At a glance
Average revenue by day of the week, computed across the rolling 90 days. Useful for sizing weekend marketing pushes, scheduling Customer Service shifts, knowing when a promotional event will produce the biggest absolute lift, and spotting structural weekly patterns. Outputs both mean and median per DOW; median is more robust to single-day spikes (e.g. Black Friday).
| What it counts | For each day of the week (Mon-Sun): MEAN(daily_revenue) and MEDIAN(daily_revenue) over all instances of that DOW in the 90-day window. With 90 days, that’s roughly 12-13 instances per DOW. |
| API field | grand_total, created_at from the orders index. Day-of-week derived from created_at in UTC by default. |
| VAT / tax treatment | Tax-inclusive (grand_total). |
| Shipping inclusion | Included. |
| Discounts | Already deducted. |
| Cancelled orders | Excluded by default. |
| Refunds | Not subtracted, the card is creation-time. |
| Currency | Mixed-currency display by default. base_grand_total for FX-neutral. |
| Multi-store scope | All Store Views by default. UK and US Store Views often have very different DOW patterns; per-Store-View slice is essential. |
| Why median is exposed alongside mean | A single huge promotional Wednesday in the 90-day window can pull Wed’s mean up by 30%; the median is unaffected. For “what’s a typical Wednesday?”, median is honest. For “what’s the absolute revenue I produce on Wednesdays?”, mean is the planning number. |
| Time window | 90D rolling. Longer windows (180D, 365D) smooth seasonality but risk including events from old promotional periods. |
| Alert trigger | None on this card directly. |
| Roles | owner, marketing |
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 B2B+DTC industrial-supplies merchant on Adobe Commerce 2.4.7. Snapshot 13 May 26, 90-day rolling. Mean and median revenue by day of week:| DOW | Mean revenue | Median revenue | Order count mean | Average DTC share | Average B2B share |
|---|---|---|---|---|---|
| Monday | $42,300 | $39,800 | 184 | 38% | 62% |
| Tuesday | $38,800 | $37,200 | 175 | 42% | 58% |
| Wednesday | $36,400 | $35,100 | 166 | 44% | 56% |
| Thursday | $34,900 | $33,800 | 161 | 47% | 53% |
| Friday | $30,200 | $29,100 | 148 | 56% | 44% |
| Saturday | $24,600 | $24,200 | 142 | 78% | 22% |
| Sunday | $22,800 | $22,600 | 134 | 81% | 19% |
| Slice | Pattern |
|---|---|
| Mon vs Sun spread | Mon is 86% higher than Sun. Typical for B2B+DTC. |
| B2B drives Mon-Tue-Wed | B2B share is 56-62% on those days vs 19-22% on weekends. |
| DTC drives Sat-Sun | DTC is 78-81% of weekend revenue. |
| Mean vs median gap on Wed | Wed mean (35,100); a single $90k Wed in the window pulled mean up. |
- Marketing planning. Friday-Sunday is DTC-dominated; weekend promos should be DTC-themed (apparel, accessories, gift items). Monday-Wednesday is B2B-dominated; bulk-purchase incentives belong here.
- Customer Service staffing. Mon-Tue absorbs ~60% of weekly RMA volume because customers placed orders on weekends and inspect/decide-to-return early in the week. Schedule heaviest CS coverage Mon-Tue mornings.
- A “Wednesday push” promotion would underperform unless explicitly B2B-targeted. The card answers “what day should I run my biggest sale?” with “Friday or Saturday for DTC, Monday or Tuesday for B2B”.
- Sunday’s revenue is 54% of Monday’s. A Sunday outage is genuinely less costly than a Monday outage; SRE incident severity should weight by DOW.
- Mean vs median for forecasting. Use median for “expected daily Wednesday” planning; use mean for absolute revenue forecasting (because mean × 90 days ≈ total revenue, by definition).
- Cross-link with Orders Over Time: order count and revenue should track proportionally per DOW. If Wed has high order count but low revenue, Wed’s AOV is depressed (perhaps due to DTC bargain-hunters); investigate.
- Compare to peer benchmark: B2B-leaning industrial-supplies merchants typically run Mon/Sun ratio of 1.6-2.0×. Pure-DTC apparel runs 1.2-1.4× (much flatter). The 1.86× here is on-norm.
