Most return workflows begin after the customer has already decided to send the product back. That is too late for margin. The expensive part of returns is not only shipping and restocking. It is the hidden drag across acquisition cost, support load, inventory planning and customer confidence.
Return intelligence moves the work upstream. Instead of asking how to process a return faster, the team asks which returns were preventable and what signal could have warned us earlier.
The useful signals are usually available before checkout. Product size variance, image mismatch, unclear specifications, repeated support questions, region-specific fit behavior, discount-led purchase intent and channel-level creative promises can all increase return risk.
A return intelligence workflow should classify risk by product, page, audience, creative and promise. For example, a fashion product might have low return risk for repeat buyers but high risk for first-time buyers from a specific ad set. A furniture product might have risk because dimensions are technically present but visually hard to understand.
The action should not always be to block the sale. Often it is to improve the decision environment: add a fit note, change the hero image, adjust the offer, surface a comparison, rewrite the size guidance or route high-risk SKUs into a review queue.
Teams that do this well treat return rate as a product and content quality signal. Reverse logistics still matters, but the bigger opportunity is preventing avoidable returns before they become operational work.