SaaS Retention Cohort Analyzer

Free v1.0.0 2 views

Transform raw user activity data into retention cohort reports with statistical analysis and benchmarks—no SQL expertise required.

What You Get

Get cohort retention tables, statistical significance tests, industry benchmarks, and actionable insights from your CSV exports in minutes instead of hours of SQL wrangling.

The Problem

Indie SaaS founders struggle with building retention cohort reports because the SQL and statistical analysis skills required are beyond their expertise. They know retention matters but can't measure it properly, leading to guessing at retention rates instead of measuring them, not knowing which cohorts retain better, missing opportunities to identify what drives long-term users, and making product decisions without retention data.

The Solution

This skill transforms raw user activity exports into comprehensive retention analysis by parsing CSV/Excel data, calculating cohort retention rates with statistical rigor, performing significance tests for segment comparisons, benchmarking against industry standards, and providing reproducible Python code and SQL queries. The computational leverage comes from pandas for data wrangling and scipy for statistical tests, while Claude provides strategic interpretation and actionable recommendations. Users get complete retention cohort tables, confidence intervals, statistical test results, visualization code, and specific next steps—solving 80%+ of the retention analysis workflow.

How It Works

  1. 1 Upload or describe your user activity data (CSV/Excel with user_id, signup_date, event_timestamp columns)
  2. 2 Skill validates data structure, checks sample sizes, and identifies any quality issues
  3. 3 Python script calculates cohort retention rates, handles incomplete cohorts, and computes confidence intervals
  4. 4 For segment comparisons, statistical significance tests are performed with power analysis
  5. 5 Results are benchmarked against industry standards (B2B SaaS, B2C, mobile apps)
  6. 6 Receive retention tables, statistical analysis, visualization code, and actionable recommendations
  7. 7 Get reproducible Python scripts and SQL queries for ongoing analysis

What You'll Need

  • CSV or Excel export with user activity data including user_id, signup_date, and event_timestamp columns
  • Definition of what counts as 'retained' for your product (login, subscription renewal, feature usage)
  • Minimum 100 users per cohort for statistical reliability (300+ for segment comparisons)
  • uv installed for running Python scripts (installation provided in workflow)

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Examples

Free vs Paid Plan Retention Comparison

Segmented retention analysis comparing free and paid plans with statistical significance testing, effect size calculations, retention curve shape analysis, and strategic recommendations for improving free tier conversion.

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Monthly Login Retention Analysis

Basic cohort retention analysis for a SaaS product tracking login activity, with confidence intervals, B2B SaaS benchmark comparisons, and prioritized recommendations for addressing retention cliffs.

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Small Sample Daily Retention with Statistical Warnings

Daily cohort retention analysis for a small dataset (40 users) demonstrating proper handling of insufficient sample sizes with Wilson confidence intervals, power analysis, and clear limitations while still providing actionable insights.

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