Brand Intelligence Analytics

Abstract

Leveraging the power of big data represents an opportunity for brand managers to reveal patterns and trends in consumer perceptions, while monitoring positive or negative associations of the brand with desired topics. This chapter describes the functionalities of the SBS Brand Intelligence App (SBS BI), which has been designed to assess brand importance and provides brand analytics through the analysis of (big) textual data. To better describe the SBS BI's functionalities, we present a case study focused on the 2020 US Democratic Presidential Primaries. We downloaded 50,000 online articles from the Event Registry database, which contains both mainstream and blog news collected from around the world. These online news articles were transformed into networks of co-occurring words and analyzed by combining methods and tools from social network analysis and text mining.

References in corpus (4)

• The Semantic Brand Score
• Distinctiveness Centrality in Social Networks
• Forecasting election results by studying brand importance in online news
• Studying the association of online brand importance with museum visitors: An application of the semantic brand score

Cited by in corpus (4)

• Forecasting consumer confidence through semantic network analysis of online news
• A new system for evaluating brand importance: A use case from the fashion industry
• Forecasting financial markets with semantic network analysis in the COVID-19 crisis
• Brand Network Booster: A new system for improving brand connectivity