The quest to predict the way brand images get hijacked

Schulich is developing a tool to help marketers predict negative brand attention

Shane Schick July 25, 2016

Screen Shot 2016-07-22 at 1.58.52 PMResearchers at York University’s Schulich School of Business say they may be close to creating a way to analyze data and create a way for marketers to see how consumers might create negative mirror-images of them online, in social media or elsewhere.

Speaking at a master class in customer experience design last week, Markus Giesler, the school’s associate professor of marketing and director of its Big Design Lab, said he and his team were analyzing and mapping conversations about brands that may turn ugly. An example might be Uber, for instance, which grew rapidly for its on-demand ridesharing service, but spawned untold numbers of memes and online content that parodied its value proposition.

“A dashboard system is in development,” Giesler said, adding that the work involved identifies trends or patterns that could be common and predictive. “The real problem is not neutralization, but early detection.”

The idea of consumers creating their own anti-advertising about certain products and services has been around for years, of course, but Giesler pointed out that social media and similar services are making it much easier for what he called “doppelganger brand images” to be distributed much more widely than ever before. He looked at the recent Pokemon Go phenomenon, for instance, where there are already GIFs and other content being posted online making fun of people obsessed with looking for digital creatures using their smartphone.

“To some extent, marketing is all about political struggles,” he said, between forces that try to co-opt brand messages and turn them back on their creators. “Success is all about winning a conversational battle.”

For years now, Schulich has been working on a methodology to combat doppelganger brand images after they’ve been seen. The steps form the acronym MINT, which stands for Monitoring, Invert, Neutralize and Transform. Companies monitor for such images, then try to “invert” them back to what the brand wants to convey, neutralize the negative conversations and, ultimately, transform such conversations so the right message is getting out as quickly and as widely as possible.

While Giesler said MINT had already proven successful, creating a way of forecasting the danger would be obviously more powerful. He showed how the analysis of negative perceptions could be broken down and put into categories (in Uber’s case, such categories included “the exploitator,” “the menace” and “the bully”). The dense patterns of conversations online were shown in colourful visuals that looked sort of like the ink blots in Rorschach tests.

Whether brands get better at staying one step ahead of doppelgänger brand images, Giesler suggested they acknowledge the propensity for having their messages distorted is always there.

“We’ve gone from a customer-centric mindset to one where all voices matter,” he said. “It’s not a matter of positioning or labelling: anybody can now be a part of what shapes a brand experience.”