AI just made our participants more discoverable 🧲
Now participants that are especially hard to find - but do exist within our panel - are identified by our matching engine and presented in your dashboard.
Here’s what changed.
The Keyword Matching Problem
Traditional participant matching relies on exact or fuzzy keyword matching.
Search for "software engineers" and matching will sometimes miss developers with similar skill sets. In these cases our matching algorithm leans on targeting criteria to fill the gaps: Industry, Function, Seniority…
But because it doesn’t instinctively understand what ‘relevant’ means, it won’t always prioritize sending matching communications to people who are a great match for your study.
This means that occasionally studies take longer than needed to fill, fill rates are lower, and our participant pool feels smaller than it actually is.
Introducing AI Matching
Now AI means it’s easier to find these hard-to-reach participants. Here’s how AI matching works:
Semantic matching instead of exact keywords
Our system now understands conceptual relationships, matching participants based on meaning rather than literal text.
This isn't just fuzzy matching that catches typos, it's genuine understanding. The system recognizes that a "brand strategist" and a "marketing specialist" might have overlapping expertise.
Progressive participant profiling
New participants start with limited data, making initial matching challenging. But here's where AI Matching gets smarter over time.
As participants engage with the platform, each screener they complete, each project they qualify for, and each study they participate in teaches the system more about their true qualifications and expertise.
Over time, we securely collect millions of data points from our participants that could help them match with more relevant research studies.
This progressive profiling enables increasingly precise matching, so your tenth project on the platform will see better matches than your first.
Intent-based project recommendations
AI Matching shows participants projects similar to ones they've previously qualified for. Instead of relying solely on profile data or stated interests, we match based on demonstrated intent and proven qualification history.
A participant who qualified for three different SaaS product studies? They'll see your enterprise software research. Someone who's participated in multiple healthcare UX studies? Your medical device usability project will surface for them.
This approach increases the likelihood that participants who see your project will qualify - because they've already proven they fit similar criteria.
The Results You Can Expect
With AI Matching, you'll see:
- More studies filled
- Studies filling faster
- Higher quality matches
- Less time spent reviewing screeners
Ready to see the difference? Launch your next project with Respondent and find your first participant in minutes.
