This project demonstrates a complete end-to-end sales analytics workflow using T-SQL. The objective is to explore a sales data warehouse, generate business KPIs, analyze customer and product performance, perform time-series analysis and apply advanced SQL techniques such as CTEs, Window Functions, Ranking Functions, and Customer Segmentation.
The project follows a real-world analytical process used by Data Analysts, Business Intelligence Analysts and Analytics Engineers to transform raw transactional data into actionable business insights.
- Explore and understand the database schema.
- Profile customer and product data.
- Develop core business KPIs.
- Analyze customer demographics and purchasing behavior.
- Evaluate product and category performance.
- Perform monthly and yearly trend analysis.
- Apply advanced SQL analytical techniques.
- Generate business-focused insights from sales data.
fact_sales
dim_customersdim_products
- Retrieved all tables in the database.
- Examined table structures and column data.
- Validated relationships between fact and dimension tables.
- Data Exploration
- Database Profiling
Analyzed:
- Unique customer countries
- Product categories
- Product subcategories
- Product catalog structure
- Data Discovery
- Dimension Analysis
Determined:
- First order date
- Last order date
- Business activity duration
- Youngest customer
- Oldest customer
- Date Functions
- DATEDIFF()
- MIN()
- MAX()
Calculated:
- Total Revenue
- Total Quantity Sold
- Average Selling Price
- Total Orders
- Total Products
- Total Customers
- Active Customers
- Aggregate Functions
- COUNT DISTINCT
- KPI Engineering
Analyzed:
- Customers by country
- Customers by gender
- Revenue generated per customer
- Customer purchase activity
- Customer Segmentation
- Revenue Analysis
- Business Reporting
Analyzed:
- Products by category
- Average product cost
- Revenue by category
- Revenue contribution by products
- Product Performance Analysis
- Category Analysis
- Revenue Attribution
Identified:
- Top 5 revenue-generating products
- Bottom 5 performing products
- Top 10 customers by revenue
- Customers with the fewest orders
- ROW_NUMBER()
- RANK()
- DENSE_RANK()
- Ranking Analysis
- Performance Benchmarking
- Business Prioritization
Generated:
- Year & Month format
- Month Start Date format
- Human-readable Month-Year format
- Trend Analysis
- Time-Series Reporting
- Date Transformations
Calculated:
- Annual Revenue
- Running Total Revenue
- Window Functions
- Cumulative Analysis
- Business Growth Tracking
Compared product performance against:
- Previous year's sales
- Average product sales performance
- LAG()
- AVG() OVER()
- YoY Analysis
- Historical Comparisons
- Performance Evaluation
Segmented products into cost ranges:
- Low Cost
- Medium Cost
- High Cost
- CASE Statements
- Product Classification
- Pricing Analysis
Customers were grouped into:
- Customer lifespan ≥ 12 months
- Spending > €5,000
- Customer lifespan ≥ 12 months
- Spending ≤ €5,000
- Customer lifespan < 12 months
- Behavioral Segmentation
- Customer Lifetime Analysis
- Business Classification Logic
Calculated:
- Revenue by category
- Percentage contribution to total revenue
- Contribution Analysis
- Revenue Distribution
- Category Performance Evaluation
Measures the average revenue generated per order.
Measures average customer spending behavior over time.
- Business KPI Design
- Customer Spending Analysis
- Revenue Optimization Metrics
- SELECT
- WHERE
- ORDER BY
- DISTINCT
- TOP
- GROUP BY
- HAVING
- INNER JOIN
- LEFT JOIN
- CASE Statements
- Date Functions
- Common Table Expressions (CTEs)
- Window Functions
- ROW_NUMBER()
- RANK()
- DENSE_RANK()
- LAG()
- Running Totals
- Customer Segmentation
- Product Segmentation
- Contribution Analysis
- Time-Series Analytics
- Which products generate the highest revenue?
- Which products underperform and require attention?
- Who are the most valuable customers?
- Which customers place the fewest orders?
- Which categories contribute the most to overall sales?
- How do sales trends change over time?
- What is the Average Order Value (AOV)?
- How does product performance compare year-over-year?
- Which customer segments drive the most revenue?
- Microsoft SQL Server
- T-SQL
- SQL Server Management Studio (SSMS)
- Developed end-to-end analytical SQL solutions.
- Built business-focused KPI reporting.
- Performed customer and product performance analysis.
- Applied advanced window functions for trend and ranking analysis.
- Implemented customer and product segmentation models.
- Generated actionable business insights from transactional sales data.
- SQL Query Optimization
- Data Exploration
- Data Profiling
- Business Intelligence Analytics
- KPI Development
- Revenue Analysis
- Customer Analytics
- Product Analytics
- Time-Series Analysis
- Window Functions
- Segmentation Analysis
- Performance Benchmarking
- Data Warehousing Concepts
Hi, I'm Purva Kalambate, an aspiring Data Analyst passionate about transforming raw data into actionable business insights.
I enjoy working with SQL, data analytics, business intelligence, and data visualization to solve real-world business problems. Through hands-on projects, I continuously strengthen my skills in data exploration, KPI development, customer analytics, product performance analysis, and advanced SQL techniques.
- SQL (T-SQL, PostgreSQL, MySQL)
- Data Analysis
- Business Intelligence
- Data Cleaning & Transformation
- Data Visualization
- Dashboard Development
- Statistical Analysis
- Problem Solving