A comprehensive product analytics framework that reverse-engineers the growth strategy of Notion—a $10B+ productivity platform. This project demonstrates advanced PM skills in metrics definition, funnel analysis, cohort retention, growth modeling, and data-driven decision-making.
Understanding what drives growth in billion-dollar products is critical for any PM. However, most PMs only see surface-level metrics without understanding:
- How to define the right North Star metric
- How to build comprehensive analytics frameworks
- How to identify and quantify growth levers
- How to communicate insights to executives
This project simulates the complete analytics infrastructure for a product like Notion, demonstrating how to think systematically about growth, retention, and monetization.
- North Star Metric Framework: Systematic approach to defining and tracking the most important metric
- Complete Funnel Analysis: 7-stage user journey with conversion tracking
- Cohort Retention Analysis: Time-based retention tracking with heatmap visualization
- Growth Lever Quantification: Data-driven prioritization of 5 major growth opportunities
- SQL Query Templates: Production-ready queries for analytics databases
- Executive Dashboards: Beautiful, interactive visualizations for stakeholder communication
- Python 3.10+: Core programming language
- Streamlit: Interactive web dashboard & data apps
- pandas & NumPy: Data manipulation and analysis
- scipy & statsmodels: Statistical analysis
- matplotlib & seaborn: Static visualizations
- plotly: Interactive dashboards and charts
- Jupyter: Exploratory analysis notebooks
product-analytics-notion-growth/
│
├── dashboard.py # 📊 Streamlit Interactive Dashboard (Main App)
├── README.md # Project documentation
├── LICENSE # MIT License
├── requirements.txt # Python dependencies
├── .gitignore # Git ignore patterns
│
├── data/ # All datasets
│ ├── raw/ # Original data
│ ├── processed/ # Analyzed data (Inputs for dashboard)
│ └── synthetic/ # Generated user data
│
├── src/ # Source code modules
│ ├── config.py # Configuration & constants
│ ├── data_generator.py # Synthetic data generation logic
│ ├── metrics_framework.py # Core metrics calculations (North Star, WAU)
│ ├── funnel_analysis.py # Funnel & drop-off logic
│ ├── cohort_analysis.py # Cohort retention logic
│ ├── growth_modeling.py # Growth projection & sensitivity models
│ ├── sql_queries.py # SQL query generation templates
│ └── visualization.py # Static chart generation (Plotly/Matplotlib)
│
├── notebooks/ # Jupyter notebooks for experimentation
├── sql/ # Generated production-ready SQL files
│
├── outputs/ # Generated analysis outputs
│ ├── figures/ # Static charts (PNG/HTML)
│ ├── dashboards/ # Legacy HTML dashboards
│ └── reports/ # Final text analysis reports
│
├── docs/ # Documentation
└── scripts/ # Execution scripts
└── run_full_analysis.py # Main analysis pipeline (Run this first)
- Python 3.10 or higher
- pip (Python package manager)
# Extract ZIP file to desired location
# Navigate to project directory
cd product-analytics-notion-growth# Create virtual environment
python -m venv venv
# Activate virtual environment
venv\Scripts\activate.batpip install -r requirements.txtpython -c "import pandas; import plotly; print('✅ All dependencies installed!')"1. Run Analysis Pipeline (Generate Data) First, generate the synthetic data and perform the analysis:
python scripts/run_full_analysis.py2. Launch Interactive Dashboard Visualize the results in the Streamlit app:
streamlit run dashboard.pyThis executes the entire workflow (~3-5 minutes):
- ✅ Generate 50,000 synthetic user profiles and events
- ✅ Calculate North Star metric and supporting KPIs
- ✅ Analyze 7-stage user funnel
- ✅ Perform cohort retention analysis
- ✅ Model 5 growth levers with revenue projections
- ✅ Generate SQL query templates
- ✅ Create interactive visualizations and dashboards
- ✅ Produce executive summary report
# Data generation only
python src\data_generator.py
# Metrics framework only
python src\metrics_framework.py
# Funnel analysis only
python src\funnel_analysis.py
# Cohort analysis only
python src\cohort_analysis.py
# Growth modeling only
python src\growth_modeling.py
# SQL queries only
python src\sql_queries.pyjupyter notebook
