A SQL-based analytics project designed to analyze customer behavior, order performance, revenue trends, and sales performance using relational data.
The Customer & Sales SQL Analysis project analyzes relational customer, order, and sales data to generate meaningful business insights.
The project focuses on extracting, transforming, analyzing, and validating data using SQL queries across multiple related tables.
The analysis covers customer purchasing behavior, order performance, revenue contribution, product-level performance, and customer segmentation.
Businesses need to understand customer purchasing patterns and sales performance to make better decisions.
Key business questions include:
- Who are the highest-value customers?
- Which customers generate the most revenue?
- Which products or categories perform best?
- What are the overall sales and order trends?
- Which customers purchase most frequently?
- What is the average order value?
- Which customers contribute significantly to total revenue?
- How does revenue vary across different customer segments?
- Analyze customer purchasing behavior
- Analyze order and sales performance
- Calculate revenue metrics
- Identify high-value customers
- Segment customers based on purchasing behavior
- Analyze product-level performance
- Apply advanced SQL techniques
- Validate data quality
- Generate actionable business insights
Key activities included:
- Checking duplicate records
- Identifying NULL values
- Validating primary and foreign key relationships
- Checking inconsistent values
- Reviewing outliers
- Validating order and customer records
- SELECT
- WHERE
- ORDER BY
- GROUP BY
- HAVING
- DISTINCT
- CASE statements
- COUNT
- SUM
- AVG
- MIN
- MAX
- INNER JOIN
- LEFT JOIN
- Multiple-table joins
- Subqueries
- Common Table Expressions (CTEs)
- Window Functions
- Ranking
- Running totals
- Customer segmentation
- Percentage contribution analysis
- Total purchases
- Number of orders
- Total revenue
- Average order value
- Purchase frequency
- Total orders
- Order trends
- Average order value
- Order frequency
- Customer order distribution
- Total revenue
- Revenue by customer
- Revenue by product/category
- Customer revenue contribution
- High-value customers
Customers were analyzed based on purchasing behavior.
Examples include:
- High-value customers
- Frequent customers
- Low-value customers
- Low-frequency customers
Window functions were used for:
- Customer ranking
- Revenue ranking
- Running revenue totals
- Customer contribution analysis
- Top-N customer identification
CTEs and subqueries were used to structure complex analytical queries.
The analysis helps identify:
- High-value customers
- Frequent purchasing customers
- Revenue concentration among top customers
- Order performance patterns
- Product/category performance
- Customer retention opportunities
Findings should be interpreted within the context of the underlying dataset.
- Create loyalty programs for high-value customers
- Target frequent customers with personalized offers
- Re-engage low-frequency customers
- Focus marketing efforts on high-revenue segments
- Monitor revenue concentration
- Use customer purchase behavior for targeted campaigns
- Improve customer retention strategies
Raw Customer & Order Data
↓
Data Validation
↓
Data Cleaning
↓
Relational Tables
↓
SQL Queries
↓
Joins & Aggregations
↓
CTEs & Subqueries
↓
Window Functions
↓
Customer Segmentation
↓
Revenue Analysis
↓
Business Insights
↓
Recommendations
customer-sales-analysis-sql/
│
├── sql/
│ ├── schema.sql
│ ├── data_analysis.sql
│ └── advanced_analysis.sql
│
├── data/
│ └── customer_orders.csv
│
├── .gitignore
│
└── README.md
- SQL
- PostgreSQL
- Relational Database Concepts
- Data Analysis
- Data Cleaning
- Data Validation
- CTEs
- Subqueries
- Window Functions
- Aggregations
- Joins
🟢 Completed
The project demonstrates end-to-end SQL analysis of customer and order data, including data validation, relational querying, customer segmentation, revenue analysis, and advanced SQL techniques.
- RFM customer segmentation
- Customer lifetime value analysis
- Sales forecasting
- Churn analysis
- Automated SQL reporting
- Power BI integration
- Customer cohort analysis
- Advanced predictive analytics
- SQL
- PostgreSQL
- Data Analysis
- Relational Data Modeling
- Data Cleaning
- Data Validation
- Joins
- Aggregations
- Subqueries
- CTEs
- Window Functions
- Customer Segmentation
- Revenue Analysis
- Business Intelligence
- Data Storytelling
- Business Problem Solving
This project is developed for portfolio and educational purposes.
The analysis and insights are based on the available dataset and should be interpreted within the context of the underlying data.
B.Tech CSE (Data Science)
Data Analytics | SQL | Power BI | Excel
GitHub: rishu-data
LinkedIn: Rishu Singh
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