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?"

4. Identify Retention Levers

For each churn driver, design an intervention:

| Churn Driver | Intervention | Metric to Track | |-------------|-------------|-----------------| | Never activated | Improve onboarding, add personal setup | Activation rate | | Lost interest | Re-engagement emails, new features | Return rate | | Poor support experience | Reduce response time, add self-serve | CSAT, support volume | | Competitor switch | Competitive analysis, feature gap fill | Win-back rate | | Budget cuts | Downgrade path, usage-based pricing | Downgrade rate |

5. Build a Retention Dashboard

Monitor weekly:

• Churn rate by segment
• Leading indicators (engagement drop, support tickets)
• Intervention effectiveness (re-engagement campaign conversion)
• Reactivation rate (how many came back?)

Sample Output

Churn analysis for Q2 (5,000 users, 8% monthly churn):

Top churn drivers:

1. (40%) Never activated — signed up, never invited team. Left in 2 weeks.
2. (25%) Lost interest — used for 1-2 months, then stopped.
3. (15%) Switched to competitor — mentioned better mobile app.
4. (10%) Too expensive — small teams on free plan hit limits.
5. (10%) Other — unknown.

Key insight: 40% of churn happens before activation. Invest in onboarding. Intervention: New guided setup flow with team invite on first login. Target: Reduce pre-activation churn from 40% to 20% → overall churn from 8% to 6%.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: uthumany
• Source: uthumany/uthy-legacy-os
• License: MIT
• Homepage: https://uthumany.github.io/uthy-legacy-os/

Install and usage instructions live in the source repository linked above.