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Predicting Customer Churn Using ML Model
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: https://doi.org/10.64388/IREV9I11-1717691
Abstract
This study focuses on customer churn prediction using machine learning techniques to help organizations identify customers who are likely to discontinue services. In today’s competitive business environment, retaining customers is more cost-effective than acquiring new ones, making churn management a strategic priority. The research utilizes customer demographic, behavioral, transactional, and interaction data to analyze patterns influencing churn. Various machine learning models, including Logistic Regression, Decision Trees, Random Forest, and Gradient Boosting, are applied and compared to determine the most effective approach. The study follows a structured methodology involving data preprocessing, feature engineering, model training, and evaluation using metrics such as accuracy, precision, recall, and ROC-AUC. The findings aim to provide actionable insights for businesses to develop targeted retention strategies, improve customer satisfaction, and reduce financial losses. Overall, the research contributes to data-driven decision-making and enhances customer relationship management through predictive analytics.
Keywords
Customer Churn, Machine Learning, Customer Retention, Customer Behavior, Data Analysis, Logistic Regression, Churn prediction model
How to cite this paper
@article{1717691,
author = {Aneesh Ahamed M, Dr. V Kanimozhi},
title = {Predicting Customer Churn Using ML Model},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {2017-2020},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717691.pdf},
abstract = {This study focuses on customer churn prediction using machine learning techniques to help organizations identify customers who are likely to discontinue services. In today’s competitive business environment, retaining customers is more cost-effective than acquiring new ones, making churn management a strategic priority. The research utilizes customer demographic, behavioral, transactional, and interaction data to analyze patterns influencing churn. Various machine learning models, including Logistic Regression, Decision Trees, Random Forest, and Gradient Boosting, are applied and compared to determine the most effective approach. The study follows a structured methodology involving data preprocessing, feature engineering, model training, and evaluation using metrics such as accuracy, precision, recall, and ROC-AUC. The findings aim to provide actionable insights for businesses to develop targeted retention strategies, improve customer satisfaction, and reduce financial losses. Overall, the research contributes to data-driven decision-making and enhances customer relationship management through predictive analytics.},
keywords = {Customer Churn, Machine Learning, Customer Retention, Customer Behavior, Data Analysis, Logistic Regression, Churn prediction model},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717691}
}