🚀 Feature Prioritization Framework with A/B Test Simulation

📋 Project Overview

A comprehensive, data-driven product management framework that demonstrates systematic feature prioritization using the RICE methodology and validates recommendations through statistical A/B testing simulation. Built to showcase real-world PM skills including prioritization, data analysis, statistical reasoning, and stakeholder communication.

Problem Statement

Product teams face the critical challenge of deciding which features to build when engineering resources are limited. Without a systematic approach, decisions are often opinion-based rather than data-driven, leading to:

• Wasted engineering effort on low-impact features
• Missed revenue opportunities
• Unclear prioritization criteria
• Lack of statistical validation

This project simulates a real-world PM scenario for a food delivery platform with 500K monthly active users, systematically evaluating 15 feature ideas and validating the top recommendation through rigorous A/B testing.

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🎯 Key Features

• RICE Prioritization Framework: Systematic evaluation of features based on Reach, Impact, Confidence, and Effort
• Statistical A/B Testing: Monte Carlo simulation with proper statistical significance testing
• Business Impact Modeling: Revenue projections and ROI calculations
• Interactive Visualizations: Executive dashboards, funnel analysis, and priority matrices
• Complete Documentation: PRDs, methodology docs, and stakeholder reports

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🛠️ Tech Stack

• Python 3.10+: Core programming language
• pandas & NumPy: Data manipulation and numerical computing
• scipy: Statistical analysis and hypothesis testing
• matplotlib & seaborn: Static visualizations
• plotly: Interactive dashboards
• Jupyter: Exploratory analysis notebooks

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🚀 CI/CD & Automation

This project utilizes GitHub Actions to ensure code quality and reproducibility.

• Workflow: Defined in .github/workflows/main.yml.
• Automation: Every push to the main branch automatically triggers:
  1. Environment Setup: Installs Python 3.12 and dependencies.
  2. Testing: Runs unit tests to ensure logic integrity.
  3. Pipeline Execution: Runs the full prioritization simulation (run_full_pipeline.py).
  4. Artifact Generation: Automatically saves the generated HTML dashboards and reports as downloadable artifacts.

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📁 Project Structure

feature-prioritization-framework/
│
├── .github/
│   └── workflows/
│       └── main.yml                   # CI/CD pipeline for automated testing & execution
│
├── README.md                          # This file
├── LICENSE                            # MIT License
├── requirements.txt                   # Python dependencies
├── .gitignore                         # Git ignore patterns
│
├── data/                              # All datasets
│   ├── raw/                           # Original feature data
│   ├── processed/                     # RICE scores and results
│   └── synthetic/                     # Simulated user behavior
│
├── src/                               # Source code
│   ├── config.py                      # Configuration
│   ├── prioritization.py              # RICE framework
│   ├── ab_test_simulator.py           # A/B testing logic
│   ├── statistical_analysis.py        # Advanced statistics
│   └── visualization.py               # Charts and dashboards
│
├── notebooks/                         # Jupyter notebooks
│   ├── 01_feature_brainstorming.ipynb
│   ├── 02_rice_prioritization.ipynb
│   ├── 03_ab_test_simulation.ipynb
│   └── 04_final_analysis.ipynb
│
├── prds/                              # Product Requirement Documents
│   ├── smart_reorder_prd.md
│   ├── group_ordering_prd.md
│   └── loyalty_gamification_prd.md
│
├── outputs/                           # Generated outputs
│   ├── figures/                       # PNG charts
│   ├── dashboards/                    # HTML dashboards
│   └── reports/                       # Text reports
│
├── tests/                             # Unit tests
│   ├── test_prioritization.py
│   └── test_statistical_analysis.py
│
├── docs/                              # Additional documentation
│   ├── methodology.md
│   ├── architecture.md
│   └── lab_logbook.md
│
└── scripts/                           # Utility scripts
    └── run_full_pipeline.py           # Complete execution

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🚀 Installation & Setup

Prerequisites:

• Python 3.10 or higher
• pip (Python package manager)
• Git (optional, for cloning)

