Match score

The match score is where the algorithm's opinion becomes visible, and participants take it more literally than they should. A "92% match" badge reads like a promise, but the number only reflects overlap in whatever data the platform collected — thin profiles produce confident-looking scores built on almost nothing. For organizers, the practical use of scores isn't the individual number but the distribution: if most pairs score low across the whole event, your registration data is too sparse or your audience segments genuinely don't fit, and both are worth knowing before the booking window opens. Teams that spot-check work backwards from scores — pull a handful of high-scoring pairs and ask whether a human would introduce these two people. The common mistake is displaying raw percentage scores to participants at all; they invite complaints ("why is this irrelevant company my top match?") and anchor expectations the data can't support. Many platforms let you show ranked lists without the number, which is usually kinder. One honest nuance: scores across platforms aren't comparable, and often not even across events on the same platform. A 78 on this year's event and a 78 on last year's may mean entirely different things, so never benchmark scores — benchmark meeting outcomes.