Churn Prediction Analytics Checklist

Instrument your product to emit usage events (logins, feature adoption, session length). Feed this to Amplitude or Mixpanel for centralized tracking.

Track both feature adoption and feature depth—power users who abandon a key feature often churn.

Map product usage events to customer accounts in your billing system (Salesforce, HubSpot). This link is critical for churn modeling.

Create a unified customer ID that exists in both product and billing to avoid lookup errors at scale.

Combine usage metrics, support interactions, and engagement signals into a single health score. Reference Gainsight or Totango for templates.

Weight health score components by their correlation to churn in your cohorts—default weights often miss your specific signals.

Create a systematic method to score accounts by churn likelihood. Use tools like Vitally or ChurnZero to baseline scoring logic.

Start with a simple rule-based model before investing in ML—domain expertise often beats complex models for churn.

Document which systems are source-of-truth, update cadence, and data quality rules. Stale product data leads to missed interventions.

Ensure product usage is updated daily and health scores refresh at least weekly for real-time intervention readiness.

Use historical churn data to build a model predicting renewal vs. churn. Include usage, support, engagement, and billing features.

Use a holdout test set from 12-18 months ago—recent data leaks information you won't have at prediction time.

Divide accounts into risk buckets (low, medium, high, critical) to tailor interventions. Don't treat all at-risk accounts the same.

Critical risk accounts should trigger automatic alerts to CSMs within hours, not days.

Identify accounts with expansion potential—high health, growing usage, but lower contract value. These are PQLs (product qualified leads).

Expansion scoring often uses inverse churn signals—heavy feature adoption without corresponding spend is a sales opportunity.

Model the likelihood each customer renews and at what value. Feeds your renewal forecast and expansion pipeline planning.

Track predicted vs. actual renewal rates by quarter to calibrate model confidence and identify blind spots.

Check model performance monthly against holdout data. Retrain quarterly to adapt to market and product changes.

If prediction accuracy drops >5% month-over-month, investigate whether new features or pricing tiers are changing churn drivers.

Automatically assign critical-risk accounts to experienced CSMs. Use Salesforce or HubSpot to trigger workflows based on risk score changes.

Route accounts 60+ days before renewal, not on renewal day—timing is everything for successful interventions.

Different churn reasons need different plays. Build playbooks for dormant users, feature-specific dropoff, and support escalations.

Playbooks should include specific email templates, call agendas, and success metrics—vague guidance fails at scale.

Trigger notifications when an account crosses a risk threshold. Use Slack integration for visibility and HubSpot automations for tasks.

Send both immediate alerts to the assigned CSM and weekly summaries to leadership—visibility drives accountability.

If you do churn, identify win-back opportunities early. Use segmented email and discounted trials to recover revenue.

Win-back campaigns often have 3-5x lower acquisition cost than new customers—prioritize them when budgets tighten.

Define actions CSMs should take when health score drops (e.g., conduct health check, usage review, feature training).

Tie health check outcomes to specific next steps—'improve training,' 'demo enterprise features,' or 'escalate to support.'

Track logo churn rate, revenue churn, and cohort-based retention. Establish monthly reporting dashboards visible to leadership.

Report both logo and revenue churn separately—large customer losses can hide in revenue churn if you focus only on one.

Track NRR as your primary metric for churn + expansion health. An NRR >100% shows net growth even without new sales.

Break NRR by cohort (e.g., 3-month-old vs. 2-year-old customers) to identify which segments drive expansion.

Measure precision, recall, and AUC of your churn model. Compare predicted churn to actual outcomes each quarter.

High precision is critical for CSM workflows—false positives waste intervention resources and erode trust in predictions.

Use individual renewal probabilities to forecast enterprise pipeline and identify gaps. Update this monthly for board reporting.

Weight forecasts by account value and segment—a high-risk $100k customer drives more urgency than a low-risk $5k one.

Calculate saved revenue from interventions (predicted churn value minus retention cost). Attribute wins to CSM efforts.

Tie intervention ROI to individual CSM performance reviews—this creates accountability and identifies your best retention leaders.