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Leveraging Co-Followership Patterns on Social Media to Identify Brand Alliance Opportunities

Leveraging Co-Followership Patterns on Social Media to Identify Brand Alliance Opportunities

Pankhuri Malhotra and Siddhartha Bhattacharyya

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The use of co-branding and brand extension strategies to access new markets (and potentially attract a larger brand audience) has grown significantly in the past few decades. The recent co-branding deal between Starbucks and Spotify—two seemingly unrelated brands—shows that mashing up two bona fide brands, especially those in diverse industries, can be a lucrative marketing strategy. By providing premium coffee-shop music, Starbucks incentivized Spotify users to join its loyalty program. In return, Spotify grew its user base by leveraging Starbucks’ offer of a free coffee for joining the music streaming service.

Although mangers have been leveraging the synergistic benefits of co-branding for decades, surprisingly few studies have sought to identify potential co-branding alliances between brands belonging to different categories. In a new Journal of Marketing article, our research team introduces a new automated scalable approach for identifying potential co-branding and brand extension opportunities using brand networks derived from publicly available Twitter followership data. We present a new construct, brand transcendence, that measures the extent to which a brand’s followers overlap with those of other brands in a new category. For example, the transcendence of a non-sports brand along any given category—for example, say sports—is based on the extent to which its followers overlap with those of other brands in the sports category. 
 
Our research team reveals cross-category branding insights in the form of brand–brand and brand–category connections, which can serve as important measures for assessing co-branding and extensions opportunities. For instance, brand–category connections capture the transcendence of brands into new categories and show that certain categories are more viable for extensions than others. Brand–brand connections, on the other hand, provide a more granular view of transcendence by revealing the individual brands that are suitable for co-branding. For instance, the transcendence vector of Mercedes shows that the strongest brand-category connections of Mercedes are to luxury, technology, and sports– making them suitable categories for extensions. Then, at the brand-brand level, we find brands such as Louis Vuitton, Nike, Tissot, and Chanel to have strong connections to Mercedes, making them potential candidates for co-branding. We also capture the asymmetric relationships between brand pairs to identify brands that may potentially benefit more from a co-branding alliance. 
 
Because user–brand relationships on social media can change, we document the fluctuations of brand connections over time and investigate the impact of such fluctuations on co-branding alliances. Understanding whether critical connections with certain brands or prospective categories have waned can help managers promptly identify the problem and take appropriate action. Similarly, identifying new connections that have formed over time can illustrate how past marketing actions can impact a brand’s transcendence in users’ minds. 
 
The cross-category connections revealed through the network can be used to assess the effectiveness of previous marketing campaigns and discover new alliance opportunities. For example, Bud Light’s connection to Pepsi reflects the co-interest patterns between the two brands and, thus, affirms the effectiveness of their previous joint marketing campaign. Similarly, Sierra Nevada’s strong connections with travel and technology brands (e.g., Southwest Airlines, Discovery, SpaceX, and Microsoft) highlight the strong co-interest with these brands and present new co-branding opportunities that may yet be known to its owners. We provide examples of both scenarios using information from external industry sources.
 
Another practical application of our method is competitor analysis, which can help managers identify the differentiating connections of brands with respect to their competitors and gauge the type of users their competitors attract. For instance, in our study, we investigate the transcendence of beer brands, Bud Light and Sierra Nevada, into different categories and gauge the type of audience these brands attract. Whereas Bud Light has high transcendence into food and dining, Sierra Nevada has high transcendence into travel, airlines, and technology. Regarding centrality, Bud Light outperforms Sierra Nevada, with stronger connections within the beer category. Thus, whereas Bud Light is positioned strongly amongst beer and food enthusiasts, Sierra Nevada resonates more with technology and travel enthusiasts.
 
Overall, the core contribution of our study is a new digital approach to analyzing audiences’ interests across a broad brand ecosystem. The cross-category insights generated by this approach can help researchers and practitioners avoid marketing myopia by identifying nontraditional branding opportunities that are difficult to infer from traditional survey-based approaches. From a managerial perspective, our brand network can efficiently and cost-effectively generate cross-category insights, given that most of the data collection and network analyses are automated. Furthermore, as our approach uses information that is publicly available on social media, it can be easily scaled to a large number of brands, with the resulting network structures reflecting the preferences of a diverse set of users. 

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From: Pankhuri Malhotra and Siddhartha Bhattacharyya, “Leveraging Co-Followership Patterns on Social Media to Identify Brand Alliance Opportunities,” Journal of Marketing.

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Pankhuri Malhotra is Assistant Professor of Marketing, University of Oklahoma, USA.

Siddhartha Bhattacharyya is Professor, Information and Decision Sciences, University of Illinois at Chicago, USA.