A new algorithm tracks people’s perception in real time via Twitter.
or decades, marketers have relied on surveys to gauge how customers perceive their brands. While this tried-and-true method does a good job of revealing how brands stack up against the competition on everything from health to luxury, it is also time-consuming and labor-intensive. By the time you have survey results in your hand, they may already be out of date.
Jennifer Cutler, an assistant professor of marketing at the Kellogg School, thinks it may be time to send many surveys into a well-deserved retirement. Instead, she and a coauthor have developed a real-time tool based on Twitter activity.
Cutler and coauthor Aron Culotta of the Illinois Institute of Technology have created an approach that allows marketers to track in real time how their company compares to others for any attribute that interests them: in minutes, a marketer can know whether customers see Tesla as more or less luxurious than Porsche, a task that previously might have taken weeks or even months to complete. This is accomplished not by tracking what users are posting to Twitter, but rather whom they follow—an approach Cutler believes offers deeper and more nuanced insights into how companies are viewed.
Consumers reveal a lot about themselves online, even when they say nothing at all.
“There’s a lot of excitement in the field of marketing about the potential to extract insights about consumers from these data, but there’s definitely been a struggle to figure out how to do that,” Cutler explains. Thus, much of that data remains untapped by marketers. Thanks to research like hers, however, “a lot of the barriers to entry and a lot of the obstacles to applying large-scale data mining for marketing insights are falling down.”
The Power of Social Media Data Mining
When marketers look to social media, they are often focused on what consumers are saying about their brands. Though Cutler believes text analysis has its place, there are serious drawbacks to relying on text alone. For example, although 20 percent of US adults have Twitter accounts, fewer than half post actively.
“Among those that write, very few are going to write about a brand, and even fewer still are going to write about your brand,” Cutler explains.
But consumers reveal a lot about themselves online, even when they say nothing at all.
These Twitter lurkers are following other users—companies, politicians, celebrities, friends—and making lists of accounts, organized by topic. Through lists, users can create their own curated newsfeeds around topics of interest (“sports,” “science,” or “politics”). And unless they have made their Twitter account private, all of this information is publicly available.
Across these many millions of user-curated lists, certain commonalities begin to emerge. @ESPN, for instance, might appear on many user lists labeled “sports” because users strongly associate it with that topic. Ditto @DogRates and “cute,” or @nytimes and “news.”
This is the basis of Cutler’s algorithm, which identifies exemplary accounts for particular topics. The tool searches for accounts that appear on many lists labeled, for instance, “environment,” and narrows those accounts down to the strongest exemplars. In the “environment” example, @SierraClub or @Greenpeace might be exemplary accounts.
The algorithm then looks for overlap between the followers of the exemplary accounts (@Greenpeace) and the followers of a particular brand (say, Toyota Prius). This information is used to compute a score between zero and one that shows how the brand is associated with the attribute. Lower scores mean most customers do not associate the brand strongly with the attribute (say, Walmart and luxury); higher scores indicate a stronger association (Toyota Prius and the environment).
To test the reliability of the method, the researchers compared their computer-generated results with traditional survey results for 239 brands. The researchers recruited survey participants online and asked them to rank each brand from one to five according to how strongly they associated it with one of three attributes: eco-friendliness, luxury, and nutrition. They found that in most cases, the survey results closely matched the results produced by the algorithm.
One interesting exception: survey participants rated Lamborghini higher on luxury than the algorithm did. It was an intriguing anomaly, considering the brand’s reputation and the eye-popping cost of its cars. So they looked more closely at Lamborghini’s Twitter account to figure out why the algorithm might have faltered.
In contrast to other companies, which use Twitter to interact with customers or share information about their product, Lamborghini’s account was a more general news feed on car technology.
“We think that it’s quite possible that people were following Lamborghini and ‘news feed’-type brands for systematically different reasons than why they might follow other brands,” Cutler explains.
Why consumers follow particular brands, and how brands use social media to achieve a variety of strategic aims, is something Cutler hopes to investigate more deeply in future research.
Overall, however, Cutler and Culotta found their tool provided a highly reliable measure of brand perception. And in contrast to the sluggish process of administering surveys, the algorithm can respond quickly to shifts in public perception or changes in a particular area of interest.
“Anytime we want to run this model, we can just query again, and if there are new players in the field—new, trendy sustainability exemplars—then we’ll catch them with the new query,” Cutler says.
She hopes marketers will realize that “it’s important to consider your followers’ social relationships and social networks on social media, not just what they say. What we’re showing here is that networks can provide a lot of extra information that is often missing in text.”
It is an insight Cutler believes can be applied much more broadly.
“Although we talk about brand perception specifically in this paper, the general idea of looking to your users’ network connections can be applied a lot of different ways,” she says. For example, she is currently at work on a project that uses similar data-mining techniques to help marketers develop customer personas.
And she hopes as social-media data mining becomes more accessible to marketers, it will allow them to gain insights into deeper and more abstract qualities of brand image.
“As we develop these new techniques, it can start to open the door to new types of questions that marketers can ask that they haven’t been able to ask before,” she says.
This article first appeared in www.insight.kellogg.northwestern.edu
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