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Card class: Non-Hero • Category: Ecommerce Platform

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At a glance

Daily order count plotted as an area chart over 90 days. The trend version of Total Orders. Where Total Orders gives you point-in-time numbers, this card shows the daily shape, peaks, troughs, weekly seasonality, and the long-term direction.
What it countsDATE_HISTOGRAM COUNT(_id) per day across every order in the 90-day window.
VAT / tax treatmentn/a, count metric.
Shippingn/a.
Discountsn/a.
RefundsNot deducted (refunded orders count on placement date).
Cancelled / voided ordersIncluded (the order existed; cancellation is a downstream state).
Currencyn/a.
Channels / sourcesAll channels contribute. Channel-shape patterns: web orders cluster around lunch / evening peaks; POS clusters around store-open hours; marketplace orders distribute more evenly across the day. Multi-channel stores see a smoother trend than single-channel.
Day-of-week effectsMost BC stores see weekend dips on weekday-categories (B2B, office supplies) and weekend peaks on weekend-categories (homewares, fashion). The 7-day rolling average smooths these effects; the daily bars expose them.
Incomplete ordersIncluded by default. They represent customer-intent events; for “successful orders only” trend, exclude status = Incomplete.
Multi-currencyThe trend is the same regardless of currency mix; count is currency-insensitive.
B2B EditionB2B order placement clusters around month-end (procurement cycles); single-month windows can hide this rhythm. The 90-day view typically shows the pattern.
Time window90D (rolling 90 days, daily granularity)
Alert triggerNone at this card; sentiment key order_count_trend is monitored at the Total Orders and BC Alert Revenue Drop levels.
Rolesowner, marketing, operations

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Calculation

DATE_HISTOGRAM COUNT(_id)
  WHERE date BETWEEN [period_start, period_end]

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Worked example

A US homewares brand on BigCommerce Pro, 90-day window from 13 Jan 26 to 12 Apr 26.
PeriodAvg orders/dayNotable peaksTrend direction
13 Jan 26 to 26 Jan 26198320 on 22 Jan (clearance flash sale)Declining (post-holiday tail)
27 Jan 26 to 9 Feb 26224280 on 8 Feb (Spring catalogue launch)Rising
10 Feb 26 to 23 Feb 26235410 on 14 Feb (Valentine’s)Stable+
24 Feb 26 to 9 Mar 26248290 on 5 Mar (mid-week spike)Stable
10 Mar 26 to 23 Mar 26232nothing notableSlight decline
24 Mar 26 to 6 Apr 26218380 on 28 Mar (Spring launch promo)Concerning
7 Apr 26 to 12 Apr 26210320 on 11 Apr (bank-holiday weekend)Continued decline
What’s interesting:
  1. The 90-day shape: peak around mid-Feb, gradual decline since. The store’s typical-day order count fell from 248 (peak) to 210 (recent), a 15% decline in 6 weeks. Concerning trend even though no individual day looks alarming. The 90-day shape is what catches slow declines that single-period summaries miss.
  2. Campaign spikes are clearly visible. Valentine’s, Spring launch, and the 11 Apr bank holiday all show as 1.5-2x baseline days. Healthy responsiveness to promotions; problematic if the underlying baseline is falling between promotions.
  3. The 28 Mar Spring launch peak (380 orders) was a single-day surge but the days after it didn’t sustain. Compare against the 14 Feb Valentine’s peak which was followed by a multi-day elevated baseline; the Spring launch retained less. That’s a customer-quality signal: the launch acquired campaign-driven first-time buyers who didn’t return.
  4. The mid-week 5 Mar spike (290) with no apparent campaign driver is suspicious. Almost always one of three causes: (a) a viral social post / influencer mention, (b) a competitor outage that pushed traffic to you, (c) a leaked discount code being aggregated. Investigate via BC Top Coupons and social listening.
  5. Day-of-week pattern visible underneath the trend. Most homewares stores see Saturday and Sunday as peak ordering days; weekends in this trend cluster 30-50% above weekdays. Healthy seasonality, not a problem.
The diagnostic playbook when the trend declines:
  1. Compare 30-day rolling avg to 30-day rolling avg from prior period, smooths out daily noise. A 5%+ rolling-avg decline is real; under 5% is noise.
  2. Cross-reference Customer Acquisition Trend. Order trend declines driven by new-customer drops are top-of-funnel; driven by repeat-customer drops are retention.
  3. Pair with BC Channel Revenue Mix. Drops usually concentrate in 1-2 channels.
  4. Audit campaign cadence. If campaigns were running steadily and have stopped, the baseline reflects the no-campaign reality, the question becomes whether your campaign cadence is sustainable as the always-on state.
  5. Check the BC Incomplete Rate trend. Rising incomplete with falling orders = checkout friction is the cause.

