Renewal risk on autopilot:
know which accounts need a call, months out
Most account teams find out a contract is at risk when the client emails asking to cancel, with weeks left to save it. We build a model that scores every account's renewal risk from usage, support tickets and engagement trends, so an account manager gets a warning months out, not a cancellation request.
The process today
A renewal that goes wrong usually announces itself far too late: a cancellation email, a quiet non-response to the renewal invoice, a champion who left the company three months ago and nobody noticed. By the time any of that reaches an account manager’s inbox, the decision has often already been made internally on the client’s side, and the save conversation is really a last-minute negotiation instead of a relationship check-in.
The signals that actually predict a churn, usage dropping, support tickets piling up unresolved, a key user who stopped logging in, almost always exist well before the cancellation request, scattered across a product database, a support tool and a CRM that nobody is cross-referencing in real time. An account manager juggling thirty accounts cannot manually watch all three systems for every client, every week.
The result is a customer success team that is reactive by structure, not by choice: they find out an account is at risk exactly when it is hardest to do anything about it, and the accounts that quietly go cold without ever complaining are often the ones nobody saw coming at all.
What the agent does
The model trains on your own CRM’s history of which accounts renewed and which churned, and learns which combinations of usage trend, support ticket volume and engagement actually preceded each outcome for your specific product, not a generic SaaS benchmark. Every account gets a health score that refreshes on a schedule, pulling live usage data, open and resolved support tickets, and engagement signals like login frequency or feature adoption.
When an account’s score crosses into risk territory, the account owner gets an alert at a lead time you configure, typically several months before the renewal date, with the specific signals behind the score: usage down forty percent over two months, three open tickets past SLA, the main admin user inactive for six weeks. That gives an account manager time to actually do something, a check-in call, an executive touchpoint, a training session, instead of a last-minute discount offer.
The whole book of accounts is visible on a dashboard sorted by risk, so a customer success lead can see where the team’s attention is most needed this week without waiting for individual alerts to surface every case.
What stays with humans
The renewal conversation, any save offer, pricing concession or escalation decision stays entirely with the account manager. The model flags risk and explains why; it does not contact the client or make a judgment call about which accounts are worth saving at what cost. A manager who knows context the model does not, a reorganisation at the client, a new champion coming in, can and should override the score.
Guards
Every risk score is logged with the signals behind it and, eventually, with the actual renewal outcome, so the model’s accuracy is tracked rather than assumed. A backtest against your past renewals and churns runs before go-live, showing how the model would have scored accounts you already know the outcome for, and a kill switch reverts to manual review in one message if the scoring ever looks unreliable.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $900 | One product line, risk model, scheduled alerts | 7 to 14 days |
| Department package | from $2,800 | Renewal risk scoring plus playbooks and account dashboards across customer success | 3 to 5 weeks |
Running cost is usually $25 to $90 a month in model usage depending on account volume.
Related
Pair this with subscription renewal reminders for the operational side of renewals once an account is confirmed healthy, and with cohort and LTV modelling so renewal risk ties back to what an account is actually worth saving for. The full package breakdown is on the AI agents service page and the automation-everything overview; for a real B2B account book, see the certification sales agent case study and the two-brand analytics hub case study.
Ready to see your renewal risk months before the cancellation email? Get in touch and we will look at your account history in the first call.
Tired of doing this by hand? We can take the whole routine off your team, not just this step: Routine takeover, from $400 →
FAQ
How much does renewal risk scoring cost?
From $900 for a single product line scored against your account history, live in 7 to 14 days. A department package adding playbooks and alerts across your customer success team usually starts at $2,800.
What data does the model need?
Usage data from your product, support ticket history, and your CRM's record of which accounts renewed versus churned in the past. More history produces a sharper model; a first version can still run on 6 to 12 months of data.
Does it replace our account managers?
No. It gives them a lead-time advantage and a reason to reach out before an account is already decided; the renewal conversation, the save offer, and the relationship stay entirely with the account manager.
How is this different from a generic churn alert?
A generic alert often fires on one signal, like a usage drop, after the fact. This model combines several signals and is trained on what actually preceded a churn in your own data, so it can flag risk earlier and with fewer false alarms.
What happens with a new account that has no history yet?
New accounts get a category-based baseline risk until they build enough of their own usage history, and the model flags them as low-confidence rather than guessing with false precision.