Leveraging the power of big data represents an opportunity for researchers and managers to reveal patterns and trends in social behaviors and consumer perceptions. This workshop presents the Semantic Brand Score (SBS), a methodology of assessment of brand importance that combines methods and tools of Text Mining and Social Network Analysis (Fronzetti Colladon, 2018). The workshop also describes the functionalities of the Semantic Brand Score BI App, which has been designed to assess brand/semantic importance, analyze brand image and mine textual data. Its analytical power extends beyond “brands”, comprising applications to study: commercial brands (e.g. Pepsi vs Coke); products (e.g. pasta vs pizza); personal brands (e.g. name and image of political candidates); set of words representing values (e.g. a company’s core values) or concepts related to societal trends (e.g. words used in media communication that impact consumers’ feeling about the state of the economy). The App generates a wide range of analytics that have been used, for example, to build predictive models to understand tourism trends, select advertising campaign testimonials, and make economic, financial and political forecasts. Gaining a deeper understanding of brand importance and image can change the way we make decisions and manage organizations in the era of big data.
Activities and Learning Objectives
Presentations followed by activity learning and discussion of small case studies.
Hands-on tutorial where participants are engaged in individual exercises/application of the App and short group work activities facilitated by the organizers.
Measure semantic/brand importance along its three dimensions (prevalence, diversity and connectivity) and evaluate semantic/brand image.
Analyze brand positioning and generate business intelligence reports.
Explain how techniques such as text analysis and use of brand mapping allow marketing managers to assess how brands are positioned in the minds of consumers or other stakeholders, and whether these associations are positive, negative or neutral.
Discuss how methods and tools of text mining, sentiment analysis and social network analysis can be used to complement/replace traditional market sensing techniques, including consumer focus groups, surveys and political polls.
Some basic knowledge of social network analysis and/or text mining could be a plus if participants plan to use this for academic purposes. However, previous knowledge of these methods is not required.
There are no strict software requirements as SBS BI is a web-based application. However, we suggest:
SBS BI workshops are regularly offered as part of three master’s programs in Data Science, Business Management and Economic Intelligence, all held at the University of Rome Tor Vergata (Italy). The workshop is also part of the course of Business Management and Analytics offered at the master’s program in Mechanical Engineering of the University of Perugia (Italy). Previous editions include: 2020 Management and Artificial Intelligence Program of Kozminski University (Poland).
Please directly apply through the conference or university websites.
Course materials will be provided to the attendees, during the course. Additional materials are linked in the following.
YOUTUBE VIDEOSSome videos describing the Semantic Brand Score, its applications and the Web App are available HERE.
Main References Please Cite as
MAIN PAPERFronzetti Colladon, A. (2018). The Semantic Brand Score. Journal of Business Research, 88, 150-160. https://doi.org/10.1016/j.jbusres.2018.03.026
WEB APPFronzetti Colladon, A., & Grippa, F. (2020). Brand Intelligence Analytics. In A. Przegalinska, F. Grippa, & P. A. Gloor (Eds.), Digital Transformation of Collaboration (pp. 125-141). Springer Nature Switzerland. https://doi.org/10.1007/978-3-030-48993-9_10
More articles, case studies and scientific materials are available HERE.