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genpark-growth-data-analyst

mcp
Guvenlik Denetimi
Uyari
Health Uyari
  • License — License: MIT
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SUMMARY

GenPark skill: genpark-growth-data-analyst based on GrowthGPT research

README.md

genpark-growth-data-analyst

Tests
License
Python
Dependencies
Coverage

GenPark Growth Data Analyst is a zero-dependency SaaS product analytics and retention econometrics engine implementing cohort retention triangle heatmaps, bivariate Granger causality F-tests on feature adoption, and discrete Kaplan-Meier churn hazard rate estimations.

flowchart TD
    A[User Birth Cohort Events] --> B[Retention Triangle Matrix Generator]
    B --> C[Empirical SaaS Retention Curve]
    D[Feature Adoption & Retention Series] --> E[Granger Causality F-Test]
    E --> F[Causal Significance & Impact Verdict]
    G[Customer Lifespan & Churn Event Stream] --> H[Hazard Rate & Survival Function]
    H --> I[Critical Retention Intervention Window]

Features

  • Cohort Retention Triangles: Calculates multi-period retention matrices and average longitudinal survival curves.
  • Granger Causality Testing: Computes F-statistics to rigorously verify whether feature adoption causes retention gains.
  • Churn Hazard Inflection: Pinpoints exact customer drop-off days to trigger proactive customer success playbooks.

Quickstart

from client import GenParkGrowthDataAnalyst

analyst = GenParkGrowthDataAnalyst()
cohorts = [{"cohort": "2026-01", "cohort_size": 100, "active_users": [100, 50, 40]}]
res = analyst.build_cohort_retention_triangle(cohorts)
print("Avg Retention:", res["average_retention_curve_pct"])

License

MIT

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