> ## 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. # Repeat Customer Rate, BigCommerce > Repeat Customer Rate 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 > The percentage of distinct customers in the period who placed more than one order. The single most predictive customer-quality KPI in commerce: a store with a 35% repeat rate has structurally lower CAC pressure than a store at 15%, regardless of paid-acquisition spend. On BigCommerce specifically, this card has a wrinkle, *registered* customers are tracked accurately by `customer_id`, *guest* customers can only be deduplicated by email, so stores with high guest-checkout rates systematically under-report repeat behaviour. Understanding the registered-versus-guest split (see [BC Guest vs Registered](/nerve-centre/kpi-cards/bigcommerce/guest-vs-registered-orders)) is essential to reading this number correctly. | | | | ----------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **What it counts** | `COUNT(distinct customer_id WHERE order_count > 1) / COUNT(distinct customer_id)` over the window. Customer is identified by `customer_id` for registered customers, by `billing_address.email` (lowercased, trimmed) for guests. Email-based grouping is fragile: typos, plus-aliasing, and case-sensitivity edge cases all under-deduplicate. | | **VAT / tax treatment** | n/a, this is a count metric. | | **Shipping** | n/a. | | **Discounts** | n/a. | | **Refunds** | **Refunded orders are still counted as orders for repeat-rate purposes.** A customer who placed two orders and refunded one still counts as repeat. This is the conventional retention definition; subtract refund-only customers manually if you want a "happy-customer" repeat rate. | | **Cancelled orders** | Excluded. A `Cancelled` order never reached the customer; the order doesn't count toward the customer's order count. | | **`Incomplete` orders** | Excluded. Abandoned-checkout artefacts do not count as orders. | | **Currency** | n/a, this is a count metric. | | **Channels / sources** | All channels aggregate. POS-customer matching to web-customer is a known gap on BigCommerce, the same physical customer who bought once on POS and once on web typically appears as two distinct `customer_id` records unless the merchant uses BC POS's customer-link feature. **This is a structural under-counting of repeat rate on multi-channel stores.** | | **Customer identity gotchas** | Three: (1) registered → guest → guest is one customer, three IDs (the registered → guest transitions break linkage). (2) Email aliasing (`susant+test@example.com` vs `susant@example.com`) creates fake-distinct customers. (3) B2B Edition company-account customers may share a single `customer_id` across multiple buyers (procurement teams), inflating the per-customer order count without representing genuine repeat behaviour. | | **Window matters** | 90D is the default but interpretation changes with window: 30D repeat rate measures *fast* repeat (subscription, consumables), 90D measures *normal* repeat (apparel, beauty), 365D measures *annual* repeat (furniture, B2B). Don't compare across windows. | | **Time window** | `90D` (rolling 90 days; settings allow 30D, 90D, 180D, 365D). | | **Alert trigger** | `<25% (Tier-1 retention floor)`. Below 25% on a 90D window is a structural retention problem; the store is leaking customers as fast as it acquires them. | | **Sentiment key** | `repeat_rate` | | **Roles** | owner, marketing | ## Calculation ``` COUNT(orders > 1 by customerId) / CARDINALITY(customerId) WHERE date BETWEEN [period_start, period_end] ``` ## Worked example A UK supplements brand on BigCommerce, 90-day window 14 Feb 26 to 14 May 26, mostly DTC subscription-style consumables. | Customer cohort | Distinct customers | Customers with >1 order | Repeat rate | Notes | | ----------------------- | ------------------ | ----------------------- | ----------- | --------------------------------------------- | | Registered (logged-in) | 4,200 | 2,100 | **50.0%** | Subscription cohort, near the theoretical max | | Guest checkout | 8,400 | 1,008 | **12.0%** | Includes inflation from email-aliasing | | **Blended (this card)** | **12,600** | **3,108** | **24.7%** | Below the 25% Tier-1 threshold | What's interesting: 1. **The blended 24.7% triggers the alert, but the registered cohort alone is 50% (excellent).** The store's actual retention is healthy; the card under-reports because guest checkouts dilute the figure. **Stores with high guest-checkout rates need to read repeat-rate alongside [BC Guest vs Registered](/nerve-centre/kpi-cards/bigcommerce/guest-vs-registered-orders)** or they'll mistakenly conclude they have a retention problem. 2. **Guest 12% repeat is structurally suspicious.** Guests should ideally show a 20-30% repeat rate (within email-deduplication accuracy); 12% suggests email-aliasing or typos are inflating the distinct-guest count and depressing the rate. **Audit a sample of guest emails**: typically 5-15% are duplicates of registered customers (same email, different cookie session creates a guest checkout against an account that exists). 3. **Subscription stores should target 50%+ on registered.** Below 30% on the registered cohort means subscriptions aren't sticking; investigate first-order experience (delivery time, packaging, product quality) and reach out to lapsed subscribers. 4. **The 24.7% blended is misleading for retention strategy.** A merchant who reads only this card and concludes "I need to invest in retention" might pour budget into loyalty-app spend that won't move this number, because the bottleneck is guest-to-registered conversion, not retention activity. **The actual lever is reducing guest-checkout rate** (offer post-purchase account creation, surface guest-order history retrieval, reduce friction in account creation). What the merchant should do: 1. **Segment by registered vs guest first.