Refund-rate anomaly. Catches batch-quality issues, broken sizing, or coupon misuse before the support inbox does.
At a glance
Real-time refund-rate anomaly: 24-hour rolling refund rate has crossed 2× the 30-day baseline. Catches batch-quality breakdowns, broken-sizing returns, fraud waves, and coupon-stacking abuse before the support inbox catches up.
Calculation
Calculated automatically from your Shopify data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.Worked example
A UK womenswear brand on Shopify Plus, normal refund rate ~6.8% (returns-friendly fashion). Friday 09 May 26, 16:00 BST. The alert fires: trailing 24h refund rate = 14.2%, 2.09× the 30D baseline of 6.8%. Decomposition of the 24h refund pool:
Six things to notice:
- The signal is concentrated. Of 108 refunds in 24h, 69 (64%) are sizing or quality issues; both are up sharply vs baseline. This is a product-quality event, not a generic refund-rate drift.
- The cause is likely a single SKU or batch. Drilling into product-level: the new “Verona dress” launched 02 May, customers returning since 06-08 May. 47 of 108 refunds are this single SKU. The dress’s measurements are running 1-1.5 sizes small.
- The action is immediate. Pause Verona-dress ads (Google, Meta), update the PDP with revised sizing guidance, email Verona-buyers proactively offering pre-paid return labels with the next size up.
- The financial impact is real. 47 dress refunds × £85 average = £4,000 of refund value already booked, with more in the pipeline (the cohort is still receiving and trying on). Pair with Refund Value for the running total.
- The repeat-rate impact is bigger. Customers who refund typically don’t reorder for 90+ days. 47 customers is a 3-month repeat-cohort hit. Cross-reference Repeat Customer Rate over the next quarter for the lagging effect.
- POS interplay. If the brand has a London showroom, customers can return the dress in person; that creates a refund without a returns-API record but still surfaces here. The split between online-returns and in-store-returns helps localise the issue: heavy in-store returns suggest the photography / sizing chart is the culprit.
Sibling cards merchants should reference together
The refund-spike alert is the trigger. The diagnostic cards:Reconciling against the vendor’s own dashboard
Where to look in Shopify Admin: Shopify doesn’t expose a single refund-rate-anomaly alert; reconstruct from:- Analytics → Reports → “Returns” filtered to the same 24h window: gives the count and value.
- Orders → Filter by Refunded with date range last 24h: gives the order list.
- Apps like Loop Returns / Returnly / AfterShip Returns: their dashboards expose return-rate trends.
Cross-connector reconciliation:
Known limitations / merchant FAQs
Why is the multiplier 2×? Should I tune it? 2× is a generic anomaly threshold. Tune based on your category and volume:- Low-refund-rate categories (electronics, beauty, B2B): 2× is too tight; small absolute spikes trip it. Use 3-4×.
- High-refund-rate categories (fashion, footwear): 2× works.
- Subscription / consumables: 2× is fine, refund pattern is steady.
- Customer accounts with very recent first orders + refunds.
- High-velocity multi-SKU orders refunded within hours.
- Repeated chargebacks on the same payment instrument.
- Open the refund list filtered to last 24h. Sort by refund value desc and reason cluster.
- Identify the cause cluster: sizing? quality? damage? changed-mind? Each has a different remedy.
- Identify the SKU concentration: are 50%+ of refunds on one SKU? That SKU is the issue. Pause its ads, update its PDP, email its buyers.
- Identify customer concentration: are 10%+ of refunds from one customer? Possible fraud. Investigate.
- Communicate proactively with customers in the at-risk cohort: those who bought the same SKU in the last 7-14 days but haven’t refunded. Offer pre-paid returns or apology credit.
- Document the cause in your ops playbook. Refund spikes from the same root cause should not recur.