Spot customers at risk of churning and prep a save plan

Customers rarely announce they are about to cancel. The signals are spread across product usage, support tickets, billing and email tone, and account managers only notice when the cancellation request arrives, too late to fix anything.

Each week the agent pulls a snapshot of every account from the tools you already have: product usage trends, open and recent support tickets with their sentiment, failed payments or downgrades, renewal dates, and how long since anyone talked to the customer. It compares each account with its own recent history and with the risk signals you define, then flags accounts that changed for the worse. For each flagged account it writes a short brief: what changed, the evidence, the renewal date, and two or three suggested next steps such as a check-in call, a training session or fixing an open ticket. Account owners get their list in Slack or the CRM; the agent does not contact customers itself.

1. Pick the signals you trust: usage drop, seats removed, ticket volume or negative sentiment, failed payments, no contact in N days, renewal within N days
2. Connect read-only access to product analytics, the help desk, billing and the CRM
3. Write down the thresholds that count as a flag, and start strict so the list stays short
4. Have the agent post a weekly list with one brief per flagged account to each account owner
5. Log what the owner did and whether the account renewed, and review the signals every quarter
6. Retire signals that flag accounts which never churn

• HubSpot or Salesforce
• Gainsight or ChurnZero
• Intercom or Zendesk
• Stripe
• Google Sheets or BigQuery + an LLM
• n8n or Zapier

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Make one of these tools? Feature your tool on every playbook where it is already listed.

Account owners decide whether and how to reach out; the agent only drafts the brief and suggested steps. Any discount, credit or contract change needs a manager's approval.

Risk scores can be wrong or biased toward accounts that simply log tickets more often, so show the evidence and not just a score. Avoid surveillance-style signals your customers would not expect, and follow your privacy policy and data agreements. Too many alerts get ignored, so cap the weekly list. Do not let a model's guess about sentiment be the only reason to treat a customer differently.

Compare churn and renewal rates for flagged accounts that got a follow-up with the period before you started, and track how many cancellations had a flag first. The value is earlier warning and a ready plan for each at-risk account, not a guaranteed save rate.