Welcome to the Coffee Shop Sales Analysis project! This repository showcases SQL-based data analysis performed on transactional data from a coffee shop. The primary objective is to derive actionable insights from sales data to enhance decision-making processes. The analysis focuses on revenue trends, customer behavior, product performance, and operational efficiency.
- Project Overview
- Database Schema
- Project Objectives
- Insights and Recommendations
- Future Enhancements
- Getting Started
- Contact
This project analyzes the sales transactions from a coffee shop, stored in the Transactions table. Key insights include total revenue, visitor counts, sales trends, product performance, and store-level analysis. The findings aim to inform business strategies, optimize operations, and improve customer experience.
- Revenue Analysis: Calculate total sales, average transaction values, and average order sizes.
- Visitor Trends: Identify peak hours, high-performing days, and unique visitor counts.
- Store Performance: Compare sales performance across store locations.
- Product Insights: Evaluate product size preferences and identify top-selling items.
- Category Contribution: Analyze category-wise sales distribution and contributions to total revenue.
- Visualize Trends: Enable visualization-ready data for further exploration.
- Revenue Overview: Analyze total revenue and average customer spend to track overall business performance.
- Peak Periods: Use insights from peak hours and high-performing days to improve staffing and inventory allocation.
- Store-Level Performance: Focus on underperforming stores for optimization and high-performing stores for replicable strategies.
- Product Preferences: Highlight popular products and sizes for better inventory planning and menu optimization.
- Category Contributions: Allocate marketing resources based on category-wise revenue contributions.
- Predictive Analysis: Implement machine learning models for forecasting sales trends.
- Customer Insights: Integrate demographic data to understand and segment customer behavior.
- Real-Time Dashboards: Leverage visualization tools like Tableau or Power BI for real-time insights.
- Clone the repository:
git clone https://github.com/meabhaykr/Coffee-Shop-Sales-Analysis-Using-SQL
- Load the sample database schema and data into your preferred SQL environment.
- Run the SQL scripts provided to explore insights.
For any questions or feedback, please contact me at meabhaykr@gmail.com.