At a glance
Average number of orders per customer in the period. The retention twin of Total Orders, where order_count answers “how many orders did the store take” this card answers “how often does the average customer buy”. Per-customer, not per-store.
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 DTC coffee subscription brand on Shopify (basic plan), single GBP storefront. Period: 12 Feb 26 to 12 May 26 (90 days).
Headline order frequency for the period: 1.80 orders per customer over 90 days.
Five things to notice:
- Subscription customers carry the average. 1,840 subscription customers contributed 5,210 orders (44% of total volume from 28% of the customer base). Without the subscription cohort, the underlying DTC frequency drops to roughly 1.4. Always segment frequency by cohort, the headline number is a weighted blend that hides the interesting story.
- Guests are excluded by design. 1,210 guest checkouts placed exactly one order each (mechanically; you can’t be a “repeat” guest in Shopify’s
customer.idmodel). Including them would drag the number to 1.66 and create the illusion that retention dropped, when really only the guest-share rose. The card excludes them so the number stays interpretable. - The mixed cohort is the upsell target. 510 customers ordered both a subscription and a one-off add-on (a grinder, a mug, a gift card). Their frequency of 3.61 is the highest in the store. Marketing’s job is to move single-product subscribers into the mixed cohort; it’s the highest-LTV behaviour pattern the data exposes.
- One-off frequency of 1.13 is healthy for the category. Coffee is a consumable; sub-1.5 frequency on a 90-day window means most one-off buyers haven’t yet rebought. Email automation (a 30-day “running low?” sequence) would lift this directly. Pair with New vs Returning to see whether the population is structurally one-off or just slow to repeat.
- B2B Edition isn’t on this storefront, so the number isn’t distorted. If the brand had a B2B portal (cafés ordering wholesale beans), 50 cafés ordering twice a week would dwarf the DTC figure. A single B2B buyer at frequency 24 has the same statistical weight as 24 DTC buyers at frequency 1, and the headline gets meaningless without segmentation.
Sibling cards merchants should reference together
Order Frequency on its own is just an arithmetic mean. The interesting questions live next to it:Reconciling against the vendor’s own dashboard
Where to look in Shopify Admin: Shopify does not expose a single “Order Frequency” tile. The closest reconstruction is via the Customers report cluster.Analytics → Reports → “Returning customer rate” and Analytics → Reports → “Customers over time”These show the populations from which frequency is computed (distinct customers, repeat customers). To get the per-customer order count yourself, export Customers → All customers (with the Orders count column) and average the
orders_count field across rows. Filter by date if you want the windowed view.
Other Shopify Admin views that look similar but are not the same number:
- Home → Customer insights: shows aggregate metrics including average orders per customer, but uses lifetime customer history, not the windowed view here. Will read higher than this card.
- Customers → Segments → Repeat customers: a customer-list filter, not a frequency metric. Useful for export, not for direct comparison.
- Apps like Klaviyo / LoyaltyLion: these compute frequency from their own event store; mailable-customer subset and time-window definitions vary. Expect 10 to 25% drift.
Cross-connector reconciliation:
Order Frequency is a Shopify-internal customer behaviour metric. It does not have direct counterparts on payment connectors (which see transactions, not customer identity). The closest cross-platform reconciliation is via analytics:
Known limitations / merchant FAQs
What’s a “good” order frequency? Category-dependent, badly. Coffee, supplements, pet food, and other consumables routinely run 3 to 6 orders per customer over 90 days; furniture, mattresses, and durable goods can sit at 1.0 to 1.2 and that’s healthy for them. The right benchmark is your own history. A 10% lift quarter-over-quarter on the same customer base is meaningful regardless of absolute level. Why are guests excluded? Guests can’t be repeat in Shopify’s data model (nocustomer.id to link a second order to). Including them mechanically pulls the average toward 1.0, swamping any real retention signal. The card therefore measures the trackable customer base. If your guest-checkout share is rising (a UX or privacy trend), pair this card with Guest vs Registered Checkout to spot the dilution before it shows up here.
My subscription store, does each recurring billing count?
Yes, every subscription billing is a separate Order.id row attached to the same customer.id, which is exactly the behaviour the metric wants. A monthly subscriber over 12 months has frequency 12 in a 365-day window. This is the right answer; the customer is placing 12 orders.
Why is this card different from “Returning Customer Rate”?
Different shapes of the same idea. Returning Customer Rate is binary per customer (did they come back yes/no?), expressed as a percentage of the cohort. Order Frequency is the count per customer, expressed as a number. A store with 50% returning rate and average return-buyer placing 3 orders has frequency ((50% × 1) + (50% × 4)) = 2.5. The two move together in trend but answer different questions.
Multi-currency, does FX affect this card?
No. Frequency is a count ratio with no money in it. A merchant taking GBP, EUR, and USD orders aggregates all of them by customer.id and the count is currency-agnostic. The companion AOV and revenue cards have the multi-currency-without-FX caveat; this one does not.
Shopify Plus vs basic plan, any behavioural differences?
Largely no, the underlying customer.id model is identical across plans. Two edge cases for Plus stores: (1) Combined Customer Records (Plus only) merges customer profiles across multiple stores, which can change the denominator if you migrate; (2) B2B Edition (Plus only) attaches orders to companies, not individual buyers, which dramatically changes the metric for any store with active B2B traffic. Basic stores see neither effect.
Refresh cadence?
Daily aggregation runs overnight on the customer index. Within-day order activity is reflected after the next overnight roll-up; this is intentional to avoid the rate flickering as orders post intraday.
B2B vs DTC, how should I interpret the blended number?
Don’t blend them if both are material. A B2B-heavy store running through B2B Edition will show an artificially-high frequency because the denominator is companies (small N) and the numerator is orders (large N). Always view B2B and DTC separately; the segmented numbers tell two different operational stories. If you’re not yet on B2B Edition and just tagging wholesale orders, filter by tag in Ask Viq.
The number moved, what should I check first?
Open Customer Count. If it dropped, you didn’t lose buying frequency, you lost buyers (and the survivors look more loyal by maths). If customer count held but frequency rose, your retention work is paying off. If both rose, you’re acquiring well and retaining well, which is rare and worth understanding before it changes.
Action playbook when frequency declines:
- Open Repeat Customer Rate. If repeat rate held but frequency dropped, your existing repeat customers are buying less often (a fatigue problem). If repeat rate dropped, fewer customers are coming back at all (an experience problem).
- Pull the New Customers trend. A surge in new acquisition mechanically dilutes frequency in the short term; the cohort hasn’t had time to repeat yet.
- Check email-program health (open rate, click rate, deliverability). The fastest lever for frequency in any DTC store is consistent post-purchase email; a 2-week deliverability outage shows up as a frequency dip 30 to 45 days later.
- Review subscription churn separately. A subscription cohort losing 5% of subscribers a month removes the highest-frequency segment first.