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Card class: Hero • Category: Ecommerce Platform

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At a glance

Percentage of customers who stopped purchasing in the last 12 months. High churn means acquisition costs never pay back, retention is the growth lever. A store with 50% churn must replace half its customer base every year just to stand still; one with 20% churn compounds returns from each cohort across multiple years. The card surfaces the structural retention health of the business in a single number that owners and CFOs use for LTV planning.
What it countsThe percentage of customers who placed at least one order in the prior 12 months but no order in the current 12 months. Computed as: customers who churned ÷ customers who were active at the start of the window.
Sample typeBackend API data from BigCommerce customer + order history, refreshed on the standard data refresh.
Why churn rate matters(1) LTV economics: every percentage point of churn reduces lifetime value by roughly 1-3 percentage points (depending on AOV and category). (2) Acquisition pressure: high churn means the merchant must constantly acquire to replace lost customers, raising CAC and depressing margins. (3) Compounding: low churn compounds, a customer retained year 2 buys year 3, year 4, year 5; a churned customer buys none of those. Stores below 20% churn typically have profitable LTV/CAC; stores above 50% rarely do.
Reading the value(1) Below 20%: healthy retention; LTV economics work. (2) 20-35%: typical ecommerce range. (3) 35-50%: investigation zone; retention strategy needs work. (4) Above 50%: alert state; the store is structurally a one-time-purchase business and acquisition cost will compound to unprofitable. (5) Cross-reference repeat_rate (the inverse signal) and cohort retention curves.
Currencypercent.
Time windowrolling 12 months.
Alert triggerchurn_rate > 20 (BAD threshold at 50%).
Sentiment keychurn_rate (LOWER_IS_BETTER in SentimentClassifier; GOOD ≤ 20%, BAD ≥ 50%).
Rolesowner, marketing, finance

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Calculation

churn_rate (%) = churned_customers ÷ active_at_start × 100

active_at_start    = COUNT(customers WHERE first_order_date < (today - 12 months)
                     AND COUNT(orders) >= 1 in [today - 24 months, today - 12 months))
churned_customers  = COUNT(customers WHERE
                     active_at_start
                     AND no_orders in [today - 12 months, today))
Edge cases:
  • Customers acquired within the last 12 months are excluded from the denominator (they had no opportunity to churn yet).
  • Subscription customers may have a different “active” definition based on subscription status.

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Worked example

A UK-based BigCommerce fashion store, customer churn reading on Wednesday 15 May 26.
MetricValueStatus
Customers active in May 2024 - May 2025 (denominator)24,500-
Customers who placed any order in May 2025 - May 2026 (retained)9,800-
Customers who did not (churned)14,700-
Churn rate60.0%Alert
Repeat customer rate (current)18.4%Below 20% threshold
Churn rate: 60.0% (well above BAD threshold of 50%). Card flags as Action Needed in red. What the customer health reading is telling us:
  1. The store is structurally a one-time-purchase business at the moment. 60% of last-year-active customers did not return this year. The acquisition treadmill is brutal: every year, the merchant must replace 60% of their active base just to stand still on revenue.
  2. What this means for LTV economics.
    • At 60% annual churn, average customer lifespan is ~1.7 years.
    • At AOV £261 and 1.7-year lifespan, average LTV is roughly £450 (assumes ~1 repeat order on average).
    • With CAC at £45 (typical for paid-led ecommerce), LTV/CAC ratio is 10x, surface-level healthy, but the absolute LTV is constrained by short lifespan.
    • A 20% reduction in churn (from 60% to 48%) typically increases LTV by 35-50% because retention compounds across the customer’s remaining lifespan.
  3. Likely root causes for 60% churn:
    • Single-purchase-friendly category: some categories (mattresses, large appliances) have naturally high churn because customers don’t buy frequently. Compare against category benchmarks.
    • Discount-driven acquisition: customers acquired via heavy discounts often don’t return at full price (cross-reference discount_dependency).
    • Weak retention marketing: no abandoned-cart, post-purchase, or re-engagement flows running.
    • Product-quality / experience issue: fulfilment delays, refund rates, OOS frustration drive lapse.
    • Competitive shift: a major competitor entered the category with better price/range/experience.
  4. The retention playbook for sustained churn reduction:
    • Phase 1 (month 1-3): stand up baseline retention flows. Post-purchase email sequence with usage tips, complementary product recommendations, and replenishment reminders. Klaviyo/Mailchimp execution; expect 3-5 percentage points churn reduction within 6 months.
    • Phase 2 (month 4-6): implement re-engagement campaign for lapsed customers (8-12 month cohort). Personalised reactivation offer, new-arrivals teaser, exit interview for non-responders. Expect another 3-5 percentage points reduction.
    • Phase 3 (month 7-12): build cohort-based LTV optimisation. Identify highest-LTV cohort signals (acquisition channel, first-product, AOV); double down on acquisition that produces those cohorts.
    • Result: churn moves from 60% → 40-45% over 12 months; LTV doubles; CAC tolerance increases (can spend more on acquisition because retention compounds returns).
  5. Cross-reference cards:
    • repeat_rate (25%): the inverse signal; should be moving in opposite direction.
    • discount_dependency: discount-driven cohorts churn at higher rates.
    • klv_* cards: ESP-side retention signals (welcome flow, post-purchase, re-engagement).
    • ga_returning_user_rate: GA-side complementary signal.
    • bc_repeat_failure_customers: customers who attempted repeat orders that failed.
The diagnostic flow:
  1. Read churn rate. Above 35% warrants action; above 50% alert.
  2. Compare against category benchmark.
  3. Decompose by acquisition cohort (which channels produce highest-churn customers).
  4. Stand up retention flow infrastructure.
  5. Re-measure annually; expect 3-10 percentage points improvement per year of focused retention investment.
Rapid-response playbook:
Time horizonAction
First weekRead churn. Compare against category. Audit retention flows.
Month 1Stand up post-purchase + abandoned-cart flows.
Month 3Run re-engagement campaign on 8-12 month lapsed cohort.
Month 6First measurable churn reduction.
Year 1Churn improves 5-15 percentage points. LTV materially increased.

