By Mark Parsons
For much of the exhibitions industry’s history, competitive advantage has been built through operational excellence. Strong sales teams, good venues, well-timed launches, and an instinctive understanding of a market have been enough to create very successful businesses.
In many cases, they still are.
But the basis of long-term value is shifting. Increasingly, the differentiator is not the event itself, but the data layer that accumulates around it – how effectively an organiser captures, structures, and learns from the information their events generate over time.
The organisers who will build the most valuable businesses in the next decade will not simply operate great shows. They will run learning systems.
Events as signals, not outcomes
At the heart of this shift is a simple but under-appreciated idea: many of the things that organisers care most about are not directly observable.
- Real demand
- Willingness to pay
- Audience intent
- Exhibitor motivations
These do not present themselves neatly in a single metric or post-show report. They are latent – they exist beneath the surface, and can only be inferred by observing patterns over time.
Viewed this way, an exhibition is not just a product or an outcome. It is a noisy signal: a moment in which fragments of underlying market behaviour briefly reveal themselves through choices, interactions, and engagement.
The organiser’s advantage does not come from any one edition performing well, but from accumulating these signals across events and years – asking, repeatedly:
What does this event teach us about the market?
So how do we do this in practice?
1. Start with a clear thesis: the data-led organiser
Most organisers still implicitly operate with an event-led thesis: “We put on shows”. A data-led organiser reframes this more fundamentally:
“We help markets discover, connect, and transact – using data as the engine”.
This shift matters because it changes how decisions are made. Instead of relying primarily on precedent or intuition, data is used to template judgement.
Consider launches.
Let’s face it, new events are traditionally green-lit based on a hunch, anecdotal exhibitor feedback, one or two anchor commitments, or a senior individual’s confidence that “the market feels ready”.
A data-led organiser asks a different question: “Where is demand already expressing itself, even if no show yet exists ? Topic engagement across content, audience overlap between adjacent sectors, repeat interest across regions, tired competitor formats – these are weak signals individually, but powerful when combined over time.
The goal is not certainty. It is reduced uncertainty.
2. Build – and protect – a proprietary data advantage
A recurring challenge in conversations about data is defensibility.
Third-party datasets, scraped websites, and bought-in data and insight can all be useful inputs. But they are not moats. If competitors can buy or scrape the same information, it cannot underpin lasting advantage.
The defensible opportunity lies in original data, generated through the organiser’s own platform both online and at the event.
Every event already produces valuable signals: how audiences navigate content, which exhibitors attract sustained attention, which topics drive return visits, which clusters convert from interest to action. Too often, these are treated as by-products rather than real assets.
The key is structure. High-value data is not about volume;it’s about discipline:
- Normalising information across shows and years
- Tracking provenance – what is audited, inferred, or estimated
- Building longitudinal views of how markets evolve
Two shows may look identical in headline metrics. Over time, their behaviour may diverge completely. One reveals deep, repeat engagement from a growing audience segment. The other relies on novelty and churn. Only one of these teaches you something durable about the market.
One thing we find highly insightful, when understanding exhibitor trends at Events Intelligence, is also looking at how new exhibitors at shows describe themselves, compared with legacy exhibitors – across large panels of exhibitors, the evolution of markets becomes far more apparent, and understandable.
3. Customer needs are real – but often latent
A thoughtful challenge to early versions of this thinking by Oliver Schmitt, of Agendum Schmitt, and Fair Network was whether organisers should simply put customer success at the centre of everything they do.
In principle, this is absolutely right, of course. Exhibitions only work when they create genuine value for all sides of the platform.
The difficulty is practical rather than philosophical.
Customer success in exhibitions are diverse, dynamic, and often poorly articulated – even by the customers themselves. What audiences say they want, what they choose, and what they respond to are frequently different things.
This is precisely where data matters.
By observing behaviour across many interactions and editions, organisers can begin to infer revealed preferencesrather than relying solely on stated ones. Over time, patterns emerge that no single exhibitor, visitor, or organiser could see in isolation.
Data does not replace customer focus. It makes it sharper.
I’ve written preiously about the power of infinite interns, and see the power of these in my own business. Current AI models can provide deep insight and enormous scale to enable teams to tease out insights from data.
4. Productise insight, not effort
Another common trap is treating data as a bespoke exercise. The same strategic questions are asked repeatedly – What should we launch next? Which exhibitors matter most? Where should sales focus? – and answered from scratch each time.
This does not scale.
High-value organisers turn insight into repeatable products: launch playbooks, market-sizing frameworks, audience similarity models, sector-weighted benchmarks. The point is not sophistication, but consistency.
When decisions are templated, human judgement improves rather than disappears. Teams spend less time debating assumptions and more time acting on evidence. The commercial effect is material: higher margins, faster scaling, and value embedded in systems rather than individuals.
In practice, I’ve yet to see an organiser think like this yet, so I have few examples – but surely some of the ‘data first’ organisers are starting to think more like this and the time will come when such an approach will be second nature and ‘business as usual’.
5. From editions to interaction graphs
One of the most powerful – and defensible – ways to think about exhibitions is not as collections of stands and attendees, but as networks of interests and interactions.
- Audiences connect to topics.
- Exhibitors connect to problems they solve.
- Content connects intent before meetings ever happen.
Over time, these interactions form an interest graph: a map of how a market actually organises itself, rather than how it describes itself.
This is extraordinarily difficult to replicate from the outside. It emerges only when an organiser owns the live platform, observes behaviour repeatedly, and connects learning across years and formats.
Each event strengthens the graph. Each interaction sharpens the signal.
For those organisers already using meeting models, you already have a deep source of intelligence as to preference and interest for a subset of the audience – by understanding who meets who, and who is selected as a desirable meeting partner, massive amounts of data is provided which can be generalised to the wider customer base. As an organiser, this insight shouldn’t sit with just your software provider, but as a key data source to understanding the value being created.
6. Think in systems, not editions
Ultimately, the shift from event-led to data-led is a shift from edition thinking to system thinking.
Edition thinking asks: Did this show perform well?
System thinking asks: What did this show teach us – and how does that inform the next decision?
In this model, even weak editions have value. They reduce uncertainty. They refine hypotheses. They add to the organiser’s understanding of the market.
Over time, learning compounds.
This is what creates defensibility. Not any single show, but the learning system behind it.
Closing thought
Exhibitions have always been about bringing people together. That does not change.
What is changing (and must change) is how value is created around those interactions. The organisers who build the most durable businesses will be those who treat data not as a reporting function, but as a strategic asset – designed intentionally, protected carefully, and applied relentlessly.
The move from event-led to data-led is not a technology project. It is a business philosophy. And increasingly, it is the difference between running great shows and building a truly high-value exhibitions business.
Mark Parsons is the founder of Events Intelligence, a data business which aims “to help organisers find their next 100 exhibitors”. He has worked with exhibition organisers for the last 20 years and also teaches Exhibition Design at Scola Politecnica di Design in Milan.
