What does social intelligence mean to you?
To me, social intelligence is the art and science of understanding people through the lens of their online conversations and behaviors. It's about uncovering patterns, emotions, and motivations that drive human action. Social intelligence is not just about listening—it's about connecting the dots between what people say, how they feel, and what they do to generate insights that inform meaningful decisions.
In my experience, social intelligence transforms raw data into stories that resonate. It's the ability to identify emerging trends, predict consumer behaviors, and gauge sentiment in real-time, enabling brands, organizations, and communities to adapt and thrive. Beyond business value, it's a tool for empathy—understanding audiences as individuals with unique needs, passions, and perspectives.
Social intelligence also means leveraging the right tools and methodologies to filter noise and focus on actionable insights. It's about blending technical expertise, strategic thinking, and creativity to uncover hidden opportunities and solve complex problems.
Ultimately, social intelligence is a bridge—linking data to decisions, insights to action, and organizations to the people they serve. It empowers us to move beyond the "what" and into the "why," driving innovation, connection, and meaningful change.
What are you doing that no-one else is to drive the social intelligence industry forward?
I'm driving the social intelligence industry forward by turning data storytelling into an accessible, teachable art form. While others focus solely on data collection or analysis, I tell complex data stories in simple ways, making sure insights are actionable for both technical experts and non-technical audiences. Social intelligence thrives when decision-makers, marketers, and creatives, not just analysts and engineers, understand its value.
Teaching plays a central role in my work. Whether lecturing to hundreds of students or training teams to leverage social listening tools, I focus on translating technical complexity into practical understanding. My favorite tool is Infegy’s API. Social listening APIs enable scale storytelling. Through visualizations, dashboards, and relatable narratives, I make even the most intricate trends—like sentiment shifts or clustering analyses—accessible to everyone.
This focus on education and empowerment transforms social intelligence from a niche field into a universally applicable resource. Equipping others to interpret data stories with clarity and confidence, I help organizations make informed decisions faster, driving more significant impact across industries.
Have you got a favourite social intelligence use case or case study from the last year?
One of my favorite social intelligence use cases is using social intelligence to detect and understand brand crises. This year, I built Infegy's Brand Risk Dashboard, which uses APIs to automate daily updates and ensure that brands have real-time insights into their reputational risks. This dashboard ranks the world's largest brands by a proprietary Brand Risk Indicator, identifying those facing heightened negative sentiment, crises, or consumer dissatisfaction.
I built this dashboard around Infegy's API, automating thousands of daily queries. The API collects post volume, sentiment, nuanced emotions like Fear and Disgust, and themes such as injuries or churn intent. It processes this data through a weighted algorithm that balances post volume with specific risk indicators, ensuring the rankings accurately reflect actual brand vulnerabilities rather than being skewed by outliers like massive post counts for more prominent brands.
The dashboard updates dynamically every day to refresh the rankings and display trend data over a rolling 30-day period. This automation gives stakeholders real-time insights into emerging crises, allowing brands to react swiftly and strategically to prevent issues from escalating. My favorite part about this is validating the algorithm - we consistently see Boeing, Starbucks, United Healthcare, and Gazprom rise to the top. (Bad for those companies, but great that my algorithm performs well).
What’s the biggest challenge technology providers face in social intelligence?
The biggest challenges in social intelligence are keeping up with data scaling challenges and adapting to new platforms. Infegy has tackled this head-on by self-hosting its infrastructure and constantly expanding its data sources.
1. Keeping Up with Scale: Social media generates enormous volumes of data, and traditional providers often rely on third-party cloud hosting that slows processing times and inflates costs. Infegy self-hosts its infrastructure, enabling rapid, real-time data processing. This approach means our platform populates queries in seconds—not hours or days—providing actionable insights when they matter most. Failure to do so forces customers to rely on slow APIs, leaving them with outdated insights that don't reflect current trends.
2. Adapting to New Platforms: The social landscape is fragmented and constantly shifting, with platforms like Threads, Bluesky, and Mastodon growing alongside giants like Reddit and TikTok. Infegy's team actively integrates new data sources to ensure clients can access the full spectrum of conversations. With these new platforms, brands can avoid missing critical moments, whether responding to a PR crisis or identifying emerging trends.
