📊 Customer Churn & Revenue Analysis — SQL Project

An end-to-end SQL analytics project analyzing customer churn, revenue trends, and lifetime value from transactional sales data — visualized in Power BI.

📌 Project Overview

This project simulates a real-world analytics workflow where SQL is the primary tool for data extraction, transformation, and analysis. It covers revenue performance, customer lifecycle segmentation, and churn identification — all built on a normalized relational database.

🎯 Objectives

📈 Analyze overall and monthly revenue performance
🛍️ Understand customer purchasing behavior
🔄 Classify customers into lifecycle segments
💎 Identify high-value customers and top-performing products
💰 Calculate Average Order Value & Customer Lifetime Value (LTV)
📊 Prepare SQL outputs for BI visualization

🗃️ Data Model

A normalized relational schema ensuring data integrity and analytical flexibility.

Table	Description
`Customers`	Customer details and signup information
`Products`	Product names, categories, and pricing
`Orders`	Order-level data including dates and status
`Order Items`	Line-item details — quantity and item price

Relationships:

One customer → Many orders
One order → Many products
One product → Many orders

💹 Revenue Analysis

Key analyses performed:

✅ Total revenue from all completed transactions
✅ Monthly revenue trends (growth, decline, seasonality)
✅ Month-over-month revenue change
✅ Revenue by customer (top contributors)
✅ Revenue by product and category

👥 Customer Behavior Analysis

Examining order frequency, recency, and total spending to identify:

One-time vs repeat customers
High-frequency purchasers
Top revenue-contributing customers

🔄 Customer Lifecycle Segmentation

Customers classified into 3 lifecycle segments:

Segment	Definition
✅ Active	Completed order within recent time window
⚠️ Churned	Last purchase beyond inactivity threshold
❌ Never Converted	Signed up but never placed a completed order

Classification is based on last completed order date — a standard industry approach for churn analysis.

💎 Customer Lifetime Value (LTV)

Total revenue generated per customer across all completed purchases.

Business use cases:

Identify high-value customers
Analyze revenue concentration
Support retention and targeting decisions

📊 Power BI Dashboard

All business logic lives in SQL. Results are visualized in Power BI with:

📌 KPI summaries (customer counts, churn rate, revenue)
📈 Monthly revenue trend analysis
🏆 Revenue breakdown by customer and product
👥 Customer count by lifecycle status

Customers with no transactions are excluded from revenue visuals but included in customer count metrics for accurate business representation.

🛠️ SQL Techniques Used

JOIN — linking customers, orders, products
CTE (Common Table Expressions) — modular query design
Window Functions — ranking, running totals, lag/lead
Aggregations — revenue, frequency, LTV calculations
CASE statements — lifecycle classification logic

🚀 How to Use

Execute SQL scripts to create tables and populate data
Run analytical queries from the provided SQL files
Review query outputs or load into Power BI for visualization
Explore insights on revenue, churn, and customer behavior

💡 Key Learnings

SQL can power end-to-end analytical workflows without additional tools
Distinguishing transactional vs non-transactional customers is critical
Churn logic can be implemented using simple, explainable rules
Relational data modeling enables flexible, scalable analysis
SQL + Power BI is a powerful combo for business intelligence

🔮 Future Enhancements

Add cohort-based churn tracking
Incorporate RFM (Recency, Frequency, Monetary) scoring
Build stored procedures for automated reporting
Connect live database to Power BI for real-time dashboards

Made with ❤️ by Arpita Randive

ArpitaRandive/Customer-Churn-Revenue-Analysis-SQL-Project-