In this workbook, I have Sales data of an E-Commerce Company for 3 months. The typical columns in the base data are:
1. Order Date/Time
2. City to which orders were shipped
3. Order Number
4. Payment Type i.e. Cash on delivery, Net Banking, EMI’s
5. Order Status i.e. Delivered or cancelled
6. SKU’s which the ordered items fall into
7. Products which the ordered SKU’s fall into
8. Categories which the ordered products fall into
Given this simple tabular representation, one may want to analyse and visualize this dataset from multiple perspectives based on user selections, such as
“What was the revenue earned from the Top 5 products in the A100 category in April for orders shipped to New Delhi?”
In this query framed above, the end user should have the leeway to select any/all of the underlined facets. So one can either choose revenue earned or Number of orders. Likewise, one can either select Top 5 products or Top 15 products/Top 5 SKU’s etc.
With relative ease, one should also be able to “Perform an affiliate analysis” showing which categories are ordered together (to study affiliations). Please review this post for an independent discussion on “Affinity Analysis”.
Furthermore, one should be able to perform a free form timeline search such as – “I would like to study growth in Total revenue of March 2-8 2015 over Feb 1-4 2015”
You may download the workbook from the link shared above.
You may watch similar videos showcasing the capabilities of Business Intelligence in MS Excel:
Here’s a video showing the capabilities of this Sales data model
You may also watch this short video to see how I visualized the revenue flow from Categories to Shipping cities during different Order periods using Custom visuals available in PowerBI desktop.
Please feel free to download the PowerBI desktop workbook of the video shown above from here.
For a detailed overview of Sankey diagrams (a Custom visual available in PowerBI desktop), you may refer to my Blog article here.
Another great Custom visual (Sand Dance) which allows data discovery has been shown at this link. At that link, you will also be able to see how I queried the underlying dataset using “Natural Language”.