Analyze Product Return Rates from Ecommerce Export Data
When you analyze product return rates from ecommerce data, you need to match order IDs across separate export files, calculate return percentages per SKU, and sum the cost of refunds plus return shipping. Quadratic joins the files, groups by product, and shows you which items are costing you money in returns.
You can see which SKUs lose money in returns and why, without joining files by hand or writing formulas to group and calculate.
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Upload your spreadsheet.xlsx .xls .csv — free to try, no accountWhat this export contains
| Order ID |
| SKU |
| Product Name |
| Order Date |
| Quantity |
| Item Price |
| Return ID |
| Return Date |
| Return Reason |
| Refund Amount |
| Return Shipping Cost |
| Order Status |
Common questions
How do I analyze product return rates from ecommerce data?
Import both your order and return export files into Quadratic. Use a JOIN or VLOOKUP formula to match Return ID or Order ID from the returns file to Order ID in the orders file. This adds return data as new columns next to each order row, so you can calculate return rate per SKU by dividing returned quantity by total quantity sold.
What is a good product return rate for ecommerce?
Average return rates are 20-30% for apparel, 5-10% for electronics, and under 5% for consumables. Compare your SKU return rates to your category average, not to an overall benchmark, because product type drives the rate more than your operations do.
How do I calculate the total cost of returns per product?
For each returned order, add Refund Amount plus Return Shipping Cost. Group by SKU and sum these costs, then divide by total units sold of that SKU to get cost per unit. This shows which products lose money even if their return rate is average.
Can I group return reasons into categories automatically?
Use Quadratic's Python cell to apply keyword matching or a language model to the Return Reason column. Map phrases like 'too small', 'too big', 'does not fit' to a 'Sizing' category, and 'broken', 'defective', 'poor quality' to 'Quality'. Then count returns by category and SKU.
How do I compare return rates before and after a sale?
Filter the joined data by Order Date to separate sale periods from regular periods. Calculate return rate for each SKU in both periods. If the rate jumps during sales, the issue is buying behavior—multiple sizes ordered with intent to return—not product quality.
Try it with your own file
DataMimi reads the file you actually have — merged cells, headers below row one, totals pasted at the bottom — and shows which rows and columns every number came from.
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