Step 1: Download/Clone the Project

# If using Git
git clone https://github.com/yourusername/feature-prioritization-framework.git
cd feature-prioritization-framework

# Or download ZIP and extract

Step 2: Create Virtual Environment (Recommended)

# Windows
python -m venv venv
venv\Scripts\activate

# macOS/Linux
python3 -m venv venv
source venv/bin/activate

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Verify Installation

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

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💻 Usage

Quick Start - Run Complete Pipeline

python scripts/run_full_pipeline.py

This executes the entire workflow:

1. Feature prioritization using RICE framework
2. A/B test simulation for top feature
3. Statistical analysis and significance testing
4. Generation of all visualizations and reports

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Run Individual Modules

1. Feature Prioritization Only

python src/prioritization.py

2. A/B Test Simulation Only

python src/ab_test_simulator.py

3. Generate Visualizations Only

python src/visualization.py

Using Jupyter Notebooks

jupyter notebook

Navigate to notebooks/ and open any notebook for interactive exploration.

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📊 Key Outputs & Synthetic Data Generation

Note: All datasets in this project are generated programmatically to simulate realistic user behavior. You do not need to download external data. When you run the scripts, they will create:

1. Generated Datasets

• Feature Ideas

  • File: data/raw/feature_ideas.csv
  • Contains: 15 brainstormed feature ideas with categories and descriptions.

• RICE Prioritization Results

  • File: data/processed/rice_scores.csv
  • Contains: Ranked features with RICE scores (reach, impact, confidence, effort)

• Synthetic User Data

  • File: data/synthetic/user_behavior.csv
  • Contains: 20,000 synthetic user records created for the A/B simulation

• A/B Test Results

  • File: data/processed/ab_test_results.csv
  • Contains: Statistical analysis results (Conversion rates, statistical significance, revenue impact)

2. Analytical Outputs

• Visualizations: outputs/figures/

• Includes:

  • RICE score bar charts
  • Effort vs Impact matrix
  • A/B test funnel analysis
  • Executive dashboard (HTML)

• Reports: outputs/reports/

• Includes:

  • Final prioritization report
  • Statistical analysis summary
  • Business impact projections

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📈 Sample Results

Top 3 Prioritized Features

Rank	Feature	RICE Score	Expected Impact
1	Smart Reorder	7,312	+18% repeat orders, ₹2.3M annual revenue
2	Instant Checkout	6,825	+12% conversion rate
3	Schedule Orders	5,940	+8% order frequency

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A/B Test Validation (Smart Reorder)

• Control Conversion Rate: 12.00%
• Treatment Conversion Rate: 14.16%
• Relative Lift: 18.0%
• P-value: 0.00001
• Result: ✅ Statistically Significant
• Recommendation: SHIP IT

🧪 Running Tests

# Run all tests
python -m pytest tests/

# Run specific test file
python -m pytest tests/test_prioritization.py

# Run with coverage
python -m pytest tests/ --cov=src

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📚 Documentation

• Methodology: Detailed explanation of RICE framework and A/B testing approach
• Architecture: System design and data flow
• Lab Logbook: Step-by-step execution workflow

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🎓 Skills Demonstrated

• Product Management: Feature prioritization, PRD writing, stakeholder communication
• Data Analysis: Statistical hypothesis testing, conversion funnel analysis
• Statistical Reasoning: A/B testing, sample size calculation, significance testing
• Business Acumen: Revenue modeling, ROI calculation, strategic thinking
• Technical Fluency: Python, data visualization, ML/statistical libraries
• Documentation: Clear technical writing, executive summaries

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🤝 Contributing

This is a portfolio project, but suggestions are welcome!

1. Fork the repository
2. Create a feature branch: git checkout -b feature/improvement
3. Commit changes: git commit -am 'Add improvement'
4. Push to branch: git push origin feature/improvement
5. Open 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

• RICE Framework: Intercom (Sean McBride)
• Statistical methods: Based on industry best practices from Google, Microsoft, Meta
• Inspiration: Real-world PM challenges in product-based companies

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📞 Support

For questions or issues:

1. Check the documentation
2. Open an issue on GitHub
3. Reach out via [email/LinkedIn]

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