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Sibling cards merchants should reference together

CardWhy pair it with Orders Over Time
Total OrdersThe point-in-time summary. This card shows the daily shape; that one shows current vs prior.
Revenue Over TimeSame shape, different axis. Together they show whether order growth = revenue growth or AOV-shift.
Total RevenueRevenue context. Order trend up + revenue trend up at same rate = volume growth; rates diverging = AOV moved.
Customer Acquisition TrendNew-customer trend. Pairing with order trend shows whether growth is driven by new or returning.
BC Channel Revenue MixChannel decomposition.
BC Revenue by HourIntra-day shape. Daily trend + hourly view = full temporal picture.
BC Weekend WeekdayDay-of-week aggregation. Strong weekend / weekday patterns visible here.
BC Alert Revenue DropReal-time alarm when this trend dips abruptly.

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Reconciling against the vendor’s own dashboard

Where to look in BigCommerce Control Panel: Analytics → Sales → “Orders over time” chart shows the daily order trend. The granularity is daily by default; switch to hourly for intra-day view (Plus / Pro). Standard plan stores need to compute manually from the order export. Why our number may legitimately differ from BC Analytics:
ReasonDirection
Incomplete order treatment. We include them; BC Analytics typically excludes.Vortex IQ HIGHER bars
Cancelled / Declined orders. Included here, may be excluded in BC Analytics.Vortex IQ HIGHER bars
Time zone. UTC vs store TZ; daily bars at the boundary shift.Boundary effects
Sync lag. Recent days are preliminary until end-of-day.Vortex IQ slightly LAGS for today
Channel coverage. Both views aggregate every channel; differences are sync timing.Same direction
Cross-connector reconciliation:
CardExpected relationshipWhat causes legitimate divergence
google_analytics.ga_purchases_trendGA4 purchase event trend should track shapeGA4 misses 10-25% of orders; absolute level differs but shape aligns.
stripe.stripe_charges_trendStripe-side charge count trendStripe sees only successful captures; this card includes Incomplete and Declined.
Same-metric documentation cross-reference:

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Known limitations / merchant FAQs

My trend has a clear weekly seasonality, what’s the right way to read it? Use 7-day rolling average instead of raw daily bars. The weekly pattern is real (weekend peaks, mid-week troughs in homewares; opposite in B2B) and obscures the underlying trend. Most stores enable rolling averages as the default chart view. The trend dropped 10% over 60 days but no single day looks alarming, is that a problem? Yes, this is exactly what slow-decline patterns look like. No single day fires an alert (the alert threshold is 20% per period); the cumulative effect is materially negative. Monitoring slow declines is what 90-day trend charts are for. Investigate via the diagnostic playbook in the Worked Example. My today bar is unusually low, did the integration break? Check the time. Today’s bar is partial until end of day. If it’s mid-afternoon and the bar reads zero, check Settings → Sources status. Most stores see today’s bar at 30-70% of normal until ~6pm local time, then it climbs to baseline. My Black Friday week dwarfs the rest of the year, can I exclude it? Yes via filtering. The rolling-90-day view will show the spike for ~3 months after; for a smoother long-term view, run 365-day or year-over-year comparisons. Why does my B2B store look so flat compared to D2C peers? B2B order volume is more even because procurement schedules drive ordering rhythm. D2C is campaign-driven and seasonal; B2B is contract-driven and steady. Both are healthy patterns. Can I overlay revenue on this chart? Not from this card directly. Pair with Revenue Over Time, the two charts together show whether order count and revenue track or diverge. Diverging trends signal AOV mix shifts. My 5 Mar spike has no apparent cause, how do I find it? Almost always one of: (a) a viral social post, (b) a competitor outage, (c) a leaked discount code. Check social platforms for spike-day mentions of your brand; check BC Top Coupons for unusual code activity that day; check status pages for major competitors. Why does my POS terminal look like it has a “ghost orders” pattern, occasional clusters of 30+ on one day, then 0 the next? Almost certainly a sync-batching issue. The terminal uploads in batches; one day’s batch lands the next day in BC. Real-time sync would smooth this out. Configure your POS to sync continuously (not in batches). Can I export this chart to share with the team? Yes, all Vortex IQ charts support PNG/CSV export from the chart menu. For embed in slides, use the PNG; for further analysis, use the CSV. Should I worry about the most recent 3 days being lower than baseline? Most recent 3 days are partially-mature. Today and yesterday will rise as orders trickle in (especially POS and Channel Manager which sync slower). Wait 48 hours before drawing trend conclusions from any 24-72 hour cliff.

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Tracked live in Vortex IQ Nerve Centre

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.