Data Analytics in Strategic Management: A Mathematical Perspective

This study investigates the transformative role of mathematics-driven data analytics in strategic management, focusing on tools such as regression analysis, clustering, and machine learning. These methodologies have reshaped decision-making processes in marketing, operations, and customer relationship management by enabling precise forecasting, customer segmentation, and operational efficiency. The systematic literature review employs the PRISMA methodology to analyze peer-reviewed articles. Key findings highlight regression analysis as a cornerstone for trend prediction and pricing strategies, clustering techniques for improving customer engagement through segmentation, and machine learning for optimizing supply chains and risk management. However, challenges such as scalability issues, integration of real-time analytics, and ethical concerns around data usage persist. The study emphasizes the need for comprehensive frameworks to align mathematical insights with strategic goals and explores the potential of emerging tools, like deep learning, in addressing these gaps. By offering practical recommendations for managers, researchers, and technology providers, the research bridges theoretical and practical dimensions, fostering innovation and organizational success.