A Survey on Customer Churn Prediction using Machine Learning Techniques

Saran Kumar · D. Chandrakala

71 citations0/0 files storedInternational Journal of Computer Applications

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Customer churn and segmentation Customer Service Quality and Loyalty AI and HR Technologies

Abstract

The fast expansion of the market in every sector is leading to superior subscriber base for service providers.Added competitors, novel and innovative business models and enhanced services are increasing the cost of customer acquisition.In such a fast set up, service providers have realized the importance of retaining the on-hand customers.It is therefore essential for the service providers to prevent churn-a phenomenon which states that customer wishes to quit the service of the company.This paper reviews the most popular machine learning algorithms used by researchers for churn predicting, not only in banking sector but also other sectors which highly depends on customer participation.

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References

2008Customer churn prediction using improved balanced random forests 2010Building comprehensible customer churn prediction models with advanced rule induction techniques 2011Customer churn prediction in telecommunications 2009Customer churn prediction by hybrid neural networks 2006CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services 2012A Customer Churn Prediction Model in Telecom Industry Using Boosting 2012A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Models 2014Churn prediction using comprehensible support vector machine: An analytical CRM application

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2025A data-driven approach with explainable artificial intelligence for customer churn prediction in the telecommunications industry 2020Churn Prediction of Customer in Telecom Industry using Machine Learning Algorithms 2023Arithmetic Optimization With Ensemble Deep Learning SBLSTM-RNN-IGSA Model for Customer Churn Prediction 2021Hybrid approach using machine learning algorithms for customers' churn prediction in the telecommunications industry

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