> ## Documentation Index > Fetch the complete documentation index at: https://docs.vortexiq.ai/llms.txt > Use this file to discover all available pages before exploring further. # Revenue by Hour, Shopify > Revenue by Hour for Shopify stores. Tracked live in Vortex IQ Nerve Centre. How to read it, why it matters, and how to act on it. **Metrics type:** [Supporting Metrics](/nerve-centre/overview#metrics-types-explained) • **Category:** [Ecommerce Platform](/nerve-centre/connectors#connectors-by-type) ## At a glance > Revenue by hour-of-day (00 to 23), aggregated across the 30D window. The £-weighted version of [Peak Order Hours](/nerve-centre/kpi-cards/shopify/peak-order-hours); reveals when the business actually earns money. | | | | ----------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | | **What it counts** | `SUM(totalPrice) GROUP BY hour(createdAt)` over the 30D window. Each hour-bucket is total customer-paid revenue placed in that hour across the period. | | **VAT / tax treatment** | Inherits store's `taxesIncluded` mode. | | **Shipping** | Included. | | **Discounts** | Deducted (post-discount). | | **Refunds** | NOT deducted. | | **Cancelled / voided orders** | Included if Shopify indexed them. | | **Currency** | Multi-currency arithmetic without FX. | | **Channels / sources** | All channels. POS sales create daytime humps in store-opening hours; B2B sales typically cluster in office hours. | | **Time window** | `30D` (default) | | **Alert trigger** | None; descriptive distribution. | | **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 home-décor DTC brand on Shopify, GMT customers primarily UK + EU. 30D window 12 Apr 26 to 12 May 26. | Hour-band (UK time) | Revenue | Share | Note | | ------------------- | ------------ | --------- | --------------------------------------- | | 06:00-09:00 | £14,200 | 4.1% | Pre-work browse-to-buy | | 09:00-12:00 | £28,600 | 8.3% | Morning office | | 12:00-14:00 | £42,800 | 12.5% | Lunch peak | | 14:00-17:00 | £37,400 | 10.9% | Afternoon coast | | 17:00-20:00 | £58,200 | 16.9% | After-work peak | | 20:00-23:00 | **£94,300** | **27.4%** | **Evening peak, primary buying window** | | 23:00-00:00 | £29,800 | 8.7% | Late-evening spillover | | 00:00-06:00 | £37,800 | 11.0% | Overnight (some EU + insomniac UK) | | **Total** | **£343,100** | **100%** | | Six things to notice: 1. **Evening dominates revenue more than orders.** The 20:00-23:00 slot is 27% of revenue but only \~22% of order count. Why? AOV is higher in the evening; customers spend more per order when relaxed at home with a cup of tea. 2. **Lunch peak is real but moderate.** 12:00-14:00 is 12.5% of revenue. Sufficient to justify ad-pacing but not the primary target. 3. **The £/order signal varies.** Compute revenue-per-order per hour: evening is \~£68 average, lunch is \~£52. The brand sells fundamentally different baskets to evening customers (premium homewares) vs lunchtime customers (impulse small-ticket). 4. **Email send-time decision.** Send at 17:00-18:00 to capture the after-work peak, NOT 20:00 (peak buy time, when emails get lost in evening inbox-flood). Test 18:30 vs 16:00 for incremental optimisation. 5. **Site-reliability is paramount during 20:00-23:00.** A 30-min outage during this window costs \~\~30% of an evening's revenue (\~\~£15-25k for this brand). Off-peak outages cost an order of magnitude less. 6. **Customer-service staffing should mirror.** Expect order-related queries to peak the next morning (10:00-12:00); staff customer-service accordingly. Most brands under-staff Mon morning despite Sun evening being the heaviest buy window. ## Sibling cards merchants should reference together Revenue by Hour is the £-side; pair with the count and pattern siblings: | Card | Why pair it with Revenue by Hour | | -------------------------------------------------------------------------------- | ------------------------------------------------------------------------ | | [Peak Order Hours](/nerve-centre/kpi-cards/shopify/peak-order-hours) | Order-count companion. Compare to find AOV-per-hour. | | [Revenue by Day of Week](/nerve-centre/kpi-cards/shopify/revenue-by-day-of-week) | Day-level cousin; combine for specific-cell decisions (e.g. Friday 8pm). | | [Weekend vs Weekday](/nerve-centre/kpi-cards/shopify/weekend-vs-weekday) | Aggregated DoW summary. | | [Total Revenue](/nerve-centre/kpi-cards/shopify/total-revenue) | Aggregate; hour buckets sum to it. | | [Average Order Value](/nerve-centre/kpi-cards/shopify/average-order-value) | The implicit AOV-per-hour ratio when paired with Peak Hours. | | [Top Discount Codes](/nerve-centre/kpi-cards/shopify/top-discount-codes) | Promo-driven hourly spikes correlate with email-send times. | ## Reconciling against the vendor's own dashboard **Where to look in Shopify Admin:** > **Shopify Admin → Analytics → Reports → "Total sales over time"** with hourly granularity (Shopify Plus only) The most direct equivalent. Pick the same window. Hour-bucket