Activation Metric Discovery Framework

Free v1.0.0 10 views

Analyze user event data to identify which behaviors predict retention. Combines statistical correlation analysis with a structured framework for evaluating activation metric candidates.

What You Get

Find your product's 'aha moment' by discovering which user actions correlate with long-term retention, ranked by predictive power with specific thresholds and validation roadmaps.

The Problem

SaaS founders and PMs struggle to identify which user actions constitute their 'aha moment' because existing analytics tools show correlation but not causation, which causes them to optimize for vanity metrics that don't predict retention. As OpenView Partners notes, 'Activation is, no doubt, a tough metric to get right.' Many struggle with understanding the importance of pinpointing the aha moment, leading to customers churning soon after signup.

The Solution

This skill analyzes user event or aggregate data to calculate correlation between behaviors and retention. It uses scipy.stats for statistical analysis (Pearson correlation, point-biserial correlation), identifies behavioral thresholds (e.g., '6+ documents' or '2+ tasks'), and applies a structured framework to evaluate candidates based on timing, user control, and measurability. For event data, it analyzes frequency patterns. For aggregate data, it identifies inflection points. Output includes ranked candidates, primary metric recommendations with thresholds, framework assessments, and A/B testing validation plans.

How It Works

  1. 1 Validate input data for sample size (minimum 100 users recommended), retention variance, and required columns (user_id, retention indicator)
  2. 2 Run statistical correlation analysis using Python scripts with pandas/scipy to calculate retention rates and correlation coefficients for each event or metric
  3. 3 Apply activation metric framework to evaluate candidates on correlation strength (>0.7 strong), early timing (first 7 days), user control, leading indicator quality, and measurability
  4. 4 Generate recommendations including primary activation metric with specific threshold, reasoning based on framework criteria, and secondary signals for composite scoring
  5. 5 Calculate user-level activation scores for aggregate data and identify users needing intervention with actionable recommendations

What You'll Need

  • CSV file with user-level data including user_id and retention indicator (retained_30d, retained_60d, etc.)
  • Minimum 100 users for statistical validity (30 minimum, but results more reliable with 100+)
  • Event/metric data from early user journey (first 7-14 days after signup)
  • Python 3.10+ with pandas, numpy, scipy (automatically handled via uv package manager)

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