Online Retail Customer Segmentation using RFM Quantiles and Clustering Technique

In the fiercely competitive landscape of modern business, making effective decisions is imperative for generating substantial revenue. With organizations employing diverse strategies to stay ahead, leveraging data for informed decision-making is crucial. Understanding individual preferences and purchasing behaviors can be challenging, but machine learning techniques offer a solution by organizing and analyzing vast datasets. Customer segmentation, particularly through K-means clustering, is instrumental in identifying target groups with similar attributes and interests. The present study focuses on customer clustering method-ologies to give businesses a competitive edge. By collecting, analyzing, and visualizing customer data, we aim to develop a data science model that utilizes K-means clustering and the RFM model (Recency, Frequency, Monetary) for existing customers. Utilizing the UK's e-commerce dataset, which delves into customer purchasing behaviors, we aim to group customers into clusters such as high-value customers, mid-range, and those at risk of churn based on RFM scores.