Quantifying Impact on Reducing Returns


Your company recently launched a new version of a popular but sometimes tricky product. The product was a popular seller but had a lot of returns and lots of support questions. This ate away at the gross margins for the item, so the product team launched a new version that tried to reduce the friction points customers had. The Product and CX teams now want you to analyze if the changes reduced support requests and improved cost savings.


Loop Returns

Filters Applied

Date Range
Product SKU

Fields Combined

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

Gorgias: ticket tags, support text

Loop: return reason, return cost, replacement cost

Demo for marketers
Demo for sales
Demo for customer success


By pulling all the support cases for this new version, you can see that the new version had significantly reduced return requests in the last six months compared to the original product and an equivalent time frame. Wanting to quantify the cost savings of this change, you turn to the ROI calculator. You plug in the cost drivers of the returns process, such as origination shipping costs, return shipping costs, processing, and the total amount of returns. You find that the reduction in support requests saved the company $85k in that period. Proving to the rest of the organization that this initiative can quantifiably save money, you start a new project to identify other high-volume, high-issue products that need a revamp to reduce customer returns.

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