How do I find out why our refund or return rate is high with iDBQuery?
Ask iDBQuery why refunds are high and it breaks the refund rate down by product, reason, channel and customer to find what is driving returns. It returns a cited explanation, so you can fix the root cause, a faulty product, a bad channel or a misleading listing, not just process the refunds.
A high refund or return rate erodes margin and signals a deeper problem, but the cause hides across orders, refund reasons and product data. iDBQuery investigates it from a plain-language question.
- It computes refund and return rates and breaks them down by product, reason, channel and customer
- It ranks the biggest contributors so you fix what matters most
- It correlates returns with a product, batch, channel or period when the data allows
- It returns a cited answer with the numbers behind it
Ask *why is our return rate up this quarter, and which products drive it?* iDBQuery might reveal that most returns come from one product line with a sizing issue sold through one channel. Follow up with *what reasons do customers give for returning it?* to confirm the root cause.
Because iDBQuery joins orders, refunds and product data into one live model, it connects a refund spike to its source rather than stopping at the rate. Cited answers let you take a specific, evidenced problem to the responsible team, whether that is fixing a listing, a product defect or a channel. E-commerce, retail and operations teams use this to cut returns at the source, protect margin, and improve the customer experience that drives repeat purchases.
Updated 2026-06-22