The investment in social listening platforms is substantial and consistently expanding which highlights its established importance in corporate decision-making. More than 30% of professionals utilize multiple social listening tools, with the highest budgetary proportion falling within the $100K–$199K annual range, according to Social Intelligence Lab’s State of Social Listening, 2025 report). This financial commitment is mirrored in its strategic application with 30% of agency professionals claiming that social listening is critical to every decision and 80% of strategists relying on it at least monthly (WARC: Future of Strategy). The primary objective for social listening is cultural & trend analysis which is incredibly valuable in the current advertising landscape that relies on brands tapping into culture with precision, consistency and creativity. However, in recent years, this social listening data has become increasingly flawed, leading to it becoming culturally deaf.
This has been coming for a long time as social listening tools have primarily been able to analyze just the text based captions on social media content. They had a revolutionary birth as they introduced the ability to monitor hashtag usage, understand the sentiment of a sentence, dissect meaning from a paragraph and quantify the impact of articles. This offered incredible insight into “water cooler” conversations that had never been available to researchers before. However, when Instagram shifted the focus to images, they quickly proved the adage that “a picture is worth a thousand words.” The problem then grew exponentially with the more recent focus on video (TikToks, Instagram Reels, Stories). The text caption became a minor footnote to the larger context of a data-rich video post. Data tools cannot genuinely listen to, watch, or read all of that content.
The second major culprit is the social media platforms (Meta, X, TikTok, YouTube, etc.). Due to valid concerns about customer privacy, they have become less willing to share data with vendors. The APIs narrowed the focus and exposure of consumers to a stifling degree. The APIs now create an “Observable Universe” of the internet, but as any astronomer knows, the difference between the Observable Universe and the actual universe is several orders of magnitude. This “Observable Universe” issue is exacerbated as real consumers retreat into unobservable corners of the internet. Even with better photo and video data, tools cannot scan rich comment sections or private DMs which are the spaces where meaning making dialogue exists.
But the most villainous actor in the deafening of social listening is AI itself. AI bots now flood the internet with an insurmountable quantity of slop content. This bot noise led to the infamous Cracker Barrel Curfuffle, where consumer opinion was massively overestimated due to perceived social media noise, leading to negative real-world decision-making. The onslaught of AI-slop content has created a deafening amount of noise that severely degrades the value of social listening. It has also made the main feed less appealing for real consumers which further drives them toward these private spaces of social media. This then creates a vicious cycle of shrinking the observable universe.
All of these issues lead to a simple and fundamental problem which is a basic principle of science, Bad Data in equals Bad Data out. The social listening data that goes into these data tools no longer encapsulates a significant proportion of the cultural activity of the users, therefore the results that come out are becoming increasingly culturally deaf.
So the current state of social listening is predominantly listening to Twitter, mostly just reading the captions, and only for very public channels/posts, which is dominated by AI. If you think you are going to get powerful true human insights out of that skewed data set, you would be wrong. And if you think you can rely on newer fancier AI agents to comb through that data more effectively and still generate good insights, you would still be wrong.
We need to shift our focus from the quantitative results that the social listening tools can provide and instead emphasize the qualitative insights that only a human can find, hear, watch and read. A human understands how to use the data platforms as a tool, but can also explore the content natively. Many social listening experts realized this a long time ago and have practiced the methodology of Netnography. I have had the privilege of working with Rob Kozinets, the author of this methodology, and know how powerful it is to combine an anthropological approach to a digital world. An honest researcher knows that they must actually meet the people (even digitally), consume the content, interact with the audience and become a part of the community to derive the most powerful insights.
After we have gone native, we should also rely on survey research to hone our insights and provide quantitative proof that helps make business decisions. Over the last three years, the industry has shifted back to focus groups and surveys, however, they are often being done the same way they were done 20 years ago. Instead, we need to layer our abilities to derive insights so that with each layer we learn more. In my ideal world, these successive layers would be Netnography to inform in-field ethnography to inform a survey that validates the findings and then we use social listening to provide some macro data points and additional qualitative evidence.
Social listening is still incredibly powerful and I will always advocate for its strengths. It provided something that had never been done before, it turned the infamous “water cooler talk” into a public record that anyone could use for research. It publicized parts of consumer’s brains that a survey could never open up. It empowered consumers to share truth to power with almost no barriers to entry. However, just listening is no longer enough and researchers need to employ more of their human senses to discover new learnings.
The views expressed in this column are those of the author Russell Pinke – head of the data strategy department at WKNY where he works on clients including McDonalds, Ford, Delta and Marriott – and does not necessarily reflect the views of Brand Innovators.
