tekstowe. — Gdańsk : University of Gdańsk ; Belgrade : University of Belgrade, 2025. — ( Proceedings of the International Conference on Information Systems Development ; ISSN 2938-5202 ). — e-ISBN: 978-83-972632-1-5. — S. [1–4]. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1734&con... [2025-12-11]. — Bibliogr. s. [4], Abstr. — R. K. Nowicki – dod. afiliacja: Czestochowa University of Technology
Autorzy (2)
- Scherer Magdalena M.
- AGHNowicki Robert
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 164938 |
|---|---|
| Data dodania do BaDAP | 2025-12-16 |
| DOI | 10.62036/ISD.2025.55 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Uniwersytet Gdański |
| Konferencja | International Conference on Information Systems Development 2025 |
| Czasopismo/seria | Proceedings of the International Conference on Information Systems Development |
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.
