Brands cannot escape the impact of consumer backlash. It seems every week another company is facing PR headaches over a messaging misstep or an insensitive ad. Consumer opinions are quickly amplified thanks to social media and one negative opinion can spiral into millions of views.
It’s quite obvious, then, why marketers do everything in their power to avoid such scenarios. And yet, public relations disasters persist. That is because traditional safeguards are penetrable. Focus groups, surveys and one to one interviews all come with their own set of challenges. But artificial intelligence is slowly changing that, at a time when ad campaigns must iterate quickly and marketers are tasked with showing clear ROI on campaigns.
Let’s call a spade a spade, the negative backlash can be irreversible. While some have embraced controversy (most recently, Gillette), others have been forced to plead forgiveness in the court of public opinion. Think Dolce & Gabbana and Burberry, two luxury brands that almost consecutively offended Chinese consumers with tone-deaf ads. As a result, D&G merchandise was pulled from the shelves in China.
It’s certainly possible that the teams behind these not so successful ads didn’t test consumer opinion before releasing these campaigns, but it’s not highly plausible. Any reputable marketing or advertising firm utilizes insights and the best firms depend on these insights through each step of ad campaign development. But their ads still failed – why?
The most likely reasoning is that they simply collected the wrong insights. What they gathered may have been applicable, but it didn’t uncover the real truth, the one eventually exposed when the campaign was released to the masses. This is where AI will wield the most influence and where marketers stand to gain the most from this evolving technology.
The internet has enabled brands and agencies alike to gather consumer feedback at record speeds. Surveys that would take weeks, even months, ten years ago, can now be conducted in a few days’ time. The benefit to marketers is obvious: a wealth of insight in a short timeframe, allowing for quicker campaign iterations.
The downside, on the other hand, is the sacrifice of quality. Quick surveys, for example, lose the in-depth conversation that focus groups provide. And the online environment also poses its own set of challenges like professional survey takers, for one, as well as bots and a biased environment with aids such as Google. Tools and techniques that have allowed for understanding consumer insights at scale tend to focus more on quantitative results. After all, it’s easier to work with numbers than open-ended text responses. However, quantitative results focus more on the “what” than the “why”, leaving a gap in understanding between the brand and its consumers. In the quantitative model, some human understanding is lost.
A large, household-name toy brand was developing a new ad campaign, centered around a television ad. After editing the first cut of the commercial, they sought consumer feedback. Rather than run just an online survey, the company tapped AI to communicate with participants on a massive scale, which allowed them to generate specific insights and ask follow-up questions in just a few hours.
In doing so, they found that their audience found the ad too heavily geared towards boys and lacked an emphasis on girls’ imagination. The feedback was strong enough that it led to additional revisions to the commercial to better incorporate multiple imagination perspectives. Had the company been unable to identify this insight prior to releasing the ad, they very well could have faced backlash about gender representation, which would have erased any good the ad could’ve done.
AI allowed the brand to run a live, online session with over 100 respondents, who all responded to open-ended questions during a real-time conversation. The AI component helped to analyze the text, group similar thoughts, and concepts together, and then ranked responses by popularity, so that the brand could easily sift through the responses of the respondents. AI helped the brand to quickly make sense of open-ended, qualitative data that typically takes hours or even days to make sense of.
This is only the tip of the iceberg; AI is overhauling the way insights are derived. Today, researchers are able to strategically sort through data and pinpoint the trends and insights in ways they were never able to before, and at a faster rate. The confluence of data analytics, AI and social media, for example, provides the ability to deeply and rapidly analyze a wide breadth and volume of consumer opinions for trends and patterns, enabling comprehensive market research.
At Remesh, our AI-powered platform enables the facilitation of a qualitative conversation at a quantitative scale. A moderator is able to ask both poll and open-ended questions to a live, online audience and our machine learning algorithm works in conjunction with proprietary natural language processing to rank and structure the open-ended responses from participants in real-time. Ultimately, this allows individuals to seamlessly analyze and present free-form responses from a conversation with a live audience on the Remesh platform in a similar way to how you would work with quantitative data.
AI technology can handle the most important part of cleansing, consolidating, and most importantly, analyzing data for in-depth analysis. AI tools can help encode large quantities of complex qualitative data and look at them en masse to determine statistical significance. It can also help researchers understand their audience on a much deeper level that has a higher likelihood of representing the population they’re looking at.
All of these applications empower brands to make well-informed decisions prior to releasing something to the public. AI can truly be a saving grace for marketers and should be used as a defense against potential consumer backlash.
Gary Ellis is co-founder and COO of Remesh, a platform designed to organize the world’s voice into truths through engaging and understanding populations in real-time and enabling informed action at the speed of conversation.
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