lazaruskmabemairiga-max/Gombe-retail-customer-segmentation

Retail customer segmentation for a POS business in Gombe, Nigeria using RFM analysis and K-Means clustering. # Retail Customer Segmentation — Gombe POS Business ## 📌 Project Overview This data science capstone project segments customers of a POS/retail business in Gombe, Nigeria using **RFM analysis and K-Means clustering**. The goal is to help retailers stop treating every customer the same and instead use customer behaviour to design targeted marketing strategies. ## 🎯 Business Problem Retail businesses often generalise customers. A customer who buys frequently and spends a lot should not receive exactly the same marketing strategy as a customer who has not purchased for a long time. ## 🚀 Objectives - Clean and prepare POS transaction data - Calculate customer RFM metrics - Standardise customer features - Build K-Means clustering models - Evaluate the clustering model - Profile customer segments - Recommend business actions ## 🛠️ Tools - Python - Pandas - NumPy - Scikit-learn - Matplotlib - Jupyter Notebook / Google Colab - Excel ## 📊 Methodology ### RFM Analysis - **Recency:** Days since the customer's last purchase - **Frequency:** Number of purchases - **Monetary:** Total amount spent ### Machine Learning K-Means clustering was tested across different values of K. The model was evaluated using: - Silhouette Score - Davies-Bouldin Index - Calinski-Harabasz Score - Inertia / Elbow Method ## 👥 Customer Segments ### 🏆 High-Value Loyal Customers with strong spending behaviour. **Recommended action:** VIP rewards, loyalty points, personalised offers and priority service. ### 🛒 Regular Moderate-Value The main opportunity group for increasing revenue. **Recommended action:** Product bundles, cross-selling and minimum-spend promotions. ### ⚠️ At-Risk Customers who have not purchased recently. **Recommended action:** WhatsApp/SMS reminders, win-back discounts and personalised promotions. ## 📈 Key Business Value The project helps a POS/retail business: - Improve customer retention - Increase average customer spending - Reactivate inactive customers - Target market …