MACHINE-LEARNING

Subscription Churn Model Confusion Matrix

This confusion matrix evaluates a subscription model that predicts whether a customer will stay or churn in a defined period. The churn-to-retain cell represents missed churn risk, while retain-to-churn represents customers who might receive unnecessary retention outreach. The correct balance depends on outreach cost, customer experience and expected value, not accuracy alone. Teams should set the outcome window before calculating the matrix and use a holdout period that reflects current customer behavior. This example uses aggregate counts and is intended for model evaluation, not for making automated customer decisions without appropriate review.

UPDATED 2026-09-25
EXAMPLESubscription Churn Model Confusion Matrix
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CASE ANALYSIS

Scenario

A retention team evaluates a model used to prioritize outreach.

Key decisions

  • Define the outcome window: State when churn is measured.
  • Review missed churn: These are actual churners predicted to stay.
  • Review unnecessary outreach: Predicted churners who stay may receive offers.

When to reuse this

Use for prioritization analysis with privacy-reviewed, aggregated results.

FAQ

Frequently asked questions

What is missed churn?01
An actual churner predicted to Retain.
Why does false churn matter?02
It can trigger unnecessary outreach or incentives.
What is the outcome window?03
The defined period used to decide whether a subscriber churned.
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