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Note that this is not an embedded interactive Power BI dashboard, which needs a Pro license and a Power BI account to see.

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Dataset & Data Cleaning (Excel)

The dataset contains an entire year of European (and UK) sales data from a retailer. You will see information about Items, Prices, Orders, Quantities, and Countries; download the data from my GitHub public repository:

I cleaned the data and performed calculations using Excel and DAX from Power BI.

Data Exploration & Analysis (Power BI)

After doing a quick analysis of the data, I decided to focus on the visuals that showed the most relevant insights from a business point of view: 1) total sales and orders in different units of time, 2) the distribution of orders and items by total value using histograms, 3) top items and 4) customizing a Europe map to visually compare sales by country.

Dashboard Design (Power BI)

For the visuals, I contrasted a dark gray color with a light one ―on this occasion I used mint green―, arranging the top sections for the main visuals and the bottom for complementary distribution charts. The result was a nice and clean view with many possibilities to interact with the dashboard.

Top Findings

• More than 90% of clients, orders, and total sales are from the UK (92.77%, 93.23%, and 90.64% respectively).
• September, October, and November are the months with the highest number of purchase orders and sales, with November being the top month with $1.39M and 2677 orders.
• Most of the items sold are below $5 in price, present in 97.5% of the purchase orders, and less than 4% of the purchase orders have items over $20.
• 91.7% of the purchase orders have a total value below $1000, with 42.1% of sales orders being between $0-250 and 34.6% between $250-500.
• Tuesday, Wednesday, and Thursday have the highest number of purchase orders, with Tuesday and Thursday being the top sales days with $1.80M and $1.82M respectively.
• Total purchase orders are higher between 12:00 PM and 04:00 PM, whereas total sales are higher between 10:00 AM-02:00 PM.

Next Steps

Due to the relevancy of items below $5, it would be advisable to perform a market basket analysis, or a client clustering analysis, focusing on that price range.