At a glance
Revenue split by day-of-week, averaged across the window. The card that turns “we did £232k this month” into “Tuesday accounts for 11% of weekly revenue and Saturday accounts for 19%”. Day-of-week patterns matter for staffing (CS coverage, fulfilment scheduling), marketing (when to send email, when to launch campaigns), and operations (when to deploy code, when to run inventory updates). Most BC stores have surprisingly consistent DoW patterns that are stable across months and seasons.
| What it counts | SUM(total_inc_tax) GROUP BY DAYOFWEEK(date_created) averaged across all weeks in the window. Output is one row per day of the week with absolute and % values. |
| VAT / tax treatment | Tax-inclusive. Same definition as BC Total Revenue. |
| Shipping | Included. |
| Discounts | Deducted. |
| Refunds | Not deducted (gross). |
| Cancelled orders | Excluded. |
Incomplete orders | Excluded. |
| Currency | Multi-currency without FX; per-currency averages. |
| Channels / sources | All channels aggregate by default; channel filter available. POS revenue typically peaks on weekends (in-store traffic); web revenue patterns vary by category. |
| Time-zone gotcha | Day-of-week is calculated in the store’s configured time zone, not UTC. A US Pacific store sees an order placed 23:30 PT Tuesday as Tuesday revenue; the same order in UTC would already be Wednesday. Stores serving multiple time zones (international DTC) see DoW patterns blurred by time-zone mismatch. |
| Sample size | The card averages across all weeks in the window. Below 4 weeks of data the patterns are noisy; below 12 weeks they may be biased by promotional events. The 90D default is the minimum for reliable signal. |
| Time window | 90D (rolling 90 days; settings allow 30D, 90D, 180D). |
| Alert trigger | None; this is a pattern-discovery card. |
| Roles | owner, marketing |
Calculation
Calculated automatically from your BigCommerce 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 US apparel brand on BigCommerce, 90-day window, all currency converted to USD for the example.| Day of week | Avg daily revenue ($) | % of weekly revenue | vs Sunday baseline |
|---|---|---|---|
| Monday | $14,200 | 13.4% | -5% |
| Tuesday | $13,800 | 13.0% | -8% |
| Wednesday | $14,500 | 13.7% | -3% |
| Thursday | $15,400 | 14.5% | +3% |
| Friday | $17,800 | 16.8% | +19% |
| Saturday | $16,200 | 15.3% | +8% |
| Sunday | $14,950 | 14.1% | baseline |
| Weekly total | $106,850 | 100% |
- Friday is the peak day (+19% vs Sunday). Classic apparel pattern: Friday-evening payday-spend, weekend-prep purchases, and the start of weekend leisure browsing. Saturday is also strong; the weekend block (Fri+Sat+Sun) is 46.2% of the week’s revenue.
- Tuesday is the trough (-8% vs Sunday). Most BC stores show this; mid-week is the lowest commerce activity. Use Tuesday for code deploys, inventory updates, and operational changes because the impact of any outage is smallest.
- The pattern is remarkably stable. Across 90 days the DoW distribution rarely shifts more than 1-2 percentage points week to week (assuming no promotional events). Once you know the pattern, you can detect anomalies easily: a Friday at 11% instead of 17% is a major red flag.
- The 4.6:1 weekend-to-Tuesday revenue ratio sets staffing. CS team should be heaviest Friday-Saturday; fulfilment scheduling should target Saturday-Monday volume; ad spend should peak Thursday-Friday for weekend conversion windows.
- Email send timing. Most categories see best email-driven conversion when sent Thursday morning (preparing for the Friday peak) and Saturday morning (capturing the in-progress weekend audience). Tuesday and Wednesday sends typically convert poorly; aspirational content (browse, save) works better than promotional content (buy now).
- Schedule code deploys for Tuesday or Wednesday. Lowest impact if something breaks.
- Schedule promotional email campaigns for Thursday or Saturday. Capture peak-day conversion.
- Stage CS team coverage to match revenue distribution. 30-40% capacity Friday-Saturday-Sunday combined.
- Use BC Revenue by Hour for sharper time-of-day signals within the days. The DoW * hour combination shows when each day’s buying happens.
- Compare DoW pattern by channel. POS will skew weekend; B2B will skew weekday-business-hours. Use channel filter to see each channel’s distinct pattern.
- Subscription / replenishment: more even distribution (subscriptions fire on cadence regardless of DoW).
- B2B: heavily Tuesday-Thursday concentrated; weekends near-zero.
- Wedding / events: Saturday peak much stronger.
- Flash sales / drops: distorts DoW for the days the drop happens, then reverts.
Sibling cards merchants should reference together
| Card | Why pair it with Revenue by Day of Week |
|---|---|
| BC Revenue by Hour | Sharper time resolution. DoW x hour is the staffing-decision matrix. |
| BC Weekend vs Weekday | Aggregated 5+2 view, useful for headline reporting. |
| Revenue Over Time | Time series. Watch DoW patterns shift over time as customer base changes. |
| Total Revenue | The total. DoW is the composition; total is the magnitude. |
| Order Count | Pair with this for “is the DoW pattern about more orders or bigger orders?” analysis. |
| AOV | Some categories see higher AOV on weekends (more time to browse, larger baskets). |
| BC Channel Revenue Mix | If channel mix shifts by DoW, the pattern’s source is the mix, not the customer behaviour. |
| Discount Over Time | Promotional-day spikes can mask DoW patterns; always read the two together. |
| Fulfillment Rate | Weekend orders often have slower fulfilment SLAs; track by DoW separately. |
| Refunds Over Time | Refund DoW pattern usually mirrors revenue with a 5-10 day lag. |
Reconciling against the vendor’s own dashboard
Where to look in BigCommerce’s own dashboard: BC Insights doesn’t natively expose a day-of-week revenue breakdown. The closest equivalent is the Sales Over Time chart with daily granularity; export to CSV and pivot byWEEKDAY() in spreadsheet for the same view.
For Plus and Enterprise tiers with custom Insights queries, the merchant can build a DoW report manually. Standard tier merchants typically rely on this card for the analysis.
Why our number may legitimately differ from the vendor’s:
| Reason | Direction | Why |
|---|---|---|
| Time zone definition | Either | We use store time zone (configured in BC Settings); BC Insights uses the same. Stores that change time zone mid-period see a DoW shift on the boundary day. |
| Daylight Saving transitions | Trivial | Twice a year a 23-hour or 25-hour day exists; we attribute orders to the local-day they were placed. |
| Promotional days | Either | Black Friday, Boxing Day, Prime Day distort DoW patterns. The 90-day window may or may not include them. Compare windows excluding promotional days for cleaner pattern. |
| Sync lag | Trivial | Webhook fanout introduces sub-day lag, doesn’t materially affect DoW averages. |
| Card | Expected relationship | Notes |
|---|---|---|
google_analytics.ga_revenue_by_dow | Pattern shape should match within ±2pp per day | GA4 attributes by session timestamp; BC by order created timestamp. The two should converge for purchases that fired both events. |