At a glance
Revenue grouped by the day of the week the order was placed (Mon, Tue, …, Sun). The pattern card that tells you which days carry the business and which days you can safely ignore for paid-media or staffing decisions.
| What it counts | SUM(totalPrice) GROUP BY day_of_week(createdAt) over the 90D window. Each weekday slot aggregates ~13 days of orders; the chart shows the average daily revenue per weekday. |
| VAT / tax treatment | Whatever totalPrice includes, the card includes. UK / EU stores see VAT-inclusive rows; US stores see tax-exclusive. |
| Shipping | Included (sits in totalPrice). |
| Discounts | Deducted (post-discount). |
| Refunds | NOT deducted. The day of the original order is what counts; refund timing doesn’t move revenue between days. |
| Cancelled / voided orders | Included if Shopify indexed them. |
| Currency | Multi-currency arithmetic without FX. Stores transacting in mixed currencies should filter to one currency for a usable view. |
| Channels / sources | All channels, weighted by their natural day-of-week distribution. POS spikes weekends; B2B spikes weekdays; online is more uniform. The blended pattern hides channel-specific signals. |
| Time window | 90D (default) |
| Alert trigger | None, descriptive pattern card. |
| Roles | owner, marketing |
Calculation
Calculated automatically from your Shopify 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 UK fashion DTC brand on Shopify, online-first with one London pop-up running Sat-Sun. 90D window 12 Feb 26 to 12 May 26.| Day of week | Total revenue (90D) | Avg per day | Share of week | Note |
|---|---|---|---|---|
| Monday | £148,200 | £11,400 | 14.4% | Email-driven traffic, Friday newsletter knock-on |
| Tuesday | £142,600 | £10,970 | 13.9% | Slightly softer, mid-week dip |
| Wednesday | £138,900 | £10,685 | 13.5% | Lowest weekday |
| Thursday | £156,300 | £12,025 | 15.2% | Pre-weekend acceleration |
| Friday | £172,400 | £13,260 | 16.8% | Peak weekday, payday + weekend planning |
| Saturday | £138,600 | £10,660 | 13.5% | Includes pop-up POS revenue |
| Sunday | £132,000 | £10,150 | 12.8% | Weekly low, no POS |
| Weekly total | £1,029,000 | £11,450 | 100% |
- Friday is peak; the weekend isn’t. Common in UK DTC fashion; counterintuitive vs retail. Customers shop ahead of the weekend, then live the weekend offline. Use this to time email sends (Thursday evening) and ad budget concentration (Thu / Fri).
- The weekend-spike POS is buried in the data. The Sat figure of £10,660/day looks weak vs Friday, but the pop-up itself probably did £4-6k/day on Saturday alone. Online demand falls sharply on weekend afternoons, masking the retail uplift. Filter by sales channel in Shopify Admin to isolate.
- Wednesday is a strategic gap. £10,685/day average is the lowest weekday. Two interpretations: cut Wednesday paid-media spend, or run mid-week promo events to lift the floor. Many brands do the second.
- The Mon-to-Sun spread is narrow (~30%). A 30% weekend-vs-Friday gap is healthy; if you see >50% spreads, the brand is over-reliant on a single day, which is a fragile revenue shape. Discount-heavy stores often see 60%+ spreads (the discount day vs everything else).
- Payday weeks shift the pattern. UK pay-day clusters at month-end; Friday spikes can be 25%+ higher in last week of the month than the first. The 90D average smooths this out but flags it on weekly comparison.
