This project analyzes customer financial profiles and transaction data using SQL to uncover demographic patterns, financial behavior, revenue trends, and customer value.
The analysis is further extended with forecasting in Excel to estimate future revenue trends.
- MySQL (Data Cleaning & Analysis)
- SQL (Aggregations, Window Functions, CTEs)
- Excel (Forecasting Analysis)
- Data Visualization
- Database creation and data loading
- Data cleaning and validation
- Table restructuring into customers and transactions tables
- Demographic analysis
- Financial profile analysis
- Transaction & revenue analysis
- Customer Lifetime Value (CLV) analysis
- Forecasting using historical revenue data
- Age group distribution of customers
- Gender-based customer segmentation
- State-wise customer distribution
- Average income by age group
- Credit score segmentation (Poor → Excellent)
- Debt-to-income ratio analysis
- Monthly revenue trend
- Revenue by state and city
- Average transaction value
- Customer Lifetime Value (CLV) calculation
- High-value customer identification
- State-wise spending behavior
- Top spending state per customer using window functions
Using historical transaction revenue data, forecasting was performed in Excel to estimate future revenue trends along with confidence bounds.
This analysis helps financial institutions and businesses:
- Understand customer demographics and financial behavior
- Identify high-value customers for targeted strategies
- Monitor revenue patterns across regions
- Support data-driven financial planning using forecasting insights
- Data cleaning and restructuring
- Aggregate functions
- CASE statements for segmentation
- Window functions (RANK)
- Subqueries
- Table creation and transformation