Cluster Analysis

Cluster Analysis

The chief purpose of the cluster technique is to locate any natural alliance among an established grouping of individuals (Bowerman, O'Connell, Orris, & Murphree, 2012). These particular individuals might be substances, themes, components or any other thing that establishes familiarity. These sets of people may well include a complete population or just a mere sampling of the entire population. In other words, cluster analysis places individuals in groups of select, all-inclusive cluster making delegates of one cluster similar in nature, while the other cluster appears disparate. Another name for this is partitioning or dissection (Triola, 2010). It is therefore safe to assume that cluster analysis is utilized during the summary process and not to find natural groupings.

The prominent characteristics of cluster analysis include:

* Simplicity of use and the ability for multiple data inputs

* A numerical technique that divides or categorizes sample data into uniform groups

* A multivariate system extensively employed in segmentation analysis. This method uses a degree of relationship or detachment to generate homogeneous clusters

* Contemplates interrelationships amongst variables, so it is not utilized for predicting. The results will have to be interpreted by the researcher grounded on his/her best judgment due to the lack of a p-value

* Multiple input data capabilities such as proximity, measures of similarity or distance

Example of a real life cluster analysis

Looking at the nationally recognized computer company Dell Inc. one can see how they used the cluster analysis technique in their reinvention initiative to reduce the complications associated with their products. Highly technical and sometimes complex product lines can have an unequivocal impact on organizations gross revenue and also has the potential to add additional costs related to sustaining their product line. The complexity issue associated with a highly technical product line can also negatively affect the customer experience. By decreasing the complexity of their product line, Dell Inc. was able to achieve higher consumer satisfaction and cash in on a higher profit margin. Dell Inc. utilized the cluster analysis to pinpoint strategic clusters of configurations purchased by consumers and also which product delivered the best value to the customer (Jandreski, Miller, & Skirvin, 2005).