XLSTAT: Perform a REBUS classification on a PLS-PM model in Excel

 

Background

XLSTAT is the first software package to provide users with the REBUS-PLS (REsponse-Based procedure for detecting Unit Segments in PLS-PM) segmentation method developed by Esposito Vinzi et al. (2008). The REBUS method, integrated into PLSPM, identifies heterogeneous groups of observations that do not respond in the same way to the defined model. It builds these classes iteratively, using the CM (Closeness Measure) index to distinguish profiles.

 

Dataset for Example

250 customers of a mobile phone operator were surveyed to answer a large number of questions to model their loyalty to the operator. The PLS-PM model is based on the European Customer Satisfaction Index (ECSI).

This tutorial will show you how to perform a REBUS classification in a PLS-PM context in Microsoft Excel using XLSTAT.

 

Setting Up

• Select the expert display mode in the XLSTAT-PLSPM options.

This dialog box appears:

• Select the expert display and save your settings.

• As in the general tutorial, build the ECSI model using the PLSPMGraph sheet and the Path Modeling toolbar.

• Click on the button  to run the PLS-PM analysis.

In the General tab:

• Activate the REBUS option in the general tab.

In REBUS tab:

• Select automatic truncation. The number of classes is defined automatically during the cluster analysis.

• The chosen threshold is 95 %. This means the algorithm has converged when more than 95 % of the units stay in the same class from one iteration to another.

 

Interpreting the Results

Three new sheets are added to the D1 and PLSPMGraph sheets:

• Worksheet REBUS: Details of the REBUS algorithm are displayed.

• Worksheet PLSPM(Class): For each Class, complete results are displayed in separate worksheets.

The first sheet contains the details of the REBUS algorithm, the dendogram, the class associated to each observation and the CM index for each unit and each class.

 

The two other sheets can be analyzed independently using the general method described in the XLSTAT-PLSPM tutorial.

Two classes were obtained. Each class of units has a different behavior, with respect to the ECSI model.

In class 1, satisfaction is mainly explained by perceived quality and image, and satisfaction is the only one to have an effect on loyalty. In class 2, satisfaction is also explained by perceived quality, but image has a significant effect on loyalty along with satisfaction.

We could also apply multi-group comparisons on the obtained classes.