Customer Relationship Management Strategies and Practices
Summary
Customer relationship management (CRM) encompasses the strategies, processes and technologies that enable organisations to acquire, retain and deepen relationships with customers. At its core, CRM integrates data analytics, customer behaviour modelling and personalised engagement across multiple channels, from traditional sales and service platforms to emerging social media and mobile applications. Modern CRM frameworks emphasise customer lifetime value and experience management, drawing on predictive analytics and machine-learning algorithms to anticipate needs and deliver timely, relevant offers. A shift from transaction-centric to relationship-centric models has seen the rise of social CRM, which leverages community interactions and user-generated content to co-create value. The integration of artificial intelligence and automation has further transformed decision-making, enabling real-time segmentation, dynamic personalisation and automated responses that enhance efficiency without diminishing human touch. As organisations embrace omnichannel orchestration, they align marketing, sales and service teams to present a seamless customer journey while upholding data privacy, ethical use and sustainable practices. Globally, CRM has proven pivotal in sectors as diverse as automotive, healthcare, finance and hospitality, where contextual insights and adaptive strategies foster loyalty, advocacy and competitive differentiation.
Research from Nature Portfolio
No recent Nature Portfolio content available.
Customer Relationship Management Strategies and Practices publication trend
The graph below shows the total number of articles in customer relationship management strategies and practices across all publications each year (not limited to Nature Index journals).
Technical terms
Social CRM: An approach that combines traditional CRM processes with social media platforms to foster customer engagement, community building and co-creation of value.
AI-integrated CRM systems: CRM solutions enhanced with artificial intelligence for automated decision-making, predictive analytics and personalised customer interactions.
Structural equation modelling: A multivariate statistical technique used to assess relationships among observed and latent variables in CRM research.
Bibliometric analysis: The quantitative study of published literature to identify trends, research themes and scholarly impact in a given field.
References
- How social CRM and customer satisfaction affect customer loyalty. Spanish Journal of Marketing - ESIC (2023).
- A novel framework for investigating organizational adoption of AI-integrated CRM systems in the healthcare sector; using a hybrid fuzzy decision-making approach. Telematics and Informatics Reports (2023).
- Customer relationship management and its impact on entrepreneurial marketing: a literature review. International Entrepreneurship and Management Journal (2022).
- From CRM to social CRM: A bibliometric review and research agenda for consumer research. Journal of Business Research (2022).
About these summaries
This Nature Research Intelligence Topic summary is created with the cited references and a large language model. We take care to ground generated text with facts, and have systems in place to gain human feedback on the overall quality of the process in line with our AI principles. We strive to create accurate and useful summaries for people unfamiliar with the research topic and that supports this goal. These pages are a beta release and will be updated as we learn how best to help people gain value from a research topic summary.
Turn complex research questions into confident strategic decisions
When you're under pressure to set direction, justify investment, or understand your competitive position, you need more than raw data — you need trusted insights you can act on.
Benchmark your performance against global peers using robust, methodologically sound analysis.
Combine quantitative metrics with qualitative expert insight to uncover strengths, gaps and emerging opportunities.
Gain tailored, decision-ready recommendations aligned to your strategic priorities.
Talk to us to learn more about our data dashboards and bespoke strategy reports.
Grow research skills, confidence and careers with training built for every stage of the research lifecycle.
Developed with Nature Portfolio journal Editors and internationally renowned experts. Discover three ways to learn:
Self-paced, online courses in convenient bite-sized units, covering key skills across scientific writing, publishing, grant writing, data analysis, and more.
Expert trainer-led workshops with hands-on exercises and real-time feedback across core research skills, delivered via interactive group sessions.
Editor-led workshops combining core principles in writing and publishing, personalised 1:1 feedback from Nature Portfolio Editors and hands-on exercises.
Explore course catalogues and workshop agendas, enquire about the options or request institutional pricing.