ELX News

Senior event leaders have more experiential data than ever. Across registration, content engagement, session attendance, sponsor interaction, sales influence, and post-event feedback, most large organizations now have access to a significant volume of information about how audiences engage.

The leadership challenge is knowing which signals matter, how to interpret them in context, and how to translate those insights into decisions that improve experience design, business outcomes, and executive confidence.

In this ELX editorial, we speak with Brian Gates, EVP of Strategy and Growth at RainFocus, to explore how AI can help event leaders move from fragmented reporting to clearer insight, stronger decision-making, and more connected audience experiences.

Many event teams now have access to significant volumes of attendee, engagement, content, and commercial data. From your perspective, what is still preventing organizations from turning that data into clear, actionable insight? 

Brian: The biggest barrier preventing organizations from utilizing data is data fragmentation and the time required to extract, load, and transform it into insights and reports. Multiple vendors for registration, leads, apps, and on-site technology spread across the event portfolio create silos of engagement data. This leads to gaps in truly understanding the full scope of engagement across a single event, let alone all the events an organization might run.

There’s also a gap in the context the data provides about the attendees themselves. This data resides well within the marketing tech stack in marketing automation platforms and CRMs. It details how an attendee relates to the business – their interests, previously purchased products, and who they’ve interacted with at the organization. That data usually doesn't make it into an event system, which leads to long registration forms attempting to fill in the blanks. By the time attendees reach registration, that information is already outdated because it was previously captured in a CRM.

While technologies have improved the management of events, sales, and marketing, the connective tissue and data remain siloed and fraught with delays and errors.

AI is often discussed in broad terms, but senior event leaders need to understand where it can create practical value. Where do you see AI having the greatest impact when applied to experiential data?

Brian: AI’s greatest value is its ability to sift through all the noise to detect patterns and trends that would otherwise require significant time and focus. AI also considerably lowers the barrier for those insights by using natural language rather than detailed SQL queries and table merges. Users can simply ask the question and AI will translate that into the right string of commands to generate the answer.

We’ve seen the greatest impact so far in the following areas:

• Behavioral Clustering: AI can detect and group attendees by their actual content consumption, behavioral patterns, and engagement. This allows for hyper-targeted follow up and revised event design.
• Predictive Monitoring: AI can analyze patterns to determine if things are deviating from the norm. Is a session experiencing a higher drop off rate than historical benchmarks? Or, is there a surge in registrations that would necessitate a room change to support demand? These items can be predicted, enabling managers to make adjustments proactively.
• Event Comparison: Previously, comparing events utilizing different data strategies and data points was nearly impossible. With AI, organizers can align free-form fields with drop-down lists or unstructured data, giving teams the ability to drive meaningful comparisons.
  
  

ELX members have been discussing how AI can help create dashboards and uncover trends from the data they receive through event technology platforms. What makes an AI-enabled dashboard genuinely useful for executive decision-making?

Brian: Ultimately, the executive dashboard must clearly communicate the impact on business outcomes and bluntly share actionable insights. When dashboards are dense with charts and graphs on strictly operational metrics, executives must do the heavy lifting of interpretation to make sense of them.

Modern dashboards can lead with natural language insights instead of forcing the CMO to interpret a bar chart.

“Sponsor leads for our platinum sponsors are up 12% due to the added exhibitor floor hours.”

This bulleted statement clearly communicates the change and impact. Use those same statements to connect event metrics to business outcomes.

“Attendance remained consistent year-over-year, reaching event goals. However, pipeline influence grew by 23% due to sales alignment in driving high-value account attendance.”

Lastly, offering projections using historical data and forecasted trends gives executives a way to contribute to the success of the event.

“Our current registration projection looks like we will be below last year's, what can we do to alter our path?”

That ownership pays back when guidance is provided and courses have been corrected.

