> ## 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 Churn Risk, Shopify > Customers with no order in 2× their typical cadence. Early-warning for retention spend. 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) > Customers with no order in 2× their typical cadence. Early-warning for retention spend. ## At a glance > The percentage of repeat customers whose latest order is more than 2× their personal average inter-order interval ago. The early-warning signal: customers who *should* have ordered again by now but haven't. Targetable for retention spend before they fully lapse. | | | | --------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **What it counts** | For each customer with at least 2 orders: compute the personal average inter-order interval (`days_between_orders`). If `days_since_last_order > 2 × personal_avg_interval`, the customer is "at risk". The card returns `at_risk_customers ÷ total_repeat_customers × 100`. | | **API endpoint** | `Admin GraphQL. Customer.id, Customer.numberOfOrders, Customer.lastOrderId, Order.createdAt`. Computed in OpenSearch from the order index, joined back to customer. | | **First-time customers excluded** | A customer with 1 lifetime order has no inter-order interval to compute. Such customers are excluded from both numerator and denominator. They appear instead in [New Customers](/nerve-centre/kpi-cards/shopify/new-customers). | | **VAT / tax treatment** | Not applicable (count metric). | | **Shipping** | Not applicable. | | **Discounts** | Not applicable. | | **Refunds** | Customers whose orders were fully refunded still count if they were paid orders. The model uses order-placed not order-survived. | | **Cancelled / voided orders** | Excluded. Cancelled orders do not contribute to the customer's inter-order cadence. | | **Currency** | Multi-currency safe (count ratio, no FX). | | **Channels / sources** | Customer-level metric, channel-blind. A customer who ordered POS once and online once has a valid inter-order interval. | | **Time window** | `90D` (the at-risk threshold uses the customer's full history, the 90D window is the snapshot date) | | **Alert trigger** | `>30%` of repeat customers at risk; sustained at-risk share above 30% indicates a retention problem | | **Roles** | owner, marketing | ## 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 An AU SMB skincare brand on Shopify, 60-day typical replenishment cycle. Snapshot date: 12 May 26. | Customer | Orders to date | Avg interval | Last order | Days since last | At risk? | | -------- | -------------- | ------------ | ---------- | --------------- | -------------------- | | Aisha L. | 8 | 58 days | 12 Mar 26 | 61 | No (within 2×) | | Ben H. | 4 | 70 days | 02 Feb 26 | 99 | No (within 2× = 140) | | Carla S. | 12 | 32 days | 22 Feb 26 | 79 | YES (79 > 64) | | Dev R. | 3 | 90 days | 10 Jan 26 | 122 | No (within 2× = 180) | | Eli T. | 6 | 45 days | 06 Mar 26 | 67 | No (within 2× = 90) | | Fran K. | 5 | 50 days | 18 Jan 26 | 114 | YES (114 > 100) | | Gita M. | 9 | 40 days | 14 Mar 26 | 59 | No | | Hugh B. | 2 | 60 days | 02 Feb 26 | 99 | No (within 2× = 120) | | ... | | | | | | Of 1,840 repeat customers in the database, 612 fit the at-risk definition: **33.3%**. The card alerts (`>30%`). Five things to notice: 1. **The threshold is personal, not global.** A customer who orders every 30 days is at risk after 60 days; a customer who orders every 120 days is at risk after 240. This catches truly lapsing customers without false-flagging slow-cadence buyers. Industry-blunt churn definitions ("no order in 90 days") miss the second cohort entirely. 2. **At-risk share is the alert.** The list of 612 at-risk customers is the action list, ranked by historical lifetime value (highest LTV first = most worth saving). A win-back email or SMS targeting the top 200 typically recovers 8 to 15% within 30 days. 3. **Carla S. is the high-priority save.** 12 orders, 32-day cadence, lapsed 79 days = 47 days overdue. She is a power-user pattern; either the brand has a new product issue, or she switched to a competitor. A personal email from the founder (not a discount) often outperforms automated offers for these customers. 4. **The 33% rate is concerning but not catastrophic.** Skincare DTC typically runs 20 to 28% at-risk in steady state. A 33% reading suggests a recent issue: a stockout on a hero SKU, a quality complaint cohort, a price increase, or a competitor launch. Cross-reference [Repeat Rate](/nerve-centre/kpi-cards/shopify/repeat-customer-rate) and [Order Frequency](/nerve-centre/kpi-cards/shopify/customer-order-frequency) for the underlying drivers. 5. **First-time customers are invisible here.