Optimizing Marketing Strategies with RFM Method and K-Means Clustering-Based AI Customer Segmentation Analysis

Abstract

Retrospectively, an organization’s capacity to comprehend the distinct needs of its clients will undoubtedly provide it with a competitive advantage in terms of delivering targeted client services and tailoring personalized marketing initiatives. This research investigated the efficiency of the k-means clustering algorithm as a technique for efficient consumer segmentation. The k-Means algorithm consolidated with RFM analysis is globally accredited as a profound partitioning clustering technique that has proven to be highly efficient in various business settings. The experimental outcomes provided persuasive evidence of the algorithm's performance in terms of consumer segmentation. The overall cluster purity evaluation was computed to be 0.95. This value demonstrated that the k-Means clustering algorithm incorporated with the RFM analysis attained a relatively high accuracy rate of 95% in terms of precisely and accurately segmenting the consumers based on their shared behaviors and characteristics. The high purity value of 0.95 illustrated the efficiency of the k-Means clustering algorithm in terms of accurately segmenting and categorizing the clients. This showcased that the algorithm efficiently organized and pinpointed consumers into distinct clusters based on their similarities, facilitating targeted marketing strategies and personalized approaches.