Churn Risk Scoring Model Builder

Free v1.0.0

Build predictive churn risk models from behavioral data, identifying early warning signals and scoring users for proactive intervention.

What You Get

Transform raw behavioral data into actionable churn risk scores with weighted scoring model, user risk rankings, and intervention playbooks in minutes instead of weeks.

The Problem

SaaS founders and customer success managers struggle to identify which trial users are at risk of churning before they leave. Without simple heuristics or predictive models, teams miss early warning signals and end up in reactive firefighting mode instead of proactively saving at-risk customers. One account can quietly drop 40% in usage over two months without anyone noticing until it's too late.

The Solution

This skill analyzes historical user behavioral data to identify which actions predict churn, builds a weighted scoring model combining multiple signals, and ranks every user by risk level (0-100 scale). It uses statistical correlation analysis (Pearson correlation) to quantify which behaviors matter most, normalizes them into weights, and calculates composite risk scores. Users get a transparent, explainable model showing exactly which behaviors drive churn risk, plus specific intervention recommendations for each risk tier (CRITICAL/HIGH/MEDIUM/LOW). The skill also identifies activation thresholds - the behavioral patterns that distinguish retained vs churned users - enabling product-led growth strategies.

How It Works

  1. 1 Validate your behavioral data CSV has user_id, numeric behavior columns, and churned outcome column
  2. 2 Run the Python analysis script using uv to calculate correlations between each behavior and churn outcome
  3. 3 Review risk signal analysis to understand which behaviors predict churn (positive vs negative correlations)
  4. 4 Identify your product's activation threshold - the behavioral combination separating retained from churned users
  5. 5 Review user risk scores to see all users ranked 0-100 by churn risk with specific risk factors
  6. 6 Build intervention playbooks for each risk tier with specific actions based on risk factors
  7. 7 Track model performance over time and refine based on which interventions actually reduce churn

What You'll Need

  • Python 3.10+ (dependencies installed automatically via uv)
  • Behavioral data in CSV format with user_id and churned columns
  • Minimum 30 users with at least 5% churn rate
  • Numeric behavioral columns (activity counts, days, frequencies, etc.)

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