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AI matchmaking: reshaping the future of events

AI matchmaking is reshaping the future of the event industry. 

The technology has become ubiquitous in all walks of life, whether when completing a personal chore or conducting your business.

We interact with it in some form on a daily business – that can be browsing Netflix recommendations, shopping on Amazon or using digital assistants such as Siri and Alexa.

Furthermore, the global revenue for AI is expected to grow at a five-year annual rate of 17.5% to £404.3 billion ($554.3bn) by 2024, according to IDC research.

Advancements in this technology have led to new and innovative solutions across a host of different industries, which includes matchmaking for events and exhibitions.

This feature is particularly important for its ability to rapidly analyse data and automate processes while keeping up the appearance of a human touch.

In this article, we discuss how AI matchmaking has become a necessary networking tool in the changing landscape of the event industry.

We set out the advantages it provides for business events and the broader role it is expected to play in creating successful events in the future. 

AI matchmaking is necessary for events

Virtual and hybrid events have opened up access to industry exhibitions, trade shows and conferences for thousands of participants around the world.

This has exponentially increased the amount and quality of data – name, job titles, geographical location, interests – being uploaded and processed during these events.

AI matchmaking is necessary for events

With a vast repository of information at your fingertips, how can this data be used to drive the right connections and enhance networking opportunities for attendees?

Processing all this data manually would be a Herculean task involving long hours, days and even weeks.

With AI matchmaking software, millions of data points can be analysed within a fraction of a second, delivering instant recommendations for the most potentially fruitful connections.

At the core of this technology is machine learning, which intelligently adapts to user choices by studying intent and past behaviour to generate more relevant options, if the desired match is not found.

With a majority of business events being shaped around virtual elements, we are witnessing increasingly data-rich scenarios.

This has made AI matchmaking software and tools an integral part of the event tech stack for organisers looking to deliver richer connections and more meaningful experiences.

How does AI matchmaking benefit events?

There are numerous benefits of using AI matchmaking for events – the biggest being the time it saves the user.

Let’s use the example of a business-to business exhibition to compare to see how this technology would work in  in the following scenarios:

Without AI matchmaking – Your attendees scan a lengthy list of exhibitors and fellow participants to find out who matches their interest. After a considerable length of time in going through different profiles, they shortlist a few people to network with.

With AI matchmaking – AI collects and processes the details of every attendee – it evaluates the choices they have made and offers the best matches in a split second. If the options are not relevant, it keeps adapting to improve the match relevance, until the attendee finds the best possible match.

AI matchmaking event benefits

The significant time savings ensure organisers can focus on other aspects of their event, save costs and improve bottom line. 

Another area in event networking where AI-matchmaking has made an enormous impact is in making people-to-object connections, where the AI algorithm makes recommendations for content, products and sessions. 

For example, ExpoPlatform’s AI-powered matchmaking software assists organisers in tailoring matchmaking rules to help attendees connect with relevant products, meetings and sessions options and recommended content.

These recommendations are generated using a blend of user preferences and dynamic interactions within the event platform.  

With attendees having access to seemingly endless presentations, company brochures, videos, webinars as well as product listings, AI matchmaking helps provide curated options to filter through the noise and connect to content and products that are actually useful.

This drastically cuts the time on research and helps the user make more informed and value-driven business decisions.    

Learn how AI matchmaking’s enhanced functionality to make people-to-object connections contributes to setting up a framework for an online marketplace model in our  free ebook.  

Let’s go into the benefits in a bit more detail:

AI-powered matchmaking for networking

Audience engagement and interaction are the two biggest challenges event organisers face during virtual events, according to Markletic research.

A lot of it is due to virtual events tending to report massive attendee numbers, which dilutes the signal-to-noise ratio – meaning the useful connections for people attending the event.  

AI matchmaking networking

This means your attendees will need to sift through everything so they can connect with relevant people, products and content that could lead to mutually beneficial business relationships.

AI-powered matchmaking services allows you to customise recommendations for attendees based on the information they have filled in.

These can be their areas of interest, geographical location and professional seniority.

This helps drive meaningful interactions, boosts peer-to-peer networking and aligns business goals.

The best part about AI matchmaking? If the user feels the match is not relevant, they can easily communicate their decision and the algorithm keeps adjusting and improving until a suitable connection is found.  

AI-driven analytics and insights to improve interactions

By analysing interactions in real-time, AI can improve attendee engagement and provide necessary assistance without human intervention. In contrast, conventional analytics only assess the content after the event concludes to identify what worked and what didn’t.

AI matchmaking improves interactions

Using AI algorithms, you can track virtual footfall and pinpoint exactly which resources are meeting their targets, or which touch points are receiving the maximum engagement. By studying the different factors influencing attendee behaviour and analysing multiple simulated scenarios, AI can provide answers to questions like:

  • Is the content being shared relevant to the attendees?
  • Are the meeting and communications channels being used effectively?
  • Are the correct attendees being targeted?

