Experiment Designer
Social listening is being promoted more than ever to drive rapid innovation and deliver instantaneous insights. What is the story behind its attention? Does it justify the hefty price tags, or is it just noisy data dressed up as valuable insights?
After multiple years of running social listening projects for Fortune 500 companies, we faced a question:
Are these insights actually useful for innovation?
We found that the tools and methods were not delivering on their promises. The typical social listening dashboard full of word clouds and counting widgets, for example, left us with a lot of noise and without any actionable data. Without robust analytics widgets, we were left with purely qualitative insights that could’ve just as easily come from a few TikTok scrolls, scraping, or interviews.
By blending AI agents with the best LLM features and platforms like YouScan for visual insights, adding machine learning for clustering and basic analytics, we’ve created something we’re proud of that we’re excited to share with clients and to apply in future projects.
Going beyond typical tools, diving deeper into what really matters for innovation.
Instead of purely quantitative metrics such as likes or impressions, our framework prioritizes “engagement”, uncovering what users actually care about. By giving each point a scale to compare it against, you know what the most important pain points are to tackle first.
Giving you more personalized insights
We can tweak the agents to find exactly the insights you need to gain from social listening, such as your customers’ pain points or the communication of competitors.
Deeper insights with less bias
By having the agents analyze the natural language of the user in a non-controlled environment and use it as input, you don’t influence the user’s response as you would in a controlled environment—with a survey or an interview.
Before diving deeper into the details of agentic social listening, let’s decode the buzz around its use cases and challenges:
By analyzing a vast amount of data points in near real-time, companies can act on emerging trends with unparalleled speed, but the true value lies elsewhere. Social listening offers a unique opportunity to gain insights on what consumers actually think, beyond what they claim in surveys or official interviews.
With the right methodology and querying strategy, you can gain a comprehensive view of your customers, brand efforts, online presence, and competitors across virtually every channel, opening a door to deeper insights.
Straight from the source
Unlike traditional surveys, where participants might tailor their responses for a reward, social listening taps into the unfiltered voice of the consumer. We don’t see the inherent bias of content creators catering their content to gain reach, likes, or followers as a negative—this can show us what resonates with the audience, providing insights into the overarching preferences and trends.
Smart filtering
Sifting through the digital chatter to find those golden nuggets becomes almost effortless when combined with the right tools. For instance, smart visual filters, audience insights, and video scripts AI transcribers can highlight specific scenes of your interest, such as recipe preparation, family time, or consumption occasions.
Despite strong limitations of location tracking in data coming from major platforms due to privacy policies, many tools will allow you to zoom into geo-located narratives, allowing companies to personalize their communication across different territories and use the authentic tone of voice of their consumer.
Hear the authentic tone of voice of your consumer
Social listening captures the genuine language consumers use, allowing brands to understand not only what people are saying, but how they’re saying it. This authentic tone offers insights into consumer sentiment, emotional triggers, and key phrases that resonate, helping refine messaging to better align with their audience’s voice.
By analyzing conversations and interactions, social listening reveals distinct behavioral patterns across different audience clusters. Whether it’s food enthusiasts, fitness fans, or eco-conscious consumers, you can see how preferences vary across groups. This insight allows for targeted strategies that cater to each unique segment, ensuring campaigns and products are more precisely attuned to each audience’s specific needs and interests.
Unlike the structured data sets obtained from surveys, the datasets obtained from social listening are noisy and random, offering less control and unstructured outputs.
What challenges do you need to prepare for when thinking about implementing social listening?
Innovation challenges
Similarly, most tools are designed more for cursory glances than deep dives into market research and innovation, designed more for scratching the surface rather than diving deep into market real trends (no likes) and innovations.
Continue reading to learn more about our approach to Social Listening paired with LLMs.
Trend misidentification
A “trend” in marketing is just the most recent blip on the radar. There’s a major difference between short-term buzz and sustainable innovation trends, with many tools missing this distinction, focusing more on immediate engagement than what is an exploitable product design trend.
Overwhelming noise
The amount of irrelevant data can be overwhelming, with ads, spam, and irrelevant content making finding useful insights difficult. Many reports are therefore likely to be based on this noisy data that might be spammy or irrelevant. Ensuring all the data is relevant—by cleaning up millions of data points—can be impossible, especially as the data filtering relies on seemingly archaic boolean queries.
With Boolean queries, you need to know your target keywords beforehand, which means you can miss out on important content points if they’re not on your radar. Contrary to that, going too broad dramatically increases the amount of irrelevant information included in your analyses.
With many of the above challenges proving to be near impossible to overcome, integrating social listening with Large Language Models (LLMs) isn’t just a nice-to-have—it’s absolutely essential if you’re serious about unlocking the full potential of social insights.
Many dashboards on platforms like Sprinklr or Talkwalker provide surface-level insights like the number of mentions per channel or word clouds with no discernible value for innovation.
Activating agents
This is where LLMs come into play. AI-powered agents can extract scoping information and label it in a way that’s more suited for data-driven decisions and finding market opportunities.
With an agentic approach to social listening, you aren’t just counting mentions—agents are diving deep into context, sentiment, and meaning.
In the example below, agents identified a cluster of pain points, created a central node, and then described actions users tend to take to alleviate those pain points.
Thanks to contextual understanding, these pain points can be easily clustered through hierarchical relationships, allowing you to pursue your specific niche of any emerging trend.
The possibilities of uncovering insights with LLM-powered agents are limitless—especially since many of the inputs are text-based. Text is where LLMs truly excel, easily understanding captions, author bios, or video scripts.
In the following example, agents extracted and isolated phrases in a different language that perfectly capture the tone of voice from a transcribed video script. These are valuable insights that can already be passed along to a marketing team or other agents, allowing the brand to tailor the communication to their ideal customer base.
By integrating agents with social listening data, we unlocked the ability to analyze each script individually and extract relevant elements, making the variables in our data set more useful for our innovation process.
Are you ready to revolutionize your business insights with agent-powered social listening?
