At a glance
The top-N most-refunded products by refund value (or refund count) over the period. The card refund-investigation always lands on. Refund value clusters disproportionately on a tiny minority of SKUs: typically 5-10 SKUs explain 30-50% of total refund value on most stores. Identifying these and acting on them (relisting with better images, fixing size charts, pulling defect batches, hiding offending SKUs) is the highest-leverage refund reduction lever available. On BigCommerce specifically, refund-without-return cases (Amazon A-to-Z claims, “keep it” goodwill) means this card may show items that never came back to the warehouse, which differs from the BC Return Status view of physically-received returns.
| What it counts | SUM(refund_line_total) GROUP BY product_id WHERE refunded_amount > 0 over the period. Sorted by refund value descending. Each line item’s refund attribution is summed per parent product. |
| VAT / tax treatment | Tax-inclusive. Refund values use customer-billed total. |
| Shipping | Included for refunds where shipping was refunded. |
| Discounts | Discounts on the original order do not affect refund value; what was paid is what’s refunded. |
| Refunds | This card is the refund population. |
| Cancelled orders | Excluded; cancelled orders generate no refund. |
| Currency | Multi-currency without FX. Per-currency aggregation. |
| Channels / sources | All channels aggregate. Per-channel toggle is essential because refund patterns differ structurally: Amazon’s A-to-Z system inflates refund values on Amazon-channel orders without corresponding returns; web refunds are typically smaller per-event but more numerous. Pair with BC Channel Refund Rate for the rate-aware view. |
| Refund-rate column | Beyond the absolute refund value, the card shows refund rate per product (refund value / gross sales). A SKU with high absolute refund value because it sells a lot is different from a SKU with high refund rate; the latter has a quality issue. Rate matters more than absolute value for diagnosis. |
| Refund vs return distinction | This card includes refund-without-return cases (immediate goodwill refunds, Amazon A-to-Z, damaged-in-transit refunds where the customer keeps the item). BC Return Status excludes these. The two cards together give you the full refund-and-returns picture. |
| B2B Edition behaviour | A single B2B refund can be tens of thousands of pounds, putting an obscure wholesale SKU at the top of this card. Filter to DTC if you want consumer-product diagnostics; the aggregate view mixes the two. |
| Time window | 90D (rolling 90 days; settings allow 30D, 90D, 180D, 365D). |
| Alert trigger | None directly; threshold alerts can fire if a SKU’s refund rate exceeds 15% (apparel) or 8% (other categories). |
| Roles | owner, operations |
Calculation
Calculated automatically from your BigCommerce 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 US apparel brand on BigCommerce, 90-day window 14 Feb 26 to 14 May 26. Total refund value across the period: $58,200 from 1,840 refund events.| Rank | Product | Gross sales ($) | Refund value ($) | Refund count | Refund rate |
|---|---|---|---|---|---|
| 1 | ”Slim-fit chino, size 32” (CHN-32-NVY) | $84,000 | $8,200 | 184 | 9.8% |
| 2 | ”Linen shirt, M” (LIN-SHT-M-WHT) | $42,000 | $5,800 | 98 | 13.8% |
| 3 | ”Wool overcoat, L” (WOL-OCT-L-CHR) | $96,000 | $4,200 | 24 | 4.4% |
| 4 | ”Slim-fit chino, size 34” (CHN-34-NVY) | $52,000 | $3,900 | 78 | 7.5% |
| 5 | ”Linen shirt, L” (LIN-SHT-L-WHT) | $34,000 | $3,400 | 52 | 10.0% |
| 6 | ”Cashmere sweater, M” (CSH-SWT-M-NAV) | $48,000 | $2,800 | 18 | 5.8% |
| 7 | ”Slim-fit chino, size 30” (CHN-30-NVY) | $28,000 | $2,400 | 48 | 8.6% |
| 8 | ”Wool overcoat, M” (WOL-OCT-M-CHR) | $52,000 | $2,200 | 14 | 4.2% |
| 9 | ”Slim-fit chino, size 36” (CHN-36-NVY) | $24,000 | $1,900 | 38 | 7.9% |
| 10 | ”T-shirt 3-pack” (TS-3PK-WHT) | $36,000 | $1,800 | 60 | 5.0% |
| Top 10 total | $496,000 | $36,600 | 614 | avg 7.4% | |
| All other SKUs (220) | $1,304,000 | $21,600 | 1,226 | avg 1.7% |
- Top 10 SKUs = 63% of refund value (from 28% of gross sales). Refund value is wildly concentrated; finding and fixing the top three closes 30%+ of total refund value.
