Deep Learning

First and foremost, this requires being able to effectively use an AI data-mining technique on large databases of information. Deep learning looks for patterns that are valuable to a business. AI uses machine learning to improve data mining in order to be able to predict future behavior. One way to do this work cost-effectively is to outsource the data mining for deep learning.

Deep Learning Example

An example of using deep learning effectively would be to scan all the LinkedIn profiles of current and past attendees for public events to develop a group of personas that represent typical attendees of an upcoming event being promoted.

Personas are like the characters in a story. The AI software constructs these personas from the LinkedIn profiles, and each persona represents a different type of attendee.

Some characteristics are automatically separate personas such as male versus female. However, others are subgroups under each persona such as the age of attendees. When constructing the personas, the AI software may discover patterns in the data that may not be apparent otherwise.

These identified patterns of personas are useful for a targeted marketing campaign. Marketing efforts can be focused to promote an upcoming event to those types of personas in the public who are potentially the most likely event attendees.

Evaluating the Effectiveness of Deep Learning

The return on investment in AI used for deep learning can be evaluated by comparing the cost of this deep learning effort with the revenues that come from increased attendees.

Deep learning can be applied to almost any database. Event planners can use the deep learning capabilities of AI software to learn much about event attendees of previous events to help improve the outcomes of future events.

Biometrics for Registration and Security

AI is useful in making the registration process more efficient by allowing preregistration combined with the on-site usage of biometrics for identification purposes. It can make the entire process more efficient and actually cool for once. When’s the last time someone said your registration setup was “cool”?

Using Biometrics for Registration and Security

Facial recognition makes the registration process much faster with less need for human assistance. This also enhances security at the event. Video surveillance scans of the crowd can approve the entry of the attendees as they pass through the entry doors without needing a human to check each of their badges.

Badges cannot be shared so easily with other people when facial recognition software is used. This helps eliminate the revenue loss that comes from badge sharing. This may offset some cost of this AI implementation. It also reduces the possibilities of unauthorized entry to the event space, which is a critical security issue for most events.

With biometrics, your only worry is for one of the Faceless Men to sneak into your event…

Evaluating Biometrics for Registration and Security

Adding AI facial recognition can get expensive at your event. But when you factor in the money you save from hiring more staff, you can potentially break even, if not save a good amount of cash.

Not only can this save money, but effective use of AI also increases your event’s security.

The value of this increased security can only be estimated. It is equal to the liability cost if the event security was breached by unauthorized persons with nefarious purposes.

Event Support

The most helpful AI for event support comes from mobile applications. Usually, producers don’t create costly custom event mobile apps. It’s much more common to modify existing apps for specific events.

These event apps are useful when scheduling the presentations at events. They help coordinate schedule changes and notify attendees of the changes.

Event Support Examples

Interactive site maps of the event location that help attendees find things is very popular. AI-driven chatbots that are also accessible using a mobile device are helpful to answer routine questions. The AI detects patterns that emerge from the questions asked and the feedback received from users when the chatbot gives satisfactory answers. In this way, the AI chatbot continually improves its responses.

And let’s not forget about brain dates, a networking hack growing in popularity. Machine learning can match attendees with the perfect networking buddy onsite, ensuring both parties greatly benefit from the interaction.

Privacy is protected because detailed identification and the meet-up information is only given to those users that agree on both sides to meet. Concierge services help attendees with special needs at the event site and perhaps off-site needs as well.

Evaluating Event Support Effectiveness

The value of providing event support is determined by the attendees’ engagement. Some of this is financial, such as the number of attendees at optional paid-training sessions. Other effectiveness metrics are not financial but show active participation in the event by attendees. The data collected from the event support systems also serves as a data resource for future AI data mining.

Summary

At the end of the day, applying artificial intelligence to event planning is a must.

By focusing on AI implementations that have clearly-defined parameters along with ways to track performance metrics, it is possible to use AI in ways that are cost-effective. In some cases, financial performance determines the positive impact.

Other times the performance metrics may be engagement factors. These could be the number of people going to special presentations at the events. Or it could be the number of people who give positive answers to surveys about AI support systems that they use at the event.

Either way, artificial intelligence is an emerging tech producers much embrace, not ignore.