Analysis | Visual | Segment | Impact on Target
The Impact on Target shows:
This new feature allows comparing a segment with a selected benchmark (either
data set, or another segment)
The differentiation/impact is computed by using the difference between the mean values of the variables, and 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:
is the analyzed segment,
is the benchmark,
is the analyzed observable variable,
the observable variable on the dataset ( on the data set defined by the segment or the benchmark),is the mean value of
of the observable variable of on the Target node, evaluated on the benchmark.is the effect
You can choose among the following Four types of effecteffects 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.
Null Value Assessment
This option allows estimating if the segment and benchmark mean values are significantly different. Two tests are proposed for answering this question:
- a Frequentist one, NHST t-test, the Null Hypothesis Significance Testing with the Welch's two-sample, two tailed t-test, and
- a Bayesian one, BEST, described in the paper by John K. Kruschke, "Bayesian Estimation Supersedes the t-test", Journal of Experimental Psychology: General, 2013.
Below is the Bayesian network used in the BEST approach. We are assuming that the samples follow a Student's t-distribution. 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: