> ## 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, BigCommerce > Revenue by Hour for BigCommerce 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 > Total revenue split by hour of the day (00-23) over the rolling period. The card the ad-scheduling, customer-service-staffing, and email-send-time teams live in, knowing your peak ordering hours lets you concentrate ad spend, staff support, and time email sends to match. | | | | ----------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | **What it counts** | `SUM(total_inc_tax) GROUP BY HOUR(date_created)` over the period, summed across all 30 days then averaged or totalled per hour-of-day. Default view shows the average revenue per hour-of-day across the period. | | **VAT / tax treatment** | **Tax-inclusive** (`total_inc_tax`). | | **Shipping** | Included. | | **Discounts** | Already deducted. | | **Refunds** | Not deducted (gross). | | **Cancelled / voided orders** | Included. | | **Currency** | Multi-currency without FX. Filter by currency for clean per-currency hour patterns. | | **Channels / sources** | All channels contribute by default. POS and marketplace-channel hours look very different from web hours; consider filtering to `channel_id = 1` for a pure web-storefront pattern. | | **Time-zone treatment** | Hour-of-day is computed in **UTC** by default. For local-time analysis use the time-zone toggle (Vortex Mind setting); the local-time view is more actionable for ad scheduling. UK store on UTC and BST handle automatically; US stores need explicit timezone configuration. | | **DST gotcha** | Daylight Saving transitions shift the same wall-clock hour by 1 UTC hour twice yearly. The first 1-2 weeks after a DST flip show a smeared hour pattern; settle to clean once the rolling window flushes the pre-DST data. | | **B2B Edition note** | B2B portal orders peak in business hours (9-17 local); retail orders peak evenings (18-22) and lunch (12-14). Mixing them produces a flatter pattern than is actionable; filter to one channel type at a time. | | **Time window** | `30D` (rolling 30 days, hour-of-day pattern smoothed across 30 days) | | **Alert trigger** | None on this card directly. Pair with [BC Alert Revenue Drop](/nerve-centre/kpi-cards/bigcommerce/revenue-drop-alert) for hour-by-hour comparison. | | **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 UK fashion brand on BigCommerce Pro, web only, 30-day window 14 Apr 26 to 14 May 26, local time (BST). | Hour (local BST) | Avg revenue/hour | Order count avg | Pattern | | ---------------- | ---------------- | --------------- | ------------------------------------------------- | | 00-06 | £180/hr | 3 | Quiet overnight, mostly insomniacs and US-overlap | | 07-08 | £620/hr | 11 | Commute browsing | | 09-11 | £980/hr | 17 | Morning at desk, "remember to order" | | 12-13 | £1,840/hr | 32 | **Lunch peak #1**, 1.6× day average | | 14-16 | £1,420/hr | 25 | Sustained afternoon | | 17-18 | £980/hr | 17 | Commute home | | 19-21 | £2,640/hr | 47 | **Evening peak #2**, 2.3× day average | | 22-23 | £1,420/hr | 25 | Late evening tail | | **Day average** | **£1,140/hr** | 20 | | What's interesting: 1. **Two clear peaks: lunch (12-13) and evening (19-21).** This is the textbook pattern for B2C UK fashion. Evening is bigger (2.3× day average) than lunch (1.6×) because shoppers have time to browse and AOV is higher when not rushed. **The single biggest hour is 20:00-21:00 BST**, that's where ad spend should index. 2. **The 19-21 evening peak is 4.4× the overnight 00-06 hours.** If your ad scheduling is flat across 24 hours, you're wasting overnight budget and underspending in the peak. Configure dayparting on Google Ads / Meta to weight 18:00-23:00 BST 2-3× the day average. 3. **Lunch peak (12-13) is mobile-skewed.** Customers on phones during lunch break. Mobile UX matters more here than during evening (which skews tablet / desktop on the couch). Cross-reference [BC Revenue by Device](/nerve-centre/kpi-cards/bigcommerce/revenue-by-device) filtered to lunch hour. 4. **Customer service staffing implication.** 2.3× volume in the evening means refund / question / chat queue spikes. Most BC merchants under-staff evening support; a single chat agent online 19-22 BST handles the bulk of issue resolution that would otherwise overflow to next-day email. 5. **Email send time.** The standard "send at 9 AM" recommendation is suboptimal for BC fashion stores. **Sending at 18:30 BST aligns with the rising evening peak**; the email arrives just as shoppers settle in, AOV typically 15-20% higher than 9 AM sends. Action priority order: 1. **Configure ad-spend dayparting** this week, weight 19-21 BST 2-3× the day average on Google Ads, Meta, TikTok. 