📊 E-Commerce Customer Analytics

🚀 Overview
This project explores customer purchasing behavior using RFM segmentation and discount impact analysis to help businesses optimize revenue.

🔍 Key Insights
✅ Identified VIP, Loyal, and Churned Customers using RFM segmentation
✅ Optimized discounting strategy (15-30% discounts maximize revenue)
✅ Analyzed spending trends by payment methods & customer segments
✅ Provided business recommendations for customer retention & growth

📌 Live Notebook on Kaggle: Check it out here

🚀 Business Recommendations

📢 How Zalando Can Optimize Customer Retention & Revenue:
1️⃣ Retarget Churned Customers with personalized offers
2️⃣ Loyalty Program for VIPs & High Spenders to maintain retention
3️⃣ Optimize Discounting Strategy (Keep between 15-30%)
4️⃣ Leverage Payment Method Insights for smoother checkouts
5️⃣ Use Personalized Marketing based on purchase history

👩‍💻 Author: [Egbe Grace Egbe]

🔗 LinkedIn: [ : www.linkedin.com/in/grace-egbe-77820b278] 🔗 Kaggle: Your Kaggle Profile

📌 GitHub Repository: https://github.com/egbe34/ecommerce-customer-analytics
📌 Live Notebook on Kaggle: https://www.kaggle.com/code/graceegbe12/e-commerce-customer-analytics