Automated customer health scoring and alerts

Problem

CSMs manage 50–200 accounts and can't manually track engagement signals across all of them. By the time a customer shows obvious churn signals (support escalation, non-renewal conversation), it's often too late to save the account.

Solution

The AI agent ingests product usage data, support ticket volume and sentiment, NPS scores, billing patterns, and engagement metrics. It calculates a real-time health score for every account and alerts CSMs when scores drop, with specific reasons and recommended actions.

Benefits

• Real-time health visibility across all accounts
• 2–4 week earlier churn warning
• Prioritized CSM attention on at-risk accounts
• Data-driven save playbooks

How to get started

1. 1

   Define health score model

   Identify your key health signals: product usage (daily active users, feature adoption), support (ticket volume, sentiment), engagement (meeting attendance, email opens), and billing (payment delays, downgrades).

2. 2

   Connect data sources

   Integrate product analytics, CRM, help desk, and billing system. The agent needs real-time access to all health signal sources.

3. 3

   Calibrate and deploy

   Backtest health scores against historical churn. Adjust signal weights until the model predicts churn accurately. Deploy alerts to CSMs via Slack or CRM.

Recommended tools

Gainsight, ChurnZero, Totango. See the full list on the AI Customer Success Agent pillar page.