churn-analysis

Churn Analysis

Overview

Churn is the silent killer of product growth. Losing 5% of users per month means losing 46% per year — you'd need 50%+ annual growth just to stay flat. This skill helps you diagnose why users leave, identify leading indicators of churn, and design retention interventions.

When to Use

• Monthly or quarterly churn is above target
• You need to understand WHY users are leaving
• You're designing retention campaigns or reactivation flows
• Building a churn prediction model
• Don't use for: one-off cancellations (talk to the user directly), products with < 100 active users (too few data points)

Instructions

1. Measure Churn Correctly

Definition: Users who stop using your product within a given period.

• User churn: % of users who stop using
• Revenue churn: % of recurring revenue lost (often different from user churn)
• Gross churn: Total lost (include downgrades and cancellations)
• Net churn: Lost - expansions (can be negative = negative churn = growth)

Segment churn by:

• Plan: Free vs. paid vs. enterprise
• Cohort: Users who joined in the same month
• Behavior: Heavy vs. light users
• Source: How were they acquired?

2. Find Leading Indicators

Churn doesn't happen overnight. Look for early warning signs:

• Engagement drop: Frequency of use decreases over 2+ weeks
• Feature decay: Stop using key features
• Support contact: Filing complaints or asking about cancellation
• Billable action stops: For revenue-churn, look for stopped billing-related actions
• Account changes: Team members leaving, admin changing

3. Analyze Churn Reasons

Quantitative:

• Cancel survey results (what did they select?)
• Last actions before churn (what did they do last?)
• Usage patterns of churned vs. retained users

Qualitative:

• Exit interviews: "What made you decide to leave?"
• Win-back conversations: "What would it take to come back?"
• Lost deal analysis: "Why did you choose competitor X?"