This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Brand collaborations have been growing in popularity as a powerful marketing tool for businesses looking to generate hype and expand their customer base, but brand managers often rely on costly and time-consuming surveys and screenings to identify potential matchups. A recent study by a Penn State researcher and collaborators proposes a new method: an artificial intelligence (AI)-powered framework that can uncover promising brand collaborations by leveraging the social media chatter of consumers.
Combining traditional brand characteristics with information gleaned from social media posts that co-mention brands, the proposed model could provide brand managers with a suite of potential collaboration opportunities tailored to their specific businesses, according to the researchers, who published their framework in the journal Information Systems Research. In a survey of brand managers, brand pairs identified as favorable by the framework had nearly a 90% chance of receiving a higher selection rate than those the model eliminated as unlikely pairings, the researchers wrote. In addition, the framework categorizes its recommendations and provides practical guidance on the best way to pursue a collaboration, giving companies clearer insights into which brand alliances they should prioritize.
"Finding brand collaborations is a needle-in-the-haystack problem," said co-author Keran Zhao, assistant professor of supply chain and information systems at Penn State's Smeal College of Business. "Brand A might consider brands B, C and D as options, but they will never comprehend the whole brand network. The advantage of this framework is that it provides a systematic evaluation of hundreds of brands, as well as a detailed explanation about the how and why behind an opportunity." The proposed framework is described by the researchers as a four-step system built around two key concepts: signal fusion—the ability to synthesize disparate bits of information into a cohesive insight—and actionability.
The first phase focuses on the former, combining real-world brand info with co-mentions on social media to create a comprehensive network of brand connections that can identify promising potential partnerships. For the study, the researchers collected brand personality survey information on characteristics like sophistication and familiarity for 450 top business-to-consumer brands, as well as 477,933 posts mentioning these brands together on the social media site Twitter, now X, in 2020. Using AI-based text classifiers, the researchers filtered out complaint-related posts and analyzed whether consumers viewed brands as complementary or competing.
They then combined these insights with brand characteristics and co-mention frequencies to develop a broader network of brand relationships that could identify promising alliance opportunities. "The four of us could sit together for eight to 10 hours reading Twitter posts, and we would still only see a small fraction of the conversations happening around these brands," Zhao said. "AI allows us to analyze this information at a much larger scale." The researchers also tested the model's ability to predict future real-world alliances that hadn't yet developed, finding that the model was able to accurately predict 213 of 251 alliances that occurred after the data sample's cutoff date.
Phase two of the framework builds upon this validation test by surveying stakeholders about the model's proposed brand alliances. In addition to a consumer survey with 350 participants that found a strong preference for the model's recommendations over pairings the model eliminated, the researchers also surveyed 57 brand managers to gauge their interest in the model's proposals. Discover the latest in science, tech, and space with over 100,000 subscribers who rely on Phys.org for daily insights. d research that matter—daily or weekly.
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