E-Commerce QC Returns Reports


Imagine you are a D2C clothing company. You noticed a spike in your overall return rate in the last month. While the CX team managed the escalations directly, you want to investigate the underlying reasons for this spike and see if there was an explanation for this rise of returns all of a sudden. You hope that understanding the causes here could prevent returns in the future.



Filters Applied

Product SKU
Date Range

Fields Combined

Shopify: product SKU, product supplier, customer name, order $, #s of orders placed

3PL: warehouse, carrier, zone, ship date

Gorgias: ticket tags, support text, agent

Returnly: Return cause, Exchange, Return Created

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You start your investigation by looking for patterns within the escalations. You see that a high incidence of returns revolved around one product in particular: a new t-shirt that was released just a month ago. From the return cause field you imported from Returnly, you find that the return reason tagged was "not in love with the fabric." This tells you that this is a Product issue. Drilling down into this lead, you can follow up on the associated tickets in Gorgias. Because you combined the Support Text field within Gorgias, you can see exactly where customers have displeasure with the T-Shirt. The support tickets all reported that the hemming inside the apparel was too itchy and made the shirt feel uncomfortable. Knowing this, you pass along this report to the Ops team, who then reached an agreement with the supplier for a refund for this particular SKU.

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