A new system for evaluating brand importance: A use case from the fashion industry

TL;DR

This paper introduces a novel system utilizing text mining and social network analysis to evaluate brand importance in the fashion industry by analyzing large-scale social media data.

Contribution

It presents the Semantic Brand Score (SBS) indicator and the SBS Business Intelligence App for assessing brand importance through textual data analysis.

Findings

Gucci had the highest SBS among the brands analyzed

The system effectively visualizes brand prominence and connections in social media data

The approach demonstrates how big data can inform brand management strategies

Abstract

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.