Keywords

Predictive Analytics, Economic Value Creation, Data-Driven Firms, Business Analytics, Financial Performance, Data-Driven Decision Making, Forecasting Accuracy, Return on Investment (ROI), Competitive Advantage, Organizational Capabilities. .

Abstract

In an increasingly data-driven business environment, predictive analytics is a fundamental capability for organizations looking to improve their economic performance. Predictive analytics enables firms to predict market trends, streamline operational processes and enhance strategic decision-making through the transformation of large volumes of structured and unstructured data into forward-looking insights. While there is increasing adoption of analytics-driven approaches across industries, empirical studies on the actual contribution of predictive analytics to the creation of economic value are scarce. Prior research often emphasizes the technical or conceptual aspects of analytics without providing much evidence for tangible bottom-line operational and financial results. This study fills this void by exploring how firms can achieve economic value creation through predictive analytics in data driven economy. The study employs a mixed-method research design that combines secondary financial data analysis with multiple case studies across different industries, drawn from manufacturing, retail, and financial services. The creation of economic value is measured by important controllable parameters such as revenue growth, operating cost optimization, return on investment and forecast accuracy. By contrasting business performance pre-adoption and post adoption of predictive analytics they are able to demonstrate tangible changes in economic outcomes. Qualitative findings from a case studies help to describe how insights driven by analytics can lead to better business outcomes.

Cite this Article

S.N. Meyyappan, Amarprakash, N.Sriram, 2026. " Economic Value Creation through Predictive Analytics: Evidence from Data-Driven Firms", International Journal of Economics, Business, Management Research Intelligence (IJEBMRI) 2(2): 46-54.