22 February 2022

Abstract

Purpose – Due to the recent development of Big Data and artificial intelligence (AI) technology solutions in customer relationship management
(CRM), this paper provides a systematic overview of the field, thus unveiling gaps and providing promising paths for future research.
Design/methodology/approach – A total of 212 peer-reviewed articles published between 1989 and 2020 were extracted from the Scopus
database, and 2 bibliometric techniques were used: bibliographic coupling and keywords’ co-occurrence.

Findings – Outcomes of the bibliometric analysis enabled the authors to identify three main subfields of the AI literature within the CRM domain
(Big Data and CRM as a database, AI and machine learning techniques applied to CRM activities and strategic management of AI–CRM integrations)
and capture promising paths for future development for each of these subfields. This study also develops a three-step conceptual model for AI
implementation in CRM, which can support, on one hand, scholars in further deepening the knowledge in this field and, on the other hand,
managers in planning an appropriate and coherent strategy.
Originality/value – To the best of the authors’ knowledge, this study is the first to systematise and discuss the literature regarding the relationship
between AI and CRM based on bibliometric analysis. Thus, both academics and practitioners can benefit from the study, as it unveils recent
important directions in CRM management research and practices.

Methodology
To achieve the research goal, we performed a literature review combined with a bibliometric analysis. Bibliometrics is
described as “the mathematical and statistical analysis of bibliographic records” (Pritchard, 1969) and is used to
establish intellectual linkages among articles and keywords, thus providing a big picture of rising trends and potential
research opportunities . Bibliometric techniques offer the advantage of introducing quantitative rigor compared to
narrative literature reviews, which might be invalidated by the
subjective bias of the researcher