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Event organizers

Event customers

Venue owners

Here’s a question

“What if you could analyze and understand your audience as if they were online?”

Like Google or Facebook analytics, but for the real-world.

What would that mean to you and your business?

You would be able to analyze user behavior at scale. You would be able to make informed decisions about what to do at certain times, where to position your assets to better serve your customers.

Create remarkable experiences based less on guesswork and theory and more on facts.

In fact, it’s possible.

As the saying goes: “You can’t improve what you don’t measure”

Perhaps you are measuring?

Many event organizers and venues do. But, as we have found, few manage to do so at scale with the reliability that online means provide.

This article will show you how you can make online analytics work at your offline event.

Online Analytics for the ‘real world’

Online analytics have become household tech for most online marketers and advertisers.

It provides the big picture and requires little manual labor to get deep insights about online user behavior.

Helping marketers measure the impact of campaigns, advertisements, and online investments.

Counted, registered, observed and analyzed at a larger scale. Without much human input.

Like Google or Facebook Analytics, but for the real world?

IoT is the key

Question is, “how”?

How do you measure your physical spaces without manual counting and observations?

IoT what?

Perhaps you have heard about the Internet of Things (IoT)?

In a nutshell, IoT enables virtually any object or person to connect to the internet and communicate with other people and things, via a microchip.

Small computers and sensors embedded into “real” things.

Things made smart. Or rather smart things.

This system of Things generates massive amounts of collective data (read: “Big Data”) that can be stored, transformed and analyzed in virtually endless ways


Smart things?

Smart things can virtually be anything. Ovens, stoves, fridges, coffee makers, vacuums, brooms, pots, windows, curtains, lights, cameras, and so on.

Perhaps the most (in)famous example is the automatic kitchen appliances making your morning coffee and breakfast as you wake up. 

The question is how much utility it actually provides to people’s lives.

A popular smart thing that you may know is one you probably have in either your hand or pocket.

Right now.

Virtually everybody has at least one device on them, always.

Smartphones, smartwatches, smart glasses (if you’re still hanging on to them), fitness trackers, smart clothes, and so on.

Each device has several antennas embedded in them. GSM (LTE or 4/3/2G), Bluetooth, WIFI, NFC, etc.

These antennas communicate with one another, all the time. Without you knowing it.

For example, when you enter a room, such as a café, restaurant, shop or an event, your smart device will start to communicate with other smart devices present.

It will start to communicate with the WIFI network (read: access points) within range.

For every call (read: ping) is a data marker.

Time. Signal strength. Unique identifiers, etc.

Place in triangular formations and this can even help determine a rather precise position of the smart devices present.

Take this example illustrating the movement of one person in a crowd, in New York city, USA. 

This is both an interesting and rather scary example of how crowd analytics can be used to track the movement of one person. In this case, a Senior Defense Department official and his wife at the Women’s March.

Please note that tracking and identifying individual people’s movement and whereabouts is NOT the intention of this article. Rather we focus on crowds and statistics based on masses of people.

Key metrics you should consider

Now that you know which technologies can be employed, let’s look at the metrics should measure against.





Let’s explore these metrics further, what they mean, and how you should integrate them into your event analytics. 


The success of your event comes down to this overall metric: How many attended the event?

As with online analytics, let’s refer to metrics such as user statistics, such as total, active, returning, device type (i.e. operating system),

Often, we see most events use queuing and check-in on the first day. That’s it.

Some may use apps and other services for engagement throughout the event. But it requires that users take action to get any valuable information in return.

In some instances, video surveillance and staff manually observe the crowd. A qualitatively good approach, yet a quantitative nightmare for most.

This, at best, only leads to vague assumptions about the overall flow and statistics of the event.

With the IoT technologies suggested above, based on attendance and device type, you can further draw informed pictures of what type of visitors attend your event, at what times, and at what their general preferences are.

For instance, “visitors with both iPhone and Macintosh systems prefer to visit certain exhibitors in the morning and attend sessions in the afternoon”

This will give you a much better indication of what you need to do to keep guests engaged at the event.

