Evolution of customer relationship management to data mining-based customer relationship management: a scientometric...

Area of Science:

• Business and Management
• Information Science
• Data Science

Background:

• Customer Relationship Management (CRM) research is extensive but lacks a focused summary comparing it with data mining-based CRM.
• Existing literature often concentrates on specific industries, leaving a gap in comprehensive overviews.

Purpose of the Study:

• To conduct a scientometric analysis of CRM research output from 2000-2020.
• To specifically evaluate and contrast CRM research with data mining-based CRM.
• To identify emerging trends and future research directions in the CRM domain.

Main Methods:

• Bibliometric data extraction from the Web of Science database (2000-2020).
• Descriptive and scientometric analysis to establish the bibliometric profile of CRM research.
• Multiple Correspondence Analysis (MCA) and clustering to map the conceptual structure of CRM and data mining-based CRM.

Main Results:

• The study maps the bibliometric profile and conceptual structure of CRM and data mining-based CRM research.
• Analysis reveals a significant trend towards machine learning and artificial intelligence techniques in future CRM research.
• Identified key research areas and the evolution of the CRM domain.

Conclusions:

• The research provides a comprehensive overview of the CRM and data mining-based CRM landscape.
• Future CRM research is strongly indicated to leverage advancements in artificial intelligence and machine learning.
• The study offers valuable insights for researchers and practitioners navigating the evolving field of CRM.