Abstract

This study examines the impact of analytical customer relationship management (aCRM) strategies, specifically the segmentation approach using RFM analysis and artificial learning methods, on customer satisfaction, revenue performance, and loyalty in businesses. The research adopts an approach that integrates data from both online and offline channels onto a single platform, providing a holistic view of customer behaviors. Combining the segmentation obtained through RFM analysis and artificial learning methods with timely campaigns has enhanced shopping opportunities for customers and increased customer satisfaction and loyalty. The use of aCRM as a strategic marketing and sales tool has enabled businesses to manage customer relationships more effectively. This paper contributes to the literature in this field by presenting in detail the advantages offered by aCRM, its application methods, and the results obtained.

Keywords:
Customer relationship management Loyalty Customer satisfaction Knowledge management Market segmentation Computer science Customer retention Field (mathematics) Revenue Loyalty business model Customer intelligence Marketing Business Mathematics

Metrics

1
Cited By
0.47
FWCI (Field Weighted Citation Impact)
15
Refs
0.70
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Customer Service Quality and Loyalty
Social Sciences →  Business, Management and Accounting →  Organizational Behavior and Human Resource Management
Customer churn and segmentation
Social Sciences →  Business, Management and Accounting →  Marketing
Technology Adoption and User Behaviour
Social Sciences →  Decision Sciences →  Information Systems and Management

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