Event Metrics



Online Metrics

Total visitors (users) Total number of unique devices per event How many unique devices attended the event at a given day Total Active users
Returned visitors (users) Number of unique devices per day How many unique devices attended the event on several days/events Returning Users
Device Type (per vendor) Number of unique devices per vendor and model Mobile, tablet, and desktop. E.g. iPhone, iWatch, iPad, Samsung Galaxy, Google Pixel, etc. Mobile / devices


Perhaps the most vital metric to determine your event success is engagement:

“How engaged were visitors and exhibitors throughout the event(s) day(s)?”

Whether visiting booths, sessions, keynotes and more throughout the event.

For individual exhibitors, this might be a straightforward counting exercise.

Count the number of visitors who have stopped by and how long they stuck around. Fairly easy.

But after one or more days, with perhaps more representatives, many visitors, and conversations, it is highly likely that the stats will become fuzzy.

It is human.

What about sessions and keynotes?

Sure, you can employ badge scanning in both scenarios. Again, it comes down to manual, human labor. Labor that could be spent more wisely on interacting with the visitors instead.

And what about the visitors who observed at a distance? Current technologies and methods just don’t cut it.

Event Metrics



Online Metrics

Engaged visitors Number of unique devices visiting one or more zones Visitors spending equal to or less than X time at a given event, booth, zone, session, etc. Total Sessions
Disengaged visitors Number of unique devices not visiting one or more zones Visitors spending less than X time at a given event, booth, zone, session, etc. Bounce rate
Dwell sessions (total, average) Number of unique devices staying at/in Y zone(s) The total and average places a visitor visits throughout the event, providing clear estimations about the overall interest of a certain place or event, at the event. Sessions per user
Dwell time (total, average) Number of unique devices staying at/in Y zone(s) for X time The total and average time a visitor spends at all or any given places / zone(s), providing depth to the interest estimation (sessions) and customer conversion rates. Session duration
Bonus stat: Distance Number of unique devices staying X time at Y < feet/meters from the booth. Visitors who have watched the booth from afar without interacting with representatives. This could be a potential lost opportunity, as some people are more introverted than other. Not Available
Bonus stat: Referral (journey) What other booths have N number of X unique device type visited before and after Y place(s), at Z time(s). Visitor journey throughout the event. This can provide insights into general preferences, based on device type(s), time of day(s), type of session(s), and type of event(s). Referral channels

Traffic flow

How attendees move throughout an event tells you something about how you’ve managed to organize the physical environments.

It can help address if there’re any obstacles along the way, like roadblocks or congestions.

Do you know when and why it happens?

Like traffic in general, observing which directions people take to get from point A to B is crucial in determining whether you have organized ideally.

How fast can you respond to such situations can potentially make or break your overall score.

A key thing to consider is that people don’t always behave rationally. Few do.

Even though you might have spent days on end planning how to best organize and coordinate your event, ensuring every square inch is accounted for, you will likely still find that visitors won’t work accordingly.

Observing your crowded audience at scale can help you see ‘jams’ occur, as they happen.

With intelligence in IoT and Big Data, it is possible to accurately predict when jams will occur.

Event Metric



Online Analytics

Flow Number of unique devices who entered, passed through and exited the event Will help you indicate the popularity of locations and churn ratios of less popular, and where people flow from and to. Click-Through-Rate / Acquisition
Waiting time Number of unique devices not moving for X time in Y zone can even be a determining factor when hiring the place for the event and the general organization. Page load time
Peak load Number of unique devices in X location(s) compared to Y location(s), per Z inch2 / meter2 By using heat map visualizations to compare the different values, higher and lower, at various locations during the event(s), booth(s), session(s), keynote(s), etc. Heat map / Geo-location
Peak hours Highest N number of unique devices at X location(s), per per Z inch2 / meter2, at Y times. Indicates at which location(s) and time(s) people are located. Heat map / Micro-conversions


Anomalies occur all the time. Before, during and after events.

They are hard to measure and predict.

Or at least, it was.

With the advent of IoT, Big Data and Machine Learning, you now have computers calculate possible outcomes based on massive chunks of data that is virtually impossible to do manually.

Abnormal behaviors in crowded places can indicate when something out of the ordinary happens during the event. This can relate to controlled outcomes, such as testing and experimentation.

But it can also be for uncontrolled events that might affect the security and safety of attendees.

