From Registration to Recommendations: How Eventiqs Builds the Matching Layer

A matching engine that says "you both work in tourism, you should meet" is not a matching engine. It is a contact list with a filter. Real matchmaking happens when registration data, intent signals, behavioral patterns and cross-edition memory are layered on top of each other.

This post is a structured look at how Eventiqs turns the data captured at registration into recommendations a B2B event organizer can stand behind.

The Problem

Most event matching tools treat the recommendation engine as a sort feature. Filter by sector, surface the top results, done. This is fine for casual networking. It collapses under B2B trade fair or conference scale, where 28,000 visitors and 600 participating companies cannot be sorted by sector and sent on their way.

Two problems show up immediately:

1. Intent invisibility. A buyer and a competitor are both in the same sector. Sector match alone gives them the same score.
2. Static recommendations. A list generated at registration ignores everything the attendee does afterward — pages opened, sessions saved, meeting requests sent.

Organizational Risk

If the matching layer is thin, three things degrade fast:

• Meeting acceptance rates fall. Recommendations feel random; visitors stop opening them.
• Exhibitor satisfaction drops. Booths host casual visitors instead of qualified prospects.
• Sponsors lose visible ROI. Sponsored visibility lands on people who do not match the sponsor's offer.

How Eventiqs Builds the Matching Layer

Eventiqs runs a four-signal matching layer on top of registration data:

1. Profile signals. Sector, sub-sector, role, geography, company size. The static layer. Pulled from registration and from any visitor database integration the organizer brings.

2. Intent signals. What the attendee said they wanted at registration — buyer/supplier/investor mode, sourcing categories, partner type. The intent layer is captured separately from profile, because the same person can be in different modes at different events.

3. Engagement signals. Pages opened, recommendations clicked, meeting requests sent or declined, sessions added to agenda, bookmarks. The behavioral layer that updates in real time.

4. Cross-edition memory. Sector trends, sponsor performance and visitor behavior carried over from prior editions of the same event, aggregated and KVKK and GDPR-aware. The layer that lets edition two start smarter than edition one.

Each potential match is scored by combining the four signals, not by any single one. A high score requires alignment in profile and intent and engagement, not just sector overlap.

Where Recommendations Actually Show Up

The matching layer is not a single feature; it powers several places in the event:

• Personal feed: every attendee's home view shows recommended people, companies and sessions.
• Meeting requests: when a participant sends a request, the platform tells the receiver why this meeting is suggested.
• Exhibitor view: participating companies see ranked prospects coming to their booth, not a flat list.
• Sponsor view: sponsors see which segments they are reaching, not just impression counts.

A Practical Checklist

Before you trust any matching layer at scale, check:

• Are profile and intent captured separately at registration?
• Does the engine react to behavior during the event, or only to registration data?
• Can you see why a match was suggested? Black-box matching is hard to defend.
• Does the platform retain (KVKK and GDPR-aware) signal across editions, or reset every year?
• Can you see match quality metrics in the live organizer dashboard?

How the Matching Layer Compounds

Eventiqs published an 82% match quality score at EMITT 2026. That number is not a one-off; it gets better as the engine sees more editions of the same event. The way it compounds:

• Each accepted meeting confirms a match pattern.
• Each declined meeting trains the engine away from a false-positive pattern.
• Each registration in a new edition arrives into a system that already understands its sector context.

This is the difference between a contact list and a matching engine.

Closing the Loop

Registration is the first signal layer. Engagement is the live layer. Cross-edition memory is the compound layer. Together, they turn 28,000 visitors into 3,500+ planned, qualified meetings — instead of 28,000 randomly distributed pairs of business cards.