Hot 'n Ready Pies

Table of Contents

• Project Overview
• Data Source
• Tools Used
• Mission Overview
• Exploratory Data Analysis
• Results & Findings
• What's Next
• Limitations

Project Overview

This project will provide insights on a year's worth of sales data from a large pizza parlor. We will be analyzing the data to find key performance indicators, as well as trends. These insights will be conveyed using an excel dashboard.

Data Source

Sales Data: The primary dataset used for this analysis is the "pizza_sales.csv" file, which contains descriptive information on each transaction made by the company.

Tools Used

• Excel (Data Processing & Visualization)
• PostgreSQL (Data Analysis)

MIssion Overview

The goal of this project is to create vizualizations that address the following areas:

KPI's

- Total Revenue
- Average Order Value
- Total Pizzas Sold
- Total Orders
- Average Pizzas Per Order

Trends

- Daily Trend for Total Orders
- Hourly Trend for Total Orders
- Percentage of Sales by Pizza Category
- Top 5 Best/Worst Sellers

Exploratory Data Analysis

KPI's

• Total Revenue

SELECT SUM(total_price) Total_revenue
FROM pizza_db

• Average Order Value

SELECT ROUND(SUM(total_price)/COUNT(DISTINCT order_id),2) AOV
FROM pizza_db

• Total Pizzas Sold

SELECT SUM(quantity) pizzas_sold
FROM pizza_db

• Total Orders

SELECT COUNT(DISTINCT order_id)
FROM pizza_db

• Average Pizzas Per Order

ROUND(SUM(quantity)::DECIMAL/COUNT(DISTINCT order_id)::DECIMAL,2) AS PizzaPerOrder
FROM pizza_db

Trends

• Daily Trend for Total Orders

• Hourly Trend for Total Orders

• Percentage of Sales by Pizza Category

• Best & Worst Sellers

Reults & Findings

Based on the analysis:

1. Daily orders are the highest on the Friday & Saturday
2. Classic Pizza accounts for the largest percentage of Total Sales and Orders
3. Large pizza represents the size of pizza with the highest percentage of Total Sales
4. The best Seller is the Classic Deluxe Pizza and the worst seller is the Brie Carrie Pizza

What's next?

Recommendations based on the results:

• Sales before 11am and after 10pm are very low; consider opening later and closing earlier to reduce overhead costs.
• Pizza sizes above large are selling slower than all other sizes; consider offering promotional deals to these sizes to increase sales percentage.
• The Brie Carrie Pizza is the least ordered pizza, consider replacing the name or ingredients of this pizza to attract more customers.

Limitations

• This dataset only provides a year's worth of data, which doesnt give us a chance to explore yearly trends.

• The dataset also fails to deliver any information about the profitability of the menu items, so certain datapoints may be misleading.

  • For example, the best selling pizza may mot be profitable, or the least selling pizza could be the most profitable.