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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Quantium Data Analysis Case Study\n\n## 📌 Project Objective\nAnalyze Quantium’s retail transaction and customer behavior data to generate actionable business insights, understand purchasing patterns, and support data-driven decision-making.\n\n---\n\n## 📂 Dataset Description\n\n**Datasets:**  \n- `QVI_transaction_data`: Retail transaction records.  \n- `QVI_purchase_behaviour`: Customer demographic and loyalty segment data.\n\nKey columns include transaction date, store number, product details, quantities, sales, and customer segments.\n\n---\n\n## 🔍 Key Findings\n- Cleaned and merged transactional and behavioral datasets for consistent analysis.\n- Identified sales trends with clear monthly seasonality and peak periods.\n- Uncovered the most popular product categories and top-selling products.\n- Profiled customer segments, revealing spending differences across demographics.\n- Used statistical tests to confirm significant differences in purchasing behavior between segments.\n\n---\n\n## ✅ Actionable Recommendations\n1. **Focus Promotions:** Target high-value segments with personalized offers during peak purchasing months.\n2. **Optimize Inventory:** Prioritize stocking top-selling products based on trends.\n3. **Customer Engagement:** Design loyalty programs for segments with lower spending to increase retention and spend per visit.\n4. **Marketing Strategy:** Align marketing campaigns with identified purchasing trends to boost sales.\n\n---\n\n## 📈 Result Impact\nThe analysis provided clear, data-driven insights that can help Quantium’s retail clients increase sales, better understand their customers, and make smarter inventory and marketing decisions.\n\nExplore the notebooks to see the detailed workflow, visualizations, and 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