Cluster analysis. A scientific approach to the study of complex phenomena

Management of any process, including marketing, involves an objective assessment of the situation on the market. Gradually progressing through all stages of the analysis of market opportunities, which include selection of target markets, development of a marketing mix, and marketing activities, one can not help but experience the need for research. At the same time, it is necessary not only to rely on the talent and experience of the analyst himself, but also on the skillful use of data processing methods.

In the modern economy with its complexity and versatility of processes, a huge amount of information to find the most significant data without the use of various statistical packages becomes very problematic.

Cluster analysis takes a special role in conducting market research. By its nature, this is a combined method, combining several methods of statistical research. It is based on the classification of multidimensional observations, for each of which there corresponds a set of descriptive variables. Cluster analysis assumes a way to classify an object by relative homogeneous (homogeneous) groups, having the initial set of variables for consideration. In other words, the objects are divided into groups. In groups, they show similarity in several ways.

Cluster analysis methods are used for a wide range of marketing tasks.

Segmentation of the market allows us to break the consumer category into clusters based on the expected benefits from the acquisition of a certain product. Each cluster can consist of consumers who are looking for similar benefits. The name was appropriately selected - the segmentation of advantages.

Analysis of customer behavior. In solving this problem, cluster analysis is used to create homogeneous consumer groups in order to model their behavior.

Determining the possibilities of a new product, it is possible to carry out its clustering by trademarks, and there is a pronounced regularity when the trademarks of the same cluster show more severe competition with each other than with brands in other clusters.

By grouping cities into clusters, you can choose the most suitable sales markets for a particular product.

Cluster analysis reduces the size of the data. Making observations on individual clusters, they then proceed to multiple discriminant analysis. This is much simpler and cheaper than considering each case separately.

The purpose of clustering is the grouping of objects according to similar characteristics. For a more objective assessment of the degree of similarity, a certain reference unit should be introduced. When forming clusters, they usually rely on two or more traits simultaneously.

Cluster analysis involves the use of a wide range of clustering methods. Among them we can single out such as the probabilistic approach, approaches based on artificial intelligence systems, a logical approach, a hierarchical approach.

Hierarchical cluster analysis involves a complex system that has a number of nested groups or clusters of different orders. This method uses two kinds of characteristics. Agglomerative (unifying) signs are adjacent to divisional (separating) signs. The number of features leads to a division into monothetic classification methods and polythetics.

Using all these methods in statistics, there are about a hundred clustering algorithms. But hierarchical cluster analysis takes the leading place in this list. Its attractiveness lies in the fact that it functions perfectly in the presence of data deficit, and even when for the available data there is no fulfillment of conditions according to the requirement of normality of distributions of random variables, as well as other requirements of classical statistical methods.