Explaining and Predicting Mobile Network–Driven Churn Using Machine Learning and SHapley Additive exPlanations–Based Attribution
Electrica, cilt.26, 2026 (ESCI, Scopus, TRDizin)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 26
- Basım Tarihi: 2026
- Doi Numarası: 10.5152/electrica.2026.25240
- Dergi Adı: Electrica
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, TR DİZİN (ULAKBİM)
- Anahtar Kelimeler: Customer churn prediction, customer retention strategies, explainable AI, key performance indicators, machine learning, SHAP analysis
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- İstanbul Gelişim Üniversitesi Adresli: Evet
Özet
Customer churn remains a critical concern in the mobile telecommunications industry, where retaining subscribers is essential for long-term profitability. This study presents an explainable machine learning framework to detect and interpret network-driven churn by integrating demographic data with key network performance indicators, including signal quality metrics e.g., reference signla received power, signal to interference plus noise ratio, download/upload throughput, and latency-related measurements derived from the radio access network. Using the CatBoost classifier on a proprietary dataset of 115 264 subscribers constructed with a balanced 50/50 class distribution to prevent majority-class bias, the model achieves an F1 score of 81.0% and an area under the receiver operating characteristic curve of 0.884, demonstrating strong discriminative performance. A central contribution is the introduction of a systematic SHapley Additive exPlanations (SHAP)-based attribution method that distinguishes network-driven churn from other churn types. For each churn prediction, positive SHAP values are extracted and categorized as network-related or non-network-related; instances where network features contribute the dominant share of the total positive SHAP attribution are labeled as network-driven churn. The framework is validated on both the proprietary Turkcell dataset and three publicly available telecom churn benchmarks, demonstrating generalizability. The proposed approach bridges predictive modeling and real-world network optimization, offering telecom operators a scalable and interpretable tool for targeted customer retention.