This capstone project focuses on segmenting ORA-FASHION’s customer base to enhance loyalty and retention through targeted marketing strategies. By employing Recency, Frequency, and Monetary (RFM) analysis in conjunction with unsupervised clustering algorithms, customers were effectively segmented into distinct groups based on their purchasing behaviors. Additionally, an Exploratory Data Analysis (EDA) was conducted, and Customer Lifetime Value (CLV) was calculated to enrich the segmentation analysis. The clustering validity was confirmed using metrics like the Silhouette Score and Davies-Bouldin Index. These insights will drive personalized marketing efforts, optimizing customer engagement and driving long-term business growth.
Customer Segmentation, RFM Analysis, k-Means, Customer Lifetime Value (CLV), Targeted Marketing