Leveraging Data Analytics for Financial Stability: A Blueprint for Sustaining SMEs in Economically Distressed Regions of the U.S

Jodian Campbell () and Paul Oyinloye ()

International Journal of Poverty, Investment and Development, 2024, vol. 4, issue 1, 54 - 72

Abstract: Purpose: Our research explores how data analytics can empower small- and medium-sized enterprises (SMEs) in economically distressed regions of the U.S. by improving financial decision-making, operational efficiency, and risk mitigation. The study aims to provide a blueprint for leveraging data-driven insights to ensure long-term financial stability and economic growth in underserved areas, particularly in the Midwest and Southern states. Methodology: The study employs a case-study approach to analyze the real-world applications of data analytics platforms in SMEs within economically distressed regions. It uses statistical analysis to compare the performance of businesses that have adopted data analytics tools with those relying on traditional methods. The methodology also includes the collection of data from various sources such as financial performance records, customer analytics, and predictive models. Findings: The research demonstrates that SMEs using data analytics experience significant improvements in financial performance. For instance, predictive analytics increased financial forecasting accuracy by 25%, and real-time business intelligence tools reduced operational costs by 15%. Businesses using customer analytics tools saw a 20% rise in customer retention and a 15% increase in revenue. The study shows that data-driven strategies can improve overall financial stability by 30% and enable SMEs to proactively mitigate risks in volatile markets. Unique Contribution to Theory, Policy, and Practice: The study contributes to the emerging body of literature on the transformative potential of data analytics in financially distressed regions, providing new insights into how real-time and predictive analytics can enhance financial decision-making in SMEs’ practices within the economically distressed regions. The study provides a practical blueprint for SMEs to adopt data analytics tools, demonstrating their impact on financial stability, operational efficiency, and long-term growth. It offers actionable recommendations for business leaders in distressed regions to integrate analytics equitably into their decision-making processes.

Keywords: Data Analytics; Financial Stability; SMEs; Predictive Analytics; Economically Distress Regions (search for similar items in EconPapers)
Date: 2024
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