** The brand may have 800 first-time buyers in the last 90 days but they do not contribute to this card. A growing first-time cohort with rising churn is the classic acquisition-without-retention scenario; pair this card with [New Customers](/nerve-centre/kpi-cards/shopify/new-customers) to see the full picture. ## Sibling cards merchants should reference together Churn-risk is a leading indicator. Pair with these to find the cause and the targeted save play: | Card | Why pair it with Churn Risk | What the combination tells you | | -------------------------------------------------------------------------------- | --------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------- | | [Repeat Rate](/nerve-centre/kpi-cards/shopify/repeat-customer-rate) | The reverse view, share of customers who *did* come back. | Falling repeat rate + rising churn risk = retention engine breaking. Same problem from two angles. | | [Order Frequency](/nerve-centre/kpi-cards/shopify/customer-order-frequency) | Average inter-order interval across the customer base. | A lengthening frequency average is a slower form of the same alert. | | [New Customers](/nerve-centre/kpi-cards/shopify/new-customers) | Acquisition counterpart. | Acquisition-without-retention is visible only when both move together: rising new + rising at-risk = leaking bucket. | | [Customer Trend](/nerve-centre/kpi-cards/shopify/customer-acquisition-trend) | Net customer movement over time. | If overall customer count is rising but at-risk is rising faster, growth is structurally fragile. | | [Customer Segments](/nerve-centre/kpi-cards/shopify/customer-spend-segments) | Who is at risk: VIP, mid, low-tier? | VIP at-risk is the highest-priority list. Always sort the at-risk list by LTV before acting. | | [Top Refunded Products](/nerve-centre/kpi-cards/shopify/top-refunding-customers) | Quality / sizing complaints predict churn. | If at-risk customers' last orders correlate with high-refund SKUs, that is the cause. | | [Email Health](/nerve-centre/kpi-cards/shopify/email-health) | Can you actually reach these customers? | A 33% at-risk share + 60% bounced-email share = no recovery channel. Fix the email file first. | ## Reconciling against the vendor's own dashboard **Where to look in Shopify Admin:** Shopify Admin does not natively compute personal-cadence-based churn risk. The closest views: * [Customers](https://admin.shopify.com/customers) → segment by *Last order date is more than X days ago* and *Number of orders is greater than 1*. You can build at-risk segments at fixed intervals (60d, 90d, 180d) but they use a global cutoff, not a personal cadence. * **Analytics → Reports → "Customer cohort analysis"**: Shopify Plus only. Shows retention by month-of-first-order. Useful as a sanity check on cohort retention; not a per-customer at-risk list. * **Apps like Klaviyo, LoyaltyLion, Yotpo**: have native predicted-churn features. Their algorithms use richer signals (browse, email-open, on-site behaviour) than this card. Expect 20 to 40% overlap; treat each as complementary. **Why our number may legitimately differ from Shopify Admin:** | Reason | Direction | Why | | ----------------------------- | ---------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Cadence model** | Big difference vs Shopify segments | Shopify Admin uses fixed-day segments (60d, 90d). This card uses personal 2× cadence. The two definitions intersect but do not match; expect 30 to 60% overlap with any single Shopify segment. | | **First-time exclusion** | Ours lower numerator | This card excludes 1-order customers (no cadence to compute). Shopify segments include them. | | **Cancelled order treatment** | Ours slightly different | The card excludes cancelled orders from the cadence calc. Shopify Admin includes order-placed events regardless. | | **Customer merging** | Either | If a customer placed orders under two different email addresses and Shopify hasn't merged them, both this card and Shopify see two separate customers (each invisible to the other's cadence). | | **Time zone** | Boundary days | Daily snapshot uses store time zone; orders near the snapshot boundary may bucket differently from Shopify. | **Cross-connector reconciliation:** | Card | Expected relationship | What causes legitimate divergence | | ------------------------------------------------------------------------------------------------- | --------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | [`klaviyo.klaviyo_inactive_subscribers`](/nerve-centre/klaviyo/klaviyo_inactive_subscribers) | Strong overlap; different inputs | Klaviyo's churn model uses email-engagement decay, this card uses purchase-cadence. Customers who open emails but stop buying appear here, not in Klaviyo's signal, and vice versa. | | [`google_analytics.ga_returning_users`](/nerve-centre/kpi-cards/google-analytics/returning-users) | Inverse signal (fewer returning users implies more churn) | GA4 sees returning *visits* not returning *buyers*. A churned customer can still browse without buying. | ***
