Cluster analysis
This feature is available in the Direct Marketing option.
Cluster Analysis is an exploratory tool designed to reveal natural groupings (or clusters) within your data. For example, it can identify different groups of customers based on various demographic and purchasing characteristics.
Example. Retail and consumer product companies regularly apply clustering techniques to data that describe their customers' buying habits, gender, age, income level, etc. These companies tailor their marketing and product development strategies to each consumer group to increase sales and build brand loyalty.
Cluster Analysis data considerations
Data. This procedure works with both continuous and categorical fields. Each record (row) represent a customer to be clustered, and the fields (variables) represent attributes upon which the clustering is based.
Record order. Note that the results may depend on the order of records. To minimize order effects, you may want to consider randomly ordering the records. You may want to run the analysis several times, with records sorted in different random orders to verify the stability of a given solution.
Measurement level. Correct measurement level assignment is important because it affects the computation of the results.
- Nominal. A variable can be treated as nominal when its values represent categories with no intrinsic ranking (for example, the department of the company in which an employee works). Examples of nominal variables include region, postal code, and religious affiliation.
- Ordinal. A variable can be treated as ordinal when its values represent categories with some intrinsic ranking (for example, levels of service satisfaction from highly dissatisfied to highly satisfied). Examples of ordinal variables include attitude scores representing degree of satisfaction or confidence and preference rating scores.
- Continuous. A variable can be treated as scale (continuous) when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. Examples of scale variables include age in years and income in thousands of dollars.
An icon next to each field indicates the current measurement level.
| Numeric | String | Date | Time | |
|---|---|---|---|---|
| Scale (Continuous) |  |
n/a |  |
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| Ordinal |  |
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| Nominal |
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You can change the measurement level in Variable View of the Data Editor (for more information, see Specifying Measurement Level) or you can use the Define Variable Properties dialog to suggest an appropriate measurement level for each field (for more information, see, Assigning the Measurement Level).
Fields with unknown measurement level
The Measurement Level alert is displayed when the measurement level for one or more variables (fields) in the dataset is unknown. Since measurement level affects the computation of results for this procedure, all variables must have a defined measurement level.
Scan Data. Reads the data in the active dataset and assigns default measurement level to any fields with a currently unknown measurement level. If the dataset is large, that may take some time.
Assign Manually. Opens a dialog that lists all fields with an unknown measurement level. You can use this dialog to assign measurement level to those fields. You can also assign measurement level in Variable View of the Data Editor.
Since measurement level is important for this procedure, you cannot access the dialog to run this procedure until all fields have a defined measurement level.
To obtain Cluster Analysis
This feature is available in the Direct Marketing option.
From the menus choose:
- Select Segment my contacts into clusters.
- Select the categorical (nominal, ordinal) and continuous (scale) fields that you want to use to create segments. (See the Data section above for information on measurement level.)
- Click Run to run the procedure.