2. **Move email sends to 18:30 BST** for promotional campaigns, A/B test for 4 weeks against 9 AM. 3. **Staff customer-service chat 19-22 BST** with at least one live agent. 4. **Mobile UX audit for lunch-peak hour** specifically, optimise for one-handed phone shopping in 5-minute windows. 5. **Quarterly: review hour pattern for shifts**, COVID-era patterns shifted toward evening; some brands now see 20:00-22:00 dominate even more strongly. ## Sibling cards merchants should reference together | Card | Why pair it with Revenue by Hour | | -------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------- | | [BC Weekend vs Weekday](/nerve-centre/kpi-cards/bigcommerce/weekend-vs-weekday) | Day-of-week × hour cross-tabs reveal the weekend afternoon pattern (very different from Tuesday lunch). | | [Revenue by Day of Week](/nerve-centre/kpi-cards/bigcommerce/revenue-by-day-of-week) | The orthogonal time cut. Together with hour-of-day they paint the full temporal pattern. | | [BC Revenue by Device](/nerve-centre/kpi-cards/bigcommerce/revenue-by-device) | Hour × device cross-tabs reveal lunch-mobile vs evening-desktop patterns. | | [Revenue Over Time](/nerve-centre/kpi-cards/bigcommerce/revenue-over-time) | The trend view; this card is the within-day pattern. | | [`google_analytics.ga_sessions_by_hour`](/nerve-centre/google_analytics/ga_sessions_by_hour) | Sessions-by-hour from GA4; correlates with revenue-by-hour but conversion rate may shift by hour. | | [`google_ads.ga_dayparting_performance`](/nerve-centre/google_ads/ga_dayparting_performance) | Ad-spend efficiency by hour; pair with this card to set dayparting bid modifiers. | | [BC Channel Revenue Trend](/nerve-centre/kpi-cards/bigcommerce/channel-revenue-trend) | Per-channel hour patterns (POS only opens 9-21 typically, marketplaces 24×7). | | [`klaviyo.kl_send_time_optimization`](/nerve-centre/klaviyo/kl_send_time_optimization) | Email send-time optimisation; align sends with evening peak. | ## Reconciling against the vendor's own dashboard **Where to look in BigCommerce Control Panel:** BC's native [Analytics → Sales](https://login.bigcommerce.com/deep-links/manage/analytics) does not surface hour-of-day patterns directly; the closest is the daily time-series. To verify hour patterns manually, [Orders → All orders](https://login.bigcommerce.com/deep-links/manage/orders) lets you sort by date\_created descending, then visually scan the timestamps. **There is no native hour-of-day view in BC**; this card is one of the highest-value adds the connector provides. **Why our number may legitimately differ from BC raw orders:** | Reason | Direction | | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------- | | **Time-zone configuration**. BC stores order timestamps in store time-zone; we offer UTC default plus a configurable local-time view. Misconfigured local-time setting shifts the entire hour pattern by N hours. | Configurable mismatches | | **DST transitions**. The UK BST/GMT flip and US DST flips smear the same wall-clock hour across 2 UTC hours during the transition week. | Transition-week noise | | **Marketplace order timestamps**. Channel Manager orders use the marketplace's order-creation timestamp, which may differ from BC's index timestamp by 30+ minutes. | Marketplace orders may shift bucket by 1 hour | | **Cancelled / amended orders**. An order created at 19:00 then amended at 21:00 retains its 19:00 timestamp; this is the correct attribution but may surprise merchants who think of it as a 21:00 transaction. | None, this is correct | **Cross-connector reconciliation (when GA4 connected):** | Card | Expected relationship | What causes legitimate divergence | | -------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- | | [`google_analytics.ga_sessions_by_hour`](/nerve-centre/google_analytics/ga_sessions_by_hour) | Sessions-by-hour pattern correlates strongly with revenue-by-hour, but conversion rate varies by hour. | Conversion rate is typically higher in evening; sessions and revenue patterns differ in shape. | | [`google_ads.ga_dayparting_performance`](/nerve-centre/google_ads/ga_dayparting_performance) | Ad spend efficiency by hour; should align with revenue peaks if ad scheduling matches demand. | Ad-spend peaks may be intentionally before demand peaks (build awareness in afternoon for evening conversion). | **The hour-of-day pattern is BC-aligned with similar cards on Shopify and Adobe Commerce; the analytical value is identical across platforms.** ***
