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Analysis | Visual | Segment | Impact on Target

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Impact

The Impact on Target shows:

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This new feature allows comparing a segment with a selected benchmark (either

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the entire

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data set, or another segment)

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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 Image Added for each observable variable Image Added  is computed as follow:


where:

 is the analyzed segment,

 is the benchmark,

Image Removed is the analyzed observable variable,

 is the mean value of the observable variable on the dataset ( Image Added on the data set defined by the segment or the benchmark),

Image Modified is the effect of the observable variable of Image Addedon the Target node, evaluated on the benchmark.

You can choose among the following Four types of effecteffects are available:

Example

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.

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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.

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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.


Info

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 Image Added and Image Added, but they share the same Image Added.

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Info

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.


Info

You can use the Preferences Image Added to modify:

  • the confidence level (for both the t-test and BEST),
  • the Monte Carlo Markov Chain parameters that are used for inference in the Bayesian network described above,
  • the ROPE size that defines an interval centered at 0, i.e. 0.2 defines the interval [-0.1, 0.1].


Example

Checking Null Value Assessment triggers the computation of both tests.

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When the mean values are estimated as significantly different, a square is added next to the Impact bar:

  • Image Added for t-test
  • Image Added for BEST