Customer-Segmentation-RFM-Analysis-with-Clustering

Customer segmentation is a popular task in data analysis, it enables organizations to target particular customer groups with customized marketing efforts. The Online Retail II data set from the ML Repository, which contains transaction data of an online retailer from 2010 to 2011, was utilized in this study to apply several clustering approaches. K-means, Hierarchical clustering and Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) were the techniques that were utilized. So, to find the most valuable customers, I used RFM analysis (Recency, Frequency, Monetary) in addition to clustering. The RFM analysis allowed us to identify the customers with the highest potential value, which can be targeted with personalized marketing strategies. Moreover, I utilized the Silhouette analysis, Davies-Bouldin and Calinski Harabasz index to evaluate the effectiveness of the various clustering technique.