Understanding Consumer Behavior Through AI-Driven Predictive Analytics

Abstract: The rapid proliferation of artificial intelligence (AI) and big data analytics has fundamentally transformed how organizations understand, predict, and influence consumer behavior. Despite the growing adoption of AI-driven predictive analytics in marketing, there remains limited theoretical integration explaining how these technologies shape consumer decision-making processes. This paper presents a conceptual framework that examines the relationships among AI-based personalization, predictive analytics capability, consumer data quality, and AI recommendation accuracy as independent variables, consumer trust as a mediator, and purchase intention and customer loyalty as dependent variables. Drawing upon the Technology Acceptance Model, the Theory of Planned Behavior, the Stimulus–Organism–Response framework, and Expectation Confirmation Theory, the study synthesizes current literature across AI in marketing, machine learning applications, recommendation systems, customer segmentation, and consumer journey analytics. The proposed framework contributes to the literature by providing a holistic theoretical model that bridges the gap between technological capabilities and behavioral outcomes. Practical implications suggest that organizations should prioritize data quality and algorithmic transparency to foster consumer trust, while ethical considerations regarding privacy and bias in AI models warrant careful governance. The findings offer actionable insights for marketers, technology developers, and policymakers seeking to leverage predictive analytics responsibly in consumer-facing applications.