Restaurant Sales Performance Dashboard

Focused on identifying Preferred payments methods and high selling menu items/orders to optimize staffing and inventory.

About the dataset used.
It is a sales data of a restaurant company operating in multiple cities in the world. It contains
information about individual sales transactions, customer demographics, and product details. The
data is structured in a tabular format, with each row representing a single record and each column
representing a specific attribute. This dataset can be commonly used for business intelligence, sales
forecasting, and customer behaviour analysis.
Classified as Internal use only
These are the main Features/Columns available in the dataset:

1. Order ID: A unique identifier for each sales order. This can be used to track individual transactions.
2. Order Date: The date when the order was placed. This column is essential for time-series analysis,
   such as identifying sales trends over time or seasonality.
3. Product: The name or type of the product sold. This column is crucial for analysing sales
   performance by product category.
4. Price: The unit price of the product. This, along with ‘Quantity Ordered’, is used to calculate the
   total price of each order.
5. Quantity: The number of units of the product sold in a single order. This is a key metric for
   calculating revenue and understanding sales volume.
6. Purchase Type: The order was made online or in-store or drive-thru.
7. Payment Method: How the payment for the order was done.
8. Manager: Name of the manager of the store.
9. City: The location of the store. This can be used for geographical analysis of sales, such as
   identifying top-performing regions or optimizing logistics.
   Using this dataset, I answered multiple questions with Insights using power bi tool.
   Q.1) Most Preferred Payment Method?
   Q.2) Most Selling Product - By Quantity & By Revenue?
   Q.3) Which city had maximum revenue and Which Manager earned maximum revenue?
   Q.4) Average Revenue of November & December month.
   Q.5) Is revenue increasing or decreasing over time?
   Q.6) Average ‘Quantity Sold’ & ‘Average Revenue’ for each product?
   Q.7) Total number of orders
   Q.8) Total Revenue