Prediction of Customer Churn in Telecom Industry: A Machine Learning Perspective

Author:

Hota Lopamudra,Dash Prasant Kumar

Abstract

The business world is becoming increasingly saturated in today's competitive environment. There is a great deal of competition in the telecommunications industry, especially due to various vibrant service providers. As a result, they have had difficulty retaining their existing customers. As attracting new customers is much more costly than retaining current ones, now is the time to ensure the telecom industry maintains value by retaining customers over acquiring new ones. Numerous machine learning and data mining methods have been proposed in the literature to predict customer churners using heterogeneous customer records over the past decade. This research gives a brief idea on the Customer Churn problem, and explores how various machine learning techniques can be used to predict customer churn via models such as XGBoost, GradientBoost, AdaBoost, ANN, Logistic Regression and Random Forest, and also compare the effectiveness of the models in term of accuracy. Keyword : Machine Learning, Customer Churn, Prediction Model, Random Forest, XGBoost, AdaBoost, GBoost

Publisher

Technoarete Research and Development Association

Cited by 5 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. A proposed hybrid framework to improve the accuracy of customer churn prediction in telecom industry;Journal of Big Data;2024-05-09

2. Customer Churn Prediction Model Using Deep Learning;Algorithms for Intelligent Systems;2024

3. Analysis of Customer Churn in Telecommunication Industry with Machine Learning Methods;Düzce Üniversitesi Bilim ve Teknoloji Dergisi;2023-10-24

4. A Review on Machine Learning-Based Customer Churn Prediction in the Telecom Industry;2023 9th International Conference on Control, Decision and Information Technologies (CoDIT);2023-07-03

5. Application of Machine Learning in Predicting Customer Satisfaction of Telecom Service Providers;2022 4th International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM);2022-11-22

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