SaaS Churn Analysis Framework

Free v1.0.0

Systematic framework for indie SaaS founders to understand why customers churn, identify pre-churn behavioral signals, and implement data-driven retention interventions.

What You Get

Transforms the frustration of 'I can see who churned but not why' into actionable insights with exit surveys, behavioral analysis, and intervention playbooks that reduce churn rate.

The Problem

Solo founders see customers cancel in Stripe but can't connect the behavioral patterns that preceded churn. They repeat the same retention mistakes and lose preventable MRR without understanding root causes or knowing which interventions actually work.

The Solution

Combines qualitative exit surveys with quantitative behavioral pattern analysis to categorize churn (voluntary vs. involuntary), identify pre-churn signals using statistical analysis, and generate intervention strategies. Includes Python scripts for analyzing customer data, email templates for re-engagement sequences, payment failure recovery playbooks, and tracking dashboards. Delivers week-by-week implementation plans with expected ROI calculations.

How It Works

  1. 1 Gather context about current churn rate, data availability (Stripe, analytics), MRR, customer count, and primary pain point
  2. 2 Categorize recent churn into voluntary (customer chose to leave) vs. involuntary (payment failure) segments with benchmark comparison
  3. 3 Design analysis approach based on data: behavioral pattern analysis (with Python script), exit survey framework, or payment failure recovery
  4. 4 Generate intervention strategies: pre-churn email sequences for at-risk customers, dunning optimization for payment failures, or post-churn learning playbooks
  5. 5 Build tracking dashboard with monthly churn metrics, intervention performance, and leading indicators for continuous monitoring
  6. 6 Deliver 3-week implementation checklist with week-by-week tasks, expected outcomes, and quantified MRR impact

What You'll Need

  • Access to payment processor (Stripe, Paddle, etc.) with ability to export subscription and cancellation data
  • For behavioral analysis: Analytics platform (PostHog, Mixpanel, Amplitude) with ability to export customer activity CSV files
  • Email sending capability for surveys and intervention sequences
  • Python 3.10+ if using behavioral analysis script (uv handles dependencies automatically)
  • 3-5 hours per week for first month of implementation

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Examples

Analytics + Payment Recovery: Dual-Track Analysis

A growing SaaS with both analytics data and payment failures. Demonstrates behavioral pattern analysis with PostHog, at-risk customer detection, combined with dunning optimization for a comprehensive retention strategy.

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Early-Stage Critical Churn: Diagnosis Framework

A new SaaS with critically high churn and limited data. Demonstrates stage-appropriate diagnosis, activation audit, founder outreach strategies, and conservative/optimistic outcome modeling.

View example

Stripe-Only Data: Understanding Unknown Churn

A SaaS founder with basic Stripe data who doesn't understand why customers are leaving. Demonstrates exit survey design, churn categorization, payment failure recovery, and implementation planning for limited-data situations.

View example
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