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Artificial intelligence in customer relationship management: bibliometric analysis

Nguyen Van Thanh Truong & Tran Cong Toan

International Journal of Electronic Customer Relationship Management2025https://doi.org/10.1504/ijecrm.2025.148918article
ABDC C
Weight
0.50

What the paper says

This study employs bibliometric analysis to investigate the landscape of research on artificial intelligence (AI) within customer relationship management (CRM). Utilising Scopus data comprising 766 articles published from 1992 to 2024 and employing VOSviewer 1.6.20 software, co-authorship, and co-occurrence keyword analysis were conducted to assess the prevalence, impact, and evolving trends in this field. The findings reveal a notable increase in the quantity and depth of research on AI in CRM, spanning diverse organisations and countries globally. The analysis identifies four key themes with significant research activity: the application of machine learning and deep learning in CRM, the utilisation of big data in CRM, and the integration of CRM with various data techniques. This review contributes a comprehensive overview of prior studies and highlights the developmental trajectories of AI within CRM, offering valuable insights for organisations, researchers, and scholars seeking to identify promising areas for further investigation.

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Cite this paper

https://doi.org/https://doi.org/10.1504/ijecrm.2025.148918

Or copy a formatted citation

@article{nguyen2025,
  title        = {{Artificial intelligence in customer relationship management: bibliometric analysis}},
  author       = {Nguyen Van Thanh Truong & Tran Cong Toan},
  journal      = {International Journal of Electronic Customer Relationship Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijecrm.2025.148918},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.