Analysis of Customer Churn in Telecommunication Industry with Machine Learning Methods

Author:

BİLİŞİK Özge Nalan1ORCID,SARP Damla Tuğba2ORCID

Affiliation:

1. Yıldız Teknik Üniversitesi

2. YILDIZ TEKNİK ÜNİVERSİTESİ

Abstract

In today's 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 company’s 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.

Publisher

Duzce Universitesi Bilim ve Teknoloji Dergisi

Subject

General Medicine

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Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Predictive Customer Analytics: Machine Learning for Churn Prediction and Retention;Ahliya Journal of Business Technology and MEAN Economies;2024-07-15

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