Overview
This project conducts a comprehensive analysis of retail sales data to uncover valuable insights that drive informed business decisions. By leveraging SQL as a powerful data management tool, we aim to enhance inventory management practices, ensuring that stock levels are optimized to meet customer demand without incurring excess costs. Additionally, we focus on identifying key sales trends over time, which helps in understanding customer purchasing behaviors and seasonal fluctuations. This data-driven approach not only aids in strategic planning but also plays a crucial role in improving customer engagement by tailoring marketing efforts and promotions to better align with consumer preferences.
Features
Sales Performance Analysis: Discover total sales trends per day, week, or month to understand purchasing patterns and seasonal peaks.
Customer Analysis: Identify the most frequent customers and analyze their average spending to tailor marketing strategies effectively.
Category Analysis: Break down sales by category to assess profitability and evaluate average sales performance across different product lines.
Product Analysis: Highlight top-selling products by quantity and revenue to focus inventory efforts and optimize pricing strategies.
Time-based Analysis: Analyze peak sales hours and days of the week to inform staffing and promotional campaigns.
Inventory Analysis: Assess the average quantity sold per product to optimize stock levels and identify items with high sales frequency.
Profitability Analysis: Calculate gross profit and profit margins by product category to gauge financial health and guide pricing decisions.
Technologies Used
PostgreSQL, Cte, Subqueries, Extract function, Window Function, Type Casting, Aggregate Functions
Contributing
Contributions are welcome! Please open an issue or submit a pull request for any enhancements or bug fixes.