Like factor analysis, cluster analysis is also a data reduction method. It, however, focuses on respondents as opposed to variables. It reduces a large pool of respondents into groups (or clusters) based on common themes. The members of each cluster group are more similar within a group than they are between groups.
Imagine that a health food store wants to understand the different types of customers that come into their store. They could use cluster analysis to group customers based on their socioeconomic status, frequency of purchase, type of products purchased, and monthly spend. Here are two examples of groups that might arise from this segmentation:
Market researchers use cluster analysis to divide the general population of consumers into market segments with data from surveys and test panels. This helps them better understand the relationships between different groups of potential customers and aids with new product development and selecting test markets.
Cluster analysis is also used in biology, medicine, computer science, crime analysis, and mathematical chemistry.