Intelligent Marketing in the AI Era: A Bibliometric Study of BI and ML Research

The rapid advancement of Business Intelligence (BI) and Machine Learning (ML) has significantly reshaped contemporary marketing research and practice. This study provides a comprehensive bibliometric analysis of BI–ML applications in marketing using 829 Scopus-indexed publications from 2015 to 2025. Descriptive performance indicators reveal a consistent increase in scientific productivity, with a marked acceleration after 2019, reflecting the growing integration of data-driven technologies within marketing decision-making. Keyword co-occurrence analysis identifies nine conceptual clusters, spanning core methodological themes such as machine learning, deep learning, and sentiment analysis, as well as application-oriented areas including customer analytics, digital marketing, and business intelligence. The thematic evolution analysis shows a clear conceptual transition: early research emphasizes big data and decision-support systems, middle-period research advances algorithmic modelling, and recent studies integrate ML within strategic BI-driven marketing contexts. These findings align with the global shift toward AI-enabled marketing environments and highlight the increasing importance of predictive analytics, personalization, and intelligent automation. This study contributes to the literature by mapping the intellectual landscape of BI–ML marketing scholarship, identifying thematic trajectories, and offering insights into emerging research opportunities related to generative AI, model interpretability, and ethical considerations in automated marketing analytics.