Artificial Intelligence in Marketing Automation: A Systematic Literature Review on Personalization, Campaign Optimization, and Customer Experience
DOI:
https://doi.org/10.47467/elmal.v6i11.9894Keywords:
Artificial intelligence, Brand equity, Campaign optimization, Customer experience, PersonalizationAbstract
This study aims to systematically review the application of Artificial Intelligence (AI) in marketing automation, with a focus on personalization, campaign optimization, and customer experience enhancement. Specifically, it addresses four research questions concerning the evolution of AI approaches in digital marketing, techniques for predicting customer behaviour and engagement, AI’s role in enhancing personalization and marketing effectiveness, and its contribution to brand equity and loyalty. A Systematic Literature Review (SLR) was conducted using the Scopus database as the primary source, covering publications from 2014 to 2025. Boolean queries were applied to identify relevant studies, followed by PRISMA-based screening to select 100 articles. Additionally, Natural Language Processing (NLP) techniques were employed through Biblioshiny to perform keyword co-occurrence analysis, thematic mapping, and trend visualization, providing an enriched understanding of thematic clusters and research evolution. Results indicate a marked increase in scholarly output on AI in marketing since 2019, peaking in 2022 and 2024. Personalization emerged as the dominant theme (83%), followed by customer experience (73%), while campaign optimization (17%) and brand equity and loyalty (27%) remain underexplored. Machine learning (27%) and deep learning (17%) were the most prevalent AI techniques, with growing adoption of clustering algorithms, NLP, and hybrid recommender systems. The integration of AI has demonstrated significant potential in improving targeting precision, engagement prediction, and customer retention strategies. This research contributes to the literature by combining an SLR approach with NLP-based bibliometric analysis to provide both a conceptual and empirical mapping of AI-driven marketing trends. It identifies research gaps, particularly in AI’s role in brand equity, and proposes future directions for interdisciplinary exploration. The findings offer actionable insights for marketing managers and policymakers to integrate AI tools strategically, prioritizing personalization and predictive analytics to maximize ROI, enhance customer loyalty, and sustain competitive advantage in digital marketplaces.
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