Treemaps

Comparing revenue distribution between functions

The hierarchy at the top shows just the numbers and levels, but the treemap chart represents the numbers proportionally, allowing leaders to visualize the difference in distribution.

• Compare popular lead sources with lead counts is the measure and lead source being the participating entity—a classic single-grouping configuration.
  
• View cost savings achieved across departments. With the departments as the grouping parameters and the cost saved as the measured amount, the chart lays out all the departments as tiles in proportion, based on money saved.
  
• Compare ad spends across channels, where channels are the parent grouping and ad spend is the measured unit.
  

Butterfly

 

• Compare revenue between two of your branches each month. With branches being compared for sum of sales revenue, grouped by closing date.
  
• Compare the performance of two reps in a given quarter. Compared between two users for average of amount of deals, grouped by closing date until today.
  

• Picklist-based comparisons
  
• Duration-based comparisons
  
• Aggregate-based comparisons
  

Business scenario

Comparing the number of deals closed for each lead sources: Duration-based comparison

Analyzing effective sales methodology, inbound vs. outbound: Picklist-based comparison

Analyze the amount versus the expected revenue between accounts: Aggregate-based comparison

Sankey

Key features

• Flow visualization: With the Sankey Chart, you can observe the movement of data between different groups.
  
• Multiple grouping fields: This chart works best when you have at least two grouping fields. You can go even further and add a third grouping to see an even more detailed mapping of your data flow.
  
• Simple configuration: The configuration for the Sankey Chart is as simple as any other chart type in Analytics. 
  

Business scenarios:

Imagine you’re a sales manager trying to get a better handle on your team’s performance and your company’s pipeline. You want to understand not just how many deals are coming in, but also which sources are contributing the most value—and how those deals are progressing through different sales stages.

• Regional revenue distribution: Visualize how revenue flows across different regions, product categories, and their corresponding annual revenue. This helps you compare which regions contribute the most to each product line and where your high-value segments lie.
  
• Ticket handling flow: Visualize the flow of support tickets from their origin channel to internal departments and finally to resolution statuses. This can reveal workload imbalances or common points of delay in your support process.
  

Cluster  

A cluster chart is similar to stacked column charts, but instead of stacking horizontally, the data is represented as vertical bars. As you create a column chart with multiple groupings, you can change the type of column chart to a cluster chart to achieve this representation.

 

In the above image, you can see the stacked column chart compares the number of lead conversions based on popular sources between countries. The stacks appearing on top of existing stacks ask you to calibrate the record count (y-axis) based on the previous stacks, which can lead to inaccurate interpretations. In this case, a cluster representation will paint a clearer picture of the analyses.

• Display total summary: Thus far, for all charts, each participating measure included labels. Now, to better understand overall contributions, a check box to display the total summary is provided under More options on the Chart Configuration page. Based on the configuration, the total revenue or the rolled-up quantities will be prominently displayed.
  
  
  
• Merge Y-axis: For charts that use two y-axis measurements, the intention is to view the progression of one entity against these two measures. Despite the scale, if the y-axis on the left is disproportionate to the values of the y-axis on the right, the plotted graph will result in a graphically and logically incorrect representation.
  
  As you can see in the image below, the plot area of the sum of amount bar and the sum of expected revenue aligns close together, while, the difference between $700,000 and $40,500 is drastic, creating inaccurate interpretations.
  
  In this enhancement, we're allowing neighboring values of measures to merge so that the interpretation can be more visually accurate.
  
  
• Clone components to a different dashboard: Dashboards in Analytics serve in unique ways for various audiences—there can be separate dashboards for the sales team, marketing team, engineering team, and so on, and the chances of using the same measure for reference is common. Thus, when you clone a chart, you can now determine the target dashboard in which the cloned chart can be placed.