- The Linen shirt (M) at 13.8% refund rate is the clear quality issue. Almost twice the rate of the next-worst SKU. Investigate: is it a fit issue (sizing chart wrong), a fabric issue (shrinkage in wash), an expectation issue (description / images don’t match reality)? Pull customer refund reasons for this SKU specifically; usually 3-5 distinct reasons explain 80% of refunds.
- Slim-fit chinos in 4 sizes all appear in top 10 (sizes 30, 32, 34, 36). This is a classic “size-fit issue across the range” pattern: customers don’t know their size in this fit, order multiple sizes, return all but one. Either improve the size guide, offer a try-before-you-buy option, or accept the structural higher refund rate as the cost of doing fit-sensitive apparel.
- Wool overcoat appears at #3 by value but only 4.4% refund rate. This is “high-AOV product with normal refund rate”; the refund value is high because the AOV is high, not because the SKU has issues. Don’t optimise this SKU for lower refund rate; it’s already healthy.
- The 220 long-tail SKUs at 1.7% blended refund rate are healthy. Most stores’ “average” SKU runs 1-3% refund rate; the top-10 outliers are where attention belongs.
- Investigate the Linen shirt (M) immediately. 13.8% refund rate on a high-volume SKU is the highest-leverage fix in the catalog. £5,800 of refund value over 90 days = £23,200/year on that single SKU.
- Address the chino sizing issue systemically. Better size guide with body measurements, “try multiple sizes” promo (free returns), or accept 7-9% as the structural rate.
- Audit refund reasons for top-3 SKUs. Free-text reasons from BC’s refund flow typically cluster into 3-5 categories per SKU; this is the diagnostic input.
- Consider relisting with better images. Apparel refund rates drop 2-4pp after image quality upgrades (multiple angles, model wearing, scale references).
- Watch for new SKUs entering the top 10. A new SKU appearing in the top-5 within 30 days of launch is the early-defect signal; pull it from active inventory pending investigation.
- Pair with BC Top Products to find SKUs that are top-revenue and top-refunded simultaneously; these are the highest-leverage targets.
Sibling cards merchants should reference together
| Card | Why pair it with Top Refunded |
|---|---|
| BC Refund Count | The store-wide refund count. Top refunded explains the bulk of it. |
| BC Refund Value | Same idea for dollars. |
| BC Refund Rate | Store-level rate. The top-refunded SKUs drive this. |
| BC Top Products | Cross-reference. SKUs that are top-revenue AND top-refunded need urgent attention. |
| BC Refunded Products | Line-item attribution for partial refunds. |
| BC Return Status | Physical-returns view. Refund-without-return cases live here but not there. |
| BC Refunds Over Time | Time-series for the top-refunded SKUs. |
| BC Channel Refund Rate | Per-channel breakdown; Amazon vs DTC refund patterns differ structurally. |
| BC Bottom Products | Often overlapping; SKUs at the bottom of revenue may also be at the top of refund rate (poor product-market fit). |
| BC Alert Refund Rate Spike | Anomaly detector that surfaces SKU-level refund spikes. |
| BC Inventory Alerts | Top-refunded SKUs may need inventory hold (don’t sell while quality issue is being investigated). |
Reconciling against the vendor’s own dashboard
Where to look in BigCommerce’s own dashboard: The closest native view is BC Control Panel → Analytics → Insights → Refunds → By Product (Plus and Enterprise tiers). For Standard tier, use the Orders export filtered to refunded orders and pivot onproduct_id in spreadsheet.
For per-channel breakdowns: Channel Manager → (channel) → Returns / Refunds reports. Amazon Channel Manager surfaces marketplace refunds separately.
Why our number may legitimately differ from the vendor’s:
| Reason | Direction | Why |
|---|---|---|
| Variant rollup | Either | We default to parent product; BC may show variants separately. |
| Refund-without-return inclusion | Ours higher | We include immediate goodwill refunds; BC’s Returns report may exclude them (since no RMA was created). |
| Marketplace refunds | Either | Amazon refunds may show with delay due to Channel Manager sync. |
| Time zone | Trivial | UTC vs store time zone. |
| Partial refund attribution | Either | Partial refunds with no line-item detail attribute to the parent order’s primary SKU; both BC and we approximate, may differ slightly. |
| B2B Edition refunds | Either | Quote-based refund attribution sometimes skips line-item detail. |
| Card | Expected relationship | Notes |
|---|---|---|
klaviyo.klaviyo_refund_by_product | Email-attributed subset | Limited to refund events on email-attributed orders; subset only. |