📊 UserFlow Analytics – Product Growth & A/B Testing

An end-to-end Product Analytics project that analyzes user behavior, customer retention, product performance, and revenue trends using Python, SQL, and interactive visualizations.

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📌 Project Overview

UserFlow Analytics is a comprehensive analytics project built on a synthetic e-commerce dataset. The project demonstrates the complete analytics workflow—from data cleaning and exploratory data analysis (EDA) to customer segmentation, funnel analysis, cohort analysis, A/B testing, SQL-based business analysis, and executive reporting.

The goal is to derive actionable business insights that help improve customer engagement, product performance, and revenue growth.

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🎯 Business Objectives

• Analyze customer purchasing behavior.
• Identify high-performing products and categories.
• Measure customer retention over time.
• Analyze user conversion funnels.
• Perform A/B testing for business decision-making.
• Generate actionable business recommendations.

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🛠️ Tech Stack

• Python
• Pandas
• NumPy
• Plotly
• DuckDB (SQL)
• SciPy
• Matplotlib
• Google Colab

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📂 Dataset

The project uses a synthetic e-commerce dataset containing:

• Users
• Products
• Orders
• Order Items
• User Events
• Product Reviews

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🔄 Project Workflow

1. Data Collection

• Import datasets
• Data validation

2. Data Cleaning

• Missing value handling
• Duplicate removal
• Datetime conversion
• Data type correction

3. Exploratory Data Analysis

• Product analysis
• Customer analysis
• Revenue analysis
• Brand analysis

4. Business KPIs

• Total Revenue
• Total Orders
• Total Users
• Total Products
• Average Order Value

5. Product Analytics

• Revenue by Category
• Top Products
• Brand Performance
• Monthly Revenue Trend

6. Customer Analytics

• Customer Segmentation
• Average Revenue by Segment
• Top Customers

7. Funnel Analysis

• View → Cart → Purchase Funnel
• Drop-off Analysis

8. Cohort Analysis

• Customer Retention Matrix
• Retention Heatmap

9. A/B Testing

• Variant Comparison
• Conversion Rate Analysis
• Chi-Square Statistical Test

10. SQL Business Analysis

Business insights generated using DuckDB SQL.

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📈 Key Insights

• Electronics generated the highest revenue.
• A small number of products contributed significantly to overall sales.
• Customer retention decreased considerably after the first purchase.
• The largest conversion drop occurred between product views and cart additions.
• Platinum customers generated the highest average revenue.
• A/B testing demonstrated how experimentation supports data-driven decision-making.

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📷 Project Screenshots

Top Viewed Producds

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Top Products

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Top Cities

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Top Brands

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Dropoff Analysis

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Customer Retention Heatmap

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A/B Testing

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💡 Business Recommendations

• Improve the View → Cart conversion stage.
• Introduce loyalty programs for high-value customers.
• Promote high-performing product categories.
• Improve customer retention using personalized campaigns.
• Continue A/B testing before implementing major product changes.

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🚀 Future Improvements

• Deploy as a Streamlit dashboard.
• Connect to Google BigQuery.
• Automate ETL pipelines.
• Implement customer churn prediction.
• Build sales forecasting models.

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📁 Project Structure

UserFlow-Analytics/
│
├── UserFlow_Analytics.ipynb
├── README.md
├── requirements.txt
├── LICENSE
│
├── images/
│   ├── revenue_trend.png
│   ├── category_revenue.png
│   ├── top_products.png
│   ├── customer_segments.png
│   ├── funnel_analysis.png
│   ├── cohort_heatmap.png
│   └── ab_testing.png
│
└── data/

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▶️ How to Run

1. Clone the repository

git clone https://github.com/YukthaK215/UserFlow-Analytics.git

2. Install dependencies

pip install -r requirements.txt

3. Open the notebook

jupyter notebook UserFlow_Analytics.ipynb

or upload the notebook to Google Colab.

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👩‍💻 Author

Yuktha K

GitHub: https://github.com/YukthaK215

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