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Metrics type: Supporting Metrics • Category: Ecommerce Platform

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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.

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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.

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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. 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 for the day-level decomposition.

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Sibling cards merchants should reference together

Peak Hours is the within-day pattern. The companions:

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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: Cross-connector reconciliation:

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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.

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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 or book a demo to see this metric running on your own data.