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VOL. 12, ISSUE 1 (2026)
Churn prediction in the E-Commerce industry using predictive analytics
Authors
Manjuladevi M
Abstract
Customer churn is a major challenge in the e-commerce industry because
acquiring new customers is significantly more expensive than retaining existing
ones. Predictive analytics provides a data-driven approach to identify
customers who are likely to stop using an online platform. By analyzing
historical customer data such as purchase behavior, browsing activity, and
interaction patterns, machine learning models can detect early signs of churn.
This article explores how predictive analytics techniques, including
classification algorithms and data mining methods, can be applied to predict
customer churn in e-commerce. It also highlights the importance of customer
segmentation, behavioral analysis, and personalized retention strategies.
Implementing churn prediction models enables e-commerce companies to improve customer
retention, enhance customer satisfaction, and increase long-term profitability.
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Pages:296-298
How to cite this article:
Manjuladevi M "Churn prediction in the E-Commerce industry using predictive analytics". International Journal of Commerce and Management Research, Vol 12, Issue 1, 2026, Pages 296-298
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