Text Clustering Techniques for Voice of Customer Analysis

A comprehensive analysis of customers is very vital for any organization for customer enlightenment. However, conventional methods are not effective in analyzing customers concerns particularly when it is online. Data generated online is extremely complicated because of its unstructured behavior, and the data set in this format is often complex and very difficult to evaluate its overall model. Further, the application of conventional methods remains monotonous as many emerging techniques are proposed by the researchers, but the most effective approach will remain thought-provoking. In this paper, we introduce effective methods to analyze customer concerns with a computational approach that perfectly apply text clustering techniques. An effective clustering technique can be applied to a large set of unstructured data since online data often contain huge amount of noise, and we will discuss different text clustering techniques to evaluate and analyze the method of text extraction from customer statements mainly on Web and social media for customer analysis.