Project Objectives:

• Analyze pizza sales performance using SQL.
• Build a Tableau dashboard to visualize KPIs and trends.

Key Metrics Analyzed:

• Total Revenue
• Average Order Value
• Sales by Pizza Size & Category
• Hourly Trends in Orders and Pizza Sold
• Weekly Trends for Total orders
• Top 5 and Bottom 5 Pizzas by Revenue, Total quantity and Total orders

📌 Data Source: Practiced and guided by Data Tutorials on YouTube. Full credit to the original creator.

📊 Interactive Tableau Dashboard:

Key Findings:

• The Thai Chicken Pizza generated the highest revenue.
• The Classic Deluxe Pizza was the most frequently ordered pizza.
• Most orders were placed between 12 PM and 1 PM, and again in the evening from 4 PM to 7 PM.
• Large-sized pizzas generated the highest revenue overall.
• The bottom 5 pizzas accounted for approximately 6.9% of total sales — highlighting an opportunity to review or improve their performance.

💬 Recommendations:

• Consider stocking more large-sized pizzas during evening hours to meet demand.
• Offer targeted discounts for unpopular pizzas during off-peak hours.
• Consider removing underperforming pizzas to reduce inventory costs.

Sample SQL Code:

                    -- Total Revenue
                    SELECT SUM(total_price) AS total_revenue
                    FROM pizza_sales;
                

                    --Average Order Value
                    SELECT SUM(total_price) / COUNT(DISTINCT order_id) AS Avg_Order_Value
                    FROM pizza_sales;
                

                    --Total Pizzas Sold
                    SELECT SUM(quantity) AS Total_Pizza_Sold
                    FROM pizza_sales;
                

                    --Total Orders
                    SELECT COUNT(DISTINCT order_id) AS Total_orders
                    FROM pizza_sales;
                

                    --Average Pizzas Per Order
                    SELECT CAST(CAST(SUM(quantity) AS DECIMAL(10,2)) / 
                    CAST(COUNT (DISTINCT order_id) AS DECIMAL(10,2)) AS DECIMAL(10,2)) AS Avg_Pizzas_Per_Order
                    FROM pizza_sales;
                

                    -- Hourly Trend for Total Pizzas Sold
                    SELECT  DATEPART(HOUR, order_time) AS order_hour,
		            SUM(quantity) AS Total_pizzas_sold
                    FROM pizza_sales
                    GROUP BY DATEPART(HOUR, order_time)
                    ORDER BY DATEPART(HOUR, order_time)
                

                    -- Weekly Trend for Total Orders
                    SELECT DATEPART(ISO_WEEK, order_date) AS week_number, 
	                YEAR(order_date) AS Order_year,
	                COUNT(DISTINCT order_id) AS Total_orders
                    FROM pizza_sales
                    GROUP BY DATEPART(ISO_WEEK, order_date), YEAR(order_date)
                    ORDER BY DATEPART(ISO_WEEK, order_date), YEAR(order_date)
                

                    --Top 5 Best Sellers by Revenue
                    SELECT TOP 5 pizza_name,
                           SUM(total_price) AS Total_Revenue
                    FROM pizza_sales
                    GROUP BY pizza_name
                    ORDER BY Total_Revenue DESC
                

                    --Lowest 5 pizzas by Revenue
                    SELECT TOP 5 pizza_name,
                            SUM(total_price) AS Total_Revenue
                    FROM pizza_sales
                    GROUP BY pizza_name
                    ORDER BY Total_Revenue ASC
                

💾 View full SQL queries on GitHub:

👉 Click here to view all SQL queries on GitHub