Bibliometric Analysis of Marketing Automation Research - Loso Judijanto (West Science Interdisciplinary Studies, 2026)
Marketing automation has become an increasingly important component of contemporary marketing strategies, enabling organizations to enhance customer engagement, improve operational efficiency, and support data-driven decision-making. Despite its growing adoption across industries, the intellectual structure and development trends of marketing automation research remain insufficiently explored. Therefore, this study aims to map the evolution, knowledge structure, and emerging themes of marketing automation research through a bibliometric analysis. Data were collected from the Scopus database and analyzed using VOSviewer to examine publication patterns, influential literature, keyword co-occurrence, co-authorship networks, institutional collaboration, and country collaboration. The findings indicate that marketing automation research is strongly associated with themes such as artificial intelligence, machine learning, predictive analytics, digital marketing, commerce, and big data. Automation emerged as the central concept linking technological and managerial dimensions of the field. Overlay visualization revealed a shift from industry-specific applications toward advanced technologies, particularly artificial intelligence and machine learning, which have become dominant research topics in recent years. Citation analysis identified machine learning and AI-based business transformation as the primary intellectual foundations of the field. Furthermore, collaboration analyses highlighted the significant contributions of India, the United States, and Germany, alongside a growing network of international research partnerships. The study concludes that marketing automation has evolved from an operational marketing tool into a strategic capability that supports personalization, customer relationship management, and business innovation. These findings provide valuable insights for academics and practitioners while identifying future research opportunities related to generative artificial intelligence, advanced analytics, customer experience optimization, and ethical considerations in automated marketing systems.