Customer Retention Optimization for SMEs Using Predictive Machine Learning Models

Abstract

Customer retention is a critical determinant of sustainable performance for small and medium-sized enterprises (SMEs), yet many SMEs continue to rely on reactive and experience-based approaches to manage customer churn. The increasing availability of customer data creates new opportunities for more proactive and data-driven retention strategies. This study develops a conceptual framework for optimizing customer retention in SMEs through the application of predictive machine learning models. The framework integrates customer data, predictive analytics, and risk-based decision-making to support early identification of churn risk and targeted retention actions. By emphasizing model interpretability, practical implementation, and resource efficiency, the study highlights how predictive insights can be effectively embedded into SME operational processes. The analysis demonstrates the potential of predictive machine learning to transform customer retention management from a reactive function into a proactive and strategic capability under SME-specific constraints.