This new feature allows comparing a segment with a selected benchmark (either the entire data set, or another segment). The comparison is done in terms of Impacts, a term combining the difference of the observable variables' mean values and their effect on the Target node.
The Impact for each observable variable is computed as follow:
where:
is the analyzed segment,
is the benchmark,
is the mean value of on the data set defined by the segment or the benchmark,
is the effect of on the Target node, evaluated on the benchmark.
Four types of effects are available:
Let's take the Perfume example for which we have defined five segments with the Breakout Variable Product, namely Prod3, Prod4, ProdG1, ProdG5 and Prod G6. Below is the table with the Standardized Total Effects of all the observable variables on Purchase Intent, and the mean values of these variables, computed on the entire data set, and on the segment represented by Prod3. These figures allows computing the Impacts of each variable. 
This option allows estimating if the segment and benchmark mean values are significantly different. Two tests are proposed for answering this question:
Below is the Bayesian network used in the BEST approach. We are assuming that the samples follow a Student's tdistribution. The segment and the benchmark have their own and , but they share the same . 
The default Confidence Level has been set to 95%. This is the same for both tests. As for the Bayesian test, the Region of Practical Equivalence (ROPE) on the Effect size around the null value has been set by default to [0.1, 0.1]. The null value is declared to be rejected if the 95% Highest Density Interval (HDI) falls completely outside the ROPE. 
You can use the Preferences to modify:

Checking Null Value Assessment triggers the computation of both tests. When the mean values are estimated as significantly different, a square is added next to the Impact bar:
