RFM-T Model Clustering Analysis in Improving Customer Segmentation

Abstract

In the dynamic landscape of business, understanding and identifying customers are paramount for effective marketing strategies. This study delves into the realm of customer segmentation, a crucial component of robust marketing strategies, particularly focusing on the widely adopted RFM (Recency, Frequency, and Monetary) model. Various new models of RFM have been explored, with a notable extension being the RFM-T model, introducing the "T" variable to represent Time. This study aims to compare the performance of the traditional RFM model with the innovative RFM-T model, assessing their efficacy in customer segmentation. Utilizing a dataset sourced from a US-based online retail platform, the study employs the K-Means algorithm for segmentation, a method commonly utilized for partitioning data points into distinct clusters. To ascertain the optimal number of clusters, the Elbow Curve approach is employed, offering insight into the granularity of segmentation. Subsequently, the Silhouette Score, a metric used to assess the cohesion and separation of clusters, is leveraged to evaluate the quality and effectiveness of both models. By conducting a comparative analysis of the traditional RFM model and its enhanced RFM-T counterpart, the study endeavors to shed light on their respective contributions to the refinement of customer profiling and segmentation strategies within the online retail industry. Through this exploration, businesses can glean valuable insights into the evolving landscape of customer segmentation, thereby enabling them to tailor their marketing efforts more precisely and effectively to meet the dynamic needs and preferences of their target audience.