any process management, including marketing, assumes an objective assessment of the situation on the market.Gradually moving through all stages of the analysis of market opportunities, which include the selection of target markets and the development of the marketing and implementation of marketing activities, unwittingly faced with the need to study.This must not only rely on the talent and experience of the analyst, but also on their skillful use of data processing techniques.
In today's economy with its complex and multifaceted processes, huge amounts of information to find the most relevant data without the use of various statistical packages becomes very problematic.
special role in marketing research takes a cluster analysis.By its nature, this combined method that combines several methods of statistical research.It is based on lies classification of multivariate observations, each of which has its own set of descriptive variables.Cluster analysis suggests a way to classify the object of a re
cluster analysis methods are used for a wide range of marketing objectives.
Market Segmentation allows you to split the consumer category into clusters on the basis of the expected benefits of the acquisition of certain goods.Each cluster may consist of consumers who are looking for similar benefits.The name he picked an appropriate - segmentation method advantages.
analysis of customer behavior.In this task, the cluster analysis is used to create a homogeneous consumer groups to model their behavior.
determine the possibility of a new product, you can make it clustering by brand, with a pronounced pattern observed when the brands of the same cluster exhibit a fierce competition with each other than with brands in other clusters.
grouping clusters in the city, you can choose the most appropriate markets for certain goods.
cluster analysis reduces the dimensionality of the data.Making observations of individual clusters, then move to multiple discriminant analysis.It is much easier and cheaper than consider each case.
aim of clustering is to group objects on similar grounds.For a more objective assessment of the degree of similarity should introduce certain standards of units.In forming clusters typically rely on two or more features at the same time.
Cluster analysis involves the use of a wide range of clustering methods.Among them are such as probabilistic approach, approaches, which are based on artificial intelligence, logical approach, hierarchical approach.
Hierarchical cluster analysis involves a complex system that has a number of sub-groups or clusters of different orders.This method uses two kinds of characteristics.Agglomerate (Unity) signs coexist with divizivnymi (Separate).The number of signs leading to the division on monothetic classification methods and polythetic.
Using all these methods in statistics, there are about a hundred of clustering algorithms.But the hierarchical cluster analysis occupies a leading place on the list.Its appeal lies in the fact that it works well with the lack of data, even when the available data is not the fulfillment of conditions according to the requirement of normally distributed random variables, as well as other requirements of the classical statistical methods.