Author
Listed:
- Tobias Olbrück
- Peter A. Gloor
- Ludovica Segneri
- Andrea Fronzetti Colladon
Abstract
Many companies are facing increased competition within their industry. One way to succeed is to understand potential customers and their needs, to differentiate from competitors with a strong brand personality and a targeted social media presence. User-generated content (e.g., Twitter tweets or Facebook posts) and its language style tell a lot about a person. We can infer feelings, behavior, and personality from this analysis. This chapter uses social media data and machine learning approaches to explore how congruence between customer and brand personality influences a company’s success. We collected and analyzed Twitter data on 29 car manufacturers in the U.S.A. and information on their potential customers. We used IBM Watson Personality Insights and Griffin Tribefinder to compute personality features for each customer and the brand’s “brand personality”. Our results show that brands with a personality similar to their customers are more successful in sales. However, there are also a few traits where customers prefer an opposite brand personality to complement their personality. This study helps marketing managers to implement strategic choices to improve their competitive advantage, aligning new products with the expectations of their customers.
Suggested Citation
Tobias Olbrück & Peter A. Gloor & Ludovica Segneri & Andrea Fronzetti Colladon, 2024.
"How does congruence between customer and brand personality influence the success of a company?,"
Chapters, in: Peter A. Gloor & Francesca Grippa & Andrea Fronzetti Colladon & Aleksandra Przegalinska (ed.), Handbook of Social Computing, chapter 10, pages 190-215,
Edward Elgar Publishing.
Handle:
RePEc:elg:eechap:21469_10
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