03 PREDICT CHURNCHURN SCORE
See who is likely to leave, from the day they sign up.
A score from 0 to 100 for every new customer, with the reasons behind it. A prepaid card, a disposable card, a card removed just before renewal: you know before the payment fails.
HOW IT WORKS
Four steps, all automatic.
- 01
A customer signs up
NoChurn reads the payment method type, the card country, the signup source and any attributes you send.
- 02
It gets a score
From 0 to 100, with a level (low, medium, high) and the factors that moved it, each with its weight.
- 03
It learns your business
The first scores use sensible defaults. Once your account has enough history, the weights are learned from your own customers.
- 04
It warns you early
If a customer removes their card a few days before renewal, they are flagged and a save email can go out before the payment fails.
WHAT YOU CONTROL
Your rules, your tone, your call.
Risk by payment method
Prepaid and disposable cards rank high, neobank cards medium, wallets, Link, bank cards and SEPA low. Adjusted to your history over time.
Your signals
Signup source, onboarding, seats, activity: anything you send as an attribute can weigh in.
Suggested actions
Ask for a second card, push onboarding, offer the annual plan, or alert your team.
Early warning window
Choose how many days before renewal a removed card should raise the flag. Seven by default.
In every flow
The score can pick the cancel offer, the tone of recovery emails, or the timing of win-back.
In your tools
Read the score from the API, or receive it by webhook as soon as it is computed.
BUILT IN
Rules you don’t have to think about.
EXPLAINED
Never a black box
Every score comes with the factors that made it, so you can see why a customer is flagged.
SAMPLE SIZE
Shown with its sample
Learned weights are shown with how many customers they are based on. Too few, and the defaults stay.
NO CARD DATA
Built without card numbers
The score uses the card type, funding and country. Never the number.
INTEGRATIONS
Works with your stack.
The score uses what each source shares: payment method details from billing, conversations from your help desk.
NoChurn runs it
- StripeA 0–100 score at signup and at every change, from the payment method, signup source and your custom data.
- Any billing, by APIA 0–100 score at signup and at every change, from the payment method, signup source and your custom data.
- ChargebeeA 0–100 score at signup and at every change, from the payment method, signup source and your custom data.
- RecurlyA 0–100 score at signup and at every change, from the payment method, signup source and your custom data.
- MaxioA 0–100 score at signup and at every change, from the payment method, signup source and your custom data.
- ZuoraA 0–100 score at signup and at every change, from the payment method, signup source and your custom data.
- PaddleA 0–100 score at signup and at every change, from the payment method, signup source and your custom data.
- Lemon SqueezyA 0–100 score at signup and at every change, from the payment method, signup source and your custom data.
- PolarA 0–100 score at signup and at every change, from the payment method, signup source and your custom data.
- BraintreeA 0–100 score at signup and at every change, from the payment method, signup source and your custom data.
- MollieA 0–100 score at signup and at every change, from the payment method, signup source and your custom data.
- GoCardlessA 0–100 score at signup and at every change, from the payment method, signup source and your custom data.
- RazorpayA 0–100 score at signup and at every change, from the payment method, signup source and your custom data.
- App StoreA 0–100 score at signup and at every change, from the payment method, signup source and your custom data.
- Google PlayA 0–100 score at signup and at every change, from the payment method, signup source and your custom data.
Feeds it with data
- IntercomTicket volume, topics and satisfaction from Intercom weigh in the score.
- ZendeskTicket volume, topics and satisfaction from Zendesk weigh in the score.
- HubSpotTicket volume, topics and satisfaction from HubSpot weigh in the score.
- Help ScoutTicket volume, topics and satisfaction from Help Scout weigh in the score.
- GorgiasTicket volume, topics and satisfaction from Gorgias weigh in the score.
- CrispTicket volume, topics and satisfaction from Crisp weigh in the score.
API
Also in the API.
Use the score in your own app: gate a trial, ask for a second card, or route a customer to your team.
API reference/v1/customers/:id/score Score, level and factors /v1/customers/:id Send attributes that feed the score customer.scored A new or updated score customer.flagged Card removed before renewal FAQ
Questions people ask.
Is this the same as a fraud score?
No. Fraud tools ask whether a payment is legitimate. The churn score asks whether a real customer is likely to leave or fail to pay, and why.
How does it know a card is prepaid?
Payment providers share the card’s funding type (credit, debit, prepaid) without the card number. NoChurn reads that field.
What can I do with a high score?
Ask for a second card, push onboarding, offer the annual plan, or alert your team. You choose which action runs for which level.
Does it work without Stripe?
Yes. Send the payment method type and your attributes through the API, and the score works the same way.
Guides on churn score
All guides →- CHURN METRICS
What is a good churn rate? Public benchmarks by industry and price point, and how to read them
Churn rate benchmarks by industry and price per customer from Recurly's public data (July 2026), voluntary vs involuntary, and how to compare yours.
6 min read
- CHURN ANALYSIS
Activation and early churn: fixing the first 30 days
How to define an activation milestone from your own data, measure early churn and time to value, find where new customers stall, and fix the first month.
6 min read
- CHURN METRICS
Annual vs monthly plans: what changes for churn
How annual and monthly plans churn differently: comparing their rates fairly, renewal notice rules, failed renewals and the selection bias behind annual.
6 min read
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