** This card alone tells you what; the segmentation tells you why. 2. **If registered repeat is healthy and blended is below 25%:** invest in account-creation conversion, not retention. 3. **If registered repeat is also low (\<35%):** the retention problem is real. Look at first-order experience, second-order timing, lapsed-customer reactivation campaigns. 4. **If POS customers don't link to web customers**, the card under-reports cross-platform repeat. Enable BC POS customer-link feature; expect the rate to jump 3-5 percentage points for stores with strong omnichannel. 5. **Pair with [BC Customer Trend](/nerve-centre/kpi-cards/bigcommerce/customer-acquisition-trend) and [BC New Customers](/nerve-centre/kpi-cards/bigcommerce/new-customers)** to see whether the repeat rate is dropping because retention got worse or because new-customer volume rose (mathematical artefact). ## Sibling cards merchants should reference together | Card | Why pair it with Repeat Rate | | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | [BC Guest vs Registered](/nerve-centre/kpi-cards/bigcommerce/guest-vs-registered-orders) | The diagnostic for whether repeat rate is structurally under-reported. Stores with >50% guest checkout will see this card depressed regardless of actual retention. | | [Customer Count](/nerve-centre/kpi-cards/bigcommerce/unique-customers) | The denominator. Repeat rate alone hides whether customer base is growing, shrinking, or stable. | | [New Customers](/nerve-centre/kpi-cards/bigcommerce/new-customers) | Inversely related. Rising new-customer volume mechanically depresses repeat rate even if retention is unchanged. | | [BC Customer Trend](/nerve-centre/kpi-cards/bigcommerce/customer-acquisition-trend) | Time-series. Track repeat rate movement to see whether changes are recent or long-standing. | | [BC Top Customers](/nerve-centre/kpi-cards/bigcommerce/top-customers) | The customers driving the repeat rate. Top 5% of repeat customers typically generate 30-50% of revenue. | | [Order Frequency](/nerve-centre/kpi-cards/bigcommerce/customer-order-frequency) | The orders-per-repeat-customer metric. Repeat rate × frequency = total customer LTV input. | | [Churn Risk](/nerve-centre/kpi-cards/bigcommerce/customer-churn-risk) | The forward-looking complement. This card looks at past behaviour; churn risk predicts who's about to stop being a repeat customer. | | [BC Channel Repeat Rate](/nerve-centre/kpi-cards/bigcommerce/repeat-customer-rate-by-channel) | Per-channel split. Marketplace channels (Amazon) typically have 5-10% lower repeat than DTC because customers there are buying the marketplace, not the brand. | | [AOV](/nerve-centre/kpi-cards/bigcommerce/average-order-value) | Repeat customers usually have higher AOV. If your AOV is rising and repeat rate is rising together, retention strategy is working. | | [Klaviyo Email Revenue Share](/nerve-centre/kpi-cards/bigcommerce/email-attributed-revenue-share) | Cross-connector. Email-driven revenue is overwhelmingly from repeat customers; Klaviyo share correlates strongly with this card. | | [Total Revenue](/nerve-centre/kpi-cards/bigcommerce/total-revenue) | Always interpret repeat rate as a contributor to revenue. A 5pp increase in repeat rate typically lifts revenue 3-7% over 6 months. | ## Reconciling against the vendor's own dashboard **Where to look in BigCommerce's own dashboard:** The closest native view is **BC Control Panel → Analytics → Insights → Customers → Returning Customers** (Plus and Enterprise tiers). The "Returning customer rate" metric is BC's own version of repeat rate; it uses a slightly different denominator definition (customers with any orders in the period regardless of when they joined). For an export, **Customers → Customers → Filter: number of orders > 1**. For Standard tier (no Insights), use the customer list with the order-count filter and divide manually. **Why our number may legitimately differ from the vendor's:** | Reason | Direction | Why | | -------------------------------- | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Customer-identity definition** | Either | BC may treat email-only customers (no `customer_id`) as guests and exclude from repeat-rate; we deduplicate by email. The choice changes the rate by 2-8 percentage points on guest-heavy stores. | | **Window boundary** | Either | We use UTC; BC uses store time zone. Edge customers around boundary-day count differently. | | **POS linkage** | Ours lower | If POS customers are not linked to web customers in BC POS settings, both we and BC under-count cross-platform repeat. The merchant can fix this in BC POS Settings → Customers. | | **B2B Edition company accounts** | Ours higher | If procurement teams share a company account, all their orders contribute to one customer's count, inflating repeat rate. BC Insights may treat each buyer as a sub-customer; we treat the company account as the customer. | | **Cancelled order treatment** | Either | We exclude cancelled orders from per-customer order counts; some BC views include them. A customer with 1 confirmed + 1 cancelled order shows as repeat in BC's view, single-order in ours. | | **Email casing** | Slight gap | We lowercase emails for deduplication; BC's internal logic varies. Stores with lots of mixed-case email entries see 1-3% gap. | **Cross-connector reconciliation (when both connectors are connected for this merchant):** | Card | Expected relationship | Notes | | ---------------------------------------------------------------------------- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | [`klaviyo.repeat_purchase_rate`](/nerve-centre/klaviyo/repeat_purchase_rate) | Should match within ±5% on stores using Klaviyo as the customer-data backbone | Klaviyo's repeat rate uses email-based customer ID universally, no registered/guest distinction. The two cards should converge on stores with strong email-capture at checkout. | ***
