Amazon Customer Lifetime Value (CLV) Analysis This project demonstrates an end-to-end analytics workflow for analyzing Customer Lifetime Value (CLV) using: Python – MySQL. Power BI . Azure (Azurite)

This project demonstrates an end-to-end data analytics pipeline for analyzing Customer Lifetime Value (CLV) using Python, MySQL, Power BI, and Azure (Azurite).

• Goal:

  • Calculate CLV for Amazon customers.
  • Identify high-value customers, revenue by region, and product category performance.

• Business Impact:

  • Helps prioritize retention strategies.
  • Maximizes marketing ROI by focusing on high-value customers.

• Python → Data generation, cleaning, CLV calculation, predictive modeling.
• MySQL → Data storage, schema creation, advanced queries.
• Power BI → Dynamic dashboard for CLV visualization and segmentation.
• Azure (Azurite) → Cloud deployment simulation.

1. Data Generation & CLV Calculation

   • Used Python with Faker to create synthetic datasets:
     • Customers, Products, Orders, CLV table.
   • CLV calculation and predictive modeling using Linear Regression.

2. Database Design & SQL Queries

   • Created normalized schema with foreign keys.
   • Loaded CSVs into MySQL.
   • Queries implemented:
     • Top 10 customers by CLV
     • Revenue by region
     • CLV segmentation (High/Medium/Low)

3. Dashboard in Power BI

   • Connected Power BI to MySQL for dynamic reporting.
   • Key visuals:
     • KPIs: Total Revenue, Avg CLV, AOV, Customer Count
     • Top Customers by CLV
     • CLV Segmentation
     • Revenue by Region
     • AOV by Category

4. Cloud Simulation

   • Simulated deployment with Azure Azurite.

• High-value customers contribute over 50% of revenue.
• Top categories: Books & Sports.
• North America is the highest revenue region.
• Predictive CLV modeling enhances retention strategies.

1. Clone this repository

git clone https://github.com/yourusername/amazon-clv-analysis.git cd amazon-clv-analysis 2. Create virtual environment bash Copy Edit python -m venv venv source venv/bin/activate # Linux/Mac venv\Scripts\activate # Windows 3. Install dependencies bash Copy Edit pip install -r requirements.txt 4. Run Python script bash Copy Edit python CLV.py ✅ 📚 SQL Scripts sql/create_tables.sql → Database schema sql/load_data.sql → Import data sql/queries.sql → Advanced insights ✅ 🚀 Future Scope Real-time CLV updates via Azure Data Factory. Add churn prediction using ML pipelines. Deploy Power BI dashboard as a web app. ✅ 🔗 Links GitHub Repo: https://github.com/Tanu272004/Amazon_CLV_Analytics_Project#amazon_clv_analytics_project LinkedIn Post: https://www.linkedin.com/in/tanmay-sharma-800599373/