At a glance
Revenue split into two buckets: weekday (Mon-Fri) vs weekend (Sat-Sun). The simplest temporal pattern card; tells you whether the business is workday-driven or leisure-driven.
| What it counts | Two-bucket aggregation: SUM(totalPrice WHERE day_of_week IN (Mon..Fri)) and SUM(totalPrice WHERE day_of_week IN (Sat,Sun)) over the 90D window. |
| VAT / tax treatment | Inherits store mode. |
| Shipping | Included. |
| Discounts | Deducted (post-discount). |
| Refunds | NOT deducted. Original-order day determines the bucket. |
| Cancelled / voided orders | Included if Shopify indexed them. |
| Currency | Multi-currency arithmetic without FX. |
| Channels / sources | All channels. POS skews retail-hours; B2B skews weekday; impulse-channels (TikTok) skew weekend evenings. |
| Time window | 90D (default 90D rolling) |
| Alert trigger | None; descriptive split. |
| 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, single warehouse. 90D window 12 Feb 26 to 12 May 26.| Bucket | Days in window | Revenue | Avg per day | Share |
|---|---|---|---|---|
| Weekday (Mon-Fri) | 65 | £758,200 | £11,665 | 73.7% |
| Weekend (Sat-Sun) | 26 | £270,800 | £10,415 | 26.3% |
| Total | 91 | £1,029,000 | £11,308 | 100% |
- Weekday-skewed for fashion DTC. Common pattern; customers shop ahead of weekend wear. The 73.7% / 26.3% split with weekday over-index of ~3 ppt is healthy for the category.
- Per-day weekend is lower. £10,415/day weekend vs £11,665/day weekday. The shape is “weekday peak with weekend cool-down”. Customers physically engage in weekend life rather than browsing online.
- Saturday and Sunday differ. This card aggregates them; use Revenue by Day of Week for the split. Saturday usually slightly higher than Sunday (people buy ahead of the week).
- Inverted patterns exist. Some categories (food/grocery, weekend hobby gear, wellness, leisure) show weekend-over-index. If yours does, ad budget should pace toward Fri-Sat-Sun.
- POS retail can flip the pattern. Weekend POS revenue from pop-up shops can swing the headline. A brand with 30% retail share that does 60% of retail on weekends will look more weekend-skewed than the pure online story warrants.
- Email send-time strategy. Weekday-skewed brands should focus email volume Tue-Thu; weekend-skewed brands should focus Fri-Sat. The split tells you which calendar to optimise around.
Sibling cards merchants should reference together
The two-bucket weekday/weekend view. Companions:| Card | Why pair it with Weekend vs Weekday |
|---|---|
| Revenue by Day of Week | The 7-day cousin; reveals which specific day drives the bucket. |
| Peak Order Hours | The within-day cousin; weekend hours often differ from weekday hours. |
| Revenue by Hour | £-version of peak hours. |
| Revenue Over Time | Trend dimension; the weekday/weekend split can be stable while overall revenue trends. |
| Total Revenue | Aggregate context. |
| Revenue by Channel | POS-heavy channels weight weekends; pure online weights weekdays. |
Reconciling against the vendor’s own dashboard
Where to look in Shopify Admin: Shopify doesn’t have a dedicated weekend/weekday split. Reconstruct from:- Analytics → Reports → “Total sales over time” with daily granularity; group manually into weekday vs weekend.
- Apps like Glew, Polar Analytics: typically expose this split.
| Reason | Direction | Why |
|---|---|---|
| Time zone | Boundary | UTC vs store time zone; orders near midnight bucket differently. |
| Refund treatment | Either | We use original-order date; Shopify Net Sales differs. |
| Multi-currency | Aggregate distortion | We don’t FX-normalise. |
| Channel filter | Either | Reports filtered to “Online Store” only differ from blended. |
| Sync lag | Ours lower for “today” | Most-recent 5-15 min may not be in. |
| Card | Expected relationship | What causes legitimate divergence |
|---|---|---|
google_analytics.ga_revenue_by_dow_bucket | Should track shape | GA4 misses 10-25% of orders; level lower, shape similar. |
Known limitations / merchant FAQs
What’s a healthy weekday/weekend split? Category-dependent:- Fashion / lifestyle DTC: 70-78% weekday (people shop ahead of weekends).
- Food, grocery, weekend leisure: 50-60% weekend-leaning.
- B2B / wholesale: 90%+ weekday; minimal weekend.
- Subscription consumables: ~75% weekday (billing cycles weight it).
- Impulse-driven (TikTok, social commerce): 50-60% weekend.
- POS / retail presence: weekend retail traffic skews the bucket.
- Social-commerce channels: TikTok and Instagram Shop weekend evenings.
- Subscription billing alignment: if subscriptions billed Friday-Saturday, the weekend bucket inflates.
- B2B-heavy mix: weekday-only ordering.
- Email-driven conversion: most email opens are Tue-Thu, driving weekday peaks.
- Bank holidays and long weekends: in the 90D window, multiple bank holidays compress the weekday bucket.
- Email send-time: weekday-skewed → Tue-Thu sends. Weekend-skewed → Fri-Sat.
- Ad-spend pacing: align with the split; don’t over-spend on the lower-revenue bucket unless conversion-rate is meaningfully better.
- Customer-service staffing: skew to the buy-bucket plus 24-48h lag.
- Inventory replenishment: weekend-deplete + Mon-restock cycle for weekend-heavy stores.
- Promo timing: launch new collections to align with the natural buying window.