Same-metric documentation cross-reference (for agencies running multiple platforms) The same definition lives on other commerce platforms. This is **not a reconciliation**, your Shopify store does not have a parallel store on BigCommerce or Adobe Commerce. These cross-links exist for cross-platform navigation only. * [`bigcommerce.churn_risk`](/nerve-centre/kpi-cards/bigcommerce/customer-churn-risk) * [`adobe_commerce.churn_risk`](/nerve-centre/kpi-cards/adobe-commerce/customer-churn-risk)
## Known limitations / merchant FAQs **Why is the at-risk percentage rising?** Three usual causes: 1. **Cohort coming due.** A successful acquisition wave 3 to 6 months ago means a wave of repeat-customers is now hitting their personal 2× threshold. Mechanical, not a problem unless the wave is fully lapsing. 2. **Quality / experience issue.** A complaint cohort, a recent stockout on a hero SKU, a price increase, or a competitor launch is driving customers away. Cross-reference [Top Refunded Products](/nerve-centre/kpi-cards/shopify/top-refunding-customers). 3. **Email-channel decay.** Win-back campaigns are not landing because the email file is decayed (high bounce, low open). Pair with [Email Health](/nerve-centre/kpi-cards/shopify/email-health). **What is "at risk" exactly?** A customer is at risk if their `days_since_last_order > 2 × personal_avg_inter_order_interval`. This personalises the threshold per customer. A 30-day-cadence customer becomes at risk at 60 days; a 180-day-cadence customer becomes at risk at 360 days. **Why are first-time customers excluded?** A customer with one lifetime order has no inter-order interval to compute. Including them would force a global default (e.g. 90 days) and dilute the personal-cadence value of the metric. First-time customers are tracked in [New Customers](/nerve-centre/kpi-cards/shopify/new-customers); their retention is best measured via cohort analysis once they have a second order. **My subscription store, does this card work?** Yes, well. Subscription customers have very tight cadence (30 / 60 / 90 day plans) so their 2× threshold is precise. A subscription customer 75 days past their 60-day cadence is a 25-day overdue cancellation in disguise. Subscription stores often see the at-risk rate spike right after a billing failure cohort. **Multi-channel: do POS-only customers appear?** Yes, if they have a customer record. Shopify creates customer records for both online and POS purchases. A POS-only customer with multiple visits has a valid cadence. The card is channel-blind. **B2B vs DTC?** B2B cadence is structurally longer (90 to 365 days between orders) and lumpier. The 2× rule still works (a 180-day-cadence B2B customer is at risk at 360 days) but the at-risk list is smaller and individually more valuable. For B2B-heavy stores, sort the at-risk list by LTV and act on the top 20 manually rather than running a generic win-back campaign. **Multi-currency, any impact?** None on the at-risk computation (which uses dates, not currency). The save-play economics differ by region: a £15 win-back voucher is meaningful in the UK, less so in the US, and may not even cover acquisition cost in some markets. Tune the offer per region. **Shopify Plus vs basic?** No definitional difference. Plus stores typically have richer customer-history depth (longer-tenure customers, multiple stores via Shopify Plus organizations). The card behaves identically; the at-risk list on Plus stores is often longer and higher-LTV. **Refresh cadence?** Daily snapshot. The personal-cadence calculation is expensive (per-customer computation across full order history) so the card refreshes once per 24 hours. Real-time variant is on the roadmap. **The card alerted, what should I do?** 1. Sort the at-risk list by LTV (highest first). 2. For the top 5%, send a personal email from the founder or account manager. No discount; ask why they have not been back. 3. For the next 20%, send a 15-20% win-back voucher with a 14-day expiry. 4. For the long tail, run an automated win-back flow (Klaviyo, Mailchimp, native Shopify Email). 5. Measure recovery rate at 30 / 60 / 90 days. Industry-typical is 8 to 15% reactivation on the targeted top tier. 6. Diagnose the root cause: stockout, quality complaint, price increase, competitor. Fix the input, not just the symptom. *** ### Tracked live in Vortex IQ Nerve Centre *Customer Churn Risk* 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](https://app.vortexiq.ai/login) or [book a demo](https://www.vortexiq.ai/contact-us) to see this metric running on your own data.