Sibling cards merchants should reference together
| Card | Why pair it with Revenue by Day of Week |
|---|---|
| Revenue Over Time | The day-by-day shape that this card aggregates. |
| Orders Over Time | Order-count DOW pattern. |
| Total Revenue | Sum across DOWs equals total revenue. |
| Revenue Breakdown | Other dimensions; pair for richer slicing. |
| Daily Order Trends | Intra-day patterns. |
| Customer Trend | Customer count DOW pattern. |
shopify.revenue_by_dow | Cross-platform peer. |
google_analytics.ga_sessions_by_dow | Traffic-side DOW; pair to assess conversion-rate consistency across the week. |
Reconciling against the vendor’s own dashboard
Where to look in Adobe Commerce Admin: Adobe Commerce does not have a “Revenue by Day of Week” report natively. Closest:Reports > Sales > Orders with daily granularity, then mentally group by DOW. Tedious; the card is faster.
Reports > Sales > Bestsellers has no DOW dimension.For a manual cross-check:
Export Sales > Orders for the period to CSV; pivot in spreadsheet on day-of-week derived from Created. Sum by DOW.
Why our number may legitimately differ from a manual Admin computation:
| Reason | Direction of divergence |
|---|---|
| Time-zone. Day-of-week derived from UTC by default. A merchant manually computing might use Store View locale; orders near midnight cross DOW boundaries. | DOW shift on edge cases |
| Currency. Card defaults to mixed; manual computation usually uses single Store View. | Material for multi-currency |
| Cancelled inclusion. Card excludes by default. | Standard |
| Mean vs median. Card defaults to median (more robust); a manual sum-divided-by-N is mean. Mean is sensitive to single big days; median is stable. | Material for stores with promo events |
| Time-zone, sync lag. Standard. | Minor |
| Card | Expected relationship | What divergence tells you |
|---|---|---|
google_analytics.ga_sessions_by_dow | Sessions DOW pattern usually flatter than revenue DOW pattern (because B2B traffic is higher-conversion than DTC) | Material divergence in DOW shape between sessions and revenue suggests conversion-rate varies materially by day. |
stripe.stripe_charge_by_dow | Stripe DOW shape ≈ this card’s DTC-side shape (since most B2B is via PO, not Stripe) | Match expected. |
Known limitations / merchant FAQs
Why does the card use UTC for DOW assignment? Consistency. UK/EU merchants and US merchants might define “Monday” differently if locale-based, leading to bizarre cross-Store-View comparisons. UTC ensures Monday is Monday everywhere. Toggle to Store View locale via the manifest if needed. Mean or median, which should I trust? Median for “what’s a typical Wednesday like?”. Mean for “what total revenue do I produce on Wednesdays in a quarter?”. Use median when a single promotional day might distort; use mean when computing forecast totals. My Sunday revenue is rising over time, what’s happening? Often DTC growth (DTC is over-represented on weekends). Could also be a Sunday-specific marketing campaign (email send, Sunday-only social ad). Cross-link withgoogle_analytics.ga_sessions_by_dow to see if Sunday traffic is rising or just Sunday conversion.
Why does my B2B-leaning store have such a steep Mon-Sun spread?
B2B accounts don’t order on weekends (procurement teams don’t work). The weekday-weekend gap is bigger than for pure-DTC. A 1.8-2.0x ratio is common for B2B-heavy.
My multi-store has different DOW patterns per Store View, expected?
Yes. UK B2B portal vs US DTC site have very different patterns. Slice per Store View for clarity.
Cancelled orders excluded by default, can I include them?
Yes via the toggle. Useful for “what’s the demand DOW shape, including orders that didn’t capture?”. Most merchants prefer the captured-only default.
Why doesn’t Reports > Sales > Orders match my mean DOW?
Reports doesn’t compute DOW. It computes daily totals which you’d then have to group by DOW manually. The card automates the manual step. If your manual computation differs, check timezone, currency, and cancelled-handling.
Do bank holidays affect this?
Bank holidays show as “low Monday” or “low whichever DOW”. Over 90 days, 1-2 bank holidays might pull a particular DOW’s mean down by 5-10%. Median is more robust to this.
Can I see DOW × hour heatmap?
Yes via the heatmap toggle on the card. Displays mean revenue by DOW × hour-of-day. Most useful for SRE on-call schedules and CS staffing optimisation.
A B2B account places one whale PO on a Sunday, does it skew the DOW pattern?
Slightly. Median is robust to this (one whale doesn’t change median if the DOW has 12-13 instances). Mean is sensitive. The card flags any DOW where mean diverges materially from median; investigate the outliers.