The impact of drivers on engagement indicates the correlation between the scores given to a driver and those given to engagement. For this calculation, the last known driver score and the last known engagement score are compared.
💡 The calculation performed is based on Pearson's coefficient, which aims to detect the presence or not of a linear relationship between two continuous quantitative variables.
It is between -1 and 1:
The drivers with a strong impact are therefore drivers that will tend to be directly linked to engagement. They can be considered as predictors of engagement because their decrease will generally be associated with a decrease in engagement, and inversely for an increase. These are therefore subjects to prioritize and/or work on because it is on these subjects that actions would have the most impact.
💡 Caution: a driver with a low impact on engagement still deserves consideration, as engagement is not the only end measure to improve. This driver may have a strong impact on mood or motivation for example. Moreover, its immediate impact is weak but could have a strong impact on the life of the company in the longer term.

The impact cloud allows you to visualize how the drivers' impacts on engagement are distributed according to their score.
To understand it:
This graph is directly related to the strengths and weaknesses, whose calculation is partly based on the impact:
The color of the points allows you to quickly find out which theme the drivers are associated with. It is also possible to fly over a theme to display the drivers and their score. By clicking on it, the user can also keep it active.
