Predictive analytics has become an indispensable tool in marketing, enabling organizations to anticipate consumer needs, personalize engagement, and optimize strategies for improved performance. This entry examines predictive analytics specifically within the context of consumer behavior, integrating advanced analytical methods with established behavioral theories such as the Theory of Planned Behavior, the Stimulus–Organism–Response (S–O–R) model, Prospect Theory, and relationship marketing principles. By linking data-driven techniques to theoretical foundations, the discussion demonstrates how predictive models transform raw data into actionable consumer insights. The entry explores key applications including purchase pattern forecasting, personalized recommendations, customer segmentation, dynamic pricing, and churn prevention. For each, examples illustrate how predictive analytics uncovers behavioral patterns, aligns with psychological and sociological constructs, and informs proactive marketing interventions. Techniques such as machine learning, data mining, and statistical forecasting are presented in a streamlined, application-driven manner, emphasizing their relevance to understanding and influencing consumer decision processes. Emerging trends and a forward-looking research agenda are also discussed, highlighting priorities such as real-time and context-aware modeling, privacy-preserving analytics, integration of multi-modal data, cross-cultural predictive modeling, longitudinal analysis of consumer identity evolution, and model explainability. Enhanced conceptual figures provide visual frameworks for linking data sources, analytical methods, theory, and consumer outcomes. By bridging predictive analytics with consumer behavior theory, this entry contributes to both marketing scholarship and practice. It demonstrates how combining rigorous analytics with behavioral insight leads to more precise, ethical, and culturally relevant marketing strategies, offering value to academics, practitioners, and policymakers engaged in data-driven decision-making.