# Navigate to (notebooks/) for interactive exploration- File:
outputs/dashboards/executive_dashboard.html - Shows: Weekly Active Collaborative Workspaces (North Star)
- Insight: Tracks engagement + network effects
- File:
outputs/figures/user_funnel.html - Shows: 7-stage conversion funnel with drop-off rates
- Insight: Identifies biggest bottlenecks
- File:
outputs/figures/(Static Charts) - Interactive Dashboard: Streamlit Web App (
dashboard.py)
- File:
outputs/figures/cohort_retention_heatmap.html - Shows: Month-over-month retention by signup cohort
- Insight: Reveals product improvements over time
- File:
outputs/figures/growth_levers.html - Shows: 5 growth opportunities ranked by revenue impact
- Insight: Data-driven roadmap prioritization
- Location:
sql/ - Includes: Production-ready queries for PostgreSQL
- Use Case: Implement in real analytics database
- File:
outputs/reports/analytics_framework_report.txt - Contains: Executive summary with recommendations
- Metric: Weekly Active Collaborative Workspaces
- Current: ~2.1M (simulated)
- Target: 5M
- Gap: 2.9M (58% to target)
| Stage | Users | Conversion | Drop-off |
|---|---|---|---|
| Signup | 50,000 | 100% | 0% |
| Activation | 30,000 | 60% | 40% |
| Engagement | 13,500 | 45% | 55% |
| Habit Formation | 4,725 | 35% | 65% |
| Collaboration | 2,363 | 50% | 50% |
| Monetization | 591 | 25% | 75% |
Overall Conversion: 1.18% (Signup → Paid)
| Rank | Lever | Impact | Confidence |
|---|---|---|---|
| 1 | SEO Content Strategy | $12.5M annual revenue | High |
| 2 | Viral Sharing Optimization | $7.8M annual revenue | Medium |
| 3 | Template Discovery | $6.2M annual revenue | High |
- Month 1 Retention: 45.2%
- Month 3 Retention: 28.7%
- Month 6 Retention: 18.9%
- Improvement: Late cohorts show 8% better retention vs early cohorts
- Defined North Star metric combining engagement + collaboration
- Built supporting KPI tree (DAU, WAU, MAU, Stickiness)
- Calculated activation, engagement, and monetization rates
- Identified activation → engagement as biggest drop-off (55%)
- Segment analysis shows enterprise users convert 6x better
- Referral channel drives 2x higher quality users
- Users who activate in <24 hours have 2x better retention
- Collaborative users have 3x higher LTV
- Product improvements visible in cohort retention curves
- SEO content strategy could add $12.5M annual revenue
- Compound effect of top 3 levers: $26.5M over 12 months
- Sensitivity analysis shows robust projections
- Production-ready queries for all key metrics
- Optimized for PostgreSQL with proper indexing
- Reusable templates for real implementations
- ✅ North Star metric definition
- ✅ Analytics framework design
- ✅ Growth strategy development
- ✅ Data-driven prioritization
- ✅ Executive communication
- ✅ Funnel analysis
- ✅ Cohort retention analysis
- ✅ Statistical modeling
- ✅ Segmentation analysis
- ✅ Predictive modeling
- ✅ Python (pandas, numpy, scipy)
- ✅ SQL query writing
- ✅ Data visualization (plotly, matplotlib)
- ✅ Statistical analysis
- ✅ Dashboard creation
- ✅ Revenue modeling
- ✅ LTV calculations
- ✅ Growth lever quantification
- ✅ ROI analysis
- ✅ Strategic recommendations
- Methodology: Detailed analytics approach
- Metrics Glossary: Definition of all metrics
- Architecture: System design and data flow
- Lab Logbook: Step-by-step execution guide
This project framework can be adapted for:
- SaaS Products: Subscription-based software analytics
- Consumer Apps: Mobile app growth analysis
- Marketplace Platforms: Two-sided marketplace metrics
- Social Networks: Engagement and virality tracking
- E-commerce: Purchase funnel optimization
This is a portfolio project, but feedback is welcome!
- Fork the repository
- Create a feature branch
- Make your changes
- Submit a pull request
This project is licensed under the MIT License - see the LICENSE file for details.
Ayush Saxena
- LinkedIn: Ayush Saxena
- GitHub: iamAyushSaxena
- Email: aysaxena8880@gmail.com
- Notion: Inspiration for the analytics framework
- Amplitude: Product analytics best practices
- Mixpanel: Cohort analysis methodologies
- Lenny's Newsletter: Growth insights and frameworks
For questions or issues:
- Check the documentation
- Review the lab logbook
- Open an issue on GitHub
- Reach out via [LinkedIn/Email]
⭐ If this project helped you, please star the repository!
💼 Looking for a PM who can do this for your product? Let's connect!