Customer Segmentation Based on RFM Attributes Using Machine Learning

The objective of this study was to segment customers for an online retail E-commerce website of a UK retailer using clustering machine learning techniques based on RFM attributes. The study applied the K-Means clustering algorithm to group customers into clusters, and various methods such as the elbow method and silhouette score were used to determine the optimal number of clusters. The study found that four customer clusters were optimal, and marketing recommendations were provided for each customer segment based on their RFM characteristics. The results and customer segments identified could be used to shape marketing campaigns for similar products with the help of a marketing expert.