CUSTOM DATA & AI

Find out why your customers leave.

Send the attributes you already have. NoChurn joins them with billing and support data, finds what goes with churn, and the AI explains it. Correlation, not causation: every insight shows its sample size.

Reduce my churn Read the docs
app.nochurn.sh/data
CUSTOM DATA & AI · Interface preview · example data

Recommended to send

Start with these. Each one is a question the AI can answer for you.

ACQUISITION

  • signup_date2026-03-14

    Do customers from some months or cohorts leave sooner?

  • signup_sourcefacebook_ads · google_ads · seo · referral

    Do some channels bring customers who churn more? Facebook Ads vs SEO, for example.

  • coupon_usedLAUNCH50

    Do discount signups stay once the discount ends?

ACCOUNT

  • plan · billing_intervalpro · monthly

    Do monthly plans leave more than annual ones?

  • company_size · seats11–50 · 3

    Do small teams or single users leave more?

  • country · industryDE · agency

    Do some countries or industries churn more?

PRODUCT USAGE

  • activated_at2026-03-16

    Do customers who never reach the “aha” moment cancel?

  • last_active_at2026-10-30

    How long of inactivity comes before a cancel?

  • key_feature_usageprojects_created: 4

    Which feature keeps customers, and which one nobody uses?

HEALTH & BILLING

  • support_tickets · nps2 · 7

    Do customers who write to support a lot, or never, leave sooner? Connect your help desk (Intercom, Zendesk, HubSpot, Help Scout, Gorgias or Crisp), or send it yourself.

  • payment_methodcard · sepa · prepaid

    Do some payment methods fail more often? Prepaid or expiring cards, for example.

  • trial_days14

    Do customers who skipped the trial, or had a short one, leave sooner?

03 PREDICT CHURNCUSTOM DATA

Send your own data. AI finds what predicts churn.

Push any attribute in one call: signup date and source, seats, usage, onboarding, NPS. NoChurn compares customers who leave with those who stay and tells you which attributes go with churn.

Sources

  • Stripe Payments, card types, plans, failures
  • Your app · API / SDK Attributes and usage events
  • Support · Intercom, Zendesk, HubSpot, Help Scout, Gorgias, Crisp Conversations, topics, satisfaction
  • CSV import Anything else you track
NoChurn stats · AI

NoChurn joins every source for each customer, computes the statistics, and the AI explains what stands out.

What comes out

  • Wrote to support 3+ times in 30 days → 2.1× more likely to cancel
  • Prepaid card at signup → 1.8× more payment failures
  • No login and no support contact for 14 days → 2.6× more likely to cancel
One call per customer, or a batch
await fetch("https://api.nochurn.sh/v1/customers/cus_…", {
  method: "PATCH",
  headers: { Authorization: `Bearer ${NOCHURN_KEY}` },
  body: JSON.stringify({
    attributes: {
      signup_date: "2026-03-14",
      signup_source: "facebook_ads",
      seats: 1,
      onboarding_done: false,
      last_active_at: "2026-10-12",
    },
  }),
});
Churn drivers
  • onboarding_done = false 3.1× more likely to cancel
  • signup_source = facebook_ads 2.4× more likely to cancel
  • last_active_at > 14 days 2.0× more likely to cancel
  • plan = annual 0.4× · stays longer

Correlation, not cause: use it to choose who gets which offer.

PROMPT

Read https://www.nochurn.sh/llms.txt,
then follow https://www.nochurn.sh/docs/quickstart/
to add NoChurn to this app. Use a test key
(nc_test_…) until I say we go live.

Stop leaking revenue.

  • Free until $300 MRR
  • No card needed
  • Live in 10 minutes
  • Never a cut of what we recover
Reduce my churn Read the docs