Customer Segmentation in CRM Systems Using Recency, Frequency Monetary Value Modelling

Abstract

In today’s world, customer-centric business strategies make an analytics review of customer trends very crucial for a company’s long-term planning, as well as its strategic organizational continuity. The CRM systems (Customer Relationship Management Systems) assist in managing and tracking interactions with both current and prospective clients, ensuring profitability through vigilant data analysis. However, growing amounts and complexities of customer data require sophisticated solutions. This study aims to determine the use of Recency, Frequency, and Monetary value (RFM) modeling in data-driven approaches to customer relations management system (CRM) segmentation within the scope of advanced data techniques. RFM metrics allow the categorization of customers based on how recently they made a purchase (Recency), their purchase frequency (Frequency), and how much they spend (Monetary). Tailored marketing, improved retention, and appropriate resource allocation can be achieved with crafted strategies improved through understanding customer engagement and value via the proposed parameters. For this study, retail transactional data collected from a CRM system repository were pre-processed and scored on RFM metrics, which were subsequently applied to unsupervised machine learning approaches, particularly K-means clustering, to develop distinct customer segments. Clusters such as high-value loyal customers, potential loyalists, and at-risk customers provided by the analysis are meaningful. The relevance and effectiveness of the segmentation were determined using key performance indicators such as customer lifetime value and churn rate. The research indicates that marketing that focuses on RFM-based clustering improves marketing activities, furthers personalization, and fosters better decisions. This research demonstrates that integrating traditional RFM modeling with contemporary data mining techniques provides a scalable and flexible framework for customer segmentation, making a significant contribution to the field of CRM and customer analytics.