Continuous scans are essential to ensure that action plans are carried out by the right number of people in the right place.

It can also help you identify the position of attendees who are out of sight.

Event Metric



Online Analytics

Controlled Anomaly What happens in X zone(s) when Y controlled instance is triggered Will help you indicate the popularity of locations and churn ratios of less popular, and where people flow from and to. Behavior / Experiments
Uncontrolled Anomaly What happens in X zone(s) when Y uncontrolled instance occurs. OR, given X forecast what is the probability of Y occurrence? Some machine learning / artificial magic. In plain language, this helps you stay alert when possible unwanted scenario is likely to occur. N/A (that I’m are aware of)

Marketing and Business Intelligence

Metrics and analytics are an excellent sales tool that your trade show and your sales team need.

They are also an effective tool for measuring KPIs of the teams in charge of the booths.

Other metrics such as the number of Prospects at the Event, based on the number of visitors, can be analyzed in addition to the number of Pre-Show Reach Outs, prospect to Meeting Ratio for scheduled meetings on the event, as well as other offline metrics.


 Identify: Hot spots

Measure the level of engagement throughout the event and keep track of the areas that have the most visitors, why they move in that direction, and at what times.

Compare the traffic within your areas of interest through segmentation and consider them in your next sales strategy.

Count the number of people entering various sections of the trade shows to better understand how many visitors visit the different types of booths.

This way you can, for instance, optimize the rental pricing of key spaces. Dynamically, even.


Identify: High Traffic Areas

Find opportunities for brand activations, events and commercial spaces based on the identification of high-transit areas.

Use geofencing to split the area into zones for deeper insights about behavior and use.

The flow of visitors after a presentation is a good indicator of the attention, acceptance, and interest that the product may have had on the audience.


Identify: Peak Hours

Identify the busiest hours at your events, when it generates the most traffic and footfall. Manage events and marketing campaigns according to your event objectives.

Cost savings are possible by optimizing the operations of the cleaning and security staff according to the number of visitors and their needs within the exhibitions during peak hours.

This can have a positive effect on customer satisfaction.


Identify: Abnormal Behavior and Patterns

Observe patterns affected by certain instances. Perhaps an announcement or something arbitrary taking place during the event.

This may indicate the effects on the crowd and can help you learn about attendee behavior at scale, giving you insights about what to do and how to steer attendees in the right direction.

Crowd nudging on steroids.

Integrate: Marketing Analytics

This is where it both gets exciting and tricky. Proceed with caution.

Since you probably already work with online analytics, it’s safe to say that, combined with your offline crowd analytics, you are nearing a potential gold mine of data and intelligence.

That is the reason for the attempted reference point between online and offline crowd analytics.

But it is ripe with complexity and may likely be a costly affair, especially in terms of human resources required (in or outsourced).

Having said that, it makes sense to consider the possibilities for future exploration.

Integrate: IT Automation

IT automation is something your IT department probably already invests in. Most cloud platforms today provide applications to get you started without too much complication.

Take Microsoft Power Automate or Azure Functions, found in Office 365 and Azure (obviously).

Both can perform basic ‘functions’ (read: tasks) that can be automated based on different parameters.

An example could be that when a certain threshold is reached (e.g. X number of people in the Y area), a function should be automatically triggered, notifying your staff to take the desired action. Either by text message or notification.

There are other alternative Software as a Service (SaaS) solutions such as IFTTT and Zapier that can integrate and automate many more applications. But they are separate from Microsoft, Amazon, and Google, and therefore you should consult with your IT department first.

Our Jayflux platform integrates deeply with Microsoft services and enables some of these functionalities out of the box, so you don’t have to overthink it.

Other Considerations

Using these technologies is not all pink clouds and candy. It presents some rather significant considerations to be made and addressed.


What if these devices, the scanners, get hacked or somehow tampered with? 

Any device is essentially prone to tampering. Whether it be digitally or physically.

Whether it’s by overloading (Denial of Service) the device, accessing its data or listening in on transmissions, there are so many ways in which IoT devices can be a security risk to your data and systems.

The question is, how do you intend to uphold the security?

Do you have the resources in place to deploy, operate, monitor and respond to when a potential breach is taking place? Or do you intend to outsource it?

