At a glance
Daily count and value of cancelled Adobe Commerce orders over the trailing 90 days. The trend view of Cancellation Rate. Useful for visualising whether a cancellation spike is a one-day anomaly, a sustained 7-to-14-day issue, or a structural change. The 90-day window catches monthly seasonality (end-of-month B2B PO restructuring, payday cycle effects) that a 30-day window would miss.
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 nutritional supplements brand on Adobe Commerce 2.4.6 with US, UK, and B2B Store Views. Window: 90 days ending Monday 4 May 26. 90-day cancellation overview:
Three notable spikes in the 90-day series:
What this is telling operations:
- Three distinct causes, none repeating: gateway, fraud rule, ops correction. None is a recurring pattern.
- The fraud rule spike (31 cancels in one day) would not have triggered the Cancellation Rate alert on its own (one bad day in a 30-day window dilutes; 31/130 = 24%, way over the 3% trigger, but the next day the rate dropped back). The trend view makes the day visible.
- Median daily cancel count (3) reflects baseline
pending_paymenttimeouts and routine ops actions; nothing alarming. - Cumulative value at risk over 90 days: $58,400 of orders cancelled. About half is timeout-driven (recoverable if customers retry, many do), about a quarter is fraud rule (false-positive cost), about a quarter is OOS-at-picking and other ops causes.
- Strategic takeaway: investing in (a) webhook resilience (timeout reduction) and (b) fraud-rule false-positive tuning would together save ~$30k of unrecovered revenue per quarter.
- Cross-checking Cancelled-Over-Time against Stripe webhook delivery success rate: the Feb 8 spike correlates within the hour. Confirms diagnosis.
- B2B portal contributed 4 cancellations across the entire 90 days. B2B is steady at near-zero, as expected. The signal is concentrated in the consumer Store Views.
Sibling cards merchants should reference together
Reconciling against the vendor’s own dashboard
Where to look in Adobe Commerce Admin:Sales > Orders filtered by Status = “Canceled” with date range = “Last 90 Days”. The Admin grid lists individual orders; you can group by date manually via export-and-pivot for the daily series.
Reports > Sales > Orders with date range. The Admin report includes cancelled orders by default but does not chart them as a separate series.For the cancellation cause:
Sales > Orders > [Order ID] > Comments has the auto-generated note for timeout cancels and the operator note for manual cancels.Why our number may legitimately differ from a manual Admin computation:
Cross-connector reconciliation (when these connectors are connected for this merchant):
Known limitations / merchant FAQs
Why does the chart bucket byupdated_at rather than created_at?
The cancellation event is what we’re measuring; updated_at reflects when the cancellation happened. A pending_payment order created on Monday that times out on Tuesday morning is a Tuesday cancellation event. Bucketing by created_at would assign it to Monday and conflate the order-creation pattern with the cancellation pattern.
Why use a 90-day window rather than 30?
Cancellation patterns often have monthly seasonality (B2B end-of-month PO restructuring, payday-cycle fraud campaigns, monthly stock counts triggering OOS-at-picking). 90 days catches three monthly cycles, enough to spot recurring patterns. 30 days is too short to distinguish “monthly recurring” from “one-off”.
Adobe Commerce vs Magento Open Source: difference?
None at the calculation. Adobe Commerce paid edition’s B2B Companies module adds approval-flow cancels (an order rejected by a Company approver moves to canceled); these appear in the chart but are typically rare. Open Source merchants without the B2B module don’t see this cause.
My multi-store Adobe Commerce, can I overlay Store Views on the same chart?
Yes, configure per-Store-View series in the manifest. Useful for spotting region-specific spikes (a UK warehouse stock-recount that doesn’t affect US, for example).
The chart shows a steady decline in cancellations, is that real progress?
Probably yes if it’s been sustained for 30+ days. Common drivers of declining cancellation: gateway-callback hardening (fewer timeout cancels), fraud-rule tuning (fewer false positives), MSI reservation-window adjustments (fewer OOS-at-picking). All three are operational maturity wins worth attributing.
Why doesn’t this card show abandoned carts?
Abandoned carts are quotes that never became orders; cancelled orders are orders that were created and then cancelled. They’re different funnel stages tracked by different tables in Adobe (quote vs sales_order). For cart abandonment trend, see Cart-Abandonment Spike Alert.
A spike on a single day, should I worry?
Not necessarily. Single-day spikes are usually one-off (a webhook burst failure, a fraud-rule tuning incident, a stock-recount). Sustained 7+ day elevation is the worry signal. The chart’s value is showing the difference between “yesterday was bad” and “the last fortnight is bad”.