SaaS Churn Pattern Analyzer

Free v1.0.0

Transform reactive churn panic into systematic analysis with exit surveys, pre-churn behavior patterns, and intervention playbooks.

What You Get

Get customer success team-level churn analysis: high-response exit surveys, statistically validated pre-churn signals, risk scoring, and data-driven intervention strategies.

The Problem

Indie SaaS founders struggle with understanding why users churn because exit surveys have low response rates (5-15%) and analytics don't show intent. This causes fighting churn blindly and losing revenue to preventable churn. They can see cancellations in Stripe but can't connect to behavior patterns or understand root causes.

The Solution

Provides three analysis modes: (1) Exit survey design with response-boosting tactics achieving 30-50% response rates through conversion optimization principles and personal outreach templates, (2) Pre-churn behavior analysis using statistical tests (t-tests, chi-square) to identify behaviors predicting churn with significance testing, generating risk scoring systems and intervention triggers, (3) Cohort investigation framework comparing acquisition sources, product changes, pricing, seasonality, and external factors to identify systemic issues. Includes Python scripts for statistical analysis (pandas, scipy.stats), SQL queries for data exploration, email templates, and measurement frameworks.

How It Works

  1. 1 Determine analysis mode based on situation: exit survey design for <10 churns, behavior pattern analysis for products with event data, cohort investigation for anomalous churn patterns
  2. 2 Gather required data: product context for surveys, event logs and subscription data for behavior analysis, cohort comparison data for anomaly investigation
  3. 3 Run analysis using appropriate method: receive survey templates and email scripts, execute Python statistical analysis on user behavior data, or apply systematic investigation framework with SQL queries
  4. 4 Interpret results and create intervention playbook with specific risk thresholds, automated triggers, email templates for each risk level, and measurement framework to track effectiveness

What You'll Need

  • For statistical analysis: Python with pandas and scipy (included in skill)
  • For behavior pattern mode: Minimum 10 churned users, event logs or analytics data, subscription/cancellation data
  • For cohort analysis: SQL database access or CSV exports, comparison cohort data
  • Understanding of product and user journey, ability to implement changes (surveys, emails, queries)

Get This Skill

Have Feedback?

Help us improve this skill by sharing your ideas and suggestions.

Request Improvements