Interpret categorical key influencers

Categorical key inflencers are the key factors or indepedent variables which makes the outcome to change depending on it. Let us understand with the following example.

Consider analyzing “Loan” explained by other fields dragged to “Explain by” field. The setting of the visualization pane is given further while scrolling down in the article. We are just showing images to understand the 2 types of key influencers initially.

Factors that influences the likelihood of increase in loan intake

We have kept the dropdown to “Increase” to see at what influences loan to increase.

Top single factor is “Interest_rate“. The “Interest_rate” of 13.12 or less is the top factor that contributes to a increase of “Loan“. More precisely, if “interest_rate” is 13.12 or less then your “loan” increases by 121.3 times.

When “Salary” increases, the “Loan” also increases. When the “Salary” increases by 48663, the “loan” also increases by 61.6 times.

People with age 46-70 are 26.61 times more likely to have a increase in “loan” intake.

Note: Select the “Only show values that are influencers” check box to filter by using only the influential values. Refer to the above image in the bottom right pane.

Interact with other visuals

Every time the user select a filter or slicer or any other visual on the canvas, the key influencers runs again as a new analysis on the new set of data.

For example, you can move “Quarter” into the report and use it as a filter. Use it to see the analysis for the third quarter of any year.

Key influencer chart after using the filter field

Consider the chart is filtered for the third quarter of any year. For the third quarter, the loan is more to likely to increase when the interest_rate is 13.56 to 13.8.

Similarly the second and third top influencer also gets changed a little after the filter. The user can investigate further for different quarters and see that the interest_rate and other factors gets changed for increase or decrease in loan intake.

Interpret continuos key influencer

We have seen how categorical fields influence the increase in loan intake. There are also continuous or numerical factors like age, price, size,population count in the “Explain by” field.

When total “units” increases, the “Sale_amt” also increases.

Let’s look at what happens when “SaleTeamSize” is moved from the table into “Explain by” field. We can notice a change in the above image as follows. When the “SaleTeamSize” is added, the “Sale_amt” gets increased from 9.32 times to 15.8 times on average.

Another example is as follows

As “Total_Published” articles increases, the likelihood of “Expected” number of articles also increases.The scatter plot in the right pane shows the average number of expected articles for each value of “Total_Published“. It highlights the slope with a trend line.

Now let us understand the Key influencers chart in detail with the help of excel datasets.

DataSet used:

The dataset used is “SaleData.xlsx “. Upload the dataset in Power BI and refer to the dataset to follow along with the below-given sections of the article.

SaleData.xlsx: A sample screenshot of the dataset is taken to get an idea of the data fields for analysis of various metrics.

Data: We will be working with “SaleData.xlsx” data with data fields as shown in the above image. The major variables used to show the charts are as follows.

• Sale_amt: The total sale amount for various items of different brands.
• Units: The total number of units sold for any given item of any brand.
• Unit_price: The price of any item.
• Region: The different regions are “east”,”west” and “central” where the sale is handled.

There are other data fields also which can affect any given metric. The user can make use of other fields as per the need or requirement for the analysis or prediction.

Load Data in Desktop:

Open Power BI Desktop

Click the “Get Data” option and select “Excel” for data source selection and extraction.

Select the relevant desired file from the folder for data load. In this case, the file is “SaleData.xlsx”. click the “Load” button once the preview is shown for the Excel data file.

The dashboard is seen once the file is loaded with the “Visualizations” Pane and “Data fields” Pane which stores the different variables of the data file.

In the “Visualizations” pane, we can select the chart type needed for our visual representation of business data. In the following image, the red colored square represents the “key influencers” chart that can be dragged for the “Report” view for further process.

Initial Key Influencer Visual: When the red-colored icon is dragged to the main visualization pane, we get the following view initially.

In this article, we are going to see Power BI key influencers charts.

We will be discussing the following topics and their basic implementation in the Power BI desktop.