Utilities and templates for funnels, churn, and A/B tests.
These are PM-friendly tools to reason about metrics, experiment design, and data-driven decisions.
- 🧮 ab_test_calculator.py → Sample size & significance calculator (CLI).
- 📈 analyze_funnel.ipynb → Funnel analysis notebook with quick visualizations.
- 🗂 funnel_example.csv → Mock funnel dataset for demos.
| Step | Users | Drop-off |
|---|---|---|
| Landing Page | 1,000 | - |
| Signup Started | 600 | 40% |
| Signup Completed | 400 | 33% |
| Activated | 250 | 38% |
➡ Funnel conversion = 25% (Landing → Activated).
Interpretation:
- Baseline = 10% conversion rate.
- As the variant’s conversion rate diverges (e.g., 12% or 14%), statistical power rises.
- Helps decide required sample size before launch.
As a Product Manager, I use these tools to:
- Validate whether experiments are statistically sound.
- Identify funnel bottlenecks and prioritize fixes.
- Make data-driven roadmap decisions instead of gut-feel.
📌 These are simplified, portfolio-friendly versions of the tools I use for product analytics and growth experiments.