Assista123/Customer-Segmentation-for-a-Retail-Chain-in-Nigeria

Title: # RFM Customer Segmentation & Clustering Project: The goal was to segment a retail customer base using RFM analysis and K-Means clustering to identify key customer groups for targeted marketing strategies. Data: Retail Sales Transaction Data. Methodology: RFM Calculation (Recency, Frequency, Monetary), Outlier Handling (Capping at P99), Data Scaling, K-Means Clustering (k=4), and PCA for visualization. Key Findings/Segments: Four segments were identified: VIPs (1.6%), Loyalists (4.1%), Promising Customers (18.1%), and At-Risk Customers (76.2%). Actionable Insights: Focus strategies include premium rewards for VIPs, upselling for Loyalists, and win-back campaigns for At-Risk customers. Setup & Run: To run this notebook, clone the repo and install the required libraries (see requirements.txt).