Why comms data collection methods must evolve 

Audiences are increasingly spending their time in more niche and private communities online. Legacy listening and analytics tools have struggled to keep pace with this behaviour shift and have increasingly limited (or no) access to the emerging platforms and digital spaces where audiences engage. As a result, data, intelligence, and insights pros must rethink their data collection approaches and adapt.  

To understand the why behind these audience behaviour shifts, FleishmanHillard UK conducted in-depth large-scale qualitative research. We learned: 

• TikTok is still THE primary channel for Gen Z, but some Gen Zers are also fragmenting toward more community-driven channels such as Discord and Reddit 

• Healthcare professionals are increasingly clustering on trusted, clinical platforms like Medscape 

• IT decision-makers congregate on technically focused and peer-supported sites like GitHub and Stack Overflow 

• C-suite executives prefer gated, invitation-only communities (e.g., Slack) over public social channels and are curating their inboxes with trusted, highly influential voices via newsletters and Substack  

 We are also seeing accelerated adoption of AI for the purposes of doing research and finding information across audience segments – introducing a new channel (or audience, depending on how you view it), that should be considered in channel and communications strategies. These movements represent a fundamental restructuring of where authentic conversations, information validation, and real engagement actually happen. 

So, given all of this, what are the implications for the way we approach information gathering ahead of developing our comms plans and measurement strategies? For those of us in research and analytics, this creates an urgent challenge: we must think differently about where and how we collect data.  

We need to expand our thinking about what constitutes as a credible data source and be willing (and able) to build custom solutions. We also need to layer data collection across an ecosystem of sources, rather than relying on any single vendor or channel. In practice, this approach can transform the work we do every day: 

• Trend analyses become richer when we monitor signals across trade media, industry newsletters, Reddit discussions, and specialised social channels simultaneously. They become even more robust when we then apply bias-mitigation frameworks to distinguish signal from noise.  

• Synthetic audience builds gain credibility when they’re grounded in data collected from the actual spaces the audience congregates, rather than relying solely on demographic, psychographic or attitudinal overlays found via secondary approaches.  

• Crisis and issues tracking can become genuinely predictive when you’re capturing emerging sentiment across dispersed communities before conversations escalate on mainstream platforms. 

As audiences have migrated toward private, curated spaces, our responsibility as researchers has grown. And our jobs have become arguably harder. We must be selective about what data we collect, transparent about our methods, and disciplined about respecting terms of service and user expectations.  

I truly believe that organisations that invest in this expanded data ecosystem approach gather better information. They understand what audiences are saying, where they’re saying it, why they’ve chosen those spaces, and what it means for strategic communications. Executing this effectively requires new tools, new thinking and a new commitment. The question for every research and analytics team isn’t whether to adapt – it’s how quickly you can.