Track customer usage and flag at-risk customers to the Customer Success Manager.
Understanding the challenge your team faces
Customer churn happens gradually. Teams often do not notice at-risk customers until it is too late to save them. Manual monitoring of usage patterns is time-consuming and easy to miss.
The solution and the flow of how this responsibility solves the problem
This AI worker continuously monitors customer usage, identifies warning signs, and alerts your CSM team to at-risk customers before they churn. It gives you time to intervene.
Your request or task
AI analyzes & executes
Ready-to-use result
Problem solved
Compare your situation before and after
Your team loses at-risk customers because there is no early warning system.
The AI worker flags at-risk customers early so your CSM team can step in and save them.
What your team can achieve with this responsibility
Identify at-risk customers before they churn.
Time to take action before customers leave.
Risk scores based on actual usage patterns.
Real indicators that this responsibility is making an impact
Catch and save at-risk customers.
Successfully retain customers flagged as at-risk.
CSMs spend time saving customers, not fighting fires.
Saved customers maintain their revenue.
Get answers to what you'd like to know
Login frequency, feature usage, support tickets, engagement trends, and custom metrics.
Machine learning model trained on your churn data.
Continuously, updating scores in real-time.
Yes. Set custom risk thresholds and health criteria.
Three easy steps to activate this responsibility
Connect customer data and define health metrics.
AI continuously monitors customer health.
CSMs receive alerts for at-risk customers and take action.