Driver analysis, sometimes referred to as contribution analysis or attribution analysis, helps explain the “why” behind the facts and figures that are called out in the story, specifically the drivers of change.
We’ve released a handful of new insights for discrete and continuous narratives that point out this information, making it easy for the reader to understand exactly what’s going on in the data and why it happened.
You can configure these drivers under the “driver” tab in the settings modal.
With Dimension Drivers, users can choose between three types of analysis (count, individual % and cumulative %), the threshold for how many drivers to call out, and also whether or not to include offsetters.
Here’s an example of how it might look in your story:
Discrete example explaining total profit: The profit of $268,224 was driven by California with $70,601, New York with $67,907 and Washington with $31,341 and offset by Texas with -$21,311, Ohio with -$16,023 and Pennsylvania with -$14,103.
Continuous example explaining change in total sales over the last month: The $7,451 increase in sales over the most recent month was driven by Furniture (+7.34% from $84,229 to $90,411) and Office Supplies (+5.31% from $68,568 to $72,206) and was offset by Technology (-2.92% from $81,216 to $78,848).
You can also show sub drivers in your story (see sub drivers in italics below):
The profit of $123,856 was driven by Technology with $64,286, led by Phones with $19,287, Accessories with $17,743 and Copiers with $15,706; Office Supplies with $51,112, led by Paper with $15,098, Binders with $12,747 and Storage with $8,173; and Furniture with $8,458, led by Chairs with $11,930 and Furnishings with $6,552.
Verbosity is a setting in the Language tab, but adjusting this changes the way the driver content is written. You’ll see more information in parentheses with higher verbosity, and you get a more concise version of the driver analysis with lower verbosity.
Metric Drivers were created as another method to explain the impact of a component metric on the overall metric. In this example, operating expenses and non operating expenses make up total expenses, so we can see how each contributes to the total.
Heres an example of what it might look like in your story: The $2.1 million in total expenses was driven by operating expenses with $1.8 million and non-operating expenses with $335,188.