Refund Report for Shopify
Report Pundit’s Refund Report records the details of money returned by a Shopify merchant to unsatisfied customers when they return the product/service. These reports help the merchant keep track of the refunds issued, monitor customer satisfaction and have a proper record of financial transactions for filing taxes.
5 from 1,800+ merchants
2,000+ data fields
Trusted by 40,000+ Shopify stores
Updated June 2026
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Why this report matters
Refunds are revenue running backwards, and most stores only see the total. The number that matters is the pattern: which product, which reason, which channel — because that’s what you can actually fix.
Shopify shows refunds on individual orders and in finance summaries, but a dedicated, filterable refund ledger you can schedule isn’t native on lower plans. So returns get reconciled at month-end and the root cause is never isolated.
A refund report ties each refund to its product, reason, and channel and computes a return rate. With returns a major drag on ecommerce margin, finding the one SKU or sizing issue behind a cluster of refunds often pays back faster than any acquisition spend.
What’s included
Order & refund date
The original order and the date the refund was processed.
Heads up: A refund is dated when processed, not when the order shipped — a late refund lands in a later period than the sale.
Refund amount
Value returned to the customer, partial or full.
Formula: Refund amount = Returned item value + refunded shipping/tax − restocking fees
Refund type
Full vs partial, and whether shipping/tax were included.
What & where
Product / variant / SKU
The item refunded, so returns can be traced to a specific product or size.
Example: Returns cluster on “Merino Crew – L” — a sizing problem, not a quality one.
Channel & location
Where the original sale and the refund occurred (online vs a specific store).
Staff
Who processed the refund — relevant for POS and support audit trails.
Reason & rate
Refund reason / note
The reason captured at refund time (damaged, wrong size, changed mind).
Heads up: Reasons are only as useful as they’re recorded; encourage staff to log a consistent reason or the analysis stays shallow.
Return rate
Refund-proneness of a product or the store overall.
Formula: Return rate = Returned units ÷ Units sold × 100
Who uses this report
Quality / merchandising
Finance
CX lead
Ops / POS manager
How to read the report
- Group by reason before anything else. “Wrong size” and “damaged” call for completely different fixes; the total tells you neither.
- Localize to the SKU. A product-level return rate hides which variant drives it; drop to the SKU to name the culprit.
- Mind the timing. Refunds are dated when processed, so a return spike can belong to last month’s sales — reconcile accordingly.
- Watch staff and channel outliers. An unusual refund pattern by one register or channel is worth a closer look, for policy and for fraud.
How to build the report in Report Pundit
- Open Report Pundit in your Shopify admin and choose Create Report (or the pre-built “Refund Report” template).
- Set the data source to Refunds / Returns.
- Add columns: Order, Refund date, Refund amount, Refund type, Product, Variant, SKU, Channel, Location, Staff.
- Add the Refund reason / note column where you capture it.
- Add a calculated Return rate column (returned units ÷ units sold).
- Set your date range and group by Product or Reason depending on the question.
- Run, confirm a known refund appears with the right amount, and Save.
- Schedule a weekly send to CX and finance, or export to Google Sheets for the quality review.
Sample report
Customization & filters
- Group by product, reason, channel, location, or staff.
- Filter to partial vs full refunds, or above a value threshold.
- Add a monthly return-rate trend column.
- Pair with the Order Report to see the full original transaction.
- Add a margin-impact column where product cost is set.
Automate & export
- Schedules — hourly, daily, weekly, monthly, or custom cron
- Delivery formats — Excel, CSV, PDF, or push to Google Sheets in real time
- Group by month or week — trends instead of a static snapshot
- Destinations — email (multiple recipients), Google Sheets, Google Drive, FTP/SFTP, Looker Studio, BigQuery
- Conditional alerts — get notified only if a channel's net sales drop more than X% week-over-week
Report Pundit vs Shopify's native Sales by Channel report
Frequently Asked Questions
Yes, where reasons are recorded at refund time. Grouping by reason separates “wrong size” from “damaged,” which point to different fixes — so encourage consistent reason logging.
Yes — a calculated column (returned units ÷ units sold), at the product or store level, so you can see how return-prone an item really is.
Refunds are dated when processed, not when the order shipped. A late return lands in the period it was refunded, which matters for reconciliation.
Benefits
Accurate Refund Tracking
Improved Customer Retention
Better Inventory Management
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