Amazon_CLV_Analytics_Project

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)

📊 Amazon Customer Lifetime Value (CLV) Analysis

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

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✅ 📌 Project Overview

• 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.

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✅ 🛠 Tech Stack

• 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.

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✅ 📂 Workflow

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.

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✅ 📊 Dashboard Preview

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✅ ⚡ Key Insights

• 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.

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✅ 📦 Installation

1. Clone this repository

git clone https://github.com/yourusername/amazon-clv-analysis.git
cd amazon-clv-analysis

2. Create virtual environment

bash
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python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows
3. Install dependencies

bash
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pip install -r requirements.txt
4. Run Python script

bash
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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/