https://github.com/rafailbesparas/quantium_data_analysis_case_study
Data analytics case study for Quantium: retail transactions, purchasing behavior, EDA, and actionable insights.
https://github.com/rafailbesparas/quantium_data_analysis_case_study
data-science pandas python scikitlearn-machine-learning
Last synced: 12 months ago
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Data analytics case study for Quantium: retail transactions, purchasing behavior, EDA, and actionable insights.
- Host: GitHub
- URL: https://github.com/rafailbesparas/quantium_data_analysis_case_study
- Owner: RafailBesparas
- Created: 2025-07-03T20:20:07.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-07-18T15:56:38.000Z (about 1 year ago)
- Last Synced: 2025-07-18T19:48:56.004Z (about 1 year ago)
- Topics: data-science, pandas, python, scikitlearn-machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 24.7 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Quantium Data Analysis Case Study
## 📌 Project Objective
Analyze Quantium’s retail transaction and customer behavior data to generate actionable business insights, understand purchasing patterns, and support data-driven decision-making.
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## 📂 Dataset Description
**Datasets:**
- `QVI_transaction_data`: Retail transaction records.
- `QVI_purchase_behaviour`: Customer demographic and loyalty segment data.
Key columns include transaction date, store number, product details, quantities, sales, and customer segments.
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## 🔍 Key Findings
- Cleaned and merged transactional and behavioral datasets for consistent analysis.
- Identified sales trends with clear monthly seasonality and peak periods.
- Uncovered the most popular product categories and top-selling products.
- Profiled customer segments, revealing spending differences across demographics.
- Used statistical tests to confirm significant differences in purchasing behavior between segments.
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## ✅ Actionable Recommendations
1. **Focus Promotions:** Target high-value segments with personalized offers during peak purchasing months.
2. **Optimize Inventory:** Prioritize stocking top-selling products based on trends.
3. **Customer Engagement:** Design loyalty programs for segments with lower spending to increase retention and spend per visit.
4. **Marketing Strategy:** Align marketing campaigns with identified purchasing trends to boost sales.
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## 📈 Result Impact
The 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.
Explore the notebooks to see the detailed workflow, visualizations, and insights.