Part 3: Matchmaking: Try it, try it, try it
AI in Eventmanagement
Meetings with impact are the key to successful networking. Matchmaking tools help to meet the right people. Artificial intelligence also provides support here. Photo: Cvent
Meetings with impact are the key to successful networking. Matchmaking tools help to meet the right people. Artificial intelligence also provides support here. Photo: Cvent
There are few platforms as suitable for networking as events. Matchmaking tools help create schedules in advance, allowing participants to plan the most promising meetings within a limited time frame. This is now also possible with artificial intelligence (AI). This is the third part of our new section, where we take a closer look at transformative AI tools in the event industry.
Networking is a central element of events and trade fairs. It not only helps make new contacts but also strengthens existing relationships and facilitates knowledge exchange. In a professional context, effective networking can advance careers, foster partnerships, and drive innovation. Despite its importance, many participants face the challenge of identifying relevant contacts at events. Especially at larger events, the sheer number of participants can be overwhelming. This is where matchmaking tools come in, making targeted connections that benefit both personal and professional goals.
How do matchmaking tools work?
Matchmaking algorithms specifically search for connections between two or more people to enable direct interaction. For example, a matchmaking algorithm analyses professional interests, expertise and goals to identify two participants who can support each other. These algorithms are more dynamic and rely on deeper analyses to create encounters that are both personally and professionally enriching.
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Jens Schindler, Director DACH at ExpoPlatform, describes it like this: "A matchmaking algorithm works on different levels." At the basic level, the algorithm compares users' interests and offers and links them with suitable content such as people, products, news or lectures. At the next level, the algorithm not only analyses terms, but also user behaviour: which content is viewed and which is clicked on? This allows the suggestions to be tailored more specifically to individual preferences. At the highest level, the algorithm recognises patterns in user behaviour and uses this knowledge to comb through the entire database. This enables it to create even more precise and relevant recommendations.
Most people will already be familiar with this principle from Netflix, Amazon or the dating app, where an algorithm analyses user behaviour and then recommends series or products. McNeel Keenan, VP Product Management at Cvent, adds: "These algorithms analyse demographic data and engagement patterns to create personalised networking suggestions."
How AI improves matchmaking
According to Schindler, people often don't know exactly what they want. Artificial intelligence helps to create this "aha" moment by recognising behavioural patterns and making suitable suggestions. "AI is particularly helpful because matchmaking is dynamically based on user behaviour."
AI matchmaking tools - a selection
1. ExpoPlatform 2. Cvent 3. InvitePeople 4. Brella 5. b2match 6. InEvent 7. MEETYOO
The quality of matchmaking is largely based on the type and amount of data fed into the algorithm. While not a large volume of data is required, Schindler explains, "the results naturally improve the more data you have." He cites classic data points from trade fair catalogs: job title, job category, decision-maker (yes/no), and type of decision.
Photo: Cvent
"Recommendations will only become more precise if both digital and physical data about the participant's engagement are incorporated into the matchmaking algorithm."
McNeel Keenan, VP Product Management bei Cvent
Keenan is also clear: "It starts with robust, secure data collection." Organisers already gather data points during registration and onboarding for the event. It's important not only to collect digital data but also to consider data from the physical world. This includes professional and personal interests, hobbies, and engagement data, such as attendance at various sessions. Cvent also uses gamification elements to encourage participants to complete their profiles during registration. For Schindler, monetising data is already on the horizon: "If you allow my algorithm to learn how to improve my offer for you based on your data, then I am prepared to pay you something or grant you a discount."
Challenges
For the system to realise its full potential, it requires data for learning that goes beyond just demographic information. In the beginning, especially with a first-time application, the benefits may not be as noticeable as desired. However, these benefits improve with the second and subsequent applications. For very small events with fewer than 500 participants, it can be challenging to generate a large enough pool of data to create a noticeable effect, as Schindler highlights. On the other hand, Keenan notes that events with several thousand participants, such as congresses or trade fairs, also present a challenge when it comes to efficiently calculating relevant matches. This requires significant computing power. Therefore, it’s important to be patient and allow enough time for all the benefits of an AI matchmaking platform to fully unfold. The benefits of AI are measurable. Schindler reports more than doubled lead scanning results at events that use AI: "Visitors who book meetings in advance experience a significantly more successful event."
Photo: Jens Schindler
"Visitors who book meetings in advance experience a much more successful event."
Jens Schindler, Director DACH at ExpoPlatform
For developers, however, one challenge with AI remains: the traceability of results. This is why not everyone is in favor of AI. "A significant downside of using generative AI is the difficulty in validating the result," says Erik Gullestad, CTO & Founder of InvitePeople. Meeting plans can be generated as quickly as marketing texts and images with generative AI tools like ChatGPT. However, unlike faulty images or texts, meeting plans can't be easily checked for errors. To address this, InvitePeople relies on a graph algorithm that determines the best possible schedule based on participants' requests, available time slots, and rooms.
Foto: InvitePeople
"In the future, when more advanced AI models are available to us, we will probably no longer need to configure the platform via a user interface. This will be done by a generative AI via text or voice input."
Stephanie Hellstedt, Managing Director at InvitePeople
The future of matchmaking
In addition to the matchmaking algorithm, there are other ways to integrate AI into the tool. InvitePeople is currently experimenting with a Gen AI chatbot. "In the future, when more advanced AI models are available to us, we will probably no longer need to configure the platform via a user interface. This will be done by a generative AI via text or voice input," explains Stephanie Hellstedt, Managing Director at InvitePeople. Cvent also wants to introduce a Gen-AI application in the matchmaking process in order to simplify the workflow and present the results more transparently.
The connection of the future
Matchmaking tools are changing the way we experience events. By combining data analysis and AI, they create a new dimension of networking. Keenan sums it up: "Over time, AI-driven matchmaking will not be a nice-to-have but rather a baseline expectation for event audiences. Their time away from home and office is limited, so attendees will expect powerful matchmaking tools to ensure every moment is maximized – whether through making valuable connections or gaining insights from the community and content provided." Therefore, Schindler advises: "Try it, try it, and try it again!"
Maxi Nessmann
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