Churn Prediction

Churn prediction uses machine learning to identify customers likely to cancel or stop using your service. By analyzing usage patterns, support interactions, engagement levels, and satisfaction signals, it flags at-risk customers before they churn—giving you time to intervene.

Warning Signals

• Declining product usage or login frequency
• Reduced feature adoption
• Increase in support tickets or complaints
• Low NPS scores or survey feedback
• Payment issues or downgrades
• Key champion leaves the company

Proactive Retention

Once at-risk customers are identified, customer success teams can intervene with targeted actions: reaching out to understand concerns, providing additional training, offering optimization consulting, or addressing specific pain points. Catching churn risk early makes retention significantly more successful than waiting for cancellation notices.