By self-hosting and embracing new platforms, Infegy ensures that brands receive fast, comprehensive, and reliable social intelligence, positioning them ahead of competitors in a rapidly changing digital environment.
A common misconception is that social listening is all Twitter data, how are you overcoming this perception?
Many social listening providers rely heavily on Twitter (now X) as their primary data source, which creates significant limitations. While Twitter has historically been a rich hub for real-time conversation, its current platform bias and fragmentation undermine its reliability as a standalone data source. Recent shifts, such as Elon Musk's acquisition, have driven progressive users to platforms like Threads and Bluesky, leaving Twitter with a more conservative-leaning user base and niche communities like cryptocurrency advocates. This segmentation skews sentiment and conversation, as seen during the 2024 U.S. election, where Trump-related discussions on Twitter were 8-10% less negative than on other platforms.
Infegy takes a different approach, using a multi-platform collection methodology that mitigates these biases. By analyzing data from diverse sources—including Threads, Bluesky, Reddit, TikTok, and blogs—we ensure a more balanced and comprehensive understanding of online sentiment. Infegy's tools also incorporate advanced persona analysis, allowing us to identify and account for platform-specific user dynamics.
This approach reduces reliance on a single, increasingly chaotic platform and provides richer, unbiased insights. In a fragmented digital landscape, Infegy's methodology ensures that brands can make decisions based on a full spectrum of conversations, not just the narrow lens of Twitter.
Let’s say you have a new client who is trying to take social listening more seriously, what advice would you give them? Where should they start?
To take social listening more seriously, align your goals, resources, and tools. Here is some advice to build that strong foundation:
Figure Out Your Needs:
Determine whether you need a simple brand monitoring tool to track mentions and sentiment or a more robust research platform for competitive intelligence, trend analysis, and audience insights. If your primary goal is to respond to branded criticism or monitor social chatter, an essential tool may suffice (and will be cheaper).
Assess Your Organization's Capabilities:
Consider what your team can realistically support. If you don't have a data science or engineering team, APIs or advanced analytics tools might not be helpful right now. Instead, focus on platforms that offer intuitive dashboards and automated insights to empower your existing team without heavy technical investment.
Pick Tools That Start Broad, Then Narrow:
Choose a platform that allows you to cast a wide net first, gathering a comprehensive view of the conversation before filtering. Starting too narrow can lead to missed insights, biased samples, and low search volume.
Experiment and Have Fun:
The best way to learn is by playing around with the platform. Look for tools that offer freedom to test queries without restrictive limits. This hands-on experimentation teaches you the tool's capabilities and helps you uncover insights you might have yet to think to look for.
Generative AI has changed the way we think about our work. What's the next big thing to shake up the industry?
Generative AI has revolutionized social listening by transforming text-based insights. Still, the next big shake-up will be multi-modal analysis—the ability to process images, audio, and video alongside text. With platforms like TikTok, Instagram, and YouTube driving much of today's engagement, understanding visual and auditory content is critical.
This shift will unlock more profound insights, like spotting trends from TikTok videos or analyzing sentiment in Instagram Stories, but it presents real technical challenges. Storing and processing massive volumes of rich media requires highly optimized computing systems to handle the data scale and complexity. Unlike text, multi-modal analysis demands advanced models capable of interpreting video frames, audio tones, and contextual nuances—all in real-time.
What are you looking forward to in social listening for 2025?
I'm excited about the continued integration of emerging platforms like Threads, Bluesky, and Mastodon into social listening tools. These platforms are carving out distinct, segmented user bases, and their inclusion will ensure insights remain relevant and representative of a diverse online landscape. At Infegy we’re quite happy to see indications that Twitter’s microblog hegemony seems to be ending. I’m also excited to see how the landscape will change with an (increasingly likely) TikTok ban.
I anticipate advancements in real-time processing and AI-driven automation. Faster, more scalable platforms will eliminate the delays that hinder social listening, ensuring brands can respond to trends and crises as they happen—not hours or days later. As these technologies evolve, social listening will move beyond monitoring to become a proactive tool for shaping strategy, helping organizations predict and influence consumer behavior in ways we've only begun to imagine.
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