> ## 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. # Customer Order Frequency, BigCommerce > Customer Order Frequency 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 > 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-platform customers** (different `customerId` per channel) may show as multiple low-frequency customers when they're really one high-frequency person; pair with cross-platform 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](/nerve-centre/kpi-cards/bigcommerce/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%** | | What's interesting: 1. **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). 2. **AOV climbs with order count: $76 → $124 across the buckets.** This is the LTV signal. A 6-10 order customer is worth \~$1,080 lifetime; a 1-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.** 3. **The 11+ bucket at 1.2% is your power-user core.** 80 customers here generate roughly $124 × 12 avg orders = $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.** 4. **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+. 5. **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. The intervention playbook by bucket: 1. **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. 2. **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. 3. **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. 4. **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. 5. **Cross-reference with [BC Top Customers](/nerve-centre/kpi-cards/bigcommerce/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](/nerve-centre/kpi-cards/bigcommerce/repeat-customer-rate) | The headline ratio. This card decomposes; Repeat Rate summarises. | | [Customer Count](/nerve-centre/kpi-cards/bigcommerce/unique-customers) | The denominator. Distribution × total = absolute customer counts per bucket. | | [BC Top Customers](/nerve-centre/kpi-cards/bigcommerce/top-customers) | The named-individual view of the 11+ bucket. | | [AOV](/nerve-centre/kpi-cards/bigcommerce/average-order-value) | AOV by bucket; rising AOV with bucket depth = LTV growth. | | [Customer Acquisition Trend](/nerve-centre/kpi-cards/bigcommerce/customer-acquisition-trend) | New 1-order customers feeding the bottom of the funnel. | | [BC Guest vs Registered](/nerve-centre/kpi-cards/bigcommerce/guest-vs-registered-orders) | Guest customers inflate the 1-order bucket; registered-only view shows the real distribution. | | [Churn Risk](/nerve-centre/kpi-cards/bigcommerce/customer-churn-risk) | The dropout side. Customers in 2+ buckets at risk of not returning. | | [`klaviyo.kl_post_purchase_flow`](/nerve-centre/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](https://login.bigcommerce.com/deep-links/manage/analytics/customers) on Plus / Pro / Enterprise has a "Order frequency distribution" tile. Standard plan stores need to compute manually from the [Customers → View](https://login.bigcommerce.com/deep-links/manage/customers) 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-platform 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 | **Cross-connector reconciliation:** | Card | Expected relationship | What causes legitimate divergence | | ------------------------------------------------------------------------------------ | -------------------------------------------------------- | ------------------------------------------------------------------------------------------- | | [`klaviyo.kl_rfm_segmentation`](/nerve-centre/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`](/nerve-centre/google_analytics/ga_repeat_users) | GA4 repeat-user count is a session-level signal | GA4 doesn't see purchase frequency; only session-revisit. | **Same-metric documentation cross-reference:** * [`shopify.order_frequency`](/nerve-centre/kpi-cards/shopify/customer-order-frequency) (planned) * [`adobe_commerce.order_frequency`](/nerve-centre/kpi-cards/adobe-commerce/customer-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 (filter `company_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](/nerve-centre/kpi-cards/bigcommerce/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](/nerve-centre/kpi-cards/bigcommerce/top-customers) to monitor concentration. *** ### Tracked live in Vortex IQ Nerve Centre *Customer Order Frequency* 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.