You noticed a spike in your overall return rate in the last month. You want to figure out which products are driving returns so you can better select your purchase orders for next season.
Shopify: product SKU, vendor, customer, order $, #s of orders placed
Gorgias: ticket tags, support text, agent
Yotpo: product, rating, topic sentiment score, opinion
Loop Returns: Return cause, Exchange, Return Created, Gift return
By looking at the breakdown of all the support requests, your team noticed a high incidence of support tickets stemming from products from one vendor in particular. Likewise, when switching to the Returns view, the vendor had higher than average rates of returns. After narrowing your focus onto one area, you expand the scope of your investigation by bringing in more contextual data. Through Loop's integration, you find that the most common return reason code with the vendor's products was "Quality Issues," backed up by the reviews you pulled in Yotpo from the customers who bought this product. Reviewers said that the material felt cheap and had a lower rating than similar products you offer. Armed with this evidence of unsatisfactory quality, the Ops team notified the vendor that they violated their SLA and were liable for damages as outlined in their agreement. As a result, the vendor then agreed to refund the items, while the Product team emphasized product quality when sourcing a new vendor.
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