Açıklanabilir Yapay Zekaya Dayalı Müşteri Kaybı Analizi ve Elde Tutma Önerisi

Year 2024, Volume: 6 Issue: 1, 13 - 23, 30.04.2024

İbrahim Kök

https://doi.org/10.46387/bjesr.1344414

Abstract

Mobil telekomünikasyon pazarında aboneler yüksek hizmet kalitesi, rekabetçi fiyatlandırma ve gelişmiş servis beklentisindedirler. Müşteri bu beklentilerini telekom servis sağlayıcısından karşılayamaması durumunda onu değiştirme yoluna gitmektedir. Hizmet sağlayıcı operatörlerin ise abone kaybı olarak nitelendirilen bu durumla başa çıkmak için abonelerin iletişim kalıpları, davranışları ve abonelik planlarına ait verileri analiz ederek stratejik öngörü sağlayan yorumlanabilir müşteri kaybı tahmin modellerine ihtiyacı vardır. Bu çalışmada biz K-En Yakın Komşu, Karar Ağacı, Rastgele Orman, Destek Vektör Makinesi ve Naïve Bayes algoritmalarına dayalı müşteri kaybı tahmin modelleri geliştiriyoruz. Aynı zamanda en başarılı algoritma sonuçlarının açıklanabilirliği ve yorumlanabilirliği için ELI5, LIME, SHAP ve karşıolgusal açıklanabilir yapay zeka yöntemleri kullanıyoruz. Bu sayede geliştirilen modeller incelenen abonelerin sadece operatörü değiştirip değiştirmediği değil aynı zamanda abone davranışına sebep olan özellikleri de çıktı olarak vermektedir. Geliştirilen açıklanabilir modeller aracılığıyla servis sağlayıcılara müşteri davranışlarının nasıl ve neden gerçekleştiğine dair kapsamlı analizler sunuyoruz.

Keywords

Müşteri Kaybı Analizi, Makine Öğrenmesi, Açıklanabilir Yapay Zeka, Abone Elde Tutma

Explainable AI-Driven Churn Analysis And Retention Recommendation

Abstract

In the mobile telecommunications market, subscribers expect high service quality, competitive pricing and improved service. If the customer is unable to meet these expectations from the telecom service provider, he/she switches. In order to cope with this situation, service provider operators need interpretable churn prediction models that provide strategic insights by analyzing data on subscribers' communication patterns, behaviors and subscription plans. In this paper, we develop churn prediction models based on K-Nearest Neighbor, Decision Tree, Random Forest, Support Vector Machine and Naïve Bayes algorithms. We also use ELI5, LIME, SHAP and Counterfactual explainable artificial intelligence methods for the explainability and interpretability of the most successful algorithm results. In this way, the developed model outputs not only whether the examined subscribers change the operator or not, but also the features that cause the subscriber behavior. Through the developed explainable models, we provide service providers with comprehensive analyses of how and why customer behavior occurs.

Keywords

Churn Analysis, Machine Learning, Explainable Artificial Intelligence, Subscriber Retention

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