This chapter synthesizes contemporary research on consumer behaviour in online retail, linking a non-linear decision journey with the drivers of purchase intention and data-driven segmentation. We first describe how digital touchpoints compress and loop the stages of need recognition, search, evaluation, purchase, and post-purchase sharing, emphasizing the roles of social proof, reviews, and algorithmic cues in accelerating choices. We then integrate evidence on the antecedents of online purchase intention—trust versus perceived risk, website quality and usability, and the dual contribution of utilitarian (convenience, value) and hedonic (flow, enjoyment) benefits—showing how each factor shapes conversion. Building on this foundation, the chapter reviews segmentation and targeting approaches suited to e-retail: demographic and generational patterns, psychographic motives and decision styles, and behaviour-based methods that exploit clickstream and transaction data (RFM, CLV, clustering). We discuss practical targeting levers—from personalized recommendations to omni-channel coordination—while noting governance considerations around privacy, fairness, and “creepiness” in personalization. The contribution is a cohesive framework that ties consumer psychology to analytics and retail operations, offering actionable guidance for scholars and managers seeking to design trustworthy, engaging, and performance-oriented digital shopping experiences.