Pizza Sales Management – SQL Analysis Project 

In this project, I analyzed a pizza sales dataset using SQL to understand sales performance, customer ordering behavior, and product-level trends. The goal was to extract meaningful insights that can help optimize revenue, identify top-selling pizzas, and improve business decision-making.

 TOOLS USED:

• MySQL 

• SQL Queries: Joins, Aggregate Functions, Date Functions

• Database Table:  pizza_sales.

• KPIs calculated from SQL

THINGS I DID FOR MEANINGFUL INSIGHTS:

• Cleaned and explored the sales dataset

•  Calculated key business metrics (KPIs)

•  Analyzed daily & hourly order trends

•  Identified top-performing pizza categories and sizes

• Found best & worst selling pizzas

• Generated insights for inventory and revenue optimization

• Used SQL date functions (MONTH, QUARTER, HOUR) for trend analysis 

 SOME SQL QUERIES:[BASICS]

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;

Daily Trend

SELECT DATENAME(DW, order_date) AS order_day, 

COUNT(DISTINCT order_id) AS total_orders 

FROM pizza_sales

GROUP BY DATENAME(DW, order_date);

 Hourly Trend

SELECT DATEPART(HOUR, order_time) as order_hours, 

COUNT(DISTINCT order_id) as total_orders

FROM pizza_sales

GROUP BY DATEPART(HOUR, order_time)

ORDER BY DATEPART(HOUR, order_time);

 Sales % by Pizza Category

SELECT pizza_category, 

CAST(SUM(total_price) AS DECIMAL(10,2)) as total_revenue,

CAST(SUM(total_price) * 100 / 

(SELECT SUM(total_price) from pizza_sales) AS DECIMAL(10,2)) AS PCT

FROM pizza_sales

GROUP BY pizza_category;

Key Insights :

Total Revenue & Sales KPIs: I found the total revenue, order count, and average order value using SQL aggregates. These KPIs gave a clear picture of business performance and buying behavior.

 Peak Ordering Times: Daily and hourly trends showed which days and times customers order the most. This helps understand demand patterns and staff scheduling needs.

 Category-wise Revenue Contribution: Some pizza categories contributed a higher share of total revenue, helping identify customer preferences and menu strengths.

 Pizza Size Performance: Analyzing sales by size revealed which pizza sizes customers preferred, useful for pricing and inventory planning.

Best & Worst Sellers: Top 5 and bottom 5 pizzas were identified using SQL aggregations, showing which products drive sales and which may need discontinuation or improvements.

Conclusion :

This SQL project helped me understand how raw sales data can be transformed into actionable insights. By analyzing KPIs, ordering trends, and product performance using SQL queries, I gained deeper clarity into the business's strengths and improvement areas. This approach can be applied to any retail or food-service business to support data-driven decision-making.