Comparative Study of Clustering Techniques in Market Segmentation

Abstract

This is a comparative study of clustering techniques, but focused in the area of market segmentation. By understanding the potential benefits of clustering large amounts of data, the work is to relate clustering into the field of competitive marketing. This is achieved by gathering data from the Twitter using necessary tools and then cautiously applying various clustering algorithms. From these algorithms, we are able to build graphs based on the output, and from these graphs, useful information can be advantages from a strategic marketing point of view. From the clustering of Twitter data, it is easy to identify potential social media influencers. For future implementation, one method is that the companies that are struggling to grow in terms of online marketing can benefit from this study, allowing them to identify their own social media influencers, identify social media trends and determine the online social media market segmentation. All of which will provide them an advantage in further promoting their company more effectively and efficiently.