Customer Relationships Management (CRM) Application for Customer Segmentation via RFM Analysis and K-Means Clustering

This paper introduces a web-based solution utilizing PostgreSQL, Python, and PHP (Laravel Framework) to automate Recency, Frequency, and Monetary (RFM) analysis and K-Means clustering for customer segmentation. The study addresses the inefficiencies in the manual method and positions the developed system as a transformative tool for businesses. The research aimed to automate and enhance customer segmentation through a web application, exploring the effectiveness of the solution in automating RFM analysis and clustering via experiments. The developed software is a user-friendly, integrated platform that not only saves time but also minimizes errors inherent in manual analyses. The findings highlight the system’s effectiveness in automating RFM analysis and the added value of comparative insights derived from clustering outcomes. Differentiating the system from the solutions on the market, clustering via Artificial Intelligence (AI) was added to the system. This enables users to compare what the clustering algorithm thinks the groups of users should be against what the RFM analysis produces. Finally, by providing full control of their data to users, the system aims to align with GDPR standards, ensuring both efficiency and legal compliance.