Documentation cross-reference (for agencies running multiple platforms) The repeat-rate metric exists on Shopify and Adobe Commerce with similar definitions. Shopify exposes the cleanest version because guest checkouts are tied to email by default; Adobe's customer-identity model is closer to BC's registered/guest split. * [`shopify.repeat_rate`](/nerve-centre/kpi-cards/shopify/repeat-customer-rate) * [`adobe_commerce.repeat_rate`](/nerve-centre/kpi-cards/adobe-commerce/repeat-customer-rate)
## Known limitations / merchant FAQs **My repeat rate dropped 8 points last month, what should I check first?** In order: (1) did new-customer volume rise (mathematical dilution), (2) did a Channel Manager integration go live (marketplace customers have lower repeat), (3) was there a major paid-acquisition push (paid-acquisition customers repeat at half the rate of organic), (4) did your loyalty / email program break (Klaviyo flow disabled, abandoned-cart sequence broken), (5) did first-order experience degrade (slower fulfilment, packaging issue). Run [BC New Customers](/nerve-centre/kpi-cards/bigcommerce/new-customers) and [Customer Trend](/nerve-centre/kpi-cards/bigcommerce/customer-acquisition-trend) for the same period; if new-customer volume rose proportionally, the dilution is the cause and the underlying repeat behaviour hasn't changed. **What's a good repeat rate?** Wildly category-dependent. Subscription / consumables: 50-65% on registered customers. Apparel / accessories: 25-35%. Furniture / one-off purchases: 8-15%. B2B: 60-80% (procurement is structurally repeat). Compare to your category, not to a global benchmark. **Why is my registered-customer repeat rate so much higher than blended?** Because guests dilute the figure. A guest who shops once, then comes back as a registered customer the second time, looks like two distinct customers each with one order. The actual person was a repeat buyer; the data shows two non-repeat buyers. **Convert guests to registered at the post-purchase moment** to capture this signal cleanly. **Should I fire a marketing campaign at non-repeat customers?** Almost certainly yes. The non-repeat cohort is the highest-leverage retention target on most stores. A 5pp lift in repeat rate via reactivation campaigns is achievable with email re-engagement flows; the lift compounds over 6-12 months. **Why does this card show 24% but my Klaviyo dashboard shows 38%?** Klaviyo deduplicates customers by email universally (no registered-vs-guest distinction) and uses a 365-day window by default. Different definition, different number. Use Klaviyo for retention strategy decisions; use this card for BC-native ops decisions; reconcile only after aligning windows and definitions. **Does this include refunded customers?** Yes. A customer who placed two orders and refunded one still counts as repeat. If you want to exclude refund-only customers, use Ask Viq: "show repeat rate excluding customers whose only repeat order was refunded". **My subscription store has 65% repeat rate and the alert still fires, why?** The alert threshold is configurable per store. Subscription stores typically set the floor higher (40-45%) because anything below that flags subscription churn. Configure in Settings → Alerts → Repeat Rate Threshold. **How does B2B Edition affect this card?** B2B Edition orders count normally. If you operate company accounts (multiple buyers per `customer_id`), the buyer's procurement team's combined activity inflates the repeat rate. For a clean per-buyer view, segment by individual user via Ask Viq. **Can I see repeat rate by acquisition channel?** Yes via Ask Viq: "show repeat rate by first-order channel for last 90 days". Typical pattern: organic-first customers repeat at 30-40%; paid-first customers repeat at 12-20%; email-first (newsletter signup, then converted) repeat at 35-50%. The split tells you where to invest acquisition spend. **Why does my POS-only customer not show as repeat even though they buy weekly?** BC POS doesn't always link to web customer records unless the cashier looks up the customer at checkout. **Train POS staff to look up customer by email or phone** at every transaction; this card jumps 3-5pp on most multi-channel stores after this is enabled. **Does this respect customer-group filtering?** No, it aggregates all groups. Use Ask Viq for filtered views: "repeat rate for customer\_group\_id 5 over last 90 days". **My CFO wants annual repeat rate, can I get that?** Yes, change the time window in card settings to 365D. Caveat: 365D repeat is mechanically higher than 90D repeat (more time for the second order to land); compare apples to apples by always using the same window for trend analysis. **The card shows 0% repeat rate, what's wrong?** Either the date window is too short (a 7D window will show near-zero repeat for most categories), or there's a data issue with `customer_id` or `email` fields not being populated. Check a sample of recent orders to confirm `customer_id` is populated for registered customers; if blank, BC's customer creation hook may be misconfigured. *** ### Tracked live in Vortex IQ Nerve Centre *Repeat Customer Rate* 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. 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