Final answer:
Cluster analysis is used to discover patterns in data, grouping similar items based on various attributes. It is especially useful in marketing for segmenting customers by preferences detected in their purchase behavior. Various clustering techniques accommodate different types of data and attributes.
Step-by-step explanation:
Cluster analysis can be used to group data by less obvious similarities, such as preference determined through purchase behavior. This technique helps in identifying patterns and categories within large datasets where similarities or differences might not be immediately apparent. Principles of similarity suggest that similar items or individuals tend to be grouped together - for instance, sports fans might be grouped by the colors of the teams they support.
Different methods of cluster analysis exist, such as hierarchical clustering, where data points are grouped based on the principle that objects are similar to a larger degree. In the context of purchase behavior, this could mean analyzing transaction data to identify customer groups with similar buying habits, even if those similarities are not immediately observable. Market segmentation can then be applied to target different customer clusters with advertising or product recommendations that are tailored to their grouped preferences.
Cluster methods vary in approach, being either divisive or agglomerative, and can handle data that is hierarchical or reticulate in structure. Attributes of the data can also dictate the method chosen, whether they are qualitative or quantitative in nature. Effective use of clustering can lead to more precise marketing strategies, improved customer understanding, and strategic decision making for businesses.