Skip to main content
Dimensional Analysis lets you rank any dimension’s values by their contribution to a KPI — the Top N drivers with the highest positive impact, or the Bottom N segments with the lowest or most negative performance.

​
How to use Dimensional Analysis

1

Open Explorer and select a KPI

Open Explorer from the sidebar to see your full KPI list, then click the metric you want to analyze. The selection window opens showing three tabs — Custom, Top N, and Bottom N. Under the Custom tab you can pick specific dimension values to create side-by-side comparisons tailored to your needs.
Opening Explorer from the dashboard and navigating to the KPI list
2

Switch to the Top N or Bottom N tab

Click the Top N or Bottom N tab to shift from manual selection to ranked results. Top N surfaces the dimension values with the highest contribution to the KPI; Bottom N surfaces the lowest-performing segments — useful for spotting underperforming channels, cohorts, or regions at a glance.
Switching between the Top N and Bottom N tabs in the Explorer dimensional analysis panel
3

Enter a number to set your ranking cutoff

Type how many results you want — for example, 5 for the top or bottom 5 dimension values. Explorer immediately ranks the results by contribution to the selected KPI, so you can see exactly which segments are carrying the most weight or dragging performance down.
Entering a number in the Top N field and Explorer updating the ranked dimension results in real time
Use Top N to build your Filter Presets — if the top 3 countries consistently account for the majority of KPI movement, create a preset that isolates them. This keeps your Top Stories feed focused on the segments that actually matter.

​
What’s next

Correlation Matrix

Understand which KPIs tend to move together before comparing dimensional slices.

Data Playground

Build custom views and freeform comparisons beyond the Top N/Bottom N structure.

KPI Attribution

See how dimensional contribution is used in story generation.