Churn risk watch
Renewal conversations go badly when the first sign of trouble arrives with the renewal notice. This agent watches your accounts against risk patterns you define, such as usage falling away, escalations stacking up or the sponsor going quiet, and flags them to the owner with the evidence, in time for someone to act.
Step by step
Define the risk patterns
We write down what worries you in terms of your own product and customers, rather than importing a generic churn model.
Establish each account's baseline
Patterns are judged against how that account normally behaves, so a seasonal customer is not flagged every quarter.
Watch on your schedule
The agent runs on the cadence you choose and compares the current picture with the baseline.
Flag matches with the evidence
Each flag names the pattern it matched and links to the usage, tickets or messages behind it.
Route to the owner
Flags go to the account owner, with a summary to the team lead rather than a copy of every flag.
The owner decides the response
The agent proposes a next step and drafts the outreach where you want it. A person chooses whether to act and sends anything that reaches the customer.
Exactly what this agent can see, touch and change
The same five controls sit behind every ollo agent. These are this one's settings — visible before you build it, not buried in an admin screen afterwards.
Context
What reaches the modelCustomer success and renewals teams carrying more accounts than can be reviewed properly by hand, where risk currently surfaces at renewal rather than before it.
Escalation
When it asks a personThe account owner decides the response and sends anything that reaches the customer
Applications & Rights
Which tools it uses, and what it may do in each
ZapierRead only
GmailRead only
NotionRead only
SlackReadWrites newCreates new records or documents. Never edits, moves or deletes anything that was already there.Verification
How you know it’s rightEvery claim links to the document it came from. A statement the agent cannot cite does not make it into the output — which is what makes the result reviewable in minutes rather than re-read end to end.
Who it’s for
Customer success and renewals teams carrying more accounts than can be reviewed properly by hand, where risk currently surfaces at renewal rather than before it.
What you’ll need
- Product usage and support data connected
- Written risk patterns per segment
- Named owners for each account
- A channel for flags and a review cadence
What you get
- A flagged account list with evidence
- A per-account risk note for the owner
- A trend view of flags over time
What it doesn’t do
It sees the signals in your systems, not the ones in the customer's head. A quiet, happy customer and a quiet, leaving customer look similar in the data, which is why flags carry evidence and go to a person rather than triggering an automatic play.
We build the first one with you
Not a template you configure alone. We sit with your team, build it on real data, and hand over the controls.
Scope
One session with the people who actually do the work. We agree what the agent reads, what it may write, and who approves.
Co-build
Built on your own data, not a sandbox. You watch it being made, so you know why it behaves the way it does.
Handover
You own the controls. Change the context, tighten the rights, move the approval gate — without coming back to us.
Parts of the work currently spread across the categories below. It does not replace any of those products outright.
Frequently asked
What if our product data is thin?
Then the agent leans on support history, meeting cadence and email traffic, and says clearly which signals it is missing. Thin data is a real constraint, and we would rather scope the agent honestly than have it flag on noise.
How do we avoid alert fatigue?
By tuning the patterns after the first runs, and by routing flags to owners rather than to a shared firehose. A watch agent that flags everything gets muted, so we start narrow and widen once the flags are being acted on.
Does it predict churn?
It matches patterns you have defined and shows the evidence. It does not produce a probability, because a number you cannot interrogate is harder to act on than a flag with the underlying tickets attached.
Can it trigger a save play automatically?
It can draft one. Sending stays with the account owner, because the wrong automated outreach to an unhappy customer costs more than the review it saved.