Part 2. MM4XL Tools > 2. Analytical Tools > Cluster Analysis > Cluster Analysis in a Nutshell

Cluster Analysis

Cluster Analysis in a Nutshell

Cluster analysis is used for segmenting items or people in homogeneous groups. Myers (1996) noticed that it is generally agreed that the most appropriate interdependence statistical techniques for segmenting markets are those known as clustering methods. Anderberg (1973) wrote:

The value of exploratory cluster analysis is primarily in the tendency for new arrangements of data units or variables to suggest relationships and principles previously unnoticed. The substantive results are not the output of the computer but the new ideas prompted in the analysts mind.

In marketing, segmentation needs arise often linked to differentiation matters, which relate to positioning and require data on behavior and attitude. But it is also often used when analyzing performance, for instance of affiliate companies, points of sale, distributors, etc.; for clustering satisfaction of internal and external customers; and also for treating profiles of products, companies, geographic regions, and so on.

The many available clustering methods divide essentially in two main groups:

  • Hierarchical methods, which group data row by row and do not require specifying in advance the desired number of clusters.
  • Partitioning methods, which assign items to a user defined number of clusters.

MM4XL makes available the most popular method from each of the above groups, Wards clustering method and Centroid method (also known as K-means clustering method), respectively. The former is typically run first, so to get an understanding of the data structure, and the latter method is used for refining the clusters.

 Cluster Analysis Software for Marketing Segmentation

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