SaaS Metrics

Churn Prediction

Churn prediction estimates which customers are at risk of churn using behavior, adoption, and account signals. The goal is intervention that improves retention.

Updated 2026-01-24

Definition

Churn prediction estimates which customers are at risk of churn using behavior, adoption, and account signals. The goal is intervention that improves retention.

Example

If usage drops below a 7-day threshold and support tickets spike, the account is flagged for proactive outreach.

How to use it

  • Start simple: usage drop-offs that predict churn in cohorts.
  • Evaluate models by retention impact, not by accuracy alone.
  • Prioritize explainable signals so CS teams know what to fix.
  • Calibrate scores by segment; enterprise and SMB patterns differ.

Common mistakes

  • Training on data that includes post-churn signals (label leakage).
  • Optimizing for AUC without measuring retention lift from interventions.
  • Triggering outreach on noisy signals that create alert fatigue.

Why this matters

This term matters because small changes compound in SaaS metrics. Use consistent definitions by cohort and segment so you can diagnose retention, payback, and growth quality.

Practical checklist

  • Write a 1-line definition for "Churn Prediction" that your team will use consistently.
  • Keep the time window consistent (weekly/monthly/quarterly) when comparing trends.
  • Segment results (channel/plan/cohort) before drawing big conclusions from blended averages.
  • Sanity-check with a related calculator from the same category on MetricKit.
  • Read the related guide (e.g., Retention & churn hub: cohorts, GRR/NRR, and retention curves) for context and common pitfalls.

Where to use this on MetricKit

Calculators

Guides