https://github.com/kshitiz1302/pizza-sales-report
The report provides insights into pizza sales trends for 2015, focusing on peak periods, customer preferences for large pizzas, and the best-performing menu items.
https://github.com/kshitiz1302/pizza-sales-report
data-cleaning data-management data-manipulation data-modeling data-storytelling data-visualization dax dax-expression dax-query mysql mysql-database mysqlworkbench powerbi powerbi-dashboards powerbi-desktop powerbi-report powerbi-visuals sql sql-server
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The report provides insights into pizza sales trends for 2015, focusing on peak periods, customer preferences for large pizzas, and the best-performing menu items.
- Host: GitHub
- URL: https://github.com/kshitiz1302/pizza-sales-report
- Owner: kshitiz1302
- Created: 2025-01-11T19:15:06.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-17T21:56:49.000Z (over 1 year ago)
- Last Synced: 2025-10-04T19:39:16.751Z (8 months ago)
- Topics: data-cleaning, data-management, data-manipulation, data-modeling, data-storytelling, data-visualization, dax, dax-expression, dax-query, mysql, mysql-database, mysqlworkbench, powerbi, powerbi-dashboards, powerbi-desktop, powerbi-report, powerbi-visuals, sql, sql-server
- Homepage:
- Size: 5.29 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Pizza Sales Report Dashboard
## Problem Statement
This report analyzes pizza sales data from January to December 2015, generating $817.86K in total revenue from 21,350 orders and 49,574 pizzas sold.
It highlights key trends such as peak sales on Friday and Saturday evenings, large pizzas dominating with 45.89% of sales, and the Thai Chicken Pizza leading in revenue at $43K.
The findings aim to guide strategic decisions for enhancing performance and customer satisfaction.
### Steps followed
- Step 1 : Prepare a CSV file for the dataset and create tables in the SQL.
- Step 2 : Import the CSV file to SQL.
- Step 3 : Perform some Data-Modeling in SQL through MYSQL and then import the dataset from the SQL Server into Power BI by establishing a direct connection of servers.
- Step 4 : Open the power query editor. In the view tab under the Data preview section, check the "column distribution," "column quality," and "column profile" options.
- Step 5 : It was observed that in none of the columns errors & empty values were present
- Step 6 : In the report view, under the view tab, theme was selected..
- Step 7 : Visual filters (Slicers) were added for three fields named "Pizza Size", "Order Date" & "Pizza Category".
- Step 9 : Four card new visuals were added to the canvas, representing Total Revenue, Total Orders, Total Pizza Sold, Average Order value & Average Pizza Per Order.
Using visual level filter from the filters pane, basic filtering was used & null values were unselected for consideration into average calculation.
Although, by default, while calculating average, blank values are ignored.
- Step 10 : Calculated column was created to extract order day from calender.
for creating new column following DAX expression was written;
order day = UPPER(LEFT(pizza_sales[Day Name],3))
Snap of new calculated column,
- Step 11 : Another calculated column was created to extract order month from calender.
for creating new column following DAX expression was written;
order month = UPPER(LEFT(pizza_sales[Month Name],3))
Snap of new calculated column,
- Step 12: New measure was created to find total revenue.
Following DAX expression was written for the same,
Total Revenue = SUM(pizza_sales[total_price])
A card visual was used to represent total revenue.
- Step 13 : New measure was created to find total orders,
Following DAX expression was written to find total order placed,
Total Orders = DISTINCTCOUNT(pizza_sales[order_id])
A card visual was used to represent this value.
Snap of total orders placed by customers
- Step 14 : New measure was created to calculate total pizza sold in whole year.
Following DAX expression was written to find pizza sold
Total Pizza Sold = SUM(pizza_sales[quantity])
A card visual was used to represent this total pizza sold.
Snap of total pizza sold
- Step 15 : New measure was created to calculate average order value in whole year.
Following DAX expression was written to find this,
Avg Order Value = [Total Revenue]/[Total Orders]
A card visual was used to represent average order value.
Snap of this measure
- Step 16 : New measure was created to calculate average pizza per order.
Following DAX expression was written to find this,
Avg Pizzas per Order = [Total Pizza Sold]/[Total Orders]
A card visual was used to represent average pizza per order.
Snap of this measure
# Snapshot of Dashboard (Power BI Service)
# Report Snapshot (Power BI DESKTOP)
# Insights
A double page report was created on Power BI Desktop
Following inferences can be drawn from the dashboard;
### [1] Total Number of Orders = 21350
Total revenue was $817.86K.
Total Pizza sold 49,570.
The average order value was $38.31.
2.32 pizzas sold per order.
### [2] Some other insights
### Pizza Category
1.1) Classic pizzas contributed the most sales 26.91% (14,888 units).
1.2) 25.46 % revenue produced by Supreme pizzas (11,987 units).
1.3) 23.96 % revenue produced by Chicken pizzas (11,050 units).
1.4) 23.68 % revenue produced by Veggie pizzas (11,649 units).
thus, maximum Classic Pizzas are at top in both sales and units sold category.
### Pizza Size
2.1) 45.89 % revenue produced by Larged sized pizzas (18,956 units).
2.2) 30.49 % revenue produced by Medium sized pizzas (15,635 units).
2.3) 21.77 % revenue produced by Regular sized pizzas (14,403 units).
2.4) 1.72% revenue produced by X-Large sized pizzas (552 units).
2.5) 0.12% revenue produced by XX-Large sized pizzas(28 units).
thus, Larged Sized Pizzas top the chart.
### Pizza Name
3.1) The Thai Chicken Pizza generated the highest revenue ($43K), while the Classic Deluxe Pizza had the most orders (2,329).
3.2) The Brie Carre Pizza had the lowest revenue ($12K) and the fewest orders (480).\
### Other
Friday and Saturday evenings had the highest sales, with July and January being the peak months.