In its most general definition, a cluster is a group of similar things or people positioned or occurring closely together. In market research, a cluster is a collection of data objects that are similar and dissimilar to each other. The primary objective of cluster analysis is to classify objects into relatively homogeneous groups based on a set of variables considered. These variables (demographics, psychographics, buying behaviors, attitudes, preferences, etc.) can be chosen according to the market research objectives; which problems are needing to be solved and which hypotheses need to be proven or debunked.
The clusters are not predefined, but rather naturally suggested by the data, revealing any similarities or dissimilarities. Therefore, it is important that the researcher has a thorough understanding of the objectives so that all pertinent data is collected and various cluster analyses can be conducted to see what patterns emerge.
Applications for Cluster Analysis
Market Segmentation: Companies can’t connect with all of their customers, but by dividing markets into groups of consumers with similar needs and wants, they can position themselves to appeal to these unique segments. Consumers may be clustered on the basis of benefits sought from the purchase of a product. Each cluster would consist of consumers who are relatively homogeneous in terms of the benefits they seek, thus allowing companies to deploy targeted marketing campaigns that promote the most alluring benefits and products to this consumer segment.
Understanding Buyer Behaviors: Cluster analysis can be used to identify homogeneous groups of buyers. Then, the buying behavior of each group can be examined separately on measures such as favorite stores, brand loyalty, price willing to pay, frequency of purchase, etc.
Identifying New Product Opportunities: By clustering brands and products, competitive sets within the market can be determined. Brands in the same cluster complete more fiercely with each other than with brands in other clusters. A company can examine its current offerings compared to those of its competitors to identify potential new product opportunities.
Selecting Test Markets: By grouping cities into homogeneous clusters, it is possible to select comparable cities to test various marketing strategies.
Data Reduction: A researcher may be faced with a large number of observations that can be meaningless unless they are classified into meaningful groups. Cluster analysis can help by reducing the information from an entire population of sample to information about specific groups.
Hypothesis Generations: Cluster Analysis is also useful when a researcher wishes to develop hypotheses concerning the nature of the data or to examine previously stated hypotheses.
Steps for Cluster Analysis
Formulate the problem – Select the variables on which the clustering will be based. The variables should describe the similarity between objects in terms that are relevant to the research problem. The variables should be selected based on past research, theory, the hypotheses being tested, or the judgment of the researcher.
Select a distance measure – An appropriate measure of distance needs to be selected to determine how similar or dissimilar the objects being clustered should be. The most commonly used measure is Euclidean distance.
Select a clustering procedure – Several clustering procedures have been developed and the one most appropriate for the problem at hand should be chosen.
Decide on the number of clusters – The number of clusters can be based on theoretical, conceptual, or practical considerations.
Interpret and profile clusters – This involves examining cluster centroids. The centroids represent the mean values of the objects contained in the cluster on each of the variables.
Asses the validity of clustering – Some methods to validate the data quality include using different methods of clustering and comparing the results or clustering on a smaller set of variables (randomly deleted) and comparing the results with the entire set of variables.
Cluster analysis has many useful applications. In the field of marketing, it is widely used for market segmentation and positioning, and to identify test markets for new product development. In social networking and social media, cluster analysis is used to identify smaller communities within larger groups. Insurance companies use it to identify groups of policy holders with highest average claim costs. While there are various software programs to assist with the analysis portion, it is best to partner with a skilled researcher to ensure the study is designed to collect relevant data from representative and adequate sample sizes.