Organising an event involves its fair share of challenges and uncertainties. Will anyone show up? Will the time, effort, and resources you've invested pay off? Will it bring the results you desire? These are all valid questions that any business has to think about. 

If only there were a way to predict the outcome of your event beforehand…

Luckily for you, there is! Using machine learning, statistical algorithms, and past data, it's possible to predict the future, and it's all backed up by science. Curious to learn more? In this article below, we explain why and how data models provide real business value for live events. 

Let's dive in!

The Challenge With Organising Events

The past few years have had a significant impact on organising corporate events. Regardless of your company's field of work, planning events can enhance its visibility, position it as a thought leader, contribute to employee engagement and motivation, and create a wide range of new opportunities.

Sounds good, doesn't it? But hosting a successful event is no walk in the park. There's more to it than just delicious snacks and high sign-up rates. Your event should bring value – both for your business and your target audience.

But here's the tricky part: Unlike with most products, the value of events are only realised as and after they happen. It takes days, weeks, and, in some cases, even months to understand their success. Moreover, this is true for all participants, attendees and hosts alike. You can sign up for an event, but there's no guarantee you'll receive what you're hoping for. Whichever way you look, there is a lot of uncertainty.

So, how can you know whether investing in events is worth it? How can you organise a successful event if your strategy only relies on guessing? Without data models, audience acquisition teams are working with one eye closed.

Thankfully, there is a solution that can make the process much easier. Using predictive models, you'll get the answers to your questions, and your acquisition team won't have to navigate in the dark.

What Are Predictive Models, and Why Do They Matter?

Imagine you're throwing a huge party (your event). Wouldn't it be great to know how many guests will show up, what kind of music they'd enjoy, or what snacks would be a hit? That's what predictive models do.

Thanks to machine learning and statistical algorithms, it's now possible to forecast 'what might happen'. Unlike descriptive analytics, which explains what happened in the past using historical data, predictive models use past data to predict the future. Here are some ideas on how your business could benefit from predictive models:

Predicting Attendance

Instead of general percentage-based predictions, machine learning offers more specific forecasts. 

Why is this important for your business? With predictions for each participant, it's possible to better marketing efforts. After all – identifying your target market is crucial for improving conversion rates and building long-lasting relationships with your clients (or employees).

 Note that there are two types of attendance models:

• One focuses on improving existing marketing channels.

• The other helps find new channels by analysing data.

Improving Exhibitor Preparedness

Another way to use predictive models is to score exhibitors based on their preparedness. This is crucial because a well-prepared exhibitor is likelier to have a successful event and become a repeat customer for the organiser.

IAEK Value Prediction

The main goal of B2B events is to match buyers with the right sellers, which isn't always easy. For example, a small business looking for marketing tools might not invest in high-end solutions like Adobe Marketo, but they would be interested in hearing from an expert in that field. Conversely, larger companies might be more inclined towards these advanced solutions.

Previously, it was challenging to predict how successful different types of attendees would be at an event and the value they'd get from it. But at The Media CTO, we've developed a model that goes beyond previous methods. The IAEK Framework uses intent data, behavioural analysis, and profiling to predict each participant's experience at the event.

Moreover, the model can also spot early warning signs, like exhibitors who might not benefit from the event despite being well-prepared. This situation can't be avoided sometimes, but event organisers can at least know what to expect and prepare for it.

Furthermore, IAEK Value Prediction can determine the value of participants not just by their attendance but how they engage at the event. To learn more about this, check out our article on defining participation in the IAEK way.

Why Predictive Models Are More Effective Than Descriptive Analytics

One of the main benefits of descriptive analytics is that it can help you track your company's performance and progress, as well as make more informed decisions. But there is a considerable drawback, too – it can't go beyond analysing data from past events. 

Once the process is completed, your team must put in the effort to find the necessary solutions and choose how to move forward. This, of course, takes time, which is not something event organisers always have. 

While we don't believe in a one-size-fits-all approach to event planning, it's clear that predictive models have significant advantages over descriptive analytics. Their forward-looking approach is more dynamic and actionable. However, in the best scenario – you should aim to have a balance of both.

How Diagnostic Models Support Predictive Models

No predictive model is perfect. For it to provide value, it must be consistently assessed and improved. This is where diagnostic models come in. Having a framework in place to organise and interpret data makes it much easier to identify the areas that need improvement. 

