GS4 Student Scholars Symposium

Predictive analytics has emerged as a transformative tool across industries, driving innovation and fostering data-driven decision-making. This scoping review explores diverse applications of predictive analytics, machine learning, and deep learning, highlighting their roles in enhancing operational efficiency, market expansion, profitability, and strategic planning. Predictive models such as decision trees, gradient boosting, and hybrid systems like Grey-Markov forecasting demonstrate exceptional precision in forecasting trends and optimizing operations, particularly in uncertain and competitive environments. Applications in smart buildings, business intelligence, and consumer sentiment analysis showcase how real-time data and machine learning enhance energy efficiency, inventory management, and risk mitigation. Deep learning models, including CNN and LSTM, excel in unstructured data analysis, offering high accuracy in predicting consumer behavior and guiding business strategies. Despite challenges such as data privacy and organizational resistance, the integration of advanced analytics methodologies underscores their scalability, adaptability, and transformative potential. This review consolidates key research contributions, illustrating the impact of predictive analytics in driving business growth, refining operational excellence, and achieving sustained competitive advantage in a data-driven landscape.