Analysis of Customer Churn in Telecommunication Industry with Machine Learning Methods

Özge Nalan Bilişik; Damla Tuğba Sarp

Makale detayları

Analysis of Customer Churn in Telecommunication Industry with Machine Learning Methods

Yazarlar:

Özge Nalan Bilişik | Damla Tuğba Sarp

Yayıncı:

Duzce University

Ana Başlıklar:

Mühendislik

Dergi:

Düzce Üniversitesi Bilim ve Teknoloji Dergisi Vol.11 Issue 4, pp.2185-2208

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001301594

pISSN:

2148-2446

Anahtar kelimeler:

Özet:

 In todays conditions, customer loyalty has gained importance with the increase in the competitive environment between companies, the development of marketing strategies and the improvement of companies. Therefore, it is essential to acquire customers for a company to survive. Retaining an existing customer in the telecommunication sector is less costly than gaining a new customer. Customer churn analysis is the process of predicting customers with high abandonment requests by examining the offers and utilizable behaviors. Customer churn analysis provides services to develop various campaigns aiming to increase the companys loyalty by predicting the customers who are planning to move to another company. In this way, it gives the company a competitive advantage. This study aims to make predictions by developing models for customer churns through data mining and machine learning methods in the telecommunication sector. In addition, we believe that the application in this article will contribute to data analysts and academicians who will want to analyze customer churn with different data sets in telecommunication and other sectors in the future. The analysis in this study is carried out on a data set obtained from an open-access database, including 20 transaction records for the customer from 7043 customers and whether the customer left the company. Among the data mining methods, Random Forest RF, Support Vector Machines SVM and Multilayer Artificial Neural Networks ANN are modeled in open-source Phyton environment. The results have shown that ANN has fared better at classifying customers than other machine learning methods.

 In todays conditions, customer loyalty has gained importance with the increase in the competitive environment between companies, the development of marketing strategies and the improvement of companies. Therefore, it is essential to acquire customers for a company to survive. Retaining an existing customer in the telecommunication sector is less costly than gaining a new customer. Customer churn analysis is the process of predicting customers with high abandonment requests by examining the offers and utilizable behaviors. Customer churn analysis provides services to develop various campaigns aiming to increase the companys loyalty by predicting the customers who are planning to move to another company. In this way, it gives the company a competitive advantage. This study aims to make predictions by developing models for customer churns through data mining and machine learning methods in the telecommunication sector. In addition, we believe that the application in this article will contribute to data analysts and academicians who will want to analyze customer churn with different data sets in telecommunication and other sectors in the future. The analysis in this study is carried out on a data set obtained from an open-access database, including 20 transaction records for the customer from 7043 customers and whether the customer left the company. Among the data mining methods, Random Forest RF, Support Vector Machines SVM and Multilayer Artificial Neural Networks ANN are modeled in open-source Phyton environment. The results have shown that ANN has fared better at classifying customers than other machine learning methods.

 In todays conditions, customer loyalty has gained importance with the increase in the competitive environment between companies, the development of marketing strategies and the improvement of companies. Therefore, it is essential to acquire customers for a company to survive. Retaining an existing customer in the telecommunication sector is less costly than gaining a new customer. Customer churn analysis is the process of predicting customers with high abandonment requests by examining the offers and utilizable behaviors. Customer churn analysis provides services to develop various campaigns aiming to increase the companys loyalty by predicting the customers who are planning to move to another company. In this way, it gives the company a competitive advantage. This study aims to make predictions by developing models for customer churns through data mining and machine learning methods in the telecommunication sector. In addition, we believe that the application in this article will contribute to data analysts and academicians who will want to analyze customer churn with different data sets in telecommunication and other sectors in the future. The analysis in this study is carried out on a data set obtained from an open-access database, including 20 transaction records for the customer from 7043 customers and whether the customer left the company. Among the data mining methods, Random Forest RF, Support Vector Machines SVM and Multilayer Artificial Neural Networks ANN are modeled in open-source Phyton environment. The results have shown that ANN has fared better at classifying customers than other machine learning methods.

 Günümüz koşullarında şirketler arasındaki rekabet koşullarının artması, pazarlama stratejilerinin gelişmesi ve şirketlerin değişimi ve gelişimi ile müşteri ve müşteri sadakati önem kazanmıştır. Bir şirketin ayakta kalabilmesi için müşteri kazanmak önemlidir. Telekom sektöründe mevcut bir müşteriyi elde tutmak, yeni bir müşteri kazanmaktan daha az maliyetlidir. Müşteri kaybı analizi teklif ve davranışların incelenerek şirketi bırakma isteği yüksek olan müşterilerin tahmin edilmesi sürecidir. Müşteri kaybı analizi, başka bir şirkete geçmeyi planlayan müşterileri tahmin ederek, şirket bağlılığını artırmaya yönelik çeşitli kampanyalar geliştirmeye yönelik hizmetler sunmaktadır. Bu sayede firmaya rekabet avantajı sağlamaktadır. Bu çalışmanın amacı, telekomünikasyon sektöründe veri madenciliği ve makine öğrenmesi yöntemleriyle müşteri kayıplarını modelleyerek tahminlerde bulunmaktır. Ayrıca bu makaledeki uygulamanın gelecekte telekomünikasyon ve diğer sektörlerde farklı veri setleri ile müşteri kayıplarını analiz etmek isteyecek veri analistlerine ve akademisyenlere katkı sağlayacağı düşünülmektedir. Bu çalışmadaki analiz, açık erişimli bir veri tabanından elde edilen, 7043 müşteriye ait 20 işlem kaydını ve müşterilerin şirketten ayrılıp ayrılmadığını içeren bir veri seti üzerinde gerçekleştirilmiştir. Veri madenciliği yöntemlerinden Rastgele Orman RF, Destek Vektör Makineleri SVM ve Çok Katmanlı Yapay Sinir Ağları YSA açık kaynaklı Phyton Ortamında modellenmiştir. Sonuçlar analiz edildiğinde, YSA müşterileri sınıflandırmada diğer makine öğrenimi yöntemlerinden daha başarılı olmuştur.

Makale detayları

Analysis of Customer Churn in Telecommunication Industry with Machine Learning Methods

Yazarlar:

Özge Nalan Bilişik | Damla Tuğba Sarp

Yayıncı:

Duzce University

Ana Başlıklar:

Mühendislik

Dergi:

Düzce Üniversitesi Bilim ve Teknoloji Dergisi Vol.11 Issue 4, pp.2185-2208

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001301594

pISSN:

2148-2446

Customer Churn

Machine Learning

ANN

SVM

RFMüşteri Kaybı

Makine Öğrenmesi

YSA

DVM

RO

Sayı

Makale detayları

Yazarlar: Özge Nalan Bilişik | Damla Tuğba Sarp

Yayıncı: Duzce University

Ana Başlıklar: Mühendislik

Dergi: Düzce Üniversitesi Bilim ve Teknoloji Dergisi Vol.11 Issue 4, pp.2185-2208

Belge Tipi: Makale

Yayın Tarihi: 10/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001301594

pISSN: 2148-2446

Sayı

Yazarlar:

Özge Nalan Bilişik | Damla Tuğba Sarp

Yayıncı:

Duzce University

Ana Başlıklar:

Mühendislik

Dergi:

Düzce Üniversitesi Bilim ve Teknoloji Dergisi Vol.11 Issue 4, pp.2185-2208

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001301594

pISSN:

2148-2446