  Today brand managers and marketing specialists can leverage huge amount of
data to reveal patterns and trends in consumer perceptions, monitoring positive
or negative associations of brands with respect to desired topics. In this
study, we apply the Semantic Brand Score (SBS) indicator to assess brand
importance in the fashion industry. To this purpose, we measure and visualize
text data using the SBS Business Intelligence App (SBS BI), which relies on
methods and tools of text mining and social network analysis. We collected and
analyzed about 206,000 tweets that mentioned the fashion brands Fendi, Gucci
and Prada, during the period from March 5 to March 12, 2021. From the analysis
of the three SBS dimensions - prevalence, diversity and connectivity - we found
that Gucci dominated the discourse, with high values of SBS. We use this case
study as an example to present a new system for evaluating brand importance and
image, through the analysis of (big) textual data.
