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Metrics type: Supporting Metrics • Category: Ecommerce Platform

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At a glance

Daily count of BigCommerce orders cancelled over the rolling 90-day period, plotted as a time series. The trend view of Cancellation Rate, here you spot anomalies (a single bad day), patterns (Mondays being worse than other days), and trajectories (worsening over a month). The diagnostic side of cancellation analysis.

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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.

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Worked example

A US homewares brand on BigCommerce Pro, 90-day window 14 Feb 26 to 14 May 26. What’s interesting:
  1. The 18 Feb spike (32 cancellations vs 8 baseline) is a single-day anomaly. Investigate that day’s order log: was a fraud-rule deployed that morning? Did a top SKU go OOS? Did Channel Manager fail? The reason is on the order-log notes for each cancellation. Often a single root cause explains 90% of a spike.
  2. The 22 Mar spike (47 cancellations) preceded a sustained rise. This is the warning sign: the spike was the leading edge of a systemic issue, not an isolated event. By the time April baseline was 18/day (2.25× normal), the merchant had been silently bleeding cancellations for 3 weeks. A second 5-alarm event after a spike is when to act, not the spike itself.
  3. The 15 Apr fix shows what intervention looks like. Daily cancellations dropped from 18 to 11 within 2 weeks. The intervention (probably a fraud-rule audit, an inventory-sync repair, or a Channel Manager reconnect) clearly worked. Document what was changed for next time.
  4. The 90-day average of 11/day = ~330/quarter cancellations. At an average order value of 90,that′s 90, that's ~30k of pre-fulfilment lost revenue. Half is recoverable with intervention; the other half is unavoidable (genuine fraud, true OOS, customer changed mind).
  5. Day-of-week pattern not visible at this resolution but typically Mondays show 30-50% above other weekdays (weekend orders processing Monday, OOS surfacing). Configure the card to show day-of-week breakdown for that view.
Action priority order:
  1. Spike investigation playbook when the chart shows a 2× day spike, drop everything: open the order log, identify the cancellation reasons, fix the root cause within 24 hours.
  2. Trend monitoring weekly the 30-day rolling average crossing 1.5× the 90-day average is the “systemic issue is forming” signal.
  3. Document interventions every time you fix a cancellation issue, log it against the chart timestamp; future investigations benefit.
  4. Per-channel monitoring filter the chart to per-channel views; Amazon-only spike vs web-only spike have different root causes.
  5. Quarterly: review baseline baseline cancellation rate may legitimately drift up as catalogue / channels grow; rebaseline expectations every quarter.

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Sibling cards merchants should reference together

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Reconciling against the vendor’s own dashboard

Where to look in BigCommerce Control Panel: Orders → All orders filter by status Cancelled and date range; the daily count is implicit in the timestamp distribution. There’s no native daily-trend chart in BC for cancellations specifically; manual export to spreadsheet gives the same view. Why our number may legitimately differ from BC: Cross-connector reconciliation: The cancellation-trend view is BC-aligned with similar cards on Shopify and Adobe Commerce.

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Known limitations / merchant FAQs

A 1-day spike with no recurrence, ignore it? Investigate, then maybe ignore. Single-day spikes often have identifiable causes (a fraud-rule deployment, a Channel Manager hiccup, a top-SKU OOS event) that are worth understanding even if isolated. Document the root cause; if the same cause recurs, you’ve already done the diagnosis. If it’s truly random and unrepeated, move on. My chart shows a Monday peak every week, why? Weekend orders processed Monday morning. Weekend orders that hit OOS-at-fulfilment, fraud-rule rejection, or address-validation issues get cancelled when the warehouse / ops team comes in Monday. Common pattern; reduce by configuring weekend-time fraud-rule auto-approval and weekend-time OOS-at-checkout rather than at-fulfilment. My cancellations climbed steadily over 30 days, what happened? Likely systemic. Check: (1) was a fraud-rule tightened recently? (2) did inventory sync degrade (more OOS-at-fulfilment)? (3) did a marketplace integration credential start expiring (orders accepted then auto-cancelled when payment fails)? (4) did supplier lead-times lengthen, pushing more orders into “can’t fulfil in advertised window” cancellation? Each has a different fix. A cancellation spike preceded a revenue drop, related? Almost certainly. Cancellations are a leading indicator: the order-creation flow worked but something downstream broke. Within 24-48 hours of a cancellation spike, revenue drops as the systemic issue persists. Watching this card lets you intervene before BC Alert Revenue Drop fires. My Amazon-channel cancellations are climbing, where do I look? Three places: (1) Channel Manager → Activity log for sync errors; (2) Amazon Seller Central → Performance for marketplace-side issues (account health, listing suppression); (3) Channel Manager fulfilment-time settings (if Amazon expects 2-day ship and you can’t deliver, cancellations cascade). Most Amazon cancellation lifts are integration-side. Why is my B2B portal showing any cancellations? B2B cancellations should be effectively zero. Any visible pattern means either: (1) credit-line failures (account hit limit); (2) account-manager training issues (orders being approved that shouldn’t); (3) portal usability problems (B2B customers misordering). Time-sensitive issue; investigate immediately. Should I export this chart for board reporting? Yes for trend communication. Boards understand “cancellations rose from 8/day to 18/day, then we fixed it and they returned to 10/day” better than “our cancellation rate is currently 2.4%”. The chart shows the management quality. My 90-day average is high but trend is flat, healthy or unhealthy? Flat-and-high means the underlying issue is structural (your channel mix has built-in cancellation, your fraud rules are appropriately strict, your marketplace cohort behaviour). Compare to similar-mix benchmarks; if structurally appropriate, accept. Flat-and-low is the gold standard. Multi-currency: can I see cancellations per currency? Yes, filter by currency. Pattern often differs: USD orders may have higher fraud-rule rejection (different fraud profile), EUR may have higher payment-timeout (3DS friction). Currency-specific patterns inform region-specific intervention. Should I notify customers proactively when their order is cancelled? Yes always, ideally with reason. “Your order #1234 was cancelled because the item went out of stock; here’s a 10% off code for your next order” recovers a meaningful share of customers. Silent cancellation is the worst experience; explicit communication preserves goodwill. What’s the relationship between this card and refund trends? Cancellation precedes refund in time (cancellation = pre-fulfilment, refund = post-fulfilment). A rising cancellation trend often correlates with later refund spikes (the unfixable issues that slip through cancellations show up as refunds). Both are revenue-leakage; pair the trends. My chart is jumpy with small numbers, can I smooth it? Toggle the rolling-average view (7-day or 14-day rolling). Daily counts on small-volume stores (<100 orders/day) swing noisily; rolling averages reveal the trend. Use daily for spike detection, rolling for trend assessment.

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Tracked live in Vortex IQ Nerve Centre

Cancelled Orders Over Time is one of hundreds of KPI pulses Vortex IQ tracks across BigCommerce and 70+ other ecommerce connectors. Nerve Centre runs the detection layer; Vortex Mind investigates the cause when something moves; Ask Viq lets you interrogate any number in plain English. Start for free or book a demo to see this metric running on your own data.