In the fast-paced world of e-commerce, understanding customers is vital for a successful business. This project focuses on segmenting customers in an online cosmetics shop using RFM (Recency, Frequency, Monetary) analysis and K-means clustering. Customers are classified into four segments ("Loyal Customers", "Potential Loyalist" "At-Risk", and "New Customers.") based on their buying patterns and engagement levels. Through analyzing customer behaviors by segment, the cosmetics shop can develop targeted marketing strategies, enhance customer satisfaction, and optimize revenue generation in the highly competitive market.
Customer Segmentation RFM.ipynb: Python notebook for the project
Download from kaggle
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