Customer Segmentation Through RFM Analysis and K-Means Clustering: Leveraging Data-Driven Insights for Effective Marketing Strategy

Abstract

Consumer segmentation is a very effective methodology that could enable organizations to gain a deeper comprehension of their consumer base and customize their tactics accordingly in order to cater to their unique requirements. Through the process of categorizing clients according to common attributes, organizations can acquire valuable knowledge regarding their requirements, inclinations, and purchasing behaviors. This comprehension empowers firms to develop focused marketing strategies and provide customized experiences that foster customer loyalty and enhance revenue generation. The prevalent categories of criteria in consumer segmentation encompass demographic, psychographic, geographic, and behavioral factors, whilst the prevailing methodologies for constructing customer segments involve rule-based and cluster-based techniques. Rule-based segmentation involves the utilization of pre-established rules to allocate clients into distinct segments. Conversely, cluster-based segmentation employs statistical techniques to identify inherent clusters or groups within the customer population. This research investigates the application of the K-Means clustering technique for the purpose of segmenting customer behavioral data into several categories, namely Platinum, Gold, Silver, Bronze, or Bad. The clustering approach employed demonstrated a notable degree of accuracy and precision. Through the implementation of an appropriate strategy for customer segmentation, organizations have the potential to strengthen their product offers, concentrate their marketing communications, and augment client loyalty.