This project analyzes pizza sales data to uncover key business insights and trends. Using SQL, Power Query, and Power BI, I explored, cleaned, and visualized the data to provide actionable recommendations for improving sales performance and understanding customer preferences.
📊 Project Objectives Calculate Key Performance Indicators (KPIs): a. Total Revenue b. Average Order Value c. Total Pizzas Sold d. Total Orders e. Average Pizzas Per Order
Visualize sales trends and patterns through interactive dashboards: a. Daily and Monthly Sales Trends b. Percentage of Sales by Pizza Category and Size c. Performance of Top and Bottom-Selling Pizzas d. Identify actionable insights to guide business decisions.
🛠 Tools & Technologies a. SQL Server Management Studio (SSMS): Data exploration and KPI calculations b. Power Query: Data cleaning and transformation c. Power BI: Data visualization and dashboard creation
📈 Key Insights a. Revenue: Total revenue generated was $817,860, reflecting strong performance. b. Order Volume: 21,000 orders placed, with an Average Order Value of $38.31. c. Daily Trends: Orders peaked on Fridays and dropped significantly over the weekend. d. Monthly Trends: Sales peaked in July, with a slight recovery in November and December.
Pizza Categories: a. Classic Pizza was the most popular category (26.91% share). b. Chicken Pizza drove the highest revenue. c. Pizza Sizes: Large pizzas dominated sales, accounting for 45.89% of orders. d. Top Performers: Thai Chicken and Barbecue Chicken pizzas were the leaders by revenue. e. Bottom Performers: Spinach-based pizzas consistently underperformed.
📂 Project Structure The repository contains: a. SQL Queries: Scripts used for data exploration and KPI calculations (/sql_scripts). b. Power BI Dashboard: Interactive dashboard file showcasing trends and insights (/dashboard). c. Data Cleaning Steps: Documented cleaning process using Power Query (/data_cleaning).
📌 Future Recommendations
- Focus on promoting top-performing pizzas to maximize revenue.
- Address low weekend sales with targeted campaigns or discounts.
- Revisit underperforming pizzas (e.g., spinach-based pizzas) to refine or replace offerings.
- Use monthly sales trends to plan for peak periods effectively.
🚀 Conclusion This project showcases how data analytics can drive strategic decision-making in the food industry. By combining SQL, Power Query, and Power BI, I delivered insights that can directly impact business outcomes.
💻 Contact Feel free to reach out if you have any feedback or questions about this project!
LinkedIn: linkedin.com/in/amitbisht181299/ Email: ab419821@gmail.com