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README.md

Sales Returns Strategy & Profitability Dashboard | Excel + Tableau

Dashboard Screenshot

Note: Also listed as “Sales Returns Strategy & Profitability Dashboard” on my resume and LinkedIn profile.

Tool Tool Focus Focus Dataset

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.


📚 Table of Contents


Objectives 🎯

  • 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.

Tools & Technologies 🛠️

Tool Use Case
Tableau KPI dashboards, reporting, visuals
Excel Data cleaning, transformation, metrics

Key Insights 📈

  • 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.

Report Access 📄


Project Files & Instructions 📂

File Name Description
Sales_Returns_Performance_Analysis_Report.docx Final project report with insights & recommendations
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)
Cleaned_Sales_Performance_Dataset.csv Cleaned dataset in CSV format
README_Sales_Returns_Performance_Analysis.md This README file

Conclusion & Recommendations 💡

  • 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.

Final Thoughts 📝

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.