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Sibling cards merchants should reference together

CardWhy merchants reach for it
repeat_rateThe inverse signal of churn.
discount_dependencyDiscount-driven cohorts have higher churn.
zero_spendRegistered customers who never bought; a different retention failure mode.
klv_dormant_subscribersEmail-side dormancy.
klv_revenue_per_recipientEmail-driven retention revenue.
bc_repeat_failure_customersCustomers who tried to repeat-order but failed.
bc_organic_recovery_rateRecovery rate of lapsed customers.

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Reconciling against the vendor’s own dashboard

Where to look in BC: Customers → Customer list with last-order-date filter; Analytics → Customer Reports if available. Why our number may differ:
ReasonDirectionWhat to do
Window definition. BC may define active/lapsed using calendar year; Vortex IQ uses 12-month rolling.VariableMatch window.
Guest customer treatment. BC may include guest checkouts as customers; Vortex IQ may exclude.VariableConfirm filter.
Subscription customer handling. Subscriptions have a different “active” definition.VariableConfigure subscription handling.
Cohort start date. Some retention tools use first-purchase month as cohort; Vortex IQ uses 12-month windows.Different lensUse cohort-specific reports for cohort analysis.
Cross-connector: complement with klaviyo.klv_dormant_subscribers and mailchimp.mc_segments_overview for ESP-side retention signals. Quick rule: confirm window definition and guest-customer handling first.

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Known limitations / merchant FAQs

Q: Our churn is 60%. Is that ecommerce-typical or unusually bad? Depends on category. Fashion typically 50-65%; beauty 40-55%; consumables/replenishment 25-35%; high-frequency DTC 20-30%. 60% is at the high end for fashion but not catastrophic. The leverage is in moving toward the lower end of your category, even 5 percentage points of improvement compounds to material LTV gain. Q: Why does the rolling 12-month definition matter? Because calendar-year definitions create comparison artefacts. A customer who bought in November and then May 14 months later would count as “lapsed” in a strict calendar definition but “active” in a rolling definition. The rolling window is more representative of actual customer behaviour. Q: We have a strong subscription business. How does subscription churn show up here? Configurable. By default, an active subscription counts as an “order in the period” because the customer is engaged. Some merchants prefer to track subscription churn separately (using subscription cancellation events) and exclude active subscribers from this card. Configure in profile settings. Q: Should we include guest customers? Default behaviour: include them if they have an email address (so re-engagement is possible). Exclude pure-anonymous guest checkouts. The rationale: guest customers are the natural starting point for retention work, if you can capture them in a flow, they convert to repeat customers at meaningful rates. Q: Our churn is improving. How fast can we see it on the card? Slowly. The metric is 12-month rolling, so improvements take 3-6 months to be visible and 12 months to fully materialise. Track shorter-window proxies (7-day post-purchase email open rate, 30-day repeat purchase rate) for faster-feedback signals during the retention build-out. Q: What’s the highest-leverage retention investment for ecommerce? By size of payoff in our merchant cohort: (1) post-purchase email sequence with replenishment timing, (2) abandoned-cart recovery flows, (3) re-engagement campaigns for 6-12 month lapsed cohort, (4) loyalty programme with tiered benefits. The first two are typically free or nearly free (Klaviyo, Mailchimp) and produce 3-7% revenue lift within 90 days. Q: Is high churn always a problem? For most ecommerce, yes. Exceptions are infrequent-purchase categories (mattresses, large appliances, life-event purchases) where 70-80% annual churn is structural. For those categories, focus less on churn reduction and more on lifetime value extension via service contracts, accessory cross-sell, or referral programmes. Q: How does churn rate relate to CAC payback? Closely. CAC payback period (months until acquisition cost is recovered from gross margin) is roughly: CAC ÷ (AOV × gross_margin × purchase_frequency). High churn shortens the customer lifespan available for payback. A store at 60% churn typically has 12-18 month payback; at 30% churn, 6-9 month payback. Halving churn roughly doubles the time available for the customer to pay back acquisition cost.

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Tracked live in Vortex IQ Nerve Centre

Customer Churn 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. Start for free or book a demo to see this metric running on your own data.