Brand Intelligence Analytics
This provides brand managers with a tool for monitoring brand trends, but it is incremental as it applies existing methods to new data in a specific domain.
The paper tackles the problem of analyzing consumer perceptions and brand associations by developing the SBS Brand Intelligence App, which processes 50,000 online articles to assess brand importance through network analysis of co-occurring words.
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.