MAKİNE ÖĞRENME ALGORİTMALARI İLE BİR TELEKOMUNİKASYON ŞİRKETİNDE MÜŞTERİ KAYIP TAHMİNİ ANALİZİ

Year 2021, Volume: 32 Issue: 3, 496 - 512, 31.12.2021

Zeynep Uyar Erdem Banu Çalış Seniye Ümit Fırat

Abstract

Bu araştırmanın amacı, makine öğrenimi algoritmalarının değerlendirilmesinin etkili bir müşteri kayıp tahmini (MKT) metodolojisine yönelik açıklayıcı bir analizini sağlamaktır. Hızla gelişen Müşteri İlişkileri Yönetimi (MİY) alanında, kaybetme eğiliminde olan müşterileri tutmak için uygun bir MKT metodolojisi önermek için, belirli müşterilerden açık kaynaklı bir veri madenciliği yazılımı olan WEKA'da oluşturulan makine öğrenimi algoritmalarını kullanarak bir telekomünikasyon şirketinden gelen anonim büyük bir veri setinden müşteri kaybını tahmin etmek için bir dizi veri madenciliği analizi yapılmıştır. Çalışma boyunca, Türkiye'deki özel bir telekomünikasyon şirketinden sırasıyla 195712, 32905 ve 228617 müşteri sayılarına sahip bireysel, kurumsal ve birleşik veri setleri kullanılarak müşteri kayıp tahminine ilişkin bir dizi deneysel analiz yapılmıştır. Müşteri kayıp durumunun tahmini için altı veri madenciliği algoritması değerlendirildi: Lojistik Regresyon, Naive Bayes, J48 ve RandomForest, Bagging ve Boosting gibi ELM şemaları. RandomForest, RandomTree'yi kullanırken, Bagging, temel öğrenme olarak J48'i kullanmaktadır. Deneysel analizler, MKT için uygulanan bu tür veri madenciliği analizlerine dayalı olarak gelecekteki müşteri kayıplarının olasılığının belirlenmesi için bazı karar ağaçlarının ve topluluk makine öğrenme sınıflandırıcılarının etkinliğini doğrulamak için şirketin tarihsel veri tabanından elde edilen reel veri kümeleri ile gerçekleştirilir. Sonuçlar, J48'in tüm veri kümelerine göre Naive Bayes'ten daha iyi performans gösterdiğini ve Lojistik Regresyon sınıflandırıcı şemasına çok benzer sonuçlar verdiğini göstermektedir. Ayrıca, Bagging büyük boyutlu veritabanını çözmediğinden ve J48, bireysel ve eksiksiz veri setlerinde benzer doğru sonuçlar verdiğinden, J48 karar ağacı sınıflandırıcısının yanı sıra müşteri kaybı tahmini için Bagging seçilebilir.

Keywords

Veri madenciliği, Müşteri Kayıp Analizi, Müşteri İlişkileri Yönetimi

CUSTOMER CHURN PREDICTION ANALYSIS IN A TELECOMMUNICATION COMPANY WITH MACHINE LEARNING ALGORITHMS

Abstract

The purpose of this study is to provide a descriptive analysis of the assessment of machine learning algorithms to an effective customer churn prediction (CCP) methodology. In the rapidly developing field of Customer Relation Management (CRM), to propose a convenient CCP methodology for retaining the customers who tend to churn, a set of data-mining analyses has been conducted to predict customer churn from a bulky dataset from customers with specific attributes in a telecommunication company by using machine learning (ML) algorithms built in an open-source data mining software, WEKA. Throughout the study, a set of experimental analyses regarding customer churn prediction are conducted by using residential, corporate, and combined datasets with the number of incidences of 195712, 32905, and 228617 respectively a private telecommunication company in Turkey. Six data mining algorithms have been evaluated to predict the customer churn status: Logistic Regression, Naive Bayes, J48, and ELM schemes such as RandomForest, Bagging, and Boosting. RandomForest uses RandomTree, whereas Bagging uses J48 as a base learner. The experimental analyses are conducted with real-world datasets acquired from the company's historical database to validate some decision trees' effectiveness and ensemble ML classifiers to determine the likelihood of future churning customers based on such data mining analyses implemented for CCP. The results show that the J48 outperforms Naïve Bayes based on all datasets, and it provides very similar results as the Logistic Regression classifier scheme. Besides, since Bagging has not solved the large-sized database and J48 has given similar accurate results in the residential and complete data sets, the J48 decision tree classifier can be chosen and Bagging for customer churn prediction.

Keywords

Customer Churn Analysis, Telecommunication, Data-mining, Customer Relation Management

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