Comparative Analysis of Clustering Techniques for Customer Segmentation: Evaluating K-Means, Hierarchical, and DBSCAN Models alongside RFM Frameworks to Enhance Marketing Strategies through Behavioral, Demographic, and Transactional Insights

The study aims at clustering, segmentation customer using K-means clustering model, hierarchical clustering model, Density-based Spatial Clustering of Applications with Noise (DBSCAN) model and customer segmentation frameworks (RFM), A few unsupervised machine learning (ML) clustering models such as K-means clustering model, hierarchical clustering model, Density-based Spatial Clustering of Applications with Noise (DBSCAN) and customer segmentation frameworks (RFM), to identify distinct and actionable customer segment based on their behavioral, demographic and transactional characteristics. The traditional model was included in the analysis since clustering models are not optimization models and the goodness of unsupervised models could only be evaluated with a practical business approach. Theresults and discussion of the paper highlightscustomer segmentation based on their historical transactional characteristics, evaluating the effectiveness of different clustering algorithms and customer segmentation framework the potential for future enhancements. The emphasis on statistical analysis and the evaluation of various clustering techniques provide valuable insights into effective customer segmentation strategies