Returns Reasons vs. Product Attributes


While we consider returns a negative, they can offer valuable information for your business. Return data is one way to get accurate feedback on your product. Customers are incentivized to return a product that didn't meet their expectations, compared to a survey that a customer might breeze past or ignore. On the other hand, customers directly say "I'm not satisfied" when they return an item, therefore highlighting any weakness in your customer journey. Imagine you are a DTC glasses company. You're interested in seeing what products are returned and trying to find a commonality between returned items. However, the sheer volume of support tickets and refund requests is overwhelming. You ask yourself if there's a better way to do this.


Loop Returns

Filters Applied

Product SKU

Fields Combined

Shopify: order_id, product_sku, product_color, order_size , vendor_id

Loop Returns: return code, return reason

Gorgias: support text, issue tag

Demo for marketers
Demo for sales
Demo for customer success


OmniPanel pulls all the relevant important information from your tech stack. So, instead of manually compiling reports from your tech stack, all your team needs to do is specify the desired fields. Because reporting is made so easy, your team sets up a variety of reports, tracking returns by vendor, style, order size, and color. Looking at color specifically, you see that glasses from one particular supplier get returned frequently, with customers citing discomfort when wearing them. You inform the supplier about the critical feedback and promise to change the material. However, three months later, you still track a high number of returns from the vendor, which you use as evidence to refund purchases and convince the product team to switch away from said supplier.

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