Retailers need flexible data and visual insights to support various teams. Therefore, yayloh offers pre-prepared Dataset Reports for export return data in CSV format. This enables retailer teams to integrate data with their analytics tools and transform it into actionable insights for areas such as 3PL, inventory, supply chain, product development, and customer service.

Every day we export and append your return requests to this google sheet. You should have access to it via your company email otherwise ask your yayloh admin to send an access request at support@yayloh.com.

Sorted By: order date

This product level dataset shows the breakdown of returns information per SKU or variant. Your inventory, supply chain, or customer service teams can use this report to analyse the most returned product variants.

This dataset represents information related to refund, discount and store credit per order ID processed via yayloh.

Sorted By: Timestamp [when refund was processed]

It gives an overview on number of return shipping labels generated per carrier per day. This log can be used to cross check the number of return shipping labels with your shipping carriers, TMS or your 3PL.

Main Attributes: Created on, Carrier, Count

This dataset gives you a financial summary of returns by return request date which includes value for orders returned, inspected (not refunded) and not refunded. It helps you accurately forecast your refunds.

Sorted By: “Return requested on”

A list of all comments made by each customer for each SKU. This will help you use qualitative comments as insights for your product design team. You can use the email IDs to enroll customers in a specific follow-up campaign, for example to understand their feedback better.

Sorted By: Product [name of the product]

This summarises the count of return requests for each status tab by return request date. You can use this dataset to track the return requests on the daily basis.

Sorted By: Request date

This dataset provides date-wise statistics on products and orders inspected by warehouse team. This can help you monitor your 3PL or warehouse performance.

Sorted By: Inspection date

The average processing time to receive the return, inspect it and process the refund by month. By using this you can monitor your team’s efficiency in processing and handling returns. The aim is to help you improve return processing speed.

Main Attributes: Request date, Avg days to receive, Avg days to inspect, Avg days to refund

Sorted By: Request date

A dynamic list with all return request attributes such as tracking status, customer information, products, shipping carrier etc. A great source of raw data for any returns analysis you would like to conduct. Warning: Keep date filter to 30 days, slow to load when you have many returns requests.

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Main Attributes: Order id, Order date, Order price, Status, Product, Reason, Return requested on, Comment, Carrier

Sorted By: Order date

A list of incoming returned products based on their tracking status, i.e. registered, in-transit, arrived at the warehouse, and inspected. This is a great way to manage stock and assist your supply chain department.

Sorted By: Product [name of the product]

This dataset consists of return rate breakdown per country. This gives a possibility to analyze your markets, market shares and return rates in your biggest markets.

Sorted By: Descending order of [number of] # returns

A detailed list of the order-specific return shipping labels generated by yayloh to help you with routine bookkeeping tasks.

Main Attributes: Order id, Shipment id, Tracking id, Carrier

This dataset gives you a comparison on number of partial vs full returned orders per day along with the total order count.

This dataset helps you analyse the top return reason per product and the total return count. You can also use an excel sheet to join this data with the product-level report to get the total order count.

This dataset give you an overview of the return information per collection. This can be used to analyse most returned collections.

This dataset visualises products that are purchased in two sizes in one order basket and forecast of quantity that would be returned. Use this report to estimate your approx. upcoming returns and regulate your stock accordingly.

This datasets give you insights of your top 100 returned products and their top 2 reasons.