Back to Cookbook

Churn Early Warning

Spot at-risk renewals 120 days out, not 12

Customer health scores lie. This skill combines product usage, support patterns, engagement trends, and stakeholder changes into a predictive model that catches churn risk months before renewal — when you can still do something about it.

House RecipeWork10 min setup

INGREDIENTS

💬Slack✈️Telegram✉️Email📄Google Docs

PROMPT

Create a skill called "Churn Early Warning". Build a churn prediction model for my customer base. Input signals: product usage trends (logins, feature adoption, API calls), support ticket volume and sentiment, NPS/CSAT scores, stakeholder engagement (meeting frequency, email responsiveness), champion job changes, and contract details (renewal date, term length, last price increase). Score each account 1-10 on churn risk. Alert at: score 7 = CSM warning, score 8 = manager escalation, score 9+ = executive save play. For each at-risk account, show: risk score, contributing signals, trend direction, days until renewal, and recommended save play. Weekly report: total at-risk ARR, accounts by risk tier, save plays in progress, and save rate.

How It Works

The skill builds a multi-signal churn prediction model. Usage declining? Support

tickets spiking? Champion left? NPS dropped? Each signal adds to a risk score that

triggers alerts at configurable thresholds — early enough to run a save play.

What You Get

  • Predictive churn scoring based on multiple signal types
  • Early warning at 120+ days before renewal
  • Signal breakdown: which factors are driving risk
  • Recommended save plays based on risk type
  • Trend tracking: is this account improving or declining?
  • Renewal pipeline report: total at-risk ARR and save play status

Setup Steps

  1. Connect available data sources, or provide recurring exports from those systems: product usage, support tickets, NPS, engagement
  2. Define signal weights (or let the model learn from historical churn data)
  3. Set alert thresholds: when to warn CSM, when to escalate
  4. Provide historical churn data for model calibration

Tips

  • Champion departure is the highest-urgency signal — react within 48 hours
  • Usage decline over 3+ weeks is more predictive than a single bad week
  • Don't rely on NPS alone — detractors sometimes renew, promoters sometimes churn
  • Run weekly save-play standup on all accounts above the risk threshold
Tags:#churn#customer-success#renewals#analytics