Sarcouncil Journal of Economics and Business Management

Abstract: Small and Medium-Sized Enterprises are a key part of the United States economy but are experiencing substantial challenges such as limited resources, changing customer behavior, and competitive markets. Predictive Business Analytics (PBA) has evolved as an important capability that small and medium enterprises (SMEs) employ to achieve data-driven intelligence to improve sales performance and retain customers. The goal of this work is to critically review available empirical evidence, industry reports, and confirmed case studies in order to examine the extent and impact of predictive analytics on SMEs. The results show that PBA improves sales forecasting accuracy, optimizes pricing and promotion more effectively, and enhances lead targeting, leading to increased conversions and marketing effectiveness. On top of that, predictive analytics also enables an early warning system for at-risk customers personalized engagement models and improved customer lifetime value, which ultimately makes retention better. The use of predictive models, customer segmentation, and churn-prediction in operational firms is illustrated by case studies for small to medium-sized U.S. companies that clearly show both strategic and practical value. The critical challenges, such as a lack of analytics expertise, poor data quality, and the requirement to integrate with prescriptive decision-making tools, are also highlighted in this review. Taken together, however, the evidence highlights PBA as a game-changing approach for SMEs by promoting a more proactive decision-making and innovation culture and long-term competitiveness.