The Language of Brands in Social Media

Discussion

Our framework helps systematically unpack a brand's image by outlining an approach to quantify its image through differential language analysis and topic modeling of online social media conversations. Our framework interprets higher-level interactions of words giving rise to topics. In doing so, it provides insights into how unique brand associations can be deduced via clusters of words that may be attributable to certain (but not all) brands. Firms can use topics uncovered in differential language analysis to judge positive and negative topic correlates associated with different brands within a category. There are a number of theoretical and practical implications of this analysis, as we outline next.

Theoretical and Practical Implications

The framework captures the extent to which social media data offer important insights into brand image perceptions and offers an approach to quantify a brand's positioning. We build on prior research by identifying how managers can use topic modeling to identify which brands consumers view as similar or dissimilar to each other, based on topics that are both positively and negatively correlated with the brands. The framework shows how the topics extracted from differential language analysis can be linked to future shifts in brand preferences, using TTV as a key metric.

We also highlight applications in the context of new product introduction and cobrand partner selection, thus providing insights into potential ways topic modeling can serve as a critical input to data-driven brand management. Topics can be linked to brands across multiple categories, thus giving managers opportunities for partnering with brands across categories and in identifying opportunities for cross-promotions, joint advertising, and the like. The Gatorade and Smirnoff example is one instance of how such partnership opportunities could be identified with the analysis.

Another benefit of the proposed approach is its ability to forecast sudden volatile shifts in brand perceptions before they are detected by traditional survey-based approaches. Social media data can be further analyzed to understand the causes driving changes in brand perceptions. Furthermore, this approach can help address whether cultural conversation have shifted, such that the brand is now less relevant to consumers. Knowing this can help managers increase the relevancy of a brand's advertising to its audience by understanding which topics dominate the conversation when consumers are discussing a given brand. Such data can reveal important clues that could help marketing managers become proactive in directing brand perceptions in the marketplace. Social media insights into brand perceptions can be important vehicles for instituting improvements that can reposition the brand and change the conversation surrounding it.

Limitations and Future Research Directions

Although our approach offers several advantages over traditional survey-based or qualitative approaches to brand image tracking, we acknowledge the possible biases that may exist even when using social media data. The data may be disproportionately skewed in favor of certain types of consumers and may not reflect the broader target audience for a given brand. In addition, our focus was primarily on Twitter, though other types of social media (e.g., Facebook) may also provide useful insights.

Potential extensions of our framework are worth pursuing. One possible direction is to combine the proposed unsupervised LDA technique with more supervised approaches such as Linguistic Inquiry and Word Count, which may offer insights into a brand's positioning. The role of topics in defining brand positioning provides a starting point for understanding how brands are categorized in consumers' minds and how this categorization (or groupings of brands with other brands having a similar topic correlations) can be exploited to generate insights into competitive market structures. However, there are ways of augmenting the text-mining approach with visual image–based data as well as videos to generate further insights into brand image (for a recent example of image-based feature extraction, see Liu, Dzyabura, and Mizik). Another potential direction for future research is to take a closer look at the dispersion statistics (standard deviation) surrounding a topic's relationship with a given brand across a corpus of a brand's messages. This could provide additional novel insights about the relationship of a topic with a brand, and when the variance increases, this could indicate a weakening of the topic–brand relationship. This analysis could also provide some important clues that could help predict future shifts in a brand's positioning.

Managers can also use the approach outlined here to examine how brand positioning shifts over time, based on external shocks such as brand crises incidents or introduction of new products within a category. This type of dynamic analysis (potentially using dynamic topic modeling) can build on previous research on the social media effects of crisis incidents. Research has also begun examining the morphology of tweets to assess how certain characteristics can drive engagement, and further research in this vein would benefit from using the proposed approach to identify topics of conversation that drive engagement.