At a glance
Distribution of customers by their lifetime order count, bucketed (1, 2, 3-5, 6-10, 11+). The headline tells you whether your customer base skews to one-time buyers (growth-mode acquisition without retention) or repeat buyers (mature retention). The shape of this distribution is the strongest predictor of long-term revenue stability.
| What it counts | Customers grouped by total COUNT(orders) per customerId over the last 90 days. Each customer falls into exactly one bucket based on their order count in that window. |
| VAT / tax treatment | n/a, customer count metric. |
| Shipping | n/a. |
| Discounts | n/a. |
| Refunds | Refunded orders still count toward the customer’s order count (we count placement, not realisation). |
| Cancelled / voided orders | Included in the per-customer count. A customer who placed and cancelled 3 orders shows in the 3-order bucket. |
| Currency | n/a. |
| Channels / sources | All BC channels contribute. A customer who orders 1x web + 2x POS shows as 3 orders. Cross-channel customers (different customerId per channel) may show as multiple low-frequency customers when they’re really one high-frequency person; pair with cross-channel identity stitching for the cleaner view. |
| Guest checkout treatment | Each guest order has customerId = 0. We treat each as a single 1-order customer; this materially inflates the 1-order bucket on guest-heavy stores. For the registered-only view, filter customerId != 0. |
| B2B Edition | B2B procurement teams typically appear in the 11+ bucket because they order monthly or weekly. Healthy B2B distribution skews high; consumer distribution skews to 1-3 orders. |
| Why this beats simple repeat-rate | Repeat-rate gives you a yes/no signal (have they ordered more than once); this card gives you the depth of engagement. A store with 30% repeat rate but 90% of repeats at 2 orders is structurally different from a store with 25% repeat rate but a healthy 11+ tail. |
| Time window | 90D (rolling 90 days, customer ordering frequency in that window) |
| Alert trigger | None at this card. Pair with Repeat Customer Rate for thresholds. |
| 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 US homewares brand on BigCommerce Pro, last 90 days from 13 Jan 26 to 12 Apr 26.| Order count bucket | Customer count | Share | Avg AOV |
|---|---|---|---|
| 1 order | 4,820 | 71.4% | $76 |
| 2 orders | 1,180 | 17.5% | $84 |
| 3-5 orders | 540 | 8.0% | $92 |
| 6-10 orders | 130 | 1.9% | $108 |
| 11+ orders | 80 | 1.2% | $124 |
| Total customers | 6,750 | 100% |
- 71.4% in the 1-order bucket is high but normal for D2C homewares. Mature D2C peers run 60-70% in single-order. Above 75% suggests acquisition is outpacing retention; below 60% suggests retention is unusually strong (or acquisition is anaemic).
- AOV climbs with order count: 124 across the buckets. This is the LTV signal. A 6-10 order customer is worth ~76. The retention math: lifting 5% of 1-order customers into 2-order territory generates more revenue than acquiring an equivalent number of new 1-order customers.
- The 11+ bucket at 1.2% is your power-user core. 80 customers here generate roughly 1,488/customer × 80 = $119,000 in 90 days. At 17% of the 90-day revenue from 1.2% of customers, the power tail is worth investing in: VIP program, early access, account-management touch.
- The 2-order bucket at 17.5% is the leverage point. These are customers who tried once, came back once. The marketing question is “what triggers their third order?” Often a simple post-second-order email flow drives the conversion to 3+.
- B2B-heavy stores see this distribution very different. B2B customers might cluster in 6-10 or 11+ buckets while retail clusters in 1-2. If the store mixes B2B and retail, run the two views separately for meaningful diagnosis.
- For high 1-order share (>75%): invest in welcome flow, second-purchase trigger campaigns, replenishment reminders. Tools: Klaviyo / Mailchimp post-purchase flow, BC’s native abandoned-browse retargeting.
- For thin 11+ tail (<1%): build a VIP program. The few customers who get there have outsized LTV; rewarding them protects revenue concentration. Most stores see VIP-program enrolment lift 11+-bucket share by 30-60% over 12 months.
- For 2-order bucket as a target: post-second-order email flow with a small incentive (“welcome to the family, here’s 10% off your next”) nudges the conversion to 3+. Test for 60 days, measure 3+-bucket growth.
- For B2B-heavy stores: confirm the distribution skews high (6-10 and 11+ should be the largest buckets). If your B2B distribution skews to 1-2 orders, accounts aren’t returning, dig into onboarding and account-management quality.
- Cross-reference with BC Top Customers to identify the 11+ cohort by name; segment your highest-LTV customers for direct relationship-building.
