Shopify Return Request Analytics

Shopify return request analytics helps you find products, variants, customers, and orders that generate after-sale work. Mixtable can measure return volume, returned quantities, return rates, reasons, open requests, and closure time beside sales and refund metrics.

Why returns deserve their own analytics

Returns are the most expensive thing most stores never measure. Each one costs return shipping, inspection, restocking, support time, and often the product itself if it cannot be resold. None of that appears in a sales report, and the refund amount captures only part of it.

Return analytics also catches problems earlier than refund analytics does. A request is created the moment a customer decides something is wrong; the refund may not land for days or weeks. Watching requests means you hear about a bad batch while it is still a handful of customers.

Use return metrics to find out:

• Which products and variants generate the most return requests relative to what they sell
• Why customers are returning things, using the reasons recorded on the request
• How many requests are sitting open, which is a queue your team still has to clear
• How long returns take to close, since a slow return is a customer deciding whether to shop with you again
• Which returns turned into refunds and which did not

The reason data is the most actionable part. “Wrong size” points at your size guide, “not as described” at your photos and copy, and “arrived damaged” at packaging or carrier handling. Each is fixable, and none of them shows up in a refund total.

Returns and refunds are related but not interchangeable. A return describes the request and the physical items. A refund describes money returned to the customer.

Return analytics metrics

Return Request Count

Counts Shopify return requests associated with the row and selected filters.

Return Request Rate

Expresses return-request activity as a rate against the metric’s eligible order activity. Use the count beside the rate so a small sample does not look more important than it is.

Return Quantity

Totals quantities recorded on Shopify return line items. This is different from Refunded Quantity, which follows refund activity.

Return Reason Count

Counts return-reason occurrences associated with returned line items. A return containing several items can contribute several reason records.

Open Return Count

Counts return requests that remain open. Use it as a workload measure rather than a product-quality measure by itself.

Average Hours To Close A Return

Measures average time between creation and closure for return requests that contain the required timestamps.

Start with Product sales & return rate

The ready-made Product sales & return rate report combines product fields with:

• Net Sales
• Net Quantity Sold
• Orders Count
• Return Request Rate
• Total Refund Amount Excluding Tax

1. Add an Reporting worksheet
2. Under Shopify data worksheet with analytics columns, select Product sales & return rate
3. Click Continue
4. Review the product fields and metrics
5. Click Create Worksheet

Add Return Request Count and Return Quantity when you need the numerator and operational volume beside the rate.

Analyze order-level return work

The Order fulfillment & delivery speed report adds Return Request Count, Open Return Count, and Average Hours To Close A Return beside recent Shopify orders and fulfillment timing.

This is useful for finding:

• Orders with several return requests
• Open return work tied to delayed deliveries
• Orders whose returns take unusually long to close
• Patterns by shipping country, fulfillment status, or order value

Filter Shopify returns

Return metrics can be filtered by:

• Return status
• Return reason

Depending on the metric, you can also use general order, customer, B2B, attribution, discount, and fulfillment filters.

Examples:

• Return Quantity for one reason across products
• Open Return Count for one customer segment
• Return Request Rate for B2B versus B2C orders
• Average Hours To Close A Return by shipping country

Keep the timeframe and metric identical when comparing filtered columns.

Returns, refunds, and refunded orders

Use the metric that matches the event:

Question	Metric family
How many return requests were opened?	Return Request Count
How many units were included in returns?	Return Quantity
Why were items returned?	Return Reason Count and reason filter
How many requests remain unresolved?	Open Return Count
How long does the process take?	Average Hours To Close A Return
How much money was refunded?	Total Refund Amount
How many units were refunded?	Refunded Quantity
What share of orders had refund activity?	Percent of Orders Refunded

Do not label Refund Count as returned units or Return Request Count as refunds.

Use raw Shopify return data for investigation

Analytics finds the pattern. A Return or Return Line Item worksheet shows the underlying return request, status, timestamps, products, variants, quantities, reasons, and customer notes.

A useful workflow is:

1. Use analytics to find the product, variant, or group with an unusual return metric
2. Open a Return Line Item worksheet
3. Filter to the relevant product, variant, reason, or date
4. Read the reason notes and customer notes
5. Record the likely product-page, sizing, quality, shipping, or support action

Use the Product Returns template

The Product Returns template compares Gross Quantity Sold, Refunded Quantity, and Percent of Orders Refunded for every product.

That template follows Shopify refund activity. Use the newer Return Request metrics when you need the return-management process itself.

Read rates with enough context

A high return rate on a product with three orders is not equivalent to the same rate on a product with thousands of orders. Keep Orders Count, Gross Quantity Sold, Return Request Count, and return rate together.

Also compare the selected timeframe with an earlier period. A recent increase after a supplier, size chart, packaging, or fulfillment change is more actionable than a stable long-term average.