AI-Powered Personalization and Consumer Buying Behaviour in Digital Markets

Abstract

Artificial Intelligence (AI)-powered personalization has emerged as a central component of contemporary digital commerce. The increasing availability of consumer data, advances in machine learning, predictive analytics, natural language processing, recommender systems, and generative AI has enabled digital businesses to deliver highly individualized experiences to consumers. Instead of presenting identical products, advertisements, prices, and messages to all users, digital platforms can analyse consumer characteristics and behavioural signals to predict preferences and customize commercial interactions. This transformation has significant implications for consumer buying behaviour, including product discovery, information search, purchase intention, decision-making, customer satisfaction, impulse purchasing, brand loyalty, and post-purchase behaviour.
This paper examines the relationship between AI-powered personalization and consumer buying behaviour in digital markets. It adopts a conceptual and analytical approach, drawing on theories of consumer behaviour, personalization, technology acceptance, information processing, and trust. The paper examines major applications of AI-powered personalization, including recommendation systems, personalized advertising, dynamic content, conversational commerce, predictive analytics, personalized pricing, and generative AI shopping assistants. It argues that personalization can reduce information-search costs and decision complexity while increasing perceived relevance and convenience. These mechanisms can positively influence purchase intention and customer engagement.