Have a question about your returns? Find it below, and we'll point you to the dataset that answers it. For what each dataset contains, see Analytics: Dataset Page.

Which products are returned most, and why?

Use Product performance. It shows the return rate and the two main return reasons for each product. For a quick top list, use Top 100 returned products.

Is a size or color causing returns?

Use Product performance by size or by color. A high return rate for one size often points to a sizing issue.

What do customers say about a product?

Use Product comments and Reason tags per product. They're great to share with your design or buying team.

What did we refund this month?

Use the Refund transaction report. It lists every refund and store credit by the date it was paid, which matches your bookkeeping.

How much will we still refund?

Use the Financial summary of returns. It shows the value of returns that are registered or inspected but not refunded yet.

How much revenue did exchanges and store credit save?

Use Financial – Exchange, refunds and store-credits for the value, or the Split version for the number of orders. If you offer store credit instead of exchanges, use Refund or Store-credit.

Which markets return most?

Use Return rate by market, and Financials by market to see the value per country.

Does our carrier invoice match the labels we created?

Use Return shipping label details to compare label by label, or Return shipping label statistics for daily totals.

Which labels failed?

Use Failed Return Shipping Label Requests. Tip: paste the order numbers into Custom list on the Manage page to work through them in one go.

Which refunds or exchanges didn't go through automatically?

Use Requests with processing errors. These need to be handled manually.

What's on its way back to the warehouse?

Use Incoming returned products by tracking status. It's handy when an exchange item is out of stock and you want to know if one is coming back.

How many returns should we staff for?

Use Return count over week to see the trend, and Return request status for today's workload.

How is our warehouse or 3PL doing?

Use Inspection status to see how many items were inspected each day, and Inspection and refund speed statistics to see how long it takes.

Who handled how many refunds?

Use Refunds by agents statistics. To see who has open requests, use Assigned to.

Do customers buy again after a return?

Use Retention: re-purchased after a return. To find first-time customers to win back, use First time buyers who returned products.

How do customers feel about returning with us?

Use Reviews and ratings.

Use Return request details with current tracking status and Exchange order details. They contain the full data behind your returns.

That's usually because datasets use different dates. Some look at when the order was placed, others at when the return was registered, or when it was refunded. So the same month can show different numbers. Each dataset's date is listed in Analytics: Dataset Page.