Part 2. MM4XL Tools > 2. Analytical Tools > Segmentation Tree > Anatomy of a Segmentation Tree Report > The Table

Segmentation Tree

The Table

With the option Print table checked, Segmentation Tree summarizes the outcome of the partition in a table like the one below.

Rows 1-5 of the table below report the basics of the user selection. Rows 6-22 concern the tree partitions. Beginning from left, in column A (cell 8 and 16) are shown the codes of the variable that was found to be the most related to the purchase of vitamins in our sample. Column B, C, and D show the other levels identified with the segmentation.

Column E shows values referring the whole dataset used for the analysis (1017 cases as shown in cell E7) and column Q shows the number of people in each of the lowest clusters. The 224 people in cell P8, for instance, can be found in the lowest left box of the tree.

Column G show the %-values referring to values in column E. Cell G8, for instance, has 22%, which is found dividing 224 by 1017 (=E8/E7*100). Column H shows values computed on sample values from column F. The value in cell H8 is obtained dividing 109 by 224 (=F8/E8*100).

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Row 7 tells us that Gender is the variable found in the dataset to relate most to the purchase of vitamins. There are 1017 people in our sample, and 361 of them have bought vitamins during the past 4 weeks.

In row 8 we see the tool identified a group of 224 women (22% of 1017), living in cities, with 2 or more kids at home, and younger than 45 years: 49% of these have bought vitamins for their household in the past 4 weeks. The same information is available for all the identified segments, which are shown at the lowest level of the tree.

In general, it is suggested that you handle with care groups including less than 20 units.

If you have many groups and you want to grasp quickly which of the groups is of interest to you, plot the values in columns G and H using Smart Mapping, another tool available in MM4XL.

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