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Card class: Non-Hero • 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.
What it countsCOUNT(orders WHERE status = 'Cancelled') aggregated daily over 90 days. The chart is an area / line plot; spikes and trends are the actionable shape.
VAT / tax treatmentn/a (count metric).
Shippingn/a.
Discountsn/a.
RefundsRefunds are distinct from cancellations on BC; this card tracks cancellations only.
Cancelled / voided ordersThis card is the cancellation view.
Currencyn/a (count metric, currency-agnostic).
Channels / sourcesAll channels contribute by default. Marketplace cancellations dominate the chart for marketplace-heavy stores; toggle channel filter for cleaner per-channel patterns.
Spike interpretationA 1-day spike >2× the 30-day average is usually one of: (1) a fraud-rule deployment that mass-rejected legitimate orders; (2) an OOS event on a top SKU triggering bulk cancellations; (3) a marketplace platform issue (Amazon API down, eBay sync failure); (4) a duplicate-order bug from a checkout glitch.
Trend interpretationA rising 30-day rolling average suggests systemic decay: fraud-rule drift, sustained OOS events, supplier issues, or marketplace-relationship deterioration. Falling trend = improvements working.
Day-of-week patternCancellations sometimes peak Monday (weekend orders processed Monday morning, OOS-at-fulfilment surfaces) or after major sales (post-Black-Friday cancellation wave).
B2B Edition noteB2B portal contributions to this chart should be near-zero; any visible B2B cancellation pattern warrants investigation.
Time window90D (longer window for trend visibility)
Alert triggerNone on this card directly.
Rolesowner, operations

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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.
Period (sample dates)Daily cancellations (avg)Notable spikesPattern
14 Feb - 28 Feb 268/daySpike 18 Feb (32 cancellations)Steady baseline, one bad day
1 Mar - 14 Mar 269/dayNoneStable
15 Mar - 31 Mar 2614/daySpike 22 Mar (47 cancellations)Rising trend, investigate
1 Apr - 14 Apr 2618/daySustained elevatedWorsening systemic issue
15 Apr - 30 Apr 2611/dayImprovement after fix on 15 AprRecovery after intervention
1 May - 14 May 269/dayNoneReturned to baseline
90D average11/day
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

CardWhy pair it with Cancelled Over Time
Cancellation RateThe percentage view; this card is the count over time.
Refunds Over TimeThe refund-trend cousin; cancellation + refund = total revenue-leakage trend.
Revenue Over TimeThe denominator context; cancellation spikes that don’t dent revenue suggest sub-population concentration.
BC Decline RateDecline trend correlates with payment-failure cancellations.
BC Incomplete RateCart-abandonment trend; precedes cancellations.
Orders Over TimeOrder creation trend; cancellations should track creation if rate is stable.
BC Channel Refund RatePer-channel split; combined with this card’s per-channel filter gives full per-channel story.
shopify.cancellation_rateCross-platform reference.

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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:
ReasonDirection
Status mapping. We use Cancelled only by default. Including Declined raises counts ~10-30% depending on fraud-rule strictness.Configurable
Time-zone. UTC default vs BC store time zone; daily buckets shift at midnight boundaries.Configurable
Marketplace cancellation timing. Channel Manager indexes Amazon cancellations on sync (may be hours after Amazon’s cancel timestamp).Vortex IQ may LAG Amazon Seller Central by hours
Channel coverage. We include all channels by default; BC views may filter.Different totals
Cancellation reason exclusions. Some merchants exclude duplicate-order cancellations (operational hygiene rather than failure); configurable.Configurable
Cross-connector reconciliation:
CardExpected relationshipWhat causes legitimate divergence
stripe.stripe_canceled_payment_intents_over_timeStripe-side cancellations correlate with web-channel cancellations.Stripe sees only Stripe-paid orders.
amazon_sp.amazon_cancellations_over_timeAmazon channel slice should match Amazon SP-API.Time-zone and sync-lag noise.
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.