How to Beat Uncertainty in Event Planning with Predictive Analytics

In the immortal words of Chucky D – “It was the best of times; it was the worst of times…”

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This sentiment encapsulates the last 18 months in the event’s industry - abundant uncertaintly, surrounded by crazy amounts of innovation and investment.  Probably one of the most endearing aspects of the event’s industry is the collective soul of the people in it. I was scanning LinkedIn updates last week, and you can’t help but feel the potential energy and promise for 2022.

Regardless of the type, style or focus of your next event, the level of planning and strategizing is more complex than it was pre-2020.  The substantial amount of time, effort, skill, coordination, and resources allocation required to get an event up, executed and down again has never been higher.

Decision-making with limited information is the name.  One can feel better and more confident about their decisions when the cone of uncertainty is reduced.  So how do we get there?

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Relationship Between Event Uncertainty and Predictive Analysis

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When event uncertainty is a problem, predictive analysis offers a reliable solution. The more robust and dependable a predictive model or approach is, the more uncertainty you can curb. The more promising your predictions are, the more certainty you can afford for your event!

Marketers have always been in the game of prediction, which is a reason why so much of marketing should be data driven.  Let’s look at how an event marketing professional can use predictive analysis to make sure they can better manage and monitor their conversion funnel and drive registrations.

What we know today – the name of the game is targeting the right people at the right time.  The problem is that it can be more challenging than ever, considering the following:

• Mailing lists are proving unreliable as many teams are still working from home.  What percentage of your historical reach was attributed to hard mailers?
• Email’s reach has also proven to be challenging due to higher-than-normal levels of employee turnover.  Do you know how much of your “list” has turned over in the last 1-2 years?
• The prospect to attendee conversion cycle has become dramatically compressed.  Decision-making for in-person travel is happening later and later.  Do you know when your audience is most likely to convert?

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Confident predictions can help event marketers create workable campaign strategies and engage, measure, and pivot - to double down on the most effective parts of their campaign.

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How To Get Started with Predictive Analytics?

Note there are two types of data sets that you can and should use during a predictive analysis. a) Quantitative, and b) Qualitative. 

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The Different Types of Predictive Analyses for Event Uncertainty

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Quantitative Predictive Analysis

Quantitative analysis refers to numerical data collection, organization, and problem diagnosis. These types of analyses help make predictions about statistics, event analytics, percentages, graphs, and other quantitative data. Plain and simple, quantitative analysis involves collecting the population of the data that is generated when someone does something, clicks online, etc.

For instance, common collection areas where predictive data is harvested from includes registration, lead retrieval, app engagement, virtual content consumption, and gamification modules.

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Qualitative Predictive Analysis

Qualitative predictive analysis is very often based on a sample of data from users who represent a portion of the population.  Qualitative studies are used to establish predictions about human behavior, alternative event discourses, and many different qualitative data patterns that can help deter eventual uncertainties.

The best example of qualitative predictive analysis involves surveying and capturing sentiment information around questions and answers.  While the data is rich, it’s traditionally challenging to be truly predictive because the event experience has to take place to understand what a user thinks about it.

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Top Use Cases for Predictive Analysis for Event Organizers

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1. Descriptive Analysis: Using historical audience composition as a comparative measure by using correlation. Use case today: Does my first live event’s audience in 2022 resemble our pre-pandemic audience?
2. Diagnostic Analysis: Using data to understand why there is a signal. Commonly referred to at root-cause analysis this is much more focused on the causality of an outcome. Use case today: A/B testing shows that our email subject lines aren’t resonating with our alumni audience.
3. Predictive Analysis: Using live data to predict trends in events. Use case today: Sales or marketing trending pre-event is used to forecast where you will end up on audience and revenue.
4. Prescriptive Analysis: An advanced form of predictive analysis; it’s about evaluating present, past, and predicted data sets AND using that to determine a course of action. Use case today: Presenting the mix of content marketing, to the right people, at the right item to get them to participate, engage, and amplify the value of an event.

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How Does Predictive Analysis Help Counter Event Uncertainty? 

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Event uncertainty stems from a mix of data-driven and behavioral misalignments. However, predictive analysts can scale down and breaking down problem areas to reduce the extent of uncertainties. The results are costs savings to you and a more predictable outcome for your exhibitors, sponsors and attendees.

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Conclusions

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Event management in a hybridized, pandemic-ridden era is becoming more decision intensive than ever before.

One way to combat the uncertainty around decisions and to work with more operational efficiency is to be more data-driven.  The list of predictive analysis above is a good place to start when trying to put your best event foot forward.

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Want to learn more?

Join Us February 2nd at 12 pm for our next webinar on predictive analytics

Save Your Spot: https://register.gotowebinar.com/register/8893706985234652427

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Additional resources from this post:

https://blogs.opentext.com/predictive-analytics/

https://mixpanel.com/blog/what-is-predictive-analytics/

https://medium.com/co-learning-lounge/types-of-data-analytics-descriptive-diagnostic-predictive-prescriptive-922654ce8f8f