Sibling cards merchants should reference together
Day-of-week is one slice of the temporal pattern. The companions:| Card | Why pair it with Revenue by DoW |
|---|---|
| Revenue by Hour | The within-day cousin. Pair to find the exact “Tuesday at 8pm” peaks for ad budget and email send-time optimisation. |
| Peak Order Hours | Distilled hour pattern across all days. Use the DoW card to qualify which day’s peak hour matters most. |
| Weekend vs Weekday | The simplified two-bucket view. Use when the audience needs less granularity. |
| Revenue Over Time | The trend dimension. DoW pattern can be stable while overall revenue trends down; both views matter. |
| Total Revenue | The denominator for the share calculations. |
| Top Discount Codes | A heavy-promo Friday inflates the Fri slice; check whether the DoW pattern is organic or promo-driven. |
| Orders Over Time | Order-count companion to revenue. AOV-per-day-of-week may differ from revenue-per-day-of-week. |
Reconciling against the vendor’s own dashboard
Where to look in Shopify Admin: Shopify doesn’t expose a dedicated day-of-week report natively; the closest views:- Analytics → Reports → “Total sales over time” with the granularity set to day. Manually group the daily totals into Mon/Tue/…/Sun for a comparison.
- Analytics → Live View: gives a real-time current-day pulse; use over a few weeks to manually calibrate the DoW chart.
- Apps like Glew, Polar Analytics, or Shopify Analytics+ (Shopify Plus) offer DoW splits; numbers should match this card to within sync-lag tolerance.
| Reason | Direction | Why |
|---|---|---|
| Time zone | Boundary days swing buckets | Shopify Admin uses store time zone; Vortex IQ uses UTC. An order placed at 23:30 BST on Sunday may show as Monday in our index but Sunday in Admin. |
| Refund recognition | Either | Shopify can show refunds applied to original order date or refund-event date depending on report; we always use original order date. |
| Multi-currency | Same caveat as Total Revenue | We don’t FX-normalise; Shopify reports do for the headline. |
| Channel filter | Either | Reports filtered to “Online Store” only differ from this card’s blended figure. |
| Sync lag | Ours lower for “today” | The most recent 5 to 15 minutes of orders may not be in. |
| Card | Expected relationship | What causes legitimate divergence |
|---|---|---|
google_ads.gads_revenue_by_day (when connected) | Should track Shopify’s pattern shape | Google Ads attributes purchases via last-click within window; Shopify is order-creation-time. Pattern shapes are similar; absolute numbers differ. |
klaviyo.kl_send_engagement_by_day | Indirect | Use to align email-send DoW with revenue-DoW peaks. |
Known limitations / merchant FAQs
My peak day is unusual, is that a problem? Not necessarily. Patterns are category-driven:- DTC fashion / lifestyle: typically Thursday or Friday peak.
- Beauty and skincare: often Sunday or Monday (planning for the week ahead).
- B2B and wholesale: Tuesday-Wednesday peak (buyers in the office, accounts-payable cycles).
- Food and grocery: Friday-Saturday peak (weekend prep).
- Subscription consumables: flat across the week (billing dates spread).
- Identify your top-2 revenue days.
- Look at your conversion-rate-by-DoW (Shopify Admin → Reports → Conversion over time, grouped). The CR peak doesn’t always match the revenue peak.
- Concentrate paid-media impressions in the 24 hours before the revenue peak (where high CR meets high intent). Most brands underweight Thursday spend even though Friday is peak.
createdAt UTC timestamp regardless of customer location. A US-customer order at 11pm PT is “Saturday” UTC; a UK-customer order at 8am BST is “Saturday” too. Both contribute to Saturday. To split, filter by ship-to country in Shopify Admin.
Action playbook for using DoW patterns:
- Email send-time: send 12-18 hours before revenue peak (Thursday eve for Friday peak).
- Ad-budget pacing: shift 30 to 50% of weekly budget to top-2 days; reduce the bottom-2 days by 20 to 30%.
- Retail / pop-up staffing: align retail hours with online peaks for cross-channel customers; staff retail Saturday afternoons even when online is soft.
- Inventory replenishment: PO arrivals timed for top-2 days minimise stockout risk.
- Customer-service capacity: mirror revenue DoW with support DoW; Monday post-weekend is the heaviest support day.