AI-powered consumer behavior analysis: A review of applications, techniques, and ethical implications

Abstract

Artificial Intelligence (AI), Machine Learning (ML), and Big Data Analytics have converged, changing how companies gain insights into, predict, and respond to consumer behavior. This paper will discuss the theory, methodology, and commercial applications of interpreting consumer behavior using AI-driven data analysis, including technologies such as recommendation systems, sentiment analysis, predictive customer behavior, and computer vision. The research, using supervised, unsupervised, and deep learning techniques, highlights the potential of AI to segment customers, personalize marketing strategies, forecast demand, implement dynamic pricing, and predict churn across various sectors such as retail, banking, healthcare, and entertainment. The paper also highlights the advantages of using AI, including enhanced personalization, increased conversion rates, and real-time consumer insights, as well as ongoing issues with data privacy, algorithmic bias, transparency, and ethical oversight. The paper is a research proposal that presents a conceptual framework, research questions, and testable hypotheses related to AI personalization, consumer trust, and purchase intention; outlines a mixed-methods design for future empirical validation; and identifies research directions for further investigation, including Emotion AI, generative AI, and Metaverse-integrated commerce. The literature synthesis suggests that AI is moving organizations away from reactive to predictive and prescriptive decision-making.