case summary

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Overview
Segmentation and classification are analytic techniques that helps firms compare and group customers who share
common characteristics (i.e., segmentation variables) into homogeneous segments and identify ways to target
particular segments of customers in a market on the basis of external variables (i.e., descriptor variables).
Segmentation refers to the process of classifying customers into homogenous groups (segments), such that each
group of customers shares enough characteristics in common to make it viable for the firm to design specific offerings
or products for it. This application identifies customer segments using needs-based variables called basis variables.
Cluster analysis helps firms to:
✓ better understand their customers
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The options for the Segmentation template are as follows:
▪ Segmentation data:
1. Number of segmentation variables: These variables serve as the basis for segmentation
and are often called basis variables. They might include customer’s needs, wants,
expectations, or preferences.
2. Number of respondents: The number of customers or respondents in the data that
need to be clustered.
▪ Descriptor data (optional):
• Include descriptor data: Check the box if you have descriptor variables. Descriptor
variables, or descriptors, are variables that do not contribute to the segment definition,
but can be used to describe them (e.g., age, gender).
• Number of descriptor variables: The total number of respondents (customers) in your
study.
▪ Out-of-sample classification data (optional – option only available when descriptor data is also
included in template):
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• Include classification data: Check the box if you have classification data. Classification
data refers to individuals for whom you only have descriptors available, and wish to
classify into their most likely segments.
• Number of respondents to classify: The number of individuals with descriptor data only
that you would like to classify into segments.
Note: the check box at the bottom of the dialog box will cause the template to populate with sample
(random) data that will allow you to run Segmentation modeling immediately so you can preview the
output produced.

It is not always clear whether a specific variable should be treated as a
segmentation variable or descriptor variable. This choice might depend
on the context, the managerial question, or the product category.
When in doubt, ask yourself the following questions: (1) Would this piece
of information tell me what that customer wants, in which case it should
be treated as segmentation variable, or (2) does this piece of information
tell me who that customer is and therefore should be treated as
descriptor variable? For example, “gender” would fall in the second
category most of the time, whereas “need for timely information” usually
falls in the former category.
After selecting the desired model options, click Run to generate the data collection template. The software generates
the required data blocks depending on whether you have included descriptor data (and classification data) and fills
with random data (if selected):
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Entering Your Data
A typical segmentation set up contains one or two data blocks that contain segmentation and/or discrimination data.
If running classification, an additional data block is used for the data to be classified.
▪ Segmentation data are required for the segmentation model. This data block contains the
respondent identifier and a column for each segmentation variable collected in the study. The
data within each column must be scaled using the same scale (e.g., 1–10), but each column can
have a different scale (e.g., 1–10 for satisfaction, 1–5 for convenience). Typically, segmentation
variables are numerical values (interval or ratio scale). The data set contains one row per
respondent in your study. If you must use basis variables that are nominal (e.g., “male” “female”),
then you can apply latent class segmentation analysis (see appendix).
▪ Descriptor data constitute an optional data block, depending on whether your study has
collected discrimination data. Recall that discrimination data enables you to differentiate one
customer from another (e.g., age, income, gender). Again, data within a column must be scaled
using the same scale, but different columns may use different scales. Typically, descriptor
variables are numerical (interval or ratio scale) or nominal (“male”, “female”). Each respondent
in your study appears in a separate row.
▪ Classification data is an optional data block. The block will contain columns that match your
Descriptor data block that is already being used for analysis.
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Running Segmentation Analyses

For the remainder of this tutorial, we will use the “OfficeStar:
Segmentation” data set that is available with the Enginius Segmentation
tutorial. To access the data set, open “Segmentation” under the Tutorials
dropdown in the Enginius Dashboard. This will automatically load the
“OfficeStar: Segmentation” data.
After you enter and/or upload your data, click on the Run Segmentation Analysis button in upper left corner to begin
the Analysis.
Analysis options
When you click on the Run Segmentation Analysis, a number of analysis options will be presented:
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Segmentation method
You may specify the number of segments (clusters) to develop during the analysis or you can allow Enginius to
determine the appropriate number of segments. If you allow Enginius to determine the number of segments
automatically, it will do so strictly on a mathematical basis. This may, or may not, be appropriate from a management
perspective but can be useful as a starting point for manually determining the number of segments.

Usually, a segmentation analysis consists of two steps when manually
determining the number of segments (i.e., using “Force number of
segments” option). First, you run the analysis with a large number of
segments (up to 9). Second, on the basis of analysis from the initial
report (discussed subsequently), you can determine the number of
segments to retain for further analysis.
Segmentation data
The dropdown box that appears under Segmentation data allows you to select the data block that corresponds to
your segmentation data. In OfficeStar, this data block is named “Segmentation data”.
A check box under the Segmentation data section allows you to choose whether to Standardize data. This option
scales all variables to 0 mean and unit variance before the analysis. Choosing this option is recommended if you have
measured the variables on different scales.
Descriptor analysis
To perform a Descriptor analysis, check the box beside “Run descriptor analysis” and then choose the data block where
your descriptor data is located.
You may also choose to run Classification analysis if you have provided classification data. Please refer to page 17 for
a description of classification analysis. One would not typically include classification analysis until the segmentation
analysis was complete.
Advanced options
Checking the Advanced checkbox will provide two additional options for running your segmentation analysis