Retail Analysis sample for Power BI: Take a tour
The Retail Analysis built-in sample contains a dashboard, report, and dataset that analyzes retail sales data of items sold across multiple stores and districts. The metrics compare this year's performance to last year's for sales, units, gross margin, and variance, as well as new-store analysis.
This sample is part of a series that shows how you can use Power BI with business-oriented data, reports, and dashboards. It was created by obviEnce with real data, which has been anonymized. The data is available in several formats: built-in sample in the Power BI service, .pbix Power BI Desktop file, or Excel workbook. See Samples for Power BI.
This tutorial explores the Retail Analysis built-in sample in the Power BI service. Because the report experience is similar in Power BI Desktop and in the service, you can also follow along by using the sample .pbix file in Power BI Desktop.
You don't need a Power BI license to explore the samples in Power BI Desktop. If you don't have a Power BI Pro or Premium Per User (PPU) license, you can save the sample to your My Workspace in the Power BI service.
Get the sample
Get the built-in sample
Open the Power BI service (app.powerbi.com), sign in, and open the workspace where you want to save the sample.
If you don't have a Power BI Pro or Premium Per User (PPU) license, you can save the sample to your My Workspace.
In the bottom-left corner, select Get data.
On the Get Data page that appears, select Samples.
Select Retail Analysis Sample, and then choose Connect.
Power BI imports the built-in sample, and then adds a new dashboard, report, and dataset to your current workspace.
Get the .pbix file for this sample
Alternatively, you can download the Retail Analysis sample as a .pbix file, which is designed for use with Power BI Desktop.
Get the Excel workbook for this sample
If you want to view the data source for this sample, it's also available as an Excel workbook. To see the raw data, enable the Data Analysis add-ins, and then select Power Pivot > Manage. To download the eight original Excel files, see Explore the Excel samples in Excel.
Start on the dashboard and open the report
In the workspace where you saved the sample, open the Dashboards tab, then find the Retail Analysis Sample dashboard and select it.
On the dashboard, select the Total Stores New & Existing Stores tile, which opens to the Store Sales Overview page in the Retail Analysis Sample report.
On this report page, you see we have a total of 104 stores, 10 of which are new. We have two chains, Fashions Direct and Lindseys. Fashions Direct stores are larger, on average.
In the This Year Sales by Chain pie chart, select Fashions Direct.
Notice the result in the Total Sales Variance % bubble chart:
The FD-01 district has the highest average Sales per Square Foot and FD-02 has the lowest Total Sales Variance compared to last year. FD-03 and FD-04 are worst performers overall.
Select individual bubbles or other charts to see cross highlighting, revealing the impact of your selections.
To return to the dashboard, select Retail Analysis Sample from the left navigation bar.
On the dashboard, select the This Year's Sales New & Existing Stores tile, which is equivalent to typing This year sales in the Q&A question box.
The Q&A results appear:
Review a tile created with Power BI Q&A
Let's get more specific.
Change the question to this year's sales by district. Observe the result. Q&A automatically places the answer in a bar chart:
Now change the question to this year's sales by zip and chain.
Notice how Power BI answers the question as you type and displays the appropriate chart.
Experiment with more questions and see what kind of results you get.
When you're ready, return to the dashboard.
Dive deeper into the data
Now let's explore on a more detailed level, looking at the districts' performances.
On the dashboard, select the This Year's Sales, Last Year's Sales tile, which opens the District Monthly Sales page of the report.
In the Total Sales Variance % by Fiscal Month chart, notice the large variability on variance % compared to last year, with January, April, and July being particularly bad months.
Let's see if we can narrow down where the issues might be.
In the bubble chart, select the 020-Mens bubble.
Observe that although the men's category wasn't as severely affected in April as the overall business, January and July were still problematic months.
Select the 010-Womens bubble.
Notice the women's category performed much worse than the overall business across all months, and in almost every month compared to the previous year.
Select the bubble again to clear the filter.
Try out the slicer
Let's look at how specific districts are doing.
Select Allan Guinot in the District Manager slicer on the top left.
Note that Allan's district outperformed in March and June, compared to last year.
With Allan Guinot still selected, select the Womens-10 bubble in the bubble chart.
Notice that for the Womens-10 category, Allan's district didn't meet last year's volume.
Explore the other district managers and categories; what other insights can you find?
When you are ready, return to the dashboard.
What the data says about sales growth this year
The last area we want to explore is our growth by examining the new stores opened this year.
Select the Stores Opened This Year by Open Month, Chain tile, which opens the New Stores Analysis page of the report.
As evident from the tile, more Fashions Direct stores than Lindseys stores opened this year.
Observe the Sales Per Sq Ft by Name chart:
Notice the difference in average sales/square foot across the new stores.
Select the Fashions Direct legend item in the Open Store Count by Open Month and Chain top-right chart. Notice, even for the same chain, the best store (Winchester Fashions Direct) significantly outperforms the worst store (Cincinnati 2 Fashions Direct) by $21.22 vs $12.86, respectively.
Select Winchester Fashions Direct in the Name slicer and observe the line chart. The first sales numbers were reported in February.
Select Cincinnati 2 Fashions Direct in the slicer and observe in the line chart that it was opened in June and appears to be the worst performing store.
Explore by selecting other bars, lines, and bubbles throughout the charts and see what insights you can discover.
Next steps: Connect to your data
This environment is a safe one to play in, because you can choose not to save your changes. But if you do save them, you can always select Get data for a new copy of this sample.
We hope this tour has shown how Power BI dashboards, Q&A, and reports can provide insights into sample data. Now it's your turn; connect to your own data. With Power BI, you can connect to a wide variety of data sources. To learn more, see Get started with the Power BI service.