RFM–K-OPT Based Machine Learning Framework for Customer Segmentation and Behavioral Profiling in Direct Marketing

Customer segmentation is an essential element of modern marketing analytics, which helps companies recognize, comprehend, and market to customers depending on their behavioral and transactional attributes. Conventional methods based on Recency, Frequency, and Monetary (RFM) analysis or on simple unsupervised clustering algorithms such as K-Means are very common, but they are usually limited by weaknesses such as sensitivity to centroid starting location, low cluster separability, and low interpretability. Such problems will cause volatile effects of segmentation and restrict the dependability of data-driven marketing choices. In an effort to deal with these concerns, this research study suggests a hybrid model, the RFM K-Means Optimization Technique (RFM–K-OPT), a combination of RFM analytics and K-Means clustering, and an iterative centroid optimization unit. The proposed structure will help to improve cluster compactness, stability, and interpretability using statistical computation and refinement of centroid positioning. The model is written in Python and tested with publicly available customer transaction data. The result of the experimental process shows a better quality of clustering with a Silhouette Coefficient of 0.83, Davies-Bouldin Index of 0.31, Calinski-Harabasz Index of 563, purity of clustering of 94.2 per cent, and an execution time of 5.4 seconds. The results suggest that the RFMK Opt model is a useful tool that offers credible and explainable customer segments, which can be used to make effective behavioral profiles and make sound judgments when it comes to making decisions in the context of direct marketing.