Pizza Sales Analysis

Project Overview

This project analyzes a pizza store's sales data using SQL and Excel to uncover key business insights and visualize performance through an interactive dashboard.

The goal of the project is to transform raw sales data into meaningful insights by calculating key performance indicators (KPIs), performing trend analysis, and presenting the results in a visually engaging Excel dashboard.

The analysis focuses on answering important business questions such as:

• How much revenue does the pizza store generate?
• When are the busiest ordering hours and days?
• Which pizza categories and sizes drive the most revenue?
• What are the best and worst performing pizzas?

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Dashboard Preview

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Business Objectives

The project aims to:

• Measure overall business performance using key KPIs
• Identify peak ordering times
• Understand customer purchasing patterns
• Analyze product performance across pizza categories and sizes
• Identify top and bottom selling pizzas

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Dataset

The dataset contains transaction-level pizza sales information including:

• order_id
• pizza_name
• pizza_category
• pizza_size
• quantity
• order_date
• order_time
• unit_price
• total_price

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Tools Used

Tool	Purpose
SQL Server / SSMS	Data analysis and KPI calculation
Excel	Data visualization and dashboard creation
Git & GitHub	Version control and project portfolio

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Key Performance Indicators (KPIs)

The following KPIs were calculated using SQL queries (Check out the raw SQL files here: Queries) :

1. Total Revenue

Total sales generated from all pizza orders.

SELECT SUM(total_price) AS Total_Revenue
FROM pizza_sales;

2. Average Order Value

Average revenue generated per order.

SELECT (SUM(total_price) / COUNT(DISTINCT order_id)) AS Avg_order_Value
FROM pizza_sales;

3. Total Pizzas Sold

Total number of pizzas sold across all orders.

SELECT SUM(quantity) AS Total_pizza_sold
FROM pizza_sales;

4. Total Orders

Total number of unique orders placed.

SELECT COUNT(DISTINCT order_id) AS Total_Orders
FROM pizza_sales;

5. Average Pizzas Per Order

Average number of pizzas purchased 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;

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Trend Analysis

1. Daily Trend for Total Orders

Analyzes which days generate the highest number of orders.

SELECT DATENAME(DW, order_date) AS order_day,
COUNT(DISTINCT order_id) AS total_orders
FROM pizza_sales
GROUP BY DATENAME(DW, order_date);

Insight Orders are highest during Friday and Saturday evenings.

2. Hourly Trend for Orders

Identifies peak hours when customers place orders.

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);

Insight

Peak ordering times occur around:

• 12 PM – 1 PM
• 5 PM – 7 PM

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Sales Distribution Analysis

1. Percentage of Sales by Pizza Category

Indicates which pizza category earns the most sales

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;

Insight

The Classic category generates the highest share of sales, closely followed by Supreme category.

pizza_category	PCT
Classic	26.91%
Supreme	25.46%
Chicken	23.96%
Veggie	23.68%

2. Percentage of Sales by Pizza Size

Shows which pizza size generates the most revenue.

SELECT pizza_size,
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_size
ORDER BY pizza_size;

Insight

Large size pizza earns the most sales

pizza_size	PCT
Large	38.05%
Medium	31.65%
Small	29.06%
X-Large	1.17%
XX-Large	0.07%

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Product Performance Analysis

1. Top 5 Best Selling Pizzas

SELECT TOP 5 pizza_name,
SUM(quantity) AS Total_Pizza_Sold
FROM pizza_sales
GROUP BY pizza_name
ORDER BY Total_Pizza_Sold DESC;

Insight

• The Classic Deluxe Pizza generates the highest sales.

2. Bottom 5 Least Selling Pizzas

SELECT TOP 5 pizza_name,
SUM(quantity) AS Total_Pizza_Sold
FROM pizza_sales
GROUP BY pizza_name
ORDER BY Total_Pizza_Sold ASC;

Insight

• The Brie Carre Pizza has the lowest sales.

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Excel Dashboard

The insights from SQL analysis were visualized in an interactive Excel dashboard.

Dashboard components include:

• KPI summary cards
• Daily order trend
• Hourly order trend
• Sales breakdown by pizza category
• Sales distribution by pizza size
• Top 5 best selling pizzas
• Bottom 5 least selling pizzas
• Interactive date filters

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Project Structure

Pizza-Sales-Analysis
|
├── Dashboard
│   └── dashboard.png
|
├── Queries
│   └── SQL analysis queries
|
├── pizza_sales.csv
│   └── dataset
│
├── Workbook.xlsx
│   └── Excel dashboard
│
├── PIZZA SALES SQL QUERIES.docx
│   └── documented queries
│
└── README.md

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Key Insights

• Total Revenue: $817,860
• Total Orders: 21,350
• Total Pizzas Sold: 49,574
• Average Order Value: $38.31
• Average Pizzas Per Order: 2.32

Additional insights:

• Friday and Saturday evenings are peak ordering times
• Large pizzas contribute the highest share of revenue
• Classic category pizzas generate the most sales
• The Classic Deluxe Pizza is the best-selling item

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Author

Pallab Dey

Thanks for checking out my Pizza Sales Analysis project!
If you like it, feel free to ⭐ the repo or fork it for your own use.

• GitHub:
• LinkedIn:
• Email: pallabdey21@gmail.com

Open to feedback, collaboration, or just chatting about data analysis / BI projects 🍕📊

© 2026 Pallab Dey