SaaS Activation Metric Finder

Free v1.0.0 2 views

Find your SaaS product's 'aha moment' through statistical correlation analysis between early user actions and retention.

What You Get

Identify which early user behaviors predict long-term retention using chi-square significance testing and correlation analysis, enabling data-driven optimization of onboarding flows.

The Problem

Indie SaaS founders struggle with defining their 'aha moment' metric because finding the action that correlates with retention requires statistical analysis they can't do, which causes optimizing the wrong behaviors and missing opportunities to improve conversion. Without data-driven insights, founders iterate blindly on onboarding, wasting time on changes that don't impact retention.

The Solution

This skill analyzes user event data to identify activation metrics through statistical correlation analysis. It calculates retention lift for each candidate action (tasks completed, features used, etc.), performs chi-square significance testing, and ranks metrics by predictive power. The analysis outputs actionable recommendations including your best activation metric, adoption rates, retention lift percentages, and onboarding strategy suggestions. It supports segment-specific analysis to find different 'aha moments' for different user types (solo vs team, free vs paid).

How It Works

  1. 1 Prepare event data exported from analytics tool (PostHog, Mixpanel, Amplitude) with user IDs, signup dates, event names, and timestamps
  2. 2 Define activation window (typically 7 days) and retention definition (e.g., active in days 30-60)
  3. 3 Run statistical analysis script using Python with pandas and scipy to calculate correlations
  4. 4 Review ranked results showing retention lift, statistical significance (p-values), and adoption rates for each candidate action
  5. 5 Interpret results to identify strongest activation signal based on lift percentage and significance
  6. 6 Generate onboarding recommendations and validation framework to test causation vs correlation

What You'll Need

  • Event data with at least 60 days of user activity to measure retention
  • Minimum 100 users with complete data for statistical reliability
  • Clear definition of retention appropriate for your product
  • Python 3.10+ (automatically managed by uv package manager)

Get This Skill

Have Feedback?

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

Request Improvements

Examples

Activation Hypothesis Validation for Project Management Tool

Comprehensive analysis confirming 'complete_task' as the best activation metric for an 800-user project management tool with +37% retention lift, including detailed A/B test design, expected impact calculations, and implementation timeline.

View example

Segment-Specific Activation Analysis for Creator Platform

Dual-segment analysis for 500 users finding different activation metrics for solo creators (create_content: +55% lift) vs teams (invite_team_member: +74% lift), with customized onboarding recommendations and implementation strategy for each segment.

View example

Simple Activation Analysis for File Sharing App

Statistical analysis for a 200-user file sharing app identifying 'invite_colleague' as the best activation metric with +45% retention lift (88% vs 43%), including ranked results table, onboarding strategy, and validation approach.

View example
View all examples