This project evaluates Sephora's return policy using data warehousing principles. The goal is to analyze product return patterns, assess their impact on inventory, and enhance return management using ETL processes, BI dashboards, and data visualization.
Sephora's high-end makeup and personal care products contribute to a volatile return rate. Customers often return products due to impulse purchases, unclear return policies, or unsatisfactory products.
- Lack of transparency in return policy:
- Some customers receive return bans without clear explanations.
- Sephora needs a structured return-tracking system.
- Operational impact of returns:
- Store staff may mislead customers on return eligibility to boost sales.
- Inventory turnover is affected by frequent returns.
- Distinguishing return sources:
- Returns may come from customers or suppliers.
- Need to evaluate supplier performance based on return rates.
📌 Solution: A data warehouse to analyze return trends, customer eligibility, supplier evaluation, and inventory impact.
| Dimension | Description |
|---|---|
| Time | Return date, transaction time |
| Product | Product ID, name, category, price |
| Supplier | Supplier ID, name, location, contract details |
| Customer | Customer ID, membership status, purchase history |
| Return Info | Return ID, reason, return method (online/in-store) |
| Fact Table | KPIs & Metrics |
|---|---|
| Return Facts | Return quantity, return value ($), return rate (%) |
| Customer Eligibility | Customer return frequency, ban status |
| Supplier Evaluation | Supplier-based return rates |
| Inventory Management | Total stock, restock quantity, return impact |
📌 Normalization: 2NF applied to minimize redundancy while maintaining business process efficiency.
🚀 Steps:
- Extract relevant data from Sephora’s internal systems (SAP, SQL, Linux-based inventory).
- Transform data to maintain consistency:
- Standardize date formats (YYYY/MM/DD).
- Convert missing values (e.g.,
99999for unknown numerical data). - Enforce referential integrity (linking dimension & fact tables).
- Load into a structured data warehouse for reporting & BI applications.
📌 ETL Automation: Automated pipelines ensure real-time updates.
👥 Users: Sephora's Sales, Supply Chain, and Operations teams.
- 📊 Total Product Returns by Membership Status (Table format)
- 🥧 Return Quantity by Method (Online vs. In-store) (Pie chart)
- 📈 Top 5 Suppliers with Highest Return Rates (Bar chart)
- 📊 Sales vs. Returns by Membership Status
- 📆 Quarterly Sales & Return Trends
- 📊 Year-End Product Inventory Levels (Tree map)
- 📆 Quarterly Inventory Turnover Analysis
📌 BI Tools Used: Tableau, Power BI
✅ Key Insights:
- Customers with high return frequency risk being banned.
- In-store staff may be influencing return rates by misinforming customers.
- Returns are highest in certain beauty product categories.
- Supplier return rates vary, affecting Sephora’s inventory management.
🔧 Recommended Actions:
- 🎯 Enhance return policy transparency to reduce customer confusion.
- 📊 Monitor supplier return rates to negotiate better contracts.
- 🛒 Improve inventory forecasting to manage stock turnover effectively.
- 🏷️ Restrict excessive returns based on customer eligibility metrics.
🛠 Data Processing & BI Tools:
- SQL, SAP, Linux-based inventory systems – Data extraction
- ETL tools (Talend, Informatica) – Data cleaning & transformation
- Tableau, Power BI – Data visualization & BI reporting
💾 Data Source: Synthetic data.
🔍 Summary:
- Sephora needs better return-tracking for customer eligibility, supplier performance, and inventory management.
- BI dashboards provide insightful return metrics to improve business operations.
📌 Future Enhancements:
- Machine learning to predict fraudulent returns.
- Integration with POS data for real-time tracking.
- Customer feedback analysis to refine return policies.