A behavioral model scores every account's churn risk, ranks who is worth saving this week, and drafts the save play.
The quiet quit
Nobody churns on the day they cancel. First the sign-ins stretch out. Then the seats go quiet, the usage curve bends down, a payment fails, a frustrated ticket lands. The account was leaving for weeks, in plain sight.
But those signals live in four different tools, and reading them across a whole book of customers is nobody's Monday morning job. So the team finds out when the email arrives, when every option left is expensive.
Churnora reads the signals first, and ranks who to save this week.
Illustrative figures used to describe the problem Churnora is built for, not results from a study we ran. They reflect what retention teams and published SaaS benchmarks consistently report: churn is expensive, it telegraphs itself in behavior first, and timely outreach saves a meaningful share of it.
How it works
Upload a CSV of customers and usage: signups, plans, logins, feature use, tickets, payment history. Column names are matched loosely, so the export you already have usually works as is.
A behavioral machine-learning model running in our own code scores each customer's churn risk from engineered features. Deterministic, inspectable, done during the import, no queue.
Open the console to a ranked list of accounts worth saving this week, each with its factors in plain language and a drafted save play: the email, the offer, the call notes. Copy, edit, send.
See the risk
These eight accounts are being scored by Churnora's actual model in your browser as you read this. Re-rank them, open one, read the factors, see the play.
Ranked accounts
Pick an account.
What it does
A real machine-learning model scores every customer from engineered behavioral features: sign-in recency, usage trend, seat utilization, support load, payment health, tenure. In our code, deterministic, published method.
0.94 held-out AUC · 8 features
Not a dashboard, a queue. The accounts most worth a person's time this week, ranked by risk and the revenue at stake.
Every score explains itself: the model's top factors, translated into words, each tied to a number in that account's own data.
The email, the offer, the call notes, drafted from the factors and matched to the cause. Copy it, make it yours, send it from your tools.
Each import is kept, so you can watch risk move as you work the saves, and prove retention efforts with numbers.
A forgiving CSV reader that accepts what your billing tool actually exports, explains every skipped row, and scores the book during the upload. Or load the sample and feel it first.
The predictor is ours and it is code. The language layer routes across leading models through one interface, so a lab changing price or policy is a setting here, not a rebuild.
Built AI-native
Plenty of products say AI and mean a prompt. Churnora's prediction is a behavioral machine-learning model in our own code, trained on engineered features, with its held-out accuracy published. That is what scores your book.
The language model does the part language models are actually good at: explaining the factors in plain words and drafting the outreach. Two parts, one honest split, stated the same way on the tin as in the code.
A logistic regression over eight engineered behavioral features, fitted with gradient descent, running in our own code. Deterministic and inspectable: the weights are published on How the model works.
It takes the factors the model computed and turns them into plain words, then drafts the save play. It never predicts, and it never touches a number. Model-agnostic, routed through one layer.
Scoring a thousand accounts takes a model. Turning factor weights into an email a human wants to send takes a language model. Churnora is the two, kept honestly apart.
Care
A score is the model's best read of the behavior in your data, offered to inform a human. A quiet account might be on vacation. Churnora ranks where your attention goes first; you decide what it deserves.
The model only sees the usage you import. It does not know your customer just hired a new team or lost a champion. The factors are shown precisely so you can bring what you know.
Encrypted in transit and at rest, never used to train shared models, and sent to a language provider only to write one account's explanation. Export or delete everything, anytime.
Read more on our care page and how your data is handled.
From operators
“We used to find out about churn from the cancellation email. Now Monday starts with the ranked list, we work the top five, and last quarter we saved eleven accounts we would have lost quietly. The factors are what sold me, I can check every one against our own data.”
“I run a two-person CS team, so a dashboard was never going to save anyone. Churnora hands us the accounts, the why, and a draft that is eighty percent of the email I would have written. The payment-failure catches alone pay for it.”
Illustrative examples of how retention teams use Churnora, not quotes from named customers.
Pricing
Switch to annual and two months are free.
Score up to 200 customers a month with the full model. Enough to know who is slipping before they cancel.
$0forever
No card required
Up to 200 customers, full risk scores and factors
For a book you work every week. Score up to 5,000 customers, get the weekly accounts-to-save list, and export everything.
$49per month, USD
$588 a year, or $492 on annual
Card payments are not switched on in this deployment yet, so this joins the list and we email you the moment it is.
Up to 5,000 customers, weekly saves, drafted plays, export
For retention as a team sport. Larger volumes, seats for the whole CS team, API access and priority support.
$149per month, USD
$1,788 a year, or $1,488 on annual
Card payments are not switched on in this deployment yet, so this joins the list and we email you the moment it is.
Up to 50,000 customers, seats, API, priority support
Questions
Import your book, or the sample one, and see your first ranked risk list in minutes. Free for 200 customers, no card needed.