Same-metric documentation cross-reference (for agencies running multiple platforms) * [`shopify.revenue_by_hour`](/nerve-centre/shopify/revenue_by_hour) (planned) * [`adobe_commerce.revenue_by_hour`](/nerve-centre/adobe_commerce/revenue_by_hour) (planned)
## Known limitations / merchant FAQs **My peak hour is 02:00 UTC, that doesn't match my customers, why?** Almost certainly a time-zone configuration issue. UTC 02:00 corresponds to 21:00 EST or 18:00 PST. If your customers are US-based, switch the card's time-zone toggle to local. UK / EU stores see UTC and BST/CEST line up cleanly during BST/CEST, then shift by 1 hour during winter (GMT/CET). **Why are weekend hour patterns missing here?** This card averages across all 30 days; weekend / weekday patterns blend. Use [BC Weekend vs Weekday](/nerve-centre/kpi-cards/bigcommerce/weekend-vs-weekday) for the day-of-week split, or filter this card to "weekdays only" / "weekends only" via the day-filter toggle. Weekend afternoons typically show a single broad peak (12-18) rather than the weekday lunch-and-evening twin peaks. **Should I shift my email send time to peak hour?** Yes for promotional emails. Send 30-60 minutes before peak so the email is in the inbox when the customer enters peak browsing mode. For UK fashion stores, send at 18:30 BST for the 19-21 evening peak. **Don't send at peak**, by the time the customer sees the email the peak window is half over. **My ad spend is spread evenly across 24 hours, what should I do?** Configure dayparting bid modifiers in Google Ads / Meta. A reasonable starting point: 1.5× bid modifier for peak hours (typically 19-21 local), 1.2× for secondary peaks (12-13 lunch), 0.7× for off-peak (00-06), 1.0× baseline elsewhere. Run for 4 weeks, measure ROAS by daypart, refine. **My B2B portal hour pattern looks completely different, why?** B2B buyers order during business hours (9-17 local) on weekdays. B2C customers order evenings and weekends. Mixing them produces a flat unhelpful pattern. Filter to B2B channel for B2B-specific scheduling decisions; filter to web for retail. **Why does my POS hour pattern peak at 14:00-15:00?** That's the typical retail-floor "midday browse" pattern, post-lunch shoppers in town centres or malls. POS doesn't have an evening peak because retail stores typically close 18:00-19:00. POS hour patterns are bounded by store opening hours and won't show late-evening tails. **My Amazon channel order hours look smeared compared to my web pattern, why?** Amazon Channel Manager pushes orders to BC in batches; the timestamp is the BC index time, not the original Amazon order time. The hour pattern is somewhat dampened. For accurate Amazon hour patterns use Amazon Seller Central's order-time-of-day report; for unified web pattern, filter this card to `channel_id = 1`. **Why is my "today" pattern jumpy compared to the 30-day average?** Single days have small samples per hour. A 100-order/day store has only 4-5 orders/hour on average; a single £400 order moves a single hour bucket by 30%+. The 30-day rolling view smooths this. **Don't make decisions on a single day's hour pattern.** Use the 30-day or 7-day view. **My peak hour shifted from 20:00 to 22:00 over the past quarter, what does that mean?** Customer behaviour drift, often correlated with industry-wide shifts (post-COVID later-evening shopping, streaming-service competition for the 20-22 window). Verify by re-running the 30-day window vs 90-day-prior. If the shift is persistent, update ad scheduling and email sends accordingly. If transient (a one-month anomaly) ignore. **Can I see hour pattern by source country?** Not directly in this card; combine [BC AOV by Country](/nerve-centre/kpi-cards/bigcommerce/aov-by-country) (filtered to a country) with the timezone-shifted hour view. Country-specific patterns differ materially: US peak is typically 20-22 local time across all timezones, UK peak is 19-21 BST, Asian markets often show midday peaks tied to commuting and work-break norms. **Multi-currency store, do I see hour patterns per currency?** Apply a currency filter. The hour pattern often shifts by currency (USD orders peak in US local time = 02-04 UTC, GBP in UK time = 19-21 UTC). Without filtering, the patterns blend; filtered they reveal the structurally different shopping habits per region. *** ### Tracked live in Vortex IQ Nerve Centre *Revenue by Hour* is one of hundreds of KPI pulses Vortex IQ tracks across BigCommerce 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.