ReturnLoop — E-commerce case study

Manual return handling doesn't scale past a few requests a day

Every return started the same way: a customer emailed support, someone manually looked up the order, checked eligibility, generated a label by hand, and sent a confirmation — usually a day or two later, often after a follow-up email asking what was happening. That's fine at a handful of returns a week. It breaks down the moment return volume tracks order volume, because the process was built around one person's inbox, not the store's growth.

What we built

We built a returns flow that resolves itself for the common case:

• Automatic order lookup from the customer's return request, no manual search
• Store return-policy rules applied automatically to approve or flag a request
• Shipping labels issued instantly for approved returns
• Automatic refund-timeline confirmation sent back to the customer

Why we didn't auto-approve every return

The easy way to cut support load is to auto-approve anything that hits the portal — it removes the most friction and is the fastest to build. It also removes the one check that actually protects margin: policy exceptions, like returns past the window or on final-sale items, would get waved through with everything else. We built the rules engine to route anything outside policy to a human instead of approving it silently, which costs a little of the automation's headline speed but keeps the portal from quietly overriding the store's own return policy.

The stack

A web application handles the customer-facing flow, a rules engine evaluates policy, and the store's existing order and shipping-carrier systems provide the order data and generate labels.