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Artificial Intelligence Applications for Marketing

Author

Listed:
  • Stefan Adrian Susanu

    (Dunarea de Jos University of Galati, Romania)

  • Gianita Bleoju

    (Dunarea de Jos University of Galati, Romania)

  • Alexandros Theodoros Petropoulos

    (University Politehnica of Bucharest, Romania)

  • Alina Matric

    (Dunarea de Jos University of Galati, Romania)

Abstract

This paper provides a comprehensive analysis of artificial intelligence (AI) applications in marketing, synthesizing over 50 academic studies and practical reports published between 2018 and 2024. The main goal is to identify dominant trends, tools used, and the impact of AI technologies on marketing strategies, as well as to highlight ethical challenges and future research directions. The adopted methodology is a systematic literature review (SLR), based on bibliometric and conceptual analysis of articles indexed in the Web of Science database. The tools used include bibliographic network analysis and thematic mapping to identify connections between concepts and the evolution of research in the field. The results highlight that AI is applied in various areas of marketing, including: predictive analytics for anticipating consumer behavior and optimizing advertising campaigns, recommendation systems that personalize offers and improve user experience, chatbots and Natural Language Processing (NLP) for automating customer interactions, programmatic advertising that uses AI for automated media buying, generative AI in creating visual and textual content for marketing campaigns, and visual analysis and dynamic pricing for real-time price adjustments based on demand and consumer behavior. The studies analyzed indicate significant benefits, such as increased return on investment (ROI), improved customer loyalty, and more efficient marketing processes. However, major challenges are also identified, including issues related to data quality and bias, concerns about data privacy and compliance with regulations such as GDPR, and the need to develop robust data governance strategies and clear ethical policies. This paper is addressed to researchers, marketing professionals, AI solution developers, and policymakers interested in the responsible and efficient integration of artificial intelligence into marketing strategies.

Suggested Citation

  • Stefan Adrian Susanu & Gianita Bleoju & Alexandros Theodoros Petropoulos & Alina Matric, 2025. "Artificial Intelligence Applications for Marketing," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 109-119.
    • Handle: RePEc:ddj:fseeai:y:2025:i:2:p:109-119
    DOI: https://doi.org/10.35219/eai15840409517
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    References listed on IDEAS

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    1. Ankush Singhal, 2024. "The Role of Artificial Intelligence in Enhancing Ad Personalization: A Study on Programmatic Advertising," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 3(1), pages 431-445.
    2. Saura, Jose Ramon & Ribeiro-Soriano, Domingo & Palacios-Marqués, Daniel, 2021. "From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets," International Journal of Information Management, Elsevier, vol. 60(C).
    3. Ebtisam Labib, 2024. "Artificial intelligence in marketing: exploring current and future trends," Cogent Business & Management, Taylor & Francis Journals, vol. 11(1), pages 2348728-234, December.
    4. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
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