Artificial intelligence has given us a powerful, low-cost tool to optimize exhibition presence to arrest and hold attention. Learn about how Beire has incorporated this data-based, scientific approach to achieve superior outcomes for clients.
Machine learning (AI) hasn’t been applied in the event industry on a large scale, yet. This is about to change; AI is coming for exhibition stand design optimization. This doesn’t mean the machines will be designing stands, but rather creative designers will have to be familiar with the scientific aspects of design that trigger interest and retain attention. Machine learning will be a critical tool to ensure creative is also effective.
Current Situation
The process of designing exhibition stands is largely broken. Presently, it depends too much on heuristic biases, stand builder cost efficiencies, uneven decision making, and arbitrary creativity.
Even though many stands may be aesthetically pleasing, the ability to quantify likely efficacy has been entirely overlooked. This is a missed opportunity since we now have the technological ability to quickly forecast how people will likely respond to a given design element. Where expensive human trials were required for this in the past (the realm of fortune 500 marketing budgets), machine learning algorithms can outperform at a fraction of the cost.
Solve for Visual Saliency
Visual saliency is incredibly important when it comes to exhibition stand design. Having a conspicuous exhibition stand that draws visitors and delivers an impression can make all the difference. There are several ways to realize superior visual saliency, one of them being predictive eye-tracking.
An example of an exhibition stand that has exceptional visual saliency is a Beire-designed stand for Sukano from the K Show 2019. What gives this stand exceptional saliency?
The Wave of The Future
Beire Exhibitions has included AI into the process by using machine learning algorithms to simulate eye-tracking analysis before putting stands to production. This optimizes exhibition stand design and creative placement to maximize impact as well as acquisition and retention characteristics.
The predictive eye-tracking allows detailed analysis of exhibition design creative work and graphics with both standard analysis, and enhanced linear-saliency analysis. Using the most advanced technology, we can tell you what draws attention by using a neural network trained using real eye-tracking data continually gathered from live sources. This machine learning ensures that the data sets are not stagnate; even if preferences start to shift over time, the algorithm will adjust accordingly.
Beire clients receive an analysis that generally addresses (3) key questions regarding their exhibition stand:
1. Is the design optimized around brand messaging or key physical areas?
2. Have we maximized saliency within the design to draw and hold attention?
3. Have we minimized the probability of environmental distractions?
The two methods we use during the creative process to address these questions are:
1. Design optimization: As marketers and designers, we often choose the design for an ad, commercial, or brochure based on our gut feeling. We rely on our experience and expertise, but regardless of the amount of experience you might have, the world is changing quickly. With this new state-of-the-art technology, you can rely on attention prediction to guide objective decision-making.
2. A/B testing: You can review and rethink your design immediately after the AI has identified which elements that get noticed. This allows you to work with the knowledge you need throughout your entire design process. Additionally, it gives you the tools to present your design more confidently since you can back it up with measurable data.
Data analysis of creative isn’t the future, it's already here.
Digital media developed for Instagram, Facebook, or GoogleAds is not developed by the creative whims of a designer and marketing team. Rigorous testing goes into the most successful advertising: the reach, impressions, new visitors, click-through rates, and all sorts of metrics are analyzed. For a product that requires much a much larger, and single-use, investment, it is almost unbelievable that such a solution is still only housed in the most innovative exhibition companies.
Machine learning is beginning to give us the power to objectively predict exhibition stand efficacy, at low cost, and well before thousands of euros are spent in production.
