ABSTRACT
Chapter 7 discusses cluster analysis, which is characterized as a data reduction technique that aims to group cases and not variables, that is, it seeks to identify groups of observations with similar characteristics. It is a useful technique in market segmentation, as we can identify groups of customers with similar behavior and preferences. As an example, imagine clients doing grocery: which group is sensible to sales, which group values product availability, which group goes on weekends or weekdays? The chapter presents two clustering approaches, the similarity measures, and the criteria to choose the number of clusters. Following this there are two examples, one of each approach. Additionally, the chapter includes the tests to interpret and name the retained clusters. The techniques are illustrated with theoretical description, followed by an example with the SPSS commands and the results tables with comments. The chapter also includes exercises, such as a road map to perform the analysis, an interpretative exercise with results tables, and a market context to guide a research design.