Unfortunately, in many cases, diagnostic models are too manual, which makes them challenging to apply. At The Media CTO, we use the CVI + CVO Framework™ and the IAEK Framework to make event organisers' lives easier – not add another task to their never-ending to-do lists. Utilising this framework we can:

Detect High-Value Anomalies

There's still much to learn and improve about the science of events. While there are regular trend patterns of behaviour you can discover, like with any other marketing field, anomalies do occur, and it's essential you spot them! Why? Anomaly detection opens up a door for a wide range of opportunities. For instance, our model based on a standardised IAEK Framework can show how these rare occurrences have happened and whether they are replicable.

Find the Highest Value Path

Imagine if you could identify the path of maximum value for each participant! This is precisely what our model can do. Whether you're hoping to determine the best direction for a specific industry, company, or demographic, our model provides the tools to do it. As a result, you can design better experiences for your audience.

Challenges of Implementing Data Models

At The Media CTO, we are confident that using data models for event planning offers many advantages. But is it a panacea that will solve all your problems? No. The IAEK Framework comes with its own complexities that must be considered.

Identifying 'Actions'

One of the main elements of the IAEK model is Action/Decision, which looks at all the actions taken before, during, and after the event. However, quantifying these activities can be challenging because often they are long-term, multifaceted and impacted by numerous variables. 

Assessing Genuine Engagement

Measuring engagement by metrics like interaction levels, participation duration, and feedback is easy. But does it really represent how meaningful and genuine the session has been? For event organisers, it's important that each interaction has been with an intent. This, however, is much more difficult to determine and requires diving into the deeper physiological aspects of participant experience.

Adapting to the Ever-Changing Environment

Throughout the last few years, the event landscape has changed significantly. Before 2020, virtual events were an exception. Now, it's a whole different story. In fact, Influencer Marketing Hub estimates that the global virtual events market will reach approximately $366.5 billion by 2027 - mainly driven by brands.

Adapting to a consistently evolving market, of course, can be tricky. It is affected not only by technological advancements but also by cultural drifts, shifting attitudes, and so much more. However, what's great about the IAEK model is that it is surprisingly versatile and can be utilised for events of all sizes.

To learn more, explore our article about the IAEK Framework.

Every Data Model for Your Event

As you can see from what we've discussed before, prediction models have the potential to become transformative for the event industry. If you'd like to test their capabilities, here's a quick insight into some of the most popular ones and what they could do for your business.

Do Demand Forecasting

Linear regression serves as an excellent tool for demand forecasting. By analysing historical data (for instance, registration numbers, time of the season) and current market trends, you can predict the turn-up, which helps with several other tasks (venue selection, catering needs, etc.). No more money wasted!

Personalise Your Content

In the current market, customers expect a more relevant and engaging experience. However, crafting a personalised experience can be quite a time-consuming task. With linear regression, however, the process gets much more straightforward. The data model helps predict what content would be most attractive to different audience segments. You can then use the information to offer sessions most suited to your audience.

Use Clustering to Segment Your Audience

Without audience segmentation, chaos is bound to happen. While this may sound dramatic, it's true. When you segment your participants based on their behaviour and preferences rather than structured data, creating targeted marketing campaigns will be much easier.

Match Attendees With Exhibitors

We already mentioned how important it is to match the right sellers with the right buyers. So, how can you practically do this? With matrix factorisation. Based on attendees' previous interactions, survey responses, and other data, it's possible to find the most suitable matches.

Build Communities

Networking is the lifeblood of any event. The opportunity to connect, share ideas and build relationships with other attendees is what makes the whole process worth it. But creating a space for communication is no piece of cake. How can you know which attendee should matched with whom? Here's where data models come in. 

Using these tools, you can group attendees with similar interests or professional backgrounds. While this is valuable for any event, it becomes even more critical in virtual and hybrid sessions, where spontaneous interactions are less likely.

The Power of Data Models

Around 328.77 million terabytes of data are created every day. It's almost a crime not to use it to your advantage! Whether you're planning a conference, a trade show, or a community gathering, leveraging data models can make a significant impact on your event's success.

However, it's not just about the numbers. The use of data models goes much further than that. It's about understanding and predicting human behaviour, desires, and needs. In fact, we have outlined the impact of many of these models in The DiG.

Remember, the goal is not just to organise an event but to create an experience that resonates with each participant. This will require time and effort, but predictive analytics can make the process more productive. 

With the strategic use of data models like IAEK, you can ensure every decision is informed, every strategy is data-driven, and every event is successful. What else could you wish for?

If you want to know how data models could benefit your events specifically, our friendly team would love to chat with you! Book your session here. Or subscribe to this blog, where you can access more valuable resources on digitalisation, AI, machine learning, and so much more.