Part 2. MM4XL Tools > 2. Analytical Tools > Segmentation Tree > Assembling Input Data

Segmentation Tree

Assembling Input Data

A standard input table to Segmentation Tree has column labels and is arranged by rows. The table here shows a data set made of 14 columns (B:O) and 1016 rows (2:1017).

 Marketing Segmentation Tree Diagram Chart Software

There are two kinds of variables. In the picture above segmenting variables are shaded in yellow and the discriminating variable is in green. You can have several discriminating variables in your data set, but one analysis is run with one discriminating variable at a time only.

Segmenting variables can be either text or figures and they cannot show missing values. In general, we recommend using as few codes as possible for each segmenting variable, and you should use meaningful but brief descriptive codes. This is true for column labels as well: short labels take less space and result in a more compact and more readable tree chart, while a few codes in a column help to keep clustered groups large enough to make sense to marketers.

Discriminant variables are dichotomous, such as Yes-No, and are used to distinguish items with the desired characteristic from items that dont have it. In our example we use the number 1 in column O to qualify the 360 interviewees out of 1016, who answered Yes when asked whether they had used any vitamin supplements in the past 4 weeks (the data set is available in the sheet you can access from the Example button in the tool form). Subjects without the characteristic (did not use vitamins) got a blank cell in column O.

Input data to Segmentation Tree, input data typically comes from survey studies, but also from other sources. We have noticed an increasing application to web data, such as web site traffic, and also to databases such as those of visitors to congresses or affiliates to associations.

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