AI-powered chatbots for conversational marketing and language translation

A scalable, user-friendly conversational AI solution with chatbots and language translation features can enhance virtual event engagement significantly.

AI matchmaking for marketing

It can automate customer support by facilitating conversations on thousands of industry-specific topics, rather than being a mere FAQ tool. For example, if the event is related to banking and financial services that have complex product offerings, it’s helpful to have a virtual agent that can answer questions and provide relevant information.

AI language translators can instantly and accurately translate any event communication, web pages and content resources into different languages. This enhances the global appeal of your virtual event and helps you reach out to a more diverse audience across different cultures.

The new way of business matchmaking

With its ability to rapidly study user patterns, AI matchmaking has changed the way connections are made at events. In addition to finding the most relevant matches, AI also allows organisers to do in-depth analysis of the audience persona, helping them facilitate the right business opportunities.

AI matchmaking for business

AI matchmaking also plays a crucial role in personalising the attendee journey. Instead of prompting random conversations, it encourages users to engage with relevant people and content, maximising every minute they spend during the event.

This leads to a greater engagement rate, better quality leads and a higher overall satisfaction score for the event, as well as more meaningful interaction prior to and after the event itself.

Person-to-object matchmaking

The main differentiator of ExpoPlatform matchmaking is that it takes into account all objects on the platform.

Every event or community participant is being linked with relevant people as well as also with different things of interest including products, exhibiting companies, speakers, sessions, news, webinars and many more.

Matchmaking algorithm explained: The matching algorithm is initialised using registration data, learning more over time based on user behaviour – this is the interactions between users.

It then uses information from peer interests and behaviour that influences recommendations for the user.

Categories are used in two ways:

  • Activity categories – this is what a person does or what a product is – it can belong to non-person objects
  • Interest categories – this is interests – it can only belong to person objects

Each person has a different level of interest for each product category  – it’s not just yes or no.

Meanwhile, all everything on the platform – people, products, sessions, exhibitors etc. – are tagged by one or more product categories.

The system then recommends things that best match the user’s current interests, which can change over time.

Registration and behavioural data

Matches are based on answers to registration questions and usage statistics and are produced by machine learning algorithms based on anonymised matchmaking data across all events where our platform has been deployed with this module.

Whenever a person interacts in any way with any object, their interests get updated. Interactions include:

  • Viewing pages (news, exhibitor/product profiles, etc.)
  • Favouriting
  • Requesting a meeting
  • Sending a message
  • Viewing a session
  • Rejecting a match

A person’s interests are updated by increasing or decreasing interests in each of the product categories in the system

Interests are increased for the categories that the object has if the interaction was positive

Similarly, interests are decreased for the categories that the object has if the interaction is negative (e.g. “not relevant”)

Using peer information

The system detects peer groups based on similarity of registration data and demographic information

Objects that gather a lot of interest from the peer group are more likely to appear to others in the group who have not yet seen/reacted to them

This is especially useful in cases where little interaction data is available about a person, and their peer’s interests may prove to be more accurate than what they’ve ticked in their product category preferences

Updating user matchmaking data

An object’s product categories can also evolve over time depending on what users interact with an object

Each positive interaction this object receives imparts some interest of the user into the object’s “activity categories”

Each negative interaction this object receives detracts some interests of the user from the object’s “activity categories”

This means that an object tagged with one category, could eventually acquire others, and/or lose the originally tagged category, depending on how users interact with it

This mechanisms allows for the correction of incorrect tagging of objects

Generating recommendations

Recommendations are specific to a person. They combine peer interests and product category interests to create a ranked list of all objects in the system.

Organisers can use a series of Matchmaking Filters to influence the recommendations shown.

Filters can be used to eliminate matches in the ranked list between specific people and/or companies based on answers to registration questions (e.g. small buyer should not match with large supplier).

Items that are at the top of the ranked list for their respective types (e.g. products, people, exhibitors, etc.) are then shown as recommendations to the person

Emails triggered using the system can include AI recommendations

Measuring success

A successful match is one that receives any positive action

  • Profile click
  • Favourite
  • Meet
  • Message

A successful match is removed from the list of matches once it’s received a qualifying action:

  • Favourite
  • Meet
  • Message

An unsuccessful match is one that has received a negative action, or, which has not received a
positive action after N showings.

An unsuccessful match is removed from the list as soon as it’s deemed to be unsuccessful.

The removal of an unsuccessful match impacts a user’s interest scores

Conclusion

The event industry – like most industries – is facing extraordinary challenges, but they also present a wealth of opportunities.

It is now essential to keep up with the digital transformation we have witnessed – that means innovating to provide new solutions to attendees and exhibitors.

AI matchmaking software has quickly become a popular tool to connect attendees with similar interests – as well as helping people find new content, products and services as per their preferences. 

This is amid a landscape where event organisers are making greater use of online interactions to improve their in-person shows alongside virtual components.