Experiential data can reveal patterns across audience behavior, content engagement, sponsor performance, sales influence, and journey design. What types of insights are organizations often surprised to discover once they begin looking across the full data set?

Brian: The most common insight organizations discover is the complete disconnect between how a user responds to a registration question and how they actually behave at an event. This continually comes up and gets back to the heart of leveraging behavioral data over purely question-based information. The best indicator of intent is action. 

A few other examples include:

• Silent Handraisers: This group will completely ignore a survey or request but their experiential journey will tell a completely different story. They may have never told you they had interest in a key new offering but they spend hours in sessions specifically about that product indicating significant interest.

   

• Attendance Paradox: Often, event teams find that low attendance sessions have a much higher conversation rate, which contradicts the narrative that packed sessions are the best. It's key not to overlook these sessions, since they often allow the smaller audience to get their specific needs met by the speakers, leading to a more impactful conversation.
  

For large organizations, events increasingly need to demonstrate business impact while still delivering meaningful human experiences. How can AI help leaders connect experience design with measurable outcomes?

Brian: Event leaders often bring a wealth of knowledge in human behavior and how to craft meaningful experiences, combined with logistical grit. AI brings a data science team and revenue intelligence. Together, event leaders can leverage AI to provide data-backed insights for good design.

For example, AI can analyze the engagement patterns of attendees who purchased offerings, attended the peer-to-peer roundtable, and received a personalized demo on the show floor. That group not only bought but did so on average 26 days ahead of their peers. This insight provides the data backing needed to support an intentional design of the event flow, guiding more attendees naturally through the same journey. It's no longer just a subjective or gut decision but a data-backed strategy for behavioral architecture.

What organizational conditions need to be in place before AI can deliver meaningful value from experiential data?

AI is only as good as the data it has to work with. So the old adage, “garbage in equals garbage out,” still very much applies and it can be exponentially more damaging given that AI responses come across as certainties. Ensuring you have a solid data strategy with consistent and accurate data is key. Second, ensure that data is available in near real time. Platforms that offer unified data profiles and governed data strategies are essential for setting up organizations for immediate success.

Looking ahead, how do you think AI will change the way senior event leaders plan, measure, and evolve their event portfolios over the next three to five years?

So much has already changed in just the last six months. At this point in our collective AI journey, nothing too far-fetched seems out of the realm of possibilities. That being said, AI is another massive tool that will greatly enhance our roles and the role we play in the attendee journey. It won’t replace either one, but it will definitely change how we work and interact with each other. 

• Personalization: My read is that personalization will shift from being something the brand needs to do for me to something I am enabled to direct from the brand. So much of the agentic capabilities and knowledge sharing that required brands to do the work is now readily available for every individual. My AI can build my agenda because it has all the context of my role, challenges, and products, and then it can give me an actionable summary to apply at work the next day automatically. That is only going to get better and more common.

• Predictive Portfolio Management: Targeting event leaders, AI will be the co-pilot to plan and deliver your events with the precision needed to drive business results rather than just reporting what happened. Return on engagement will enable you to specifically target events, audiences, and experiences to hit your revenue targets. It will give you advice (with charts and models) showing why the roadshow should move to Austin from Dallas, providing a clear picture of how that move will boost audiences and revenue.
  

  AI will eliminate the tedious work and give event leaders a near limitless supply of experts so they can operate solely as strategic visionaries who are absolutely critical to the revenue goals of the organization.
  
  

  ---

  What emerges from this conversation is a more mature view of AI’s role in event strategy.

  For senior event leaders, the value lies in faster interpretation, stronger pattern recognition, and clearer links between audience behavior and business outcomes. AI can help teams compare performance more meaningfully, identify signals earlier, and build dashboards that support executive decision-making.

  That value depends on the quality of the underlying data, the strength of the organization’s data strategy, and the judgment of the leaders using it.

  For event leaders, the opportunity is to use intelligence more effectively, so that experience design, measurement, and strategic decision-making become more connected.