Müşteri Kayıp Tahmini için Sinir Ağı Tabanlı Karşılaştırmalı Analiz

Abstract

Müşteri kaybı, müşterinin bir işletmeyle bağlantısının kesilmesi anlamına gelir. Müşteri kaybıyla ilgili gider, hem kaybedilen geliri hem de yeni müşteriler kazanmak için gereken pazarlama harcamalarını kapsar. Müşteri kaybının azaltılması her işletmenin en önemli hedefidir. Müşteri kayıp tahmini, işletmelerin yüksek kayıp riski olan müşterileri belirleyerek bu müşterileri ellerinde tutmalarını sağlayan stratejiler geliştirmelerine katkıda bulunacaktır. Dijital dünyada müşteri kayıp tahmini modellerinin geliştirilmesinin önemi her geçen gün artmaktadır. Bu çalışmada, anonim bir telekomünikasyon şirketinden elde edilen müşteri verileri kullanılarak müşteri kayıp tahmini için MLP tabanlı yapay sinir ağı modeli geliştirilmiştir. Geliştirilen model kNN, LR, NB, RF ve SVM ile karşılaştırılmıştır. Uygulanan modellerin tahmin sonuçları tartışılmış ve deneysel sonuçlar, karşılaştırılan tüm modellerin %70'in üzerinde doğruluğa sahip olduğunu göstermiştir. Deneysel sonuçlar, geliştirilen MLP tabanlı yapay sinir ağı modelinin diğer modellere göre yaklaşık %95 doğrulukla en başarılı sınıflandırma performansına sahip olduğunu göstermiştir.

Keywords

• Yapay Sinir Ağları
• Makine Öğrenmesi
• MLP
• Müşteri Kayıp Tahmini

Neural Network Based a Comparative Analysis for Customer Churn Prediction

Abstract

Customer churn refers to a customer's disconnection from a business. The expense associated with customer churn encompasses both the forfeited revenue and the marketing expenditures required to acquire new customers. Mitigating customer churn stands as the foremost objective for every business. Customer churn prediction will contribute to developing strategies enabling businesses to retain these customers by identifying customers with a high risk of loss. In the digital world, the importance of developing customer churn prediction models is increasing daily. In this study, MLP based artificial neural network model was developed for customer churn prediction using customer data from an anonymous telecommunications company. The developed model was compared with kNN, LR, NB, RF, and SVM. The prediction results of the applied models were discussed, and the experimental results showed that all the models compared had over 70% accuracy. Experimental results showed that the developed MLP-based artificial neural network model has the most successful classification performance compared to other models with approximately 95% accuracy.

Keywords

• Artificial Neural Network
• Machine learning
• MLP
• Customer Churn Prediction

References

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