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.
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.
Of 1,840 repeat customers in the database, 612 fit the at-risk definition: 33.3%. The card alerts (
>30%).
Five things to notice:
- 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.
- 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.
- 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.
- 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 and Order Frequency for the underlying drivers.
- 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 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: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 → 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.
Cross-connector reconciliation:
Known limitations / merchant FAQs
Why is the at-risk percentage rising? Three usual causes:- 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.
- 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.
- Email-channel decay. Win-back campaigns are not landing because the email file is decayed (high bounce, low open). Pair with Email Health.
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; 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?
- Sort the at-risk list by LTV (highest first).
- For the top 5%, send a personal email from the founder or account manager. No discount; ask why they have not been back.
- For the next 20%, send a 15-20% win-back voucher with a 14-day expiry.
- For the long tail, run an automated win-back flow (Klaviyo, Mailchimp, native Shopify Email).
- Measure recovery rate at 30 / 60 / 90 days. Industry-typical is 8 to 15% reactivation on the targeted top tier.
- Diagnose the root cause: stockout, quality complaint, price increase, competitor. Fix the input, not just the symptom.