Research on Machine Learning-Driven Customer Churn Warning and Retention Optimization Strategy for SMEs

Zhijun Liu

Financial Economics Insights, 2026, vol. 3, issue 2, 30-40

Abstract: This research article explores the application of machine learning techniques to predict customer churn and optimize retention strategies for small and medium-sized enterprises (SMEs). By leveraging predictive analytics, the study aims to provide actionable insights into customer behavior, enabling SMEs to proactively address churn risks and enhance customer loyalty. The paper outlines a systematic approach, including model development, evaluation, and strategy optimization, to empower SMEs in sustaining competitive advantage in dynamic markets.

Keywords: Customer Churn; Machine Learning; Retention Strategies; SMEs; Predictive Analytics (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://soapubs.com/index.php/FEI/article/view/2101/1930 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:axf:feiaaa:v:3:y:2026:i:2:p:30-40

Access Statistics for this article

More articles in Financial Economics Insights from Scientific Open Access Publishing
Bibliographic data for series maintained by Yuchi Liu ().