🧪 A/B Testing Framework — E-Commerce Checkout Optimization

A production-ready A/B testing pipeline for analyzing the impact of a redesigned checkout button on e-commerce conversion rates. Includes full statistical analysis, power calculations, Bayesian inference, and automated reporting.

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📋 Table of Contents

• Project Overview
• Experiment Design
• Results Summary
• Project Structure
• Installation
• Usage
• Statistical Methods
• Key Findings
• Contributing
• License

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📌 Project Overview

This project documents a real A/B test conducted on an e-commerce platform to evaluate whether changing the checkout button color and copy (from grey "Proceed" to orange "Buy Now — Secure Checkout") significantly improves conversion rates.

Business Question:
Does a more prominent, urgency-driven checkout button increase purchase completion rates without negatively affecting average order value or return rates?

Test Duration: 14 days
Total Users: 48,239
Platform: E-Commerce Web (Desktop + Mobile)

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🔬 Experiment Design

Parameter	Value
Hypothesis	New button increases conversion rate
Null Hypothesis	No difference in conversion rate
Control (A)	Grey "Proceed" button
Treatment (B)	Orange "Buy Now — Secure Checkout" button
Traffic Split	50% / 50%
Significance Level (α)	0.05
Statistical Power (1-β)	0.80
MDE (Minimum Detectable Effect)	2% relative lift
Metric	Primary: Conversion Rate; Secondary: AOV, Return Rate

Sample Size Calculation

Pre-experiment power analysis determined a minimum of 21,000 users per variant was required to detect a 2% relative lift with 80% power at α = 0.05.

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📊 Results Summary

Metric	Control (A)	Treatment (B)	Lift	p-value	Significant?
Conversion Rate	3.82%	4.51%	+18.1%	0.0003	✅ Yes
Avg Order Value	$67.40	$68.10	+1.0%	0.412	❌ No
Return Rate	8.2%	8.4%	+0.2%	0.718	❌ No
Bounce Rate	42.1%	41.8%	-0.7%	0.631	❌ No

Conclusion: The treatment button produced a statistically significant +18.1% lift in conversion rate (p < 0.001) with no adverse effects on order value or return rates. Recommend full rollout.

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📁 Project Structure

ab-testing-project/
│
├── 📂 data/
│   ├── raw/                    # Raw experiment logs
│   │   └── ab_test_raw.csv
│   └── processed/              # Cleaned, feature-engineered data
│       └── ab_test_cleaned.csv
│
├── 📂 notebooks/
│   ├── 01_EDA.ipynb            # Exploratory data analysis
│   ├── 02_SampleSize.ipynb     # Power analysis & sample size
│   ├── 03_StatisticalTests.ipynb  # Frequentist hypothesis testing
│   ├── 04_BayesianAnalysis.ipynb  # Bayesian A/B analysis
│   └── 05_FinalReport.ipynb    # Full report with visuals
│
├── 📂 src/
│   ├── analysis/
│   │   ├── __init__.py
│   │   ├── power_analysis.py   # Sample size & power calculations
│   │   ├── frequentist.py      # Z-tests, Chi-squared, t-tests
│   │   └── bayesian.py         # Bayesian inference & credible intervals
│   ├── visualization/
│   │   ├── __init__.py
│   │   └── plots.py            # All plotting functions
│   └── utils/
│       ├── __init__.py
│       └── data_loader.py      # Data ingestion & preprocessing
│
├── 📂 tests/
│   ├── test_frequentist.py
│   ├── test_bayesian.py
│   └── test_data_loader.py
│
├── 📂 results/
│   ├── figures/                # All generated charts/plots
│   └── reports/
│       └── final_report.pdf
│
├── 📂 docs/
│   ├── experiment_design.md    # Full experiment design doc
│   └── statistical_methods.md  # Methods & assumptions
│
├── .gitignore
├── LICENSE
├── README.md
├── requirements.txt
└── setup.py

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⚙️ Installation

Prerequisites

• Python 3.10+
• pip or conda

Setup

# Clone the repository
git clone https://github.com/Achal2593/A-B-Testing-Project.git
cd ab-testing-project

# Create virtual environment
python -m venv venv
source venv/bin/activate   # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

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🚀 Usage

Run Full Analysis Pipeline

# Generate synthetic data (or replace with real data in data/raw/)
python src/utils/data_loader.py --generate

# Run frequentist analysis
python src/analysis/frequentist.py

# Run Bayesian analysis
python src/analysis/bayesian.py

# Generate all visualizations
python src/visualization/plots.py

Run Tests

pytest tests/ -v --cov=src --cov-report=term-missing

Launch Jupyter Notebooks

jupyter lab notebooks/

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📐 Statistical Methods

Frequentist Approach

• Two-proportion Z-test for conversion rates
• Welch's t-test for average order value
• Chi-squared test for categorical metrics
• Bonferroni correction for multiple comparisons

Bayesian Approach

• Beta-Binomial conjugate model for conversion rates
• Monte Carlo simulation (100,000 samples)
• 95% Highest Density Interval (HDI) reporting
• Probability of superiority calculation

Validity Checks

• Sample Ratio Mismatch (SRM) test
• Novelty effect analysis (first 3 days excluded from final analysis)
• Segment analysis (mobile vs desktop, new vs returning users)

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🔑 Key Findings

1. +18.1% conversion lift is statistically and practically significant
2. Bayesian analysis shows 99.7% probability that Treatment B is superior
3. Effect is consistent across mobile (17.9%) and desktop (18.4%) segments
4. No cannibalization of AOV or return rate — pure upside
5. Estimated annual revenue impact: +$412,000 based on current traffic

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📈 Visualizations

Conversion Rate Comparison (Control vs Treatment)

Bayesian Posterior Distributions & Lift

Daily Conversion Rates Over Experiment Duration

Segment Analysis — Device & User Type

Average Order Value Distribution

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🛠️ Tools Used

Tool	Purpose
Python 3.10+	Core programming language
pandas	Data manipulation and preprocessing
NumPy	Numerical computations
SciPy	Statistical tests (z-test, t-test)
statsmodels	Power analysis and proportion tests
Matplotlib / Seaborn	Data visualizations
PyMC / ArviZ	Bayesian inference
pytest	Unit testing
GitHub Actions	CI/CD pipeline

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🚀 How to Run

# 1. Clone the repository
git clone https://github.com/Achal2593/A-B-Testing-Project.git
cd A-B-Testing-Project

# 2. Install dependencies
pip install -r requirements.txt

# 3. Generate data
python src/utils/data_loader.py --generate

# 4. Run full analysis
python src/analysis/frequentist.py
python src/analysis/bayesian.py

# 5. Generate all charts
python src/visualization/plots.py

# 6. Run tests
pytest tests/ -v

👤 About Me

Achal Wakade | Aspiring Data Analyst | Available immediately 🔗 LinkedIn | GitHub

## How to Paste on GitHub

1. Go to your repo → click `README.md`
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3. Scroll to the very bottom of the file
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📄 License

This project is licensed under the MIT License...

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Built with Python · pandas · scipy · pymc · matplotlib · seaborn

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🤝 Contributing

Contributions are welcome! Please read CONTRIBUTING.md and submit a Pull Request.

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Built with Python · pandas · scipy · pymc · matplotlib · seaborn