Sibling cards merchants should reference together
| Card | Why pair it with Customer Order Frequency |
|---|---|
| Repeat Customer Rate | The headline ratio. This card decomposes; Repeat Rate summarises. |
| Customer Count | The denominator. Distribution × total = absolute customer counts per bucket. |
| BC Top Customers | The named-individual view of the 11+ bucket. |
| AOV | AOV by bucket; rising AOV with bucket depth = LTV growth. |
| Customer Acquisition Trend | New 1-order customers feeding the bottom of the funnel. |
| BC Guest vs Registered | Guest customers inflate the 1-order bucket; registered-only view shows the real distribution. |
| Churn Risk | The dropout side. Customers in 2+ buckets at risk of not returning. |
klaviyo.kl_post_purchase_flow | The retention-marketing engine. |
Reconciling against the vendor’s own dashboard
Where to look in BigCommerce Control Panel: Analytics → Customers on Plus / Pro / Enterprise has a “Order frequency distribution” tile. Standard plan stores need to compute manually from the Customers → View export with the Orders column. For LTV-aware decomposition, BC Marketing app integrations (Klaviyo, Mailchimp) typically include a frequency / RFM segmentation that overlaps with this card. Why our number may legitimately differ from BC Analytics:| Reason | Direction |
|---|---|
| Window definition. We use last 90 days; BC may use lifetime or different window. A high-LTV B2B customer with 30 lifetime orders may show as 12 in our 90-day cut. | Different scope |
| Guest customer treatment. We treat each guest order as a separate 1-order customer; BC may aggregate guests differently. | Vortex IQ HIGHER 1-order count |
Channel mix. Cross-channel customers with different customerId per channel may show as multiple 1-order customers. | Vortex IQ HIGHER 1-order count |
| Cancelled / refunded orders. We count placement; BC may exclude. | Vortex IQ HIGHER bucket counts |
| B2B Edition aggregation. B2B accounts may aggregate at company level in BC Analytics; we count at customer level. | BC HIGHER bucket-depth on B2B |
| Sync lag. Recent orders may not be reflected. | Boundary effects |
| Card | Expected relationship | What causes legitimate divergence |
|---|---|---|
klaviyo.kl_rfm_segmentation | Klaviyo’s RFM frequency tier should align with this card | Klaviyo includes time-since-last-order; we don’t. The two are correlated but not identical. |
google_analytics.ga_repeat_users | GA4 repeat-user count is a session-level signal | GA4 doesn’t see purchase frequency; only session-revisit. |
shopify.order_frequency(planned)adobe_commerce.order_frequency(planned)
Known limitations / merchant FAQs
My 1-order share is 80%, is that bad? Above industry baseline for D2C (typical 60-70%). Either acquisition is outpacing retention (common for growth-mode stores), or first-purchase experience is poor. Run a survey on 1-order customers to identify the second-purchase blocker; common answers: product didn’t meet expectations, price too high, no reason to return. Why is my 11+ bucket so thin? Either young store (no time for customers to reach 11+) or weak retention. For stores under 12 months old, sub-2% in 11+ is normal. For 3+ year-old stores, sub-2% suggests retention investment. Build a VIP / loyalty program; the 11+ tail responds well to recognition. Should B2B and retail be separated in this view? Yes for hybrid stores. B2B customers cluster in 6-10 and 11+ buckets; retail clusters in 1-3. Mixing them gives you a bimodal distribution that’s hard to act on. Run two views: B2B-only (filtercompany_id IS NOT NULL) and retail-only.
Why does my guest-heavy POS-only store look like 95% 1-order?
Because each POS guest checkout has customerId = 0 and we treat them as separate 1-order customers. The actual customer-frequency distribution might be very different if you knew which walk-ins were the same human. POS stores need to capture loyalty-program enrolment to see real frequency.
My 2-order bucket is large (25%), is that good?
Yes. A robust 2-order bucket is the leverage point for retention; these customers have proven they like you, you just need to convert them to 3+. Stores with 25% in 2-order bucket and good post-purchase email flow typically see 30-50% of those customers reach 3 orders within 90 days.
Does this card track repeat purchase rate?
Inversely. Repeat rate = (customers in 2+ buckets) / total customers. So 100% - 1-order share = repeat rate. The two cards are mathematically related; this one shows depth, repeat rate shows binary.
My subscription store has unusual distribution, why?
Subscription products bias the distribution toward high-frequency: a customer with 6 monthly subscription deliveries shows as 6 orders. The right segmentation for subscription stores is to separate one-time-purchase customers from subscription customers; their LTV mechanics are different.
Can I see this distribution by acquisition cohort?
Not directly from this card. Pair with cohort filters in Klaviyo or Mailchimp; most ESPs let you build “customers acquired in [month]” cohorts and overlay frequency distribution. We’re working on a cohort-aware version; track the V2 backlog.
My 1-order bucket grew month-over-month, what changed?
Almost always increased acquisition. Run Customer Acquisition Trend to confirm; if new customers spiked and the 1-order bucket grew proportionally, that’s healthy. If new customers are flat but 1-order grew, retention is failing in the 2+ buckets.
My B2B store has 11+ at 25%, is that too high?
No, that’s healthy B2B. Procurement teams legitimately order weekly or bi-weekly for years. Concentration risk is real (losing one 11+ B2B customer hurts), but mature B2B distributions look this way. Pair with BC Top Customers to monitor concentration.