Cluster analysis is an exploratory data analysis tool for solving classification problems. Cluster analysis is a technique of grouping individuals or objects into unknown groups i.e. segmenting the market. The objective is to find out a group of customers in the market place that are homogeneous i.e., they share some characteristics so that they can be classified into one group.
The technique examines similarities between observations of entities based on profiles of their scores on a number of measured characteristics. Using your customer base, you may be able to form clusters of customers who have similar buying habits or demographics. You can take advantage of these similarities to target offers to subgroups that are most likely to be receptive to them. Each cluster thus describes, in terms of the data collected, the class to which its members belong; and this description may be abstracted through use from the particular to the general class or type.
Cluster analysis is thus a tool of discovery. It may reveal associations and structure in data which, though not previously evident, nevertheless are sensible and useful once found.
The cluster/group so found out should be large enough so that the company can develop it profitably, as the ultimate objective of a company is to serve the customer and earn profits. The group of customers that the company hopes to serve should be large enough for a company so that it is an economically viable proposition for the company.
Cluster analysis is neither a single technique nor a statistical technique. It is a mathematical formula for dividing data into classes, without a preconceived notion of what those classes are, based on relationships within the data. There are many different
ways to do this, and some of them use statistical probabilities or statistical quantities such as sum of squares at various points. But overall, the techniques themselves are not really statistical, as they give you no means of assessing likelihood
Applications of cluster analysis include:
- Determining the number and composition of market segments
- Facilitating the selection of test markets
- Identifying groups of people with common purchasing interests in segmentation studies; this helps to identify target markets and provides information for establishing product positioning and developing promotional themes.
Thus we can conclude that cluster analysis is a method of classifying mountains of data into meaningful piles, as it is data reduction tool that creates more meaningful clusters/groups.