Identified return-related profit loss drivers and regional patterns using Excel and Tableau for strategic recommendations.
Note: Also listed as “Sales Returns Strategy & Profitability Dashboard” on my resume and LinkedIn profile.
This project analyzes sales trends and return patterns to identify financial losses and optimize profitability. The dataset highlights key insights into high-return product categories, customer behavior, and regional return patterns.
- Objectives
- Tools Used
- Key Insights
- Report Access
- Project Files & Instructions
- Conclusion & Recommendations
- Final Thoughts
- Analyze return rate and financial loss from returned items.
- Identify high-return product categories and segments.
- Support decision-making to improve sales performance and reduce losses.
| Tool | Use Case |
|---|---|
| Tableau | KPI dashboards, reporting, visuals |
| Excel | Data cleaning, transformation, metrics |
- Over $23,000 in profit was lost due to returned products, with California and Texas responsible for over 35% of that loss.
- Top return categories: Binders, Paper, Phones.
- Return Rate: ~8% of all orders involved returns.
- High-loss regions: California, Texas, and New York drove major return-related losses.
| File Name | Description |
|---|---|
Sales_Returns_Performance_Analysis_Report.pdf |
Final project report with insights & recommendations |
Sales_Performance_Analysis_Dashboard.twbx |
Tableau workbook for interactive exploration of the dashboard |
Sales_Returns_Performance_Analysis_Dashboard.png |
Static image preview of the Tableau dashboard |
Cleaned_Sales_Performance_Dataset.xlsx |
Cleaned dataset used for analysis (Excel format) |
README_Sales_Returns_Performance_Analysis.md |
This README file |
- Monitor & Reduce High-Return Products: Improve quality and service for Binders, Paper, and Phones to minimize return impact.
- Implement Location-Based Return Strategies: Focus on California, Texas, and New York with revised policies and better product tracking.
- Maximize Profitability: Optimize inventory for low-return, high-margin categories like Copiers and Appliances.
This project demonstrates core capabilities expected of Business Analysts, Operations Analysts, and CRM Specialists—turning raw transaction data into actionable business insights. It demonstrates the ability to identify performance issues, support decision-making, and propose strategic improvements based on data.
⚠️ This project is part of a business-focused analytics portfolio designed to support CRM, operations, and BI roles. For more projects, visit my main GitHub portfolio.