Mining brand perceptions from twitter social networks

Aron Culotta, Jennifer Cutler

Research output: Contribution to journalArticlepeer-review

92 Scopus citations

Abstract

Consumer perceptions are important components of brand equity and therefore marketing strategy. Segmenting these perceptions into attributes such as eco-friendliness, nutrition, and luxury enable a fine-grained understanding of the brand’s strengths and weaknesses. Traditional approaches towards monitoring such perceptions (e.g., surveys) are costly and time consuming, and their results may quickly become outdated. Extant data mining methods are unsuitable for this goal, and generally require extensive hand-annotated data or context customization, which leads to many of the same limitations as direct elicitation. Here, we investigate a novel, general, and fully automated method for inferring attribute-specific brand perception ratings by mining the brand’s social connections on Twitter. Using a set of over 200 brands and three perceptual attributes, we compare the method’s automatic ratings estimates with directly-elicited survey data, finding a consistently strong correlation. The approach provides a reliable, flexible, and scalable method for monitoring brand perceptions, and offers a foundation for future advances in understanding brand-consumer social media relationships.

Original languageEnglish (US)
Pages (from-to)343-362
Number of pages20
JournalMarketing Science
Volume35
Issue number3
DOIs
StatePublished - May 1 2016

Keywords

  • Attribute ratings
  • Big data
  • Brand image
  • Data mining
  • Market structure
  • Perceptual maps
  • Social media
  • Social networks
  • Twitter

ASJC Scopus subject areas

  • Business and International Management
  • Marketing

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