Product usage data feeds an automated scoring flow that flags accounts at risk of churning before they cancel, giving the team time to step in.
Typical results
Daily
risk scan of every account
Days
of warning before a cancellation
100%
of accounts scored automatically
Figures reflect typical results for this kind of automation.
The challenge
The customer success team usually learned about unhappy accounts only when a cancellation request arrived, leaving no time to intervene.
Before and after
Monthly churn rate
−15%At-risk accounts reached before cancelling
16×Advance warning before a cancellation
The automation
We connected usage data to a daily flow that reviews signals such as declining logins and unused features, scores each account, and sends an alert with context whenever an account crosses a risk threshold.
How it works
- 01
Usage data flows in
Logins, feature usage and account activity sync into the flow every day.
- 02
Signals reviewed
Declining logins, unused features and quiet accounts are spotted automatically.
- 03
Every account scored
A churn-risk score updates daily for each account in the base.
- 04
Threshold crossed, alert sent
The customer success team gets an alert with full context on why the account is at risk.
- 05
Team steps in early
Outreach happens days before the customer considers leaving — churn drops 15%.
Starts from
- Usage data
Delivers
- Churn alerts
The impact
Monthly churn dropped by 15% as the team began reaching out to at-risk accounts days before they considered leaving.
Questions about this automation
Which signals feed the churn-risk score?
Common inputs are login frequency, use of core features, number of active seats and the upcoming renewal date, pulled from the product database or analytics tool and the CRM. Each signal gets a weight, and the score compares an account with its own recent baseline, so a usually quiet account is not flagged by mistake.
What does the customer success team receive when an account is flagged?
An alert in Slack or email with the account, its current score, the signals that moved it, such as logins down by half in two weeks, and the renewal date. The same details are written to the account record in the CRM, so the next touch can be planned and tracked there.
Can the scoring rules be adjusted over time?
Yes. Weights and thresholds sit in a simple settings table the team can edit, and each alert can be marked as useful or a false alarm, which shows over time which signals really predict cancellations. Simple automations like this are usually live within days once the usage data is connected.