Measuring Digital Advertising Effectiveness Using Predictive Analytics

Abstract

The rapid expansion of digital platforms has fundamentally transformed advertising strategies, making real-time measurement and optimization of advertising effectiveness a strategic imperative for firms. Traditional metrics such as impressions and click-through rates, though widely used, offer limited predictive insight into long-term consumer behavior and return on investment. Predictive analytics, powered by machine learning and big data technologies, enables marketers to forecast customer responses, optimize media allocation, and personalize advertising content at scale. This study examines how predictive analytics enhances the measurement of digital advertising effectiveness by integrating behavioral data, engagement metrics, and conversion patterns. A comprehensive conceptual framework grounded in marketing analytics theory and the stimulus–organism–response (S-O-R) model is proposed. The study offers theoretical contributions and managerial implications for data-driven advertising strategy.