📊 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.

🎯 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

🛠️ 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

📁 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)

🚀 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!')"

💻 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):

✅ 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

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

📊 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

📈 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

🧪 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

🎓 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

📚 Documentation

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

🎯 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

🤝 Contributing

This is a portfolio project, but feedback is welcome!

Fork the repository
Create a feature branch
Make your changes
Submit a pull request

📄 License

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

👤 Author

Ayush Saxena

🙏 Acknowledgments

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

📞 Support

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!

FilesExpand file tree

README.md

Latest commit

History