Microsoft, Amazon, and Google provide the most fundamental tools to help you mitigate security risks.

Please consult with IoT professionals to get a better picture of where you need to look for first and over time.

We will explore this subject in another article later. Sign up for our mailing list to be on top of what we post.


What about scanning or sniffing on people’s mobile devices, isn’t that against privacy regulations?

Essentially, yes. These scanners do read unique markers from people’s devices, evidently giving you access to identify who they might be. Do note, you can’t see the name of the person, who they are and where they come from, what their favorite movies are and who they date.

That’s entirely up to Google and Facebook.

But you will be able to see a unique MAC address, which is regarded as an indirect identification. The EU’s General Data Privacy Regulation (GDPR) states that:


an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person

“Natural persons may be associated with online identifiers provided by their devices, applications, tools and protocols, such as internet protocol addresses, cookie identifiers or other identifiers such as radio frequency identification tags. This may leave traces which, in particular when combined with unique identifiers and other information received by the servers, may be used to create profiles of the natural persons and identify them”



Put in plain English, MAC and IP addresses are different identifiers, but with an almost distinct purpose: a unique identifier. Since MAC addresses can be correlated with time and location (i.e. sensor location), it can be used to create profiles of ‘natural persons’.

But, there’s a way. It comes down to what you store, how you process it, and how and for how long you store it.

Jayflux securely scans, transmit, and store personal identifiers, such as MAC addresses, hashed and salted (read: encrypts), and process the information according to the GDPR and Danish Data Authorities’ demands.

Stay tuned for future articles about privacy protection and crowd analytics by signing up for our newsletter.

Questions and Answers

Now that we have covered what is possible, let’s consider some of which you might already have in use.


“We have a check in system”

Check-in solutions are great. It provides an opportunity to meet and greet guests, as they arrive.

Plus, it’s a ubiquitous system for registering, queuing, and entering an event.

But what happens after that? During the event?

Maybe it’s time for something smarter?


“We have badges and scanners?”

Ah yes, the trusty old badge scanners.

Did you know that according to GDPR, you are required to ask the attendees for consent collecting that information? If not, you are practically violating their data privacy.

And, who starts a good customer conversation with:

“Hey, nice to meet you. Can I scan your badge? You can win an iPhone” (This an actual encounter from just one month ago, end of February 2020).

Maybe it’s time for something more intelligent?


“We use staff and consultants for observations”

Manual counting and observations have been the norm for many years. Manual tasks performed by either your own personnel or hired consultants.

Perhaps you make use of tools such as The Triangle, The Rule of Right Turns, The Wide Aisles, The Bull’s-Eye, and The Zoom Zone?

Let’s face it. It’s a tiresome, expensive and limited approach. It’s very hard to scale and make informed predictions and timely adjustments.

Maybe it’s time for something less manual?


“We have an app for that”

Ok great. You are getting closer.

There are plenty of event organizer apps out there. They generally have all the required features for hosting a great event, interactions across organizer and participants, and analytics for the data hungry.

Some might even have location tracking enabled. Hopefully they have implemented adequate privacy functionality.

But can you live with an accuracy of up to 13 meters? Researchers from University of Singapore found that in urban settings, GPS location has an estimated error margin between 7-13 meters.

At your venue, it may well be much less precise.

App-based solutions are primarily for communication and coordination purposes.

Perhaps enable text and semantic analyses to analyze how the overall discourse throughout the event. A good app platform should have these embedded in their solution.

The point is, communication platforms won’t do it alone.

Final Remarks

I hope you find the article worthwhile your time and has inspired you to explore further. If so, please share the content however and to whomever you like. As long as it’s in good faith 😊

If you want to learn how we do and see it for yourself, click the button below and get a demo with us.

If you would like to learn more about Jayflux and the technology behind it, please visit our homepage at


“What you don’t measure, you can’t improve. So start measuring now”

Stay safe and sound out there. But don’t be afraid to explore and experiment.

Yours truly.


Nicolai Harbech

Nicolai Harbech

Director of Growth and Revenue

I helm the commercial side of Jayflux, leading all customer facing initiatives.

Human behavior and psychology, and new tech are are my favorite topics to explore and work with.

Any questions, please shoot me a mail by clicking my image.