Refunded orders / total orders. Persistent >5% signals product quality or expectation mismatch.
At a glance
Refunded order count divided by total order count over the rolling 30 days. The first canary on product quality, sizing accuracy, or post-purchase expectation issues. APAC fashion and homeware merchants typically sit at 3 to 6%; persistent >5% signals an issue worth investigating before retention erodes.
Calculation
Calculated automatically from your Shopline data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.Worked example
An APAC fashion brand running a Hong Kong Shopline store, rolling 30D ending 27 Apr 26. The brand sells womenswear with sizes XS to XL. Their target refund rate is below 6% (category benchmark). Last 30 days:
What it means. 7.16% is above threshold and the trend is sharply up. The sizing cluster (22 + 8 = 30 of 51 refunds, 59%) is the dominant cause and points to a specific product line. Drilling deeper, 18 of the 22 “too small” refunds are on a single SKU run (a new linen blouse style launched 3 weeks ago). The fix is a sizing recalibration on that SKU’s listing (relabel the photography size as M-fit-S, or re-cut the next batch).
The “colour different” cluster (9 of 51) is photographic; the studio shot on a sunny day shows the linen blouse as cream while it actually arrives as oat. Retake the product photos with neutral lighting; expected impact is ~2pp drop in the next 30D.
The action plan: relist the linen blouse with corrected size labelling and re-shot photography; expected refund rate trajectory is back to 5 to 5.5% within 30 to 45 days.
Sibling cards merchants should reference together
Refund rate is best read alongside the cards that explain why and what value:Reconciling against the vendor’s own dashboard
Where to look in Shopline’s own dashboard:Shopline Admin -> Reports -> Refunds report Filter by date range; the “refund count / order count” tile is the apples-to-apples comparison.Why our number may legitimately differ from Shopline’s Admin:
Internal identity:
shopline_refund_rate = COUNT(refunded orders) / COUNT(total orders) over the same 30D window. The numerator is the refund count surfaced by shopline_refunds_over_time; the denominator is shopline_order_count. Mathematical identity, not reconciliation.
Known limitations / merchant FAQs
What is a normal refund rate for an APAC fashion brand? 3 to 6% for established brands; up to 8% for fast-fashion or sizing-heavy categories. The threshold of 5% is the rule-of-thumb floor; sustained >7% is a quality or expectation problem worth investigating. Why is the alert at 5% not 10%? Because by 10% the damage is already structural, retention has dropped, customer-acquisition cost has not been recovered, and the brand reputation has likely taken a hit. 5% catches the issue while it is still a 30 to 45 day fix, not a 6-month rebuild. My refund rate spiked overnight; what should I check first? (1)shopline_alert_refund_spike for the rolling 24h delta, (2) drill-down on this card to see refund reason codes (sizing, photo, delivery, damage), (3) cross-reference shopline_top_products to find the SKU concentration. Sudden spike + single SKU dominance = product-quality issue on a recent batch; sudden spike + spread across SKUs = process issue (delivery, packaging).
Does this include cancelled orders?
No. Cancelled-before-payment orders never had a refund (no money moved). Cancelled-after-payment orders trigger a refund and would count, but we treat the cancellation as primary and exclude from this rate to avoid double-counting with shopline_cancellation_rate.
My refund rate dropped because I tightened my returns policy. Is that good?
Operationally yes, strategically maybe not. Refund rate dropping while shopline_repeat_rate also drops means the policy hardening cost you customer trust. The healthier signal is refund rate dropping while repeat rate holds or rises; that means the underlying quality improved, not just the policy.
Why does this exclude pending disputes?
Because a dispute can take 14 to 60 days to resolve, and including pending disputes inflates the rate during the dispute window then deflates it on resolution. We wait for resolution to count consistently. The drill-down shows pending disputes separately for awareness.
How does this compare to refund rate on Shopify or BigCommerce?
Definitionally the same; the threshold “good vs bad” is the same; the underlying data is similar. The APAC-specific note is reason-code mix: APAC merchants see a higher proportion of sizing and customs refunds than Western markets, which changes the action playbook (more sizing-photography fixes, more cross-border-fulfilment investment).
Does this include subscription order refunds?
Yes. Each subscription order is a separate order record in Shopline, so a refunded monthly subscription contributes to both numerator and denominator the same as a one-time purchase.
Should I aim to drive this to zero?
No. A refund rate near zero usually indicates an over-strict returns policy that is suppressing genuine returns at the cost of customer trust. The healthy band is 3 to 6% for fashion; near-zero is rarely a quality signal, more often a policy signal.