totals should match this card to within sync-lag tolerance, allowing for time-zone offset. Other Shopify Admin views: * **Live View**: real-time today's-revenue ticker; useful for verifying current-day peaks. * **Apps like Glew, Polar Analytics**: hourly revenue breakdowns standard. **Why our number may legitimately differ from Shopify:** | Reason | Direction | Why | | -------------------- | ---------------------- | ---------------------------------------------------------------------------- | | **Time zone** | Hour-bucket shift | UTC vs store time zone. Convert manually for comparison. | | **Refund treatment** | Either | Shopify Net Sales adjust for refunds; we show gross. | | **Multi-currency** | Aggregate distortion | We don't FX-normalise; per-currency hourly slices are clean, blended is not. | | **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. | **Cross-connector reconciliation:** | Card | Expected relationship | What causes legitimate divergence | | ------------------------------------------------------------------------------------------ | --------------------- | --------------------------------------------------------------------------------- | | [`google_analytics.ga_revenue_by_hour`](/nerve-centre/google_analytics/ga_revenue_by_hour) | Should track shape | GA4 misses 10-25% of orders due to ad blockers; hourly shape close, levels lower. | | [`klaviyo.kl_revenue_by_send_time`](/nerve-centre/klaviyo/kl_revenue_by_send_time) | Lagged correlation | Email-attributed revenue lags send-time by 1-3 hours; pair to optimise send-time. | ***
Documentation cross-reference (for agencies running multiple platforms) * [`bigcommerce.shp_revenue_by_hour`](/nerve-centre/bigcommerce/shp_revenue_by_hour) * [`adobe_commerce.shp_revenue_by_hour`](/nerve-centre/adobe_commerce/shp_revenue_by_hour)
## Known limitations / merchant FAQs **Why is the chart in UTC?** Vortex IQ uses UTC for consistency. Convert by subtracting your customer's primary time zone offset. UK summer = UTC+1; US Pacific = UTC-7/-8. **My peak revenue hour and peak order hour differ. Why?** AOV varies by hour. Evening shoppers tend to buy higher-AOV baskets than midday or morning shoppers. The order-peak captures volume; the revenue-peak captures monetary value. Both matter. **Should I run flash sales at peak revenue hour or peak order hour?** Generally peak revenue hour is the right target if your goal is total \$. But if you're testing a discount code's adoption rate, peak order hour gives more redemption volume to evaluate. **Why is overnight revenue meaningful in some windows?** Three usual reasons: 1. **Customers in different time zones.** UK store getting 11% overnight is mostly EU + insomniac domestic. 2. **Subscription billings**. Recurring orders trigger automatically at billing-day midnight; appears as overnight revenue. 3. **B2B / wholesale** with international buyers placing orders during their workday. **My multi-region store, can I split the curve by region?** Not on this card directly. Filter the underlying orders by ship-to country in Shopify Admin for per-region hourly views. Multi-region blending is the main reason hourly curves look "softer" than they should; segmentation usually reveals sharper peaks. **Why is my Black Friday hourly curve different?** BFCM and major sale events have completely different hourly distributions: * **Pre-launch midnight rush**: sales often hit hardest at 00:00 launch. * **Mid-morning second wave**: when customers wake to news of the sale. * **Late-night final push**: customers grabbing deals before sale ends. Don't draw conclusions about normal hourly patterns from BFCM data; exclude the sale week from your 30D window for clean reads. **Does subscription billing distort my chart?** Mildly. Subscription billings hit on the original-purchase-time recurring; if many subscriptions started 9am Tuesday, the next billings appear as 9am Tuesday revenue. The pattern reinforces over time. **Action playbook for using this card:** 1. **Identify peak revenue hour**: that's when ad-spend ROI is highest. 2. **Identify low revenue hour**: experiment with off-peak budget shifts. 3. **Email send-time**: 1-3 hours before peak revenue hour. 4. **Site-reliability monitoring**: monitor most intensively in peak windows. 5. **Customer-service staffing**: lag-staff for the morning following peak hours. 6. **Ops / fulfilment**: pick-and-pack workflow can be timed to absorb the previous evening's order volume by next-morning shipping cut-off. *** ### Tracked live in Vortex IQ Nerve Centre *Revenue by Hour* is one of hundreds of KPI pulses Vortex IQ tracks across Shopify and 70+ other ecommerce connectors. Nerve Centre runs the detection layer; Vortex Mind investigates the cause when something moves; Ask Viq lets you interrogate any number in plain English. [Start for free](https://app.vortexiq.ai/login) or [book a demo](https://www.vortexiq.ai/contact-us) to see this metric running on your own data.