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How Social Media and AI Enable Companies to Track Brand Reputations in Real Time

How Social Media and AI Enable Companies to Track Brand Reputations in Real Time

Roland T. Rust, William Rand, Ming-Hui Huang, Andrew T. Stephen, Gillian Brooks and Timur Chabuk

Organizations’ brand reputations can rise and fall based on brand-related events. For example, when Goya CEO Robert Unanue suggested that the 2020 U.S. presidential election was fraudulent, that controversial assertion likely offended a large segment of the population. How can we tell? In a new Journal of Marketing study, our research team demonstrates that by using artificial intelligence (AI)-based text analysis of social media, we can monitor the extent to which brand reputation rises and falls over time. What’s more, by merging this social media monitoring with the Rust-Zeithaml-Lemon customer equity drivers, we can show exactly which dimensions of brand reputation are changing. 
 
All marketers know that brands are important and that stakeholders’ views of the brand are reflective of many different factors.  Also, brand reputation may rise or fall over time, due to events that affect the brand. The fact that brand reputation is not constant makes it essential for companies to monitor their brands continuously, to determine whether a brand’s reputation is changing, and to evaluate which aspects of the brand are causing these changes. Survey-based approaches exist, but surveying stakeholders on a daily basis is generally too expensive to be practical. Another approach is to infer what is happening to brand reputation by mining social media.  Automatic, AI-based text analysis of social media posts is a realistic alternative. 

Because Twitter is widely used by people to express opinions about brands and is often monitored by the public, we chose it as our platform to explore and baseline results. By analyzing millions of Twitter tweets, we were able to demonstrate that our brand reputation tracker accurately reflected major brand events in real-time. For example, when it was revealed that Facebook had improperly shared personal information with an outside company (Cambridge Analytica), our brand tracker reflected that right away with a decline in brand reputation. On the positive side, when Google added new features, its brand reputation ratings went up. 

It is one thing to know that brand reputation is improving or declining, but another thing entirely to figure out why. To ensure the actionability of our brand reputation tracker, we sorted the tweets according to the Rust-Zeithaml-Lemon customer equity drivers, which have been applied by many Fortune 500 companies. The three main drivers of customer equity according to this framework are value, brand, and relationship. The value driver considers the rational or objective aspects of the brand, such as price, quality, or convenience. The brand driver considers the emotional or subjective aspects of the brand, such as attitude toward the brand, or perceptions of the brand’s ethics. The relationship driver focuses on the aspects of the brand that create switching costs, such as loyalty programs or knowledge of the brand. These three drivers, along with their sub-drivers, help managers know where to focus, making the brand tracker managerially relevant and actionable. 

Our brand reputation tracker is unique in that it can reflect the impact of brand events in real-time and connect them in a more granular way to managerially specific drivers of brand reputation. Managers can use it to drive programs that enhance their brands’ standing with customers, forging deeper relationships and ultimately delivering more revenues to their bottom line.

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From: Roland Rust, William Rand, Ming-Hui Huang, Andrew Stephen, Gillian Brooks, and Timur Chabuk, “Real-Time Brand Reputation Tracking using Social Media,” Journal of Marketing.

Roland T. Rust is Distinguished University Professor and David Bruce Smith Chair in Marketing, and Executive Director of the Center for Excellence in Service, Robert H. Smith School of Business, University of Maryland, USA.

William Rand is Associate Professor of Marketing, Poole College of Management, North Carolina State University, USA.

Ming-Hui Huang is Distinguished Professor, Department of Information Management, College of Management, National Taiwan University, Taiwan.

Andrew T. Stephen is Associate Dean of Research and L’Oreal Professor of Marketing, Säid Business School, University of Oxford, UK.

Gillian Brooks is Assistant Professor in Marketing, King’s Business School, King’s College London, UK.

Timur Chabuk is Vice President of Machine Learning and Advanced Analytics at Perceptronics Solutions Inc., USA.