Case Study: Text-Mining Customers View Point and Perceived Value About Brand

Abstract

paper describes how text mining techniques can be applied in the analysis of consumer voice to gain useful and actionable business insights for marketers. The technique is illustrated via its application to understand Brand’s perceived value of certain automobile brands. This case study shows the use of text mining techniques to understand brand’s perception vis-a-vis competition from their opinion, sentiment and reactions. As the amount of online text increases, the demand for text classification to aid the analysis and management of text is increasing. Data acquisition in this case is not costly, information is rich in nature, classification of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. In this paper, we mention about a procedure of classifying text using the concept of association rule of data mining and correspondence analysis for Brand perception. Voice of the customer analysis can have significant value for organizations looking to listen to and understand the customer’s “voice” (e.g., from surveys, social media, complaints or web chat) to improve operations and help direct strategy. This approach can, ultimately, help improve customer satisfaction, Net Promoter Score (NPS) and loyalty while reducing churn and dormancy, thus increasing revenues. Consumers’ Experience about a brand depends upon their expectations and engagement across touch-points of the brand. Assess Customers Purchase, Usage & Service experience and mindset from social media as emerged touch point helps to understand Brand Imagery.

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