Decision Tree is a feature in C-clone that allows user to select Gain and Volume of unique users from an audience to target specific users
How does it work ?
1- Select a Cclone segment then click on a graph one point (Volume of Unique Users and Gain)
Once the advertiser has clicked on the dot corresponding to the audience he wants to target, he will get the corresponding segmentation tree at the bottom of the interface
2- Decision Tree is displayed with the different segments and volume from selected previous point
This tree enables to identify and visualize the key predictors (most explanatory variables) explaining a performance (conversion…)
These explanatory variables are the result of the Khi2 test which calculates the dependency of the variables vs the variable to explain
The red nodes are those that constitute the audience selected
Explanation
Negative segment = Sum of “Database” % points in red (0.70+1.80+0.83+1.06 = 4.39)
Positive Segment = Sum of “% Target” points in red (1.09+2.06+1.06+1.08 = 5.29)
number of Unique Users = Sum of “Volume Target” points in red (140+59+73+6 = 278)
performance index = top figure in red (32.33; 7.23; 17.31; 1.40)
For the first segment, the strongest explanatory variable is that they most probably have a psychotherapy (>=0.5 index)
3- Audience can be saved
Keep label of the audience created automatically. The pattern of the label is standardized
Illustration:
Cclone main segments label = test
CCclone newly created label = test_4_UU_4.39_G_13.25
«4» is the second point of the abacus, starting from the left
« UU 4.39 » means 4.39% of the Negative segment
« G » is the expected performance
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