When analyzing data in market research, we cannot expect to be able to examine every individual respondent separately. While each individual detail of research is important, it can become tedious and too time-consuming to evaluate hundreds and even thousands of distinctive cases of data at the same time. However, there is a method of segmentation that allows us to analyze this information without oversimplifying it, but at a level that is easier to process. Enter cluster analysis - an approach used in market research that allows researchers to identify and define patterns in data that had not been apparent before but could give significant meaning to the data once discovered.
What is clustering?
Cluster analysis is a statistical method used to group similar objects into respective categories and is sometimes referred to as segmentation analysis, taxonomy analysis, or clustering. In market research, a cluster is defined as a group or collection of data that is similar and dissimilar to each other. Clustering is typically performed during the beginning phases of research and is used to “sort” data into categories or groups based on a set of variables. These variables, ranging from demographics, psychographics, buying behaviors, customer attitudes, etc., can be chosen and adjusted according to any market research objectives. Although there are many methods used to perform cluster analysis, the three primary methods used regularly are hierarchical cluster, k-means cluster, and two-step cluster. These three methods can be applied in different ways depending on the size and type of the dataset - and what the researcher determines is the best approach to reach the desired outcome.
How can it be used in marketing?
When cluster analysis is performed as part of market research, specific groups can be identified within a population. The analysis of these groups can then determine how likely a cluster is to purchase products or services. If these groups are defined clearly, a marketing team can then target varying clusters with tailored, targeted communication. By dividing respondents into segments of consumers with similar needs and wants, businesses can then begin to find the best ways to appeal to these segments, better position themselves, expand to new markets, and develop products or services that cater to each cluster. If researchers want to understand consumer behaviors, cluster analysis can be used to identify similar groups of consumers and their behaviors can then be examined through variables such as favorite product/service, brand loyalty, pricing, and even frequency of purchases. From here, clustering can help businesses differentiate and become more competitive, especially if other brands are not catering to a specific group that is uncovered by clustering.
Key Takeaway
Cluster analysis is a method in research marketing that defines patterns in data that cannot be revealed by looking at data individually and relies on categorizing groups of people based on factors such as overall consumer habits or demographics. Clustering has many uses as organizations and brands can implement clustering in their research process to improve their brand positioning, identify and expand on new markets, or create new products/services for the right consumers.
