This article describes how to assign respondents to clusters or segments you've created in another data file. There are two basic approaches:
- Assign respondents to segments in the new data file using the same variables as used when forming the segments, or,
- Predict segment membership based on a different set of variables.
Requirements
A data file containing a k-means cluster analysis.
Method
There are at least two ways to apply segments to a new data file:
Method 1: Assign respondents to segments in the new data file using the same variables as used when forming the segment
A three-cluster k-means solution is shown below. To allocate people in a new data file using these segments:
- Click on the k-means solution and make sure that Automatic is not checked (this option is in Properties > R Code in the object inspector.
- Take a copy of the line of code that looks similar to this (with different variable names):
kmeans = KMeans(data.frame(understand, shop, key, value, interested),
- Select the data set in the Data Tree.
- Press Update in the Object Inspector and select the new data file.
- From the toolbar, select Calculation > Custom Code.
- In the R Code box in the Object Inspector, paste in the copied code, and modify it so that it looks like this (the key bits to retain from your pasted code are kmeans or whatever it has been changed to and the variable names):
predict(kmeans, newdata = data.frame(understand, shop, key, value, interested))
- Give the variable an appropriate Name and Label.
- Change the Structure of the variable to Mutually exclusive categories (Nominal) (this setting is found in the Object Inspector under Properties > Inputs).
- Press Labels (below DATA VALUES) and enter any labels you desire and press OK.
Method 2: Predict segment membership using a different set of variables
In this scenario, segments have been formed and then a predictive model is used to predict segment membership on either:
- A completely different set of variables (e.g., demographics, or some other data available in a customer database).
- A subset of the variables used to create the segments. (Tip: if you are building a predictive model based on exactly the same variables as used to create segments, you are making a mistake, and should instead use the approach described in the previous section).
The output above from a multinomial logit (MNL) model (Anything > Advanced Analysis > Regression > Multinomial Logit), predicting segment membership based on firmographics. The goal is to now predict segment membership in a new data file, that contains the same predictor variables.
- Click on the model output and make sure that Automatic is not checked (this option is in Properties > R Code in the Object Inspector).
- Take a copy of the line of code that looks similar to this (with different variable names):
glm = Regression(segmentsGXVYS ~ q1 + q2 + q3 + q4 + q5, - Click on the data set in the Data Tree.
- Press Update in the Object Inspector and select the new data file.
- From the toolbar, select Calculation > Custom Code.
- In the R Code box in the Object Inspector, paste in the copied code, and modify it so that it looks like this (the key bits to retain from your pasted code are glm or whatever it has been changed to and the variable names):
predict(glm, newdata = data.frame(q1, q2, q3, q4, q5))
- Give the variable an appropriate Name and Label.
- Change the Structure of the variable to Mutually exclusive categories (Nominal) (this setting is found in the Object Inspector under Properties > Inputs).
- Press Labels (below DATA VALUES) and enter any labels you desire and press OK.
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