ABSTRACT The rapid expansion of digital consumer data has challenged traditional approaches to understanding behavior in digital marketing. Existing reviews often focus on individual methods and give limited guidance on how analytical techniques compare or how they should be selected for specific marketing tasks. This article presents a structured narrative review of artificial intelligence and data‐driven techniques used in consumer behavior analysis, based on a curated body of peer‐reviewed studies. It organizes the field into key methodological families, including clustering, association rule mining, sentiment analysis, predictive modeling, time‐series forecasting, anomaly detection, path and sequence analysis, social network analysis, deep learning and multimodal analytics, generative AI and foundation models, agentic AI, causal analysis, and explainable AI. Building on this taxonomy, the paper offers a comparative synthesis of how each technique's conceptual assumptions, methodological reliability, data requirements, and typical outputs relate to its suitability for segmentation and profiling, personalization and targeting, fraud and risk detection, and engagement or journey optimization. The review also examines challenges related to data quality, integration, interpretability, scalability, and real‐time deployment, together with ethical and regulatory issues such as algorithmic bias, transparency, privacy, data sovereignty, and accountability. Finally, it outlines future research directions around self‐adaptive AI systems, agentic consumer interfaces, edge computing and localized intelligence, participatory data governance, quantum‐inspired analytics, and interdisciplinary methodological innovation. By linking analytical techniques to concrete marketing objectives under explicit ethical and regulatory constraints, the study provides researchers and practitioners with a structured basis for selecting, combining, and governing methods in consumer behavior analytics.
Theodorakopoulos et al. (2026) studied this question.