Datasets are ready-made reports you can download as CSV files and share with your teams, such as product, finance or warehouse.

To export a dataset, go to Analytics > Datasets, choose a dataset and a date range, and export it. See How to export and download data from datasets.

To find the right dataset for a specific question, see Which dataset should I use?

Before you start: date filters

Datasets filter your date range on different dates:

Order date: when the customer placed the order.
Request date: when the customer registered the return.
Action date: when an action took place, such as a refund, an inspection or a label being created.

As a result, two datasets can show different numbers for the same period. Each dataset below states which date it uses.

Also note:

Return rate is the number of returned items divided by the number of ordered items, for orders placed in the date range. Discount lines are not counted as items.
Large datasets can take time to export. Use a shorter date range, for example 30 days, or use Schedule & Download.

Products

Identify which products, sizes and colors are returned most, and why.

Dataset	Content
Product performance	Ordered, returned and exchanged quantities, return rate, and the two most common return reasons per product. Also available by size and by color. Filters on order date.
Product level report	Returns, return rate and revenue per SKU or variant. Filters on order date.
Collection level report	Returns and revenue per collection. Filters on order date.
Top 100 returned products	The 100 most returned products and their main return reasons. The Split version shows how these returns were resolved: refund, store credit or exchange. Filters on order date.
Top reasons by product	All return reasons per product, with the number of returns for each. Filters on order date.
Product comments	Customer comments per product, with the customer's email address. Filters on request date.
Reason tags per product	The tags customers selected with their return reason. Filters on request date.

Track refunds, forecast upcoming refunds, and measure the value retained through exchanges and store credit.

Dataset	Content
Refund transaction report	All refunds and store credits processed through yayloh, per order. Filters on refund date.
Financial summary of returns	The value of returns that are registered, inspected or awaiting refund. Useful for forecasting. Filters on request date.
Financial – Exchange, refunds and store-credits	The value retained through exchanges and store credit. The Split version shows the same as order counts. Filters on request date.
Refund or Store-credit	The number of customers who chose store credit instead of a refund. Intended for stores that offer store credit instead of exchanges. Filters on request date.
Return rate by market	Return rate per country. Financials by market shows revenue and return value per country. Filters on order date.

Reconcile labels with carrier invoices, and identify returns that require action.

Dataset	Content
Return shipping label details	All labels created, with order, tracking number and carrier. Return shipping label statistics shows daily totals per carrier. Filters on label date.
Failed Return Shipping Label Requests	Orders where a label could not be created, with the error message. Filters on failure date.
Requests with processing errors	Returns where the automatic refund, store credit or exchange failed and requires manual handling. Filters on error date.

Plan stock and staffing, and monitor warehouse performance.

Dataset	Content
Incoming returned products by tracking status	Products on their way back, from registered to inspected. Filters on request date.
Return request status	The number of return requests per status. Filters on request date.
Return count over week	Returns per week, for warehouse staffing. Filters on request date.
Inspection status	Products and orders inspected by your warehouse. Filters on inspection date.
Partial and full return statistics	The number of partially and fully returned orders per day. Filters on order date.
Possible returns based on double-sizing	Orders containing the same product in two or more sizes, which are likely to be returned. Filters on order date.

Measure how quickly returns are handled, and how work is distributed across your team.

Dataset	Content
Inspection and refund speed statistics	The average number of days to receive, inspect and refund a return, per month. Filters on request date.
Refunds by agents statistics	Refunds and store credits processed per team member. Filters on refund date.
Assigned to	Open requests per team member. Filters on request date.

Understand the customer return experience, and identify customers to re-engage.

Dataset	Content
Reviews and ratings	Customer ratings and reviews of the return experience. Filters on order date.
Retention: re-purchased after a return	Customers who placed a new order within 30 days of a return. Filters on request date.
First time buyers who returned products	First-time customers who returned products, for re-engagement campaigns. Filters on request date.

Build your own reports from the complete data.

Dataset	Content
Return request details with current tracking status	All return requests with customer, products, reasons, tracking and carrier. Limit the date range to 30 days. Filters on request date.
Exchange order details	All exchange orders, with the original and the new variant. Filters on request date.
Daily return requests google sheet	Your return requests, added to a Google Sheet daily. To request access, ask your yayloh admin to contact support@yayloh.com.

Additional datasets may be available in your account. The datasets above are the most commonly used.