Small businesses often face challenges in leveraging data-driven strategies due to resource constraints, limiting their ability to optimize operations, forecast finances, and retain customers effectively. This study presents a machine learning framework designed to support small business management by focusing on key areas: customer retention, financial forecasting, customer segmentation, and inventory management. Utilizing real-world small business data, we evaluate various machine learning models, including Random Forest, Gradient Boosting, K-Means Clustering, Lasso Regression, and ARIMA, to determine the optimal algorithms for each business function. Our findings reveal that Random Forest excels in customer retention, Lasso Regression performs well in financial forecasting, K-Means effectively segments customers, and ARIMA accurately predicts inventory requirements. By integrating these models into a cohesive framework, small businesses can gain actionable insights without the need for extensive computational resources. This framework thus enables small businesses to implement cost-effective, ethical, and interpretable machine learning solutions, empowering them to compete more effectively in data-driven markets. This research contributes to the growing field of AI in small business, providing a practical approach for deploying machine learning models that meet the specific needs of small enterprises.

Machine learning, customer retention, financial forecasting, inventory optimization

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