DATA-DRIVEN CONSUMER PSYCHOLOGY: HOW ANALYTICS SHAPES PURCHASE DECISION-MAKING IN THE DIGITAL MARKETPLACE

Abstract

AI-driven analytics has revolutionized digital commerce, affecting how organizations interact with customers and how consumers shop. The present study from an interdisciplinary perspective that combines the theories of business management, data analytics, and consumer behavior, investigating how algorithm-based customization affects trust, risk perception, and purchase intentions of young digital consumers. Drawing on a structured survey of 150 university-age respondents alongside recent secondary industry data, the study finds that perceived personalization is positively associated with trust in AI recommendations (r = .61), which in turn is the strongest predictor of purchase intention (r = .66). Perceived privacy risk exerts a moderate negative influence on both trust and purchase intention. The study presents a conceptual framework linking analytics inputs, psychological processing, and purchase behavior, supported by descriptive statistics, correlation analysis, and visual mapping of the digital purchase funnel. Findings carry practical implications for marketing managers seeking to balance personalization benefits against consumer privacy concerns, and contribute an integrative, cross-disciplinary perspective to the growing literature on AI-consumer interaction.