This project analyzes product return trends for a fictional Superstore. Using Tableau, I explored return rates, geographic patterns, and product-level data to help uncover the root causes behind high product returns. The goal: help the company improve product quality, customer satisfaction, and operational efficiency.
Key Questions Explored:
Which products and categories have the highest return rates?
How do geographic trends impact return volume?
Are certain customer segments or time periods more prone to returns?
What can Superstore do to reduce returns going forward?
Insights Uncovered:
Office Supplies (especially binders and paper) had the highest return volume due to their large share of total sales.
Consumer segment customers returned more items than Corporate or Home Office buyers.
High-value items like appliances, tables, and copiers had the highest return rates — 25% or more in many cases.
Return spikes occurred in August and December (Back-to-School and Holiday seasons).
California led all states in returns (over 300), followed by Washington, New York, and Texas.
Visual Breakdown:
Sales vs Returns Correlation by category
Return Rate by Product Type and Segment
Returns by State (with linked customer detail)
Return Rate Trends by Month
Top Returned Products
Recommendations:
Enable customer feedback to understand return reasons
Improve product descriptions and quality checks
Adjust return policies to reduce abuse while staying customer-friendly
Monitor seasonal trends to prepare inventory and logistics
Evaluate delivery partners and include delivery experience in feedback
Tools Used:
Tableau (Dashboard creation & interactive filtering)
Excel (Data cleaning and prep)
Superstore Dataset (Sales, returns, customer segments, product data)
Project Assets: