> ## 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. # Peak Order Hours, Shopify > Peak Order Hours 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 > Order volume by hour-of-day (00 to 23), aggregated across the 90D window. Reveals when shoppers actually buy; the foundation for ad budget pacing, email send-time, and customer-service staffing. | | | | ----------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **What it counts** | `COUNT(orders) GROUP BY hour(createdAt)` over the 90D window. Each of 24 hour-buckets aggregates \~90 days of orders. | | **VAT / tax treatment** | Not applicable, count metric. | | **Shipping** | Not applicable. | | **Discounts** | Not applicable. | | **Refunds** | Not applicable; original order-creation hour is what counts. | | **Cancelled / voided orders** | Included if Shopify indexed them. | | **Currency** | Multi-currency safe (count metric). | | **Channels / sources** | All channels. POS skews into store-opening hours; online has a different curve. The blended pattern hides channel-specific signals; filter by channel in Shopify Admin for clean reads. | | **Time window** | `90D` (default 90D rolling) | | **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 US homeware DTC brand on Shopify, predominantly online, customers in PT/MT/CT/ET zones. 90D window 12 Feb 26 to 12 May 26. | Hour band (UTC) | Local PT equivalent | Orders (90D) | Note | | --------------- | ------------------------- | ------------ | --------------------------------------- | | 04:00-08:00 | 21:00-01:00 PT (prev day) | 38,200 | Late-evening East Coast peak | | 08:00-12:00 | 01:00-05:00 PT | 18,400 | Quiet, overnight | | 12:00-16:00 | 05:00-09:00 PT | 24,800 | Morning starting | | 16:00-20:00 | 09:00-13:00 PT | 32,500 | West Coast morning + East Coast lunch | | 20:00-00:00 | 13:00-17:00 PT | 41,200 | Daytime peak | | 00:00-04:00 | 17:00-21:00 PT | 56,800 | **Evening peak**, primary buying window | The single-hour peak: 02:00 UTC (19:00 PT) at \~14,800 orders/90D, \~165 orders/day average. Six things to notice: 1. **Evening dominates.** Customers buy after dinner. The 18:00-21:00 PT slot accounts for \~40% of all daily orders. Email and social-ad budget should concentrate just before this window (16:00-18:00 PT). 2. **Lunch is a secondary peak.** 12:00-13:00 PT shows a smaller second hump; customers browse and buy during lunch breaks. Less significant than evening but real. 3. **Time-zone blending.** The brand serves four US time zones simultaneously. Each customer's "8pm" is staggered three hours across the country. The aggregate peak is therefore wider (and shallower) than a single-time-zone shop's peak. Filter to one ship-to region for cleaner per-zone reads. 4. **Overnight is dead.** 02:00-05:00 PT (post-midnight) is when most US shoppers sleep; \~3-5% of daily volume. Don't pace ads here; the CPCs may be cheaper but the conversion rate is poor. 5. **Workday weakness.** 09:00-12:00 PT (morning workday) is softer than expected; people don't shop while focused on work tasks. Brands targeting in-office workers see this dip clearly. 6. **Saturday afternoon spike.** Aggregated across days, Saturday afternoons (12:00-15:00 PT) show a notable bump; weekend-leisure shopping is a real signature. Pair with [Weekend vs Weekday](/nerve-centre/kpi-cards/shopify/weekend-vs-weekday) for the day-level decomposition. ## Sibling cards merchants should reference together Peak Hours is the within-day pattern. The companions: | Card | Why pair it with Peak Hours | | -------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ | | [Revenue by Hour](/nerve-centre/kpi-cards/shopify/revenue-by-hour) | Same shape but £-weighted. AOV-per-hour may differ from order-count-per-hour; pair to find premium-buying windows. | | [Revenue by Day of Week](/nerve-centre/kpi-cards/shopify/revenue-by-day-of-week) | Day-level cousin. Combine to find specific cells (e.g. "Tuesday 8pm") rather than day-or-hour averages. | | [Weekend vs Weekday](/nerve-centre/kpi-cards/shopify/weekend-vs-weekday) | The aggregated two-bucket version of DoW. | | [Total Orders](/nerve-centre/kpi-cards/shopify/total-orders) | Denominator; for context. | | [Customer Countries](/nerve-centre/kpi-cards/shopify/orders-by-country) | Geographic split; multi-region stores see blended pattern shift if region mix shifts. | | [Top Discount Codes](/nerve-centre/kpi-cards/shopify/top-discount-codes) | Promo-driven hours often spike at email-send time; cross-reference. | ## Reconciling against the vendor's own dashboard **Where to look in Shopify Admin:** Shopify exposes hour-level granularity in: * **Analytics → Live View**: real-time map and order-by-hour ticker (today only). * **Analytics → Reports → "Sessions over time"** with hourly granularity: traffic by hour. * **Reports → Sales over time** with hourly granularity (Shopify Plus only): orders by hour. * **Apps like Glew, Polar Analytics**: typically expose hourly distribution charts. **Why our number may legitimately differ from a manual reconstruction:** | Reason | Direction | Why | | ---------------------- | ---------------------- | ---------------------------------------------------------------------------------------------------------------------------------- | | **Time zone** | Shifted bucket | Shopify Admin uses store time zone; we use UTC. Each hour bucket represents different local time. Convert manually for comparison. | | **Granularity** | Same | Both bucket by hour. | | **Channel filter** | Either | Reports filtered to "Online Store" only differ from this blended figure. | | **Customer time zone** | Same | Both record `createdAt` from the server's perspective; the customer's local time is not stored separately. | | **Sync lag** | Ours lower for "today" | Most-recent 5 to 15 minutes of orders may not be in. | **Cross-connector reconciliation:** | Card | Expected relationship | What causes legitimate divergence | | -------------------------------------------------------------------------------------------- | ----------------------- | --------------------------------------------------------------------------------------------------- | | [`google_analytics.ga_sessions_by_hour`](/nerve-centre/google_analytics/ga_sessions_by_hour) | Should track shape | GA4 measures sessions, not orders. Sessions peak earlier than purchases (browsing then buying lag). | | [`klaviyo.kl_email_engagement_by_hour`](/nerve-centre/klaviyo/kl_email_engagement_by_hour) | Use to align send times | Email-send-time should be 1-3 hours before peak buy time for maximum revenue lift. | ***
Documentation cross-reference (for agencies running multiple platforms) * [`bigcommerce.shp_peak_hours`](/nerve-centre/bigcommerce/shp_peak_hours) * [`adobe_commerce.shp_peak_hours`](/nerve-centre/adobe_commerce/shp_peak_hours)
## Known limitations / merchant FAQs **Why is the chart in UTC instead of my local time?** Vortex IQ uses UTC for all time-series for consistency across stores in different time zones. To convert: identify your store's primary customer time zone, subtract the UTC offset (PT = -8 winter / -7 summer; ET = -5/-4; BST = +1; CET = +1/+2). The chart is a fixed shape; the labels just shift. **My peak time is unusual. Is it a problem?** Patterns are category-driven: * **Apparel and beauty**: 19:00-22:00 local (post-work, leisure browsing). * **Food and grocery**: 17:00-19:00 local (pre-dinner planning). * **B2B**: 09:00-15:00 local (in-office hours). * **Subscriptions**: spread by billing-day cycles, less hourly variation. * **Mobile-first impulse (TikTok-driven)**: 21:00-00:00 local (late-evening scroll-to-buy). **Why does my peak hour shift seasonally?** Daylight savings + behaviour shifts. Summer evenings are longer (more outdoor time, slightly later peak); winter evenings are shorter (peaks earlier). Holiday seasons (Black Friday, Christmas) have entirely different shapes from normal trading. Use 90D windows to smooth, but expect annual cyclicality. **My multi-region store, can I see per-region peaks?** Not directly on this card. Filter the underlying orders by ship-to country in Shopify Admin for per-region hourly distributions. Single-region peak times are usually 2-3× sharper than the blended global view. **Why is my email click-revenue at 9am while my peak is 8pm?** Email lifecycle: sent → opened → clicked → ... → purchased. Customers click in the morning (commute, coffee), browse, then return in the evening to buy. The buy-time peak (this card) is the relevant target for paid-media; the click-time peak is for email-engagement metrics. Don't confuse them. **Should I run flash sales at peak hour?** Counter-intuitively, no. Flash sales work best when announced \~2-3 hours before peak buy hour, giving customers time to browse and decide. Announcing during peak buy hour catches customers who already had purchase intent without urgency-driving them. **Does this card include POS sales?** Yes by default. POS skews heavily toward store-opening hours (10:00-19:00 typically). Brands with retail presence see a daytime hump that pure-online stores don't have. Filter to "Online Store" channel for the e-commerce-only view. **Action playbook for using this card:** 1. **Identify your top-3 peak hours**: ad budget should pace 1.5-2× normal during these hours. 2. **Identify the lull windows (>50% below peak)**: reduce ad-spend; experiment with budget elsewhere. 3. **Email send timing**: 1-3 hours before peak hour is typical optimal. Test 2-3 windows with A/B; what works varies meaningfully by category. 4. **Customer-service staffing**: roughly track buy hours plus 24-48h lag (queries about today's orders peak tomorrow morning). 5. **Inventory replenishment**: PO arrivals timed to land before peak hours (warehouse can pick the resolved-OOS SKUs same-day). 6. **Site reliability**: monitor server load and CDN health intensively during peak; downtime during peak hour costs disproportionately more than off-peak. *** ### Tracked live in Vortex IQ Nerve Centre *Peak Order Hours* 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.