Bridging the gap between Marketing and Big Data: Improving Market Segmentation using Big Social Data Analytics

Elisa Rossini

Student thesis: Master thesis

Abstract

Big Social Data Analytics is acquiring increasing importance in many fields from the visualization and exploration of the online discourse, to the prediction of company's sales. Indeed, given the incredible expansion of social media and the accessibility of the Social Data therein, marketing actors have greater possibilities of using this data for improving or adjusting marketing strategies. The literature on the use of big social data for marketing segmentation purposes is recent and limited to applied cases, therefore this thesis has the aim of bridging the gap between big social data and marketing segmentation. This has been done through the definition of a methodology to extract psychographic and demographic attributes from different text variables of a Twitter users’ database. As a result, the application of the proposed methodology has shown that the integration of the results from the mining of Twitter users texts can not only efficiently contribute to market segmentation, but also provide a deep and comprehensive understanding of the market, including its opportunities and threats.

EducationsCand.merc.smc Strategic Market Creation, (Graduate Programme) Final Thesis
LanguageEnglish
Publication date2017
Number of pages82
SupervisorsRavi Vatrapu