https://github.com/shwetabishtt/sales-analysis
This project is about pizza sales analysis which includes working on SQL, to find out insights and also dashboarding by the use of Power BI
https://github.com/shwetabishtt/sales-analysis
powerbi sql sqlserver
Last synced: 21 days ago
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This project is about pizza sales analysis which includes working on SQL, to find out insights and also dashboarding by the use of Power BI
- Host: GitHub
- URL: https://github.com/shwetabishtt/sales-analysis
- Owner: shwetabishtt
- Created: 2024-11-11T10:14:25.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-11T14:07:42.000Z (over 1 year ago)
- Last Synced: 2025-01-11T20:34:23.905Z (over 1 year ago)
- Topics: powerbi, sql, sqlserver
- Homepage:
- Size: 3.79 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Pizza Sales Analysis
Pizza Sales Analysis has a comprehensive year's worth of sales data from a fictitious pizza place. The dataset includes detailed information about each order, such as the date and time of purchase, the types of pizzas served, their sizes, quantities, prices.
## Dataset Overview
Dataset contains valuable information that could help us optimize our operations, boost sales, and enhance customer satisfaction. Here's a quick rundown of what you can expect from the dataset:
- Date and Time: We have meticulously recorded the date and time of each order, allowing us to analyze customer behavior and identify potential peak hours.
- Pizza Details: Each entry includes comprehensive details about the pizzas ordered, including their types, sizes, quantities, prices, and the tantalizing ingredients that create an unforgettable culinary experience.
## Analysis
1. **Customer Traffic Analysis**: Uncovering how many customers visits each day and exploring whether certain times of day experience higher footfall.
2. **Bestselling Pizzas**: Analyzing the data to find out which pizzas are the most popular among our customers. Identifying bestsellers can inform our marketing strategies and help us focus on promoting these crowd favorites.
3. **Revenue and Seasonality**: Calculating the total revenue generated over the year to get an overall picture of our financial performance. Additionally, investigating whether there are any seasonal patterns in sales to plan for peak and slow periods.
4. **Menu Optimization and Promotions**: Using the data to identify pizzas that are underperforming or receiving little attention.
## Key Questions Explored
- **Total Revenue**: Total Revenue generated over the period.
- **Average Order Value**: Average order value throughout the year.
- **Total Pizza Sold**: Total number of Pizzas sold.
- **Total Orders**: Total Orders placed.
- **Average Pizzas Per Order**: Average Pizzas ordered per order.
- **Daily Trend for Total Orders**: By days of the week, trend for sales throughout the year.
- **Monthly Trend for Total Orders**: Monthly Trend for Total Orders to analuze seasonality.
- **% of Sales by Pizza Category**: Percentage of total sales, each 4 Categories contributes.
- **% of Sales by Pizza Size**: Percentage of total sales, each of 5 sizes Contributes.
- **Top 5 Best Sellers by Revenue, Total Quantity & Total Orders**: Top 5 Best selling Pizza by Revenue, Total Quantity & Total Orders.
- **5 lowest Sellers by Revenue, Total Quantity & Total Orders**: 5 lowest selling Pizzas by Revenue, Total Quantity & Total Orders.
- **Number of Customers each day & Busiest hours**: Number of customer served each day and busy operating hours.
- **Average Orders & Pizzaper Day**:Average Orders placed per Day & Pizzas sold per day.