A major restaurant chain was managing data of its restaurants from different locations. Restaurants generate large amount of data daily, so it was a tedious task to fetch and analyze the data to enable the owner to see trends. The challenge was to first consolidate data from all the locations into a single place and then measure key KPIs like:
- Sales and guest count for different locations
- Daily sales and average check amount for different locations
- Item comparison by day, item, and location
We developed this Power BI solution for a customer by extracting data from APIs and feeding them into Power BI for the analysis. This solution summarized statistics on how all stores and restaurants were performing in sales, guest visits, and several other attributes.
After taking data from their APIs, we designed a hybrid Microsoft Azure-based database which consolidated all of their data, then imported all the data into Power BI desktop to create reports.
The analysis of Sales and Guest count by location uses a combination chart for each hour and can be filtered using a slicer at the top. Power BI's bookmarking and buttons features allow easy navigation BACK to the previous page and the NEXT page, and a RESET button can be used to clear all filters applied to the tab.
Owners see a more granular view with the Total Sales by Weekday of Locations chart, and the overall status of lunch and dinner revenues, the values of Guest count and Total Sales are displayed using rotating tiles.
Top Selling items can be filtered using a slicer to measure which food item is providing the most revenue. which also shows lunch and dinner average check amount with just a click.
Finally, there is an analysis of Pizza and Burger orders. The column chart shows the percentage of Total Sales of Pizza v/s Burger in Dinner by each location. The Tornado chart compares the percentage of Total Sales of Pizza v/s Burger in Lunch by location. The Sankey chart displays the distribution of sales for lunch and dinner from every location.