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All Journal International Journal of Engineering, Science and Information Technology
Maulana, Muhammad Rafli
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Digital Marketing Data Mapping using bibliometric Analysis Maulana, Muhammad Rafli; Irawan, Ade; Setiawan, Adi; Agustina, Agustina
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.849

Abstract

This research examines the importance of digital data marketing as a response to technological advances and changes in the macro environment. The main focus of this research is the role of technologies, such as big data, artificial intelligence (AI), and predictive analytics, in driving successful marketing strategies, particularly for small and medium-sized enterprises (SMEs).To achieve this goal, the researcher used a qualitative research method with a historical approach, aiming to identify trends and developments in the relevant literature. In addition, bibliometric analysis was applied with the help of the VOSviewer tool. This analysis observed a significant growth in the study of digital data marketing. From the search using Publish or Perish covering the period of 1951 to 2025, a total of 1. 354 relevant articles were found. A focus on keyword co-occurrence revealed 6,260 keywords related to digital data marketing. After filtering the keywords into those that appeared at least three times and eliminating less relevant terms, 284 were found and divided into 16 clusters. This bibliometric analysis provides valuable insights for developing digital data marketing research through clear visualization of trends.
Co-Authors Ade Irawan Agustina Wiwik Handayani