Sentiment analysis
Real-time NLP on support tickets, emails, and Slack mentions detects frustration, disappointment, and threat-to-leave signals before they escalate to cancellation.
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Problem
Your last ten churns had warning signs in support tickets, emails, and Slack — but nobody read them until the customer was already gone, the deal was closed, and the revenue was unrecoverable. The signals were there; the system to catch them was not.
Solution
ChurnRadar reads support tickets, emails, and Slack mentions for churn signals, predicts which accounts will cancel 60–90 days before renewal with high confidence, and drafts the retention response while you still have time to act — turning preventable losses into recoverable conversations.
Features
Churn prediction
ML trained on historical churn patterns predicts which accounts will cancel 60–90 days before renewal, giving your team time to intervene with real data.
Competitor mentions
AI flags when customers mention alternatives in tickets, calls, or reviews — an early warning signal that your competitive position may be weakening.
Automated response
LLM drafts personalized, empathetic retention responses addressing the specific pain point detected — ready for your CS team to review and send.
Health scoring
Every account gets a continuous health score based on engagement metrics, support sentiment, and usage trends — visible on a single dashboard.
Playbook automation
Pre-built retention playbooks trigger automatically when an account crosses the churn-risk threshold, eliminating the lag between detection and action.
How it works
- 1ChurnRadar ingests support tickets, emails, and Slack mentions and applies NLP to detect frustration, disappointment, and threat-to-leave signals across every customer interaction.
- 2ML models trained on historical churn predict which accounts will cancel 60–90 days before renewal, assigning each a continuous health score for prioritization.
- 3When an account crosses the risk threshold, a tailored retention response is drafted automatically for your team to review and send while you still have time.
FAQ
- How does ChurnRadar detect churn signals?
- It applies real-time NLP to support tickets, emails, and Slack mentions, detecting frustration, disappointment, and threat-to-leave signals before cancellation occurs.
- How far in advance does it predict churn?
- ML models trained on historical churn predict cancellations 60–90 days before renewal, giving your team enough time to intervene with a real plan.
- Does it flag competitor mentions?
- Yes — it flags when customers mention alternatives in tickets, calls, or reviews as an early warning signal of competitive risk.
- Can it draft retention responses?
- Yes — LLM drafts personalized, empathetic retention responses addressing the specific pain point detected, ready for your CS team to customize and send.