Customer churning analysis using machine learning algorithms

B. Prabadevi · R. Shanmuka Shalini · B. R. Kavitha

95 citations0/0 files storedInternational Journal of Intelligent Networks

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

Businesses must compete fiercely to win over new consumers from suppliers. Since it directly affects a company's revenue, client retention is a hot topic for analysis, and early detection of client churn enables businesses to take proactive measures to keep customers. As a result, all firms could practice a variety of approaches to identify their clients early on through client retention initiatives. Consequently, this study aims to advise on the optimum machine-learning strategy for early client churn prediction. The data included in this investigation includes all customer data going back about nine months before the churn. The goal is to predict existing customers' responses to keep them. The study has tested algorithms like stochastic gradient booster, random forest, logistics regression, and k-nearest neighbors methods. The accuracy of the aforementioned algorithms are 83.9%, 82.6%, 82.9% and 78.1% respectively. We have acquired the most effective results by examining these algorithms and discussing the best among the four from different perspectives.

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References

2019Customer churn prediction in telecom using machine learning in big data platform 2015A comparison of machine learning techniques for customer churn prediction 2019A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector 2021Customer churn prediction system: a machine learning approach 2013A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services

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