📊 Product Analytics Deep-Dive: Reverse Engineering Notion's Growth

🎯 Project Overview

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.

📸 Project Preview

Problem Statement

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.

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🎯 Key Features

• 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

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🛠️ Tech Stack

• 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

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📁 Project Structure

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)

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🚀 Installation & Setup

Prerequisites

• Python 3.10 or higher
• pip (Python package manager)

Step 1: Download/Extract the Project

# Extract ZIP file to desired location
# Navigate to project directory
cd product-analytics-notion-growth

Step 2: Create Virtual Environment (Windows)

# Create virtual environment
python -m venv venv

# Activate virtual environment
venv\Scripts\activate.bat

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Verify Installation

python -c "import pandas; import plotly; print('✅ All dependencies installed!')"

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💻 Usage

1. Run Analysis Pipeline (Generate Data) First, generate the synthetic data and perform the analysis:

python scripts/run_full_analysis.py

2. Launch Interactive Dashboard Visualize the results in the Streamlit app:

streamlit run dashboard.py

This executes the entire workflow (~3-5 minutes):

1. ✅ Generate 50,000 synthetic user profiles and events
2. ✅ Calculate North Star metric and supporting KPIs
3. ✅ Analyze 7-stage user funnel
4. ✅ Perform cohort retention analysis
5. ✅ Model 5 growth levers with revenue projections
6. ✅ Generate SQL query templates
7. ✅ Create interactive visualizations and dashboards
8. ✅ Produce executive summary report

Run Individual Modules

# 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.py

Using Jupyter Notebooks

jupyter notebook
# Navigate to (notebooks/) for interactive exploration

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📊 Key Outputs

1. North Star Metric Dashboard

• File: outputs/dashboards/executive_dashboard.html
• Shows: Weekly Active Collaborative Workspaces (North Star)
• Insight: Tracks engagement + network effects

2. User Funnel Analysis

• File: outputs/figures/user_funnel.html
• Shows: 7-stage conversion funnel with drop-off rates
• Insight: Identifies biggest bottlenecks

3. Visualizations

• File: outputs/figures/ (Static Charts)
• Interactive Dashboard: Streamlit Web App (dashboard.py)

4. Cohort Retention Heatmap

• File: outputs/figures/cohort_retention_heatmap.html
• Shows: Month-over-month retention by signup cohort
• Insight: Reveals product improvements over time

5. Growth Levers Prioritization

• File: outputs/figures/growth_levers.html
• Shows: 5 growth opportunities ranked by revenue impact
• Insight: Data-driven roadmap prioritization

6. SQL Query Templates

• Location: sql/
• Includes: Production-ready queries for PostgreSQL
• Use Case: Implement in real analytics database

7. Final Report

• File: outputs/reports/analytics_framework_report.txt
• Contains: Executive summary with recommendations

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📈 Sample Results

North Star Metric

• Metric: Weekly Active Collaborative Workspaces
• Current: ~2.1M (simulated)
• Target: 5M
• Gap: 2.9M (58% to target)

User Funnel Performance

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)

Top 3 Growth Opportunities

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

Cohort Insights

• 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

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🧪 Key Insights Demonstrated

1. Metrics Framework

• Defined North Star metric combining engagement + collaboration
• Built supporting KPI tree (DAU, WAU, MAU, Stickiness)
• Calculated activation, engagement, and monetization rates

2. Funnel Analysis

• Identified activation → engagement as biggest drop-off (55%)
• Segment analysis shows enterprise users convert 6x better
• Referral channel drives 2x higher quality users

3. Cohort Retention

• Users who activate in <24 hours have 2x better retention
• Collaborative users have 3x higher LTV
• Product improvements visible in cohort retention curves

4. Growth Modeling

• 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

5. SQL Proficiency

• Production-ready queries for all key metrics
• Optimized for PostgreSQL with proper indexing
• Reusable templates for real implementations

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🎓 Skills Demonstrated

Product Management

• ✅ North Star metric definition
• ✅ Analytics framework design
• ✅ Growth strategy development
• ✅ Data-driven prioritization
• ✅ Executive communication

Data Analysis

• ✅ Funnel analysis
• ✅ Cohort retention analysis
• ✅ Statistical modeling
• ✅ Segmentation analysis
• ✅ Predictive modeling

Technical Skills

• ✅ Python (pandas, numpy, scipy)
• ✅ SQL query writing
• ✅ Data visualization (plotly, matplotlib)
• ✅ Statistical analysis
• ✅ Dashboard creation

Business Acumen

• ✅ Revenue modeling
• ✅ LTV calculations
• ✅ Growth lever quantification
• ✅ ROI analysis
• ✅ Strategic recommendations

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📚 Documentation

• Methodology: Detailed analytics approach
• Metrics Glossary: Definition of all metrics
• Architecture: System design and data flow
• Lab Logbook: Step-by-step execution guide

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🎯 Use Cases

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

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🤝 Contributing

This is a portfolio project, but feedback is welcome!

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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👤 Author

Ayush Saxena

• LinkedIn: Ayush Saxena
• GitHub: iamAyushSaxena
• Email: aysaxena8880@gmail.com

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🙏 Acknowledgments

• Notion: Inspiration for the analytics framework
• Amplitude: Product analytics best practices
• Mixpanel: Cohort analysis methodologies
• Lenny's Newsletter: Growth insights and frameworks

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📞 Support

For questions or issues:

1. Check the documentation
2. Review the lab logbook
3. Open an issue on GitHub
4. Reach out via [LinkedIn/Email]

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⭐ If this project helped you, please star the repository!

💼 Looking for a PM who can do this for your product? Let's connect!