Every return has a root cause. Find it and fix it for good.
Returns aren't just refunds to process. The patterns point straight at the cause — a listing photo, a supplier's sizing chart, a handful of customers gaming the policy.
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What’s slipping through
- ?One product drives a third of returns because the listing photo doesn't match.
- ?The same product returns more on one channel than another, and nobody knows why.
- ?A handful of customers game your policy with serial free refunds.
- ✓Shows why returns happen: which products, listings, channels and customers.
- ✓Points you upstream — fix the listing, push the supplier with evidence.
- ✓Feeds repeat-abuser and refund-without-return signals into the shared customer profile.
31%
of returns can trace to a single product with a listing that doesn't match.1
1 Illustrative pattern. Actual concentration varies by catalogue.
How it works
Connect orders and returns
Return reasons, products, channels and customers ingested automatically.
Patterns clustered
Returns grouped by product, listing, supplier, channel and customer.
Root causes surfaced
The specific listing, sizing chart or supplier behind each cluster.
Fixes prioritised
Ranked by how much margin each fix would recover.
What you get
Product-level drivers
Which SKUs drive disproportionate returns, and why.
Channel comparison
Where the same product returns more, and what differs.
Supplier quality signals
Sizing and quality issues clustered by supplier, with evidence.
Refund-without-return
Refunds issued where the item never came back.
Serial abuser flags
Return patterns well outside normal behaviour, fed into the shared customer profile.
Margin impact
What each root cause is costing you, ranked.
Works with what you already run
No migration. Shipherd reads from your existing stack.
Frequently asked questions
How do I reduce ecommerce return rates?
Start by identifying root causes rather than processing returns faster. Most return volume concentrates in a small number of products, and the cause is usually specific and fixable — a listing image that misrepresents the item, an inaccurate supplier sizing chart, or a channel-specific expectation mismatch.
Why does the same product return more on one channel?
Usually because the listing differs. Marketplace listings often carry different images, dimensions or descriptions from your own site, and the gap between what a customer expects and what arrives drives the return.
What is refund-without-return leakage?
It is a refund issued where the item never actually came back. At small volumes it is invisible; at scale it becomes material, and it is one of the clearer signals of policy abuse. Shipherd feeds it into a shared customer profile alongside signals from damage claims and marketplace refunds, so a pattern spanning all three becomes visible.
Can returns data be used with suppliers?
Yes, and it is one of the more valuable outputs. Return clusters attributable to a supplier's sizing or quality give you evidence to renegotiate terms or push for correction, rather than absorbing the cost.
Does this integrate with my returns portal?
Shipherd reads return data from your order and marketplace systems, so it works alongside whatever returns portal you already use.
How much of a return rate is actually addressable?
It varies, but return volume is typically concentrated rather than evenly spread, which means a small number of fixes usually addresses a disproportionate share.
Ready to stop the leaks?
Live in days, not months. No cost to start. You only pay us if we recover.