At a glance
The day-by-day count and value of Credit Memos created over the last 90 days, plotted as a time series. Lets Operations spot batch postings, weekly cycles, post-promotion return waves, and supplier-defect spikes that aggregate-only cards smooth away.
Calculation
Calculated automatically from your Adobe Commerce 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 B2B-leaning industrial-supplies merchant on Adobe Commerce 2.4.7. Snapshot taken 13 May 26, looking back 90 days. Aggregate stats over the window:
Drill into the 6 outlier days:
Pattern analysis:
- Day-of-week bias is strong. Monday averages 18 Credit Memos vs Sunday’s 3. CS teams batch-post on Monday morning after the weekend’s RMAs queue up. This is a posting artifact, not a real refund spike.
- Tuesday is the second-highest day for the same reason; carry-over from Monday’s backlog.
- Friday afternoon spikes appear before holiday weekends (President’s Day Friday 14 Feb showed 24 vs Friday baseline 14). CS clears the queue before time off.
- The 6 Mar single-day $28,900 spike is one large B2B return masking the daily count. The dollar series shows a clear spike; the count series shows a moderate spike. Reading both axes catches this.
- The supplier-error wave starting 14 Apr is the most actionable signal. The uniform-line refunds are still elevated 4 weeks in (28 Apr at 36, well above mean+2σ). Operations should check if the corrective action (sizing chart correction, supplier rework) is landing.
- Cross-link with Top Refunded Products filtered to the post-14 Apr window: the top 10 refunded SKUs should now be uniform-line items if our hypothesis is correct.
Sibling cards merchants should reference together
Reconciling against the vendor’s own dashboard
Where to look in Adobe Commerce Admin:
Reports > Sales > Refunds with “Show By” set to “Day”, date range matching this card. The chart and table show daily refund value (using base_grand_total). The count series is not shown; for daily count Admins must use the Credit Memos grid.
Sales > Operations > Credit Memos sorted by Created descending; export to CSV and group by date for a daily count. Adobe Commerce admin does not natively chart the count series.
Why our number may legitimately differ from Adobe Commerce Admin:
Cross-connector reconciliation (when these connectors are connected for this merchant):
Known limitations / merchant FAQs
Why is Monday always the highest day? CS teams batch-post weekend RMAs on Monday morning. The customer’s actual return decision happened over the weekend; the Credit Memo is dated when CS clicks “Submit”. This is a posting artifact. To see “true” daily refund pressure, look at when the order was placed (not when the Credit Memo was created) using the cohort-refund variant. Can I see this by Customer Group (B2B vs DTC)? Yes. Apply a Customer Group filter via the chart legend. Useful when you suspect a single channel is driving the pattern. A single-day spike, real or batch posting? Three diagnostic checks: (1) does the count series spike but value series stay normal? Then it is many small partial refunds, often a backlog clear. (2) Does value spike but count not? Then it is one whale refund, drill into Top Refunded Products for the SKU. (3) Both spike together? Real event, possibly a supplier defect or a faulty batch shipment. Why is Sunday always so low? CS doesn’t process refunds on Sundays for most merchants. The data is correct; the operational rhythm is the cause. Use 7-day rolling rather than daily for trend. My multi-currency, the value series looks weirdly wavy, why? The default uses mixed-currencygrand_total. A day with one large refund in USD plus normal day in GBP looks like a spike if you’re reading the chart in GBP-mental-model. Toggle to base_grand_total for FX-neutral comparison.
Why does today’s last data point look low?
Because today is incomplete. The day-to-date count cannot match a full day. Most charts dim or annotate “in progress” on the trailing day. If yours doesn’t, ignore the trailing data point or annotate it manually.
The 90-day window doesn’t include my Black Friday spike, can I extend?
The default is 90 days. The card supports 180-day and 365-day views via the manifest. Beyond that, OpenSearch index retention defaults to 13 months; older data needs a custom backfill query.
Can I overlay an event marker (promo end, supplier change, system upgrade)?
Yes via the Vortex IQ workspace annotations. Mark events on the timeline and they overlay across all time-series cards (this one, Revenue Over Time, Orders Over Time) for incident-correlation.
Why don’t stripe.stripe_refunds_over_time and this card always match day-by-day?
Even on a Stripe-paid order, the day a Credit Memo is posted in Adobe and the day the refund hits Stripe’s API can differ by a few seconds (Adobe calls Stripe API; the API call returns; both write timestamps). On day boundaries, this matters. Aggregate over 7 days and they match closely.