Run a driver analysis (relative importance)

The driver analysis answers the question behind the number: not how high a result is, but why it moves. In DataLion it is implemented as a relative importance analysis and runs on the R engine – directly on your data, with no R knowledge required.

What the driver analysis does

The analysis breaks down the explained variance of a target measure – NPS or satisfaction, for example – across the individual influencing factors and shows, as a ranking, which drivers contribute most. This tells you which factor explains the most and which explains hardly anything – so you can direct resources to the drivers with real leverage.

How to proceed

Select the target measure (e.g. NPS, overall satisfaction, repurchase intention).
Select the explanatory variables (the possible drivers, e.g. image dimensions or experience building blocks).
Run the relative importance analysis – it is one of the more than 20 predefined methods on the R engine.
Read off the ranking of the drivers by explained importance.

The analysis runs on your weighted data – with no export to R, SPSS or Python.

In addition: regressions

To check the direction and strength of individual effects, complement the driver analysis with regressions (linear, ordinal and others). You will find the basics under Statistical analyses with the “Advanced statistics” chart type.

Compare drivers by segment

Run the analysis for different segments and compare whether a factor – price, for example – works differently for new customers than for existing ones. Cross tables are well suited to comparing segments side by side.

Tip: The driver analysis is the heart of NPS and satisfaction studies. Back up differences between segments as well, with significances.