At a glance
Daily order count over trailing 30 days. The most-used operational chart on Adobe Commerce dashboards: warehouse staffing, customer-service capacity planning, and ad-budget pacing all reference this curve. Adobe Commerce stores typically show a strong weekday weekend pattern with monthly cycles overlaid (B2B end-of-month PO clusters).
| What it counts | COUNT(orders) GROUP BY date(created_at) over 30 days. Each day is a bucket; 30 buckets per series. Optional 7-day moving average overlay. |
| API field | _id, created_at from GET /rest/V1/orders. |
| VAT / tax treatment | n/a, count metric. |
| Shipping inclusion | n/a. |
| Discounts | n/a. |
| Credit Memo refund treatment | An order later refunded counts on the original-order day. |
state machine inclusion | All states except canceled (cancelled orders aren’t operationally meaningful for warehouse and customer service planning). |
pending_payment quirk | Included. Net-30 B2B orders count as “orders placed” even though no money has captured. For “orders that will need fulfillment” use Unfulfilled Orders. |
Multi-currency grand_total vs base_grand_total | n/a. |
Store View scope (store_id) | All Store Views by default; per-Store-View overlays useful for spotting region-specific patterns (a UK Store View campaign that doesn’t affect US). |
| Time window | 30D daily granularity. |
| Alert trigger | None by default. |
| Roles | owner, marketing, operations |
Calculation
Worked example
A homewares brand on Adobe Commerce 2.4.6, US/UK/B2B Store Views. 30-day window ending Monday 4 May 26. Daily order count summary:| Metric | Value |
|---|---|
| Average daily orders | 240 |
| Standard deviation | 48 |
| Highest day | 392 (Mother’s Day Saturday) |
| Lowest day | 142 (Easter Sunday) |
| 7-day moving average (latest) | 252 |
| Day | Median orders |
|---|---|
| Mon | 285 |
| Tue | 308 |
| Wed | 304 |
| Thu | 285 |
| Fri | 248 |
| Sat | 168 |
| Sun | 154 |
- Order volume is mid-week heavy. B2B procurement clusters Tuesday/Wednesday; warehouse staffing should be heaviest Wednesday/Thursday for the lagged dispatch.
- The Mother’s Day spike (392 on the campaign Saturday) is 60% above weekly median, expected for the campaign. Worth comparing the cohort acquired that day for 90-day repeat behaviour.
- The Easter Sunday trough (142) is the lowest of the period. B2B is closed; consumer demand is reduced. Plan warehouse and customer service to reflect this.
- The 7-day moving average of 252 is up 8% vs the same window a quarter ago. Steady growth.
- Cross-checking Customer Trend: customers per day track orders per day closely (orders-per-customer hovers around 1.05). A sudden divergence (orders rising faster than customers) indicates AOV-rising-by-larger-baskets or repeat-orders-from-same-customer-in-day patterns.
- Action: warehouse staffing for the next 14 days should target 250-300 orders/day capacity; surge-staffing reserved for the next campaign push (planned for week 4).
Sibling cards merchants should reference together
| Card | Why pair it with Daily Order Trends |
|---|---|
| Total Orders | Single-period summary. |
| Orders Over Time | Longer-window trend (90 days). |
| Customer Trend | Customer-level companion. |
| Revenue Over Time | Dollar-weighted view. |
| Revenue by Day-of-Week | The DOW pattern decomposition. |
| Daily Leakage | Operational fulfillment slice. |
google_analytics.ga_orders_over_time | Top-of-funnel comparison. |
shopify.daily_order_trends | Cross-platform peer. |
Reconciling against the vendor’s own dashboard
Where to look in Adobe Commerce Admin:Reports > Sales > Orders with the date range. Adobe’s report shows daily totals natively when granularity is set to “Day”.
Dashboard > Orders widget shows the recent days at a glance.
Sales > Orders with date filter for individual order verification.For multi-Store-View:
Switch scope to specific Store View; each Store View has its own daily totals.Why our number may legitimately differ from Admin:
| Reason | Direction of divergence |
|---|---|
| Time-zone. Admin in Store View timezone; card UTC. ±1 day per bucket. | Boundary-day differences |
canceled exclusion. Card excludes; Admin includes unless filtered. | Card lower |
| Multi-Store-View aggregation. Card sums; Admin defaults to one Store View. | Card higher than per-Store-View admin view |
| Sync lag. Card uses OpenSearch sync (5-15 min); Admin live. | Negligible at daily |
| Pair | Expected relationship | What divergence tells you |
|---|---|---|
google_analytics.ga_orders_over_time | GA4 ≈ Adobe × (1 - tracking gap) | 10-25% lower is normal. |
stripe.stripe_charges | Stripe-paid orders only; subset | Stripe is a subset; net-30 B2B orders skew Adobe higher. |
Known limitations / merchant FAQs
Why does my chart drop on weekends but my GA4 sessions stay flat? B2B procurement is weekday-only; consumer browsing continues on weekends but at lower conversion. The order-per-session ratio drops on weekends, dragging order count down even though traffic is steady. Adobe Commerce vs Magento Open Source: any difference? None at the calculation. Both editions show the same trend. My multi-store Adobe Commerce, can I see per-Store-View overlays? Yes, configure per-Store-View series in the manifest. Useful for spotting regional campaign effects. The chart shows 0 orders on a recent day, what happened? Two causes: actual zero-order day (Christmas, holidays) or sync gap (the OpenSearch index missed orders). Cross-check Adobe Admin for ground truth. Why includepending_payment orders in the count?
B2B net-30 orders are placed-orders even though uncaptured. Excluding them would understate the operational pipeline.
A spike day shows up but my warehouse didn’t see it, why?
Most likely B2B net-30 orders. They’re placed (the sale is real, the customer committed) but no payment has captured and no shipment is required for some hours/days. Cross-check Unfulfilled Orders for the warehouse-relevant view.
Why does the chart use created_at rather than updated_at?
Day-of-business is the order-creation day; subsequent state changes don’t move the order to a different day for capacity planning purposes.
The pattern looks different from my GA4 chart, why?
GA4 measures purchase events (which fire on Adobe order create); they should track. Material divergence indicates GA4 tag-fire issues at checkout.