Unlocking the Power of Neural Signals: Enhancing Market-Level Efficiency of Banner Advertisements

The average internet user is bombarded with a stream of online banners on a daily basis. From promotional offers to brand advertisements, these digital displays attempt to capture our attention while navigating websites, tempting us to engage with the advertised content. But what differentiates an effective banner from a forgettable one? How can marketers cut through the noise and leave a lasting impression on their target audience?

For a while now, marketers have recognized the significance of combining EEG (electroencephalography), eye-tracking, and behavioral measurements. In previous articles we already explored how these methods can significantly enhance market-level efficiency, for example in music and movies.

Spending time with online banners and their retailers

Recently, Kislov et al. (2022) dived into the power of these measurements in the market-level efficiency of online banners. They aimed to predict online banner efficiency, which they defined as the time spent on the retailer’s website after users clicked one of the banners.

In order to describe efficiency, neural metrics that we have heard before were harnessed: engagement and valence were indexed from the brain, and attentional factors were distilled from eye-tracking data. Interestingly, the attention to the online banner was separated for each component: the duration subjects fixated on the picture, text or brand in the online banner were reported.

Moreover, the behavioral likeability was included as well, where subjects indicated whether they liked the ad. Why, you might ask? In all neuromarketing studies where real behavior is forecasted, the survey results provide a measure of conventional research. When the predictability of these neural measures is not extending the predictive gain there’s no added value. We might just ask people in that case. Would be easy, right?

Guess what?

Since you’re reading our neuromarketing blog, I’m assuming you would’ve guessed; the survey data from the lab was not really predictive for the behavior of the population. Only 9% of variance in time spent on the website after they clicked the banner could be explained by the subjective likability index.

What’s kind of shocking for me as a neuromarketing fanatic, is the fact that the attentional metrics were not predictive at all. The time subjects in the lab looked at the picture, text or brand in the online banner was in no way predictive for the time that their peers spent on the website.

However, when we turn to the brain activity, there’s some value! For the indexes from EEG, engagement and valence, the model could significantly predict 45% of the variance in time spent on the website. The most important indicator was not valence, but rather the neural engagement with the online banner during the lab session.

What if we put everything together?

That’s where it gets interesting! When the predictive model combined Eye Tracking and EEG, suddenly 79% of variance in time spent on the website was explained. The most important predictors here were EEG engagement and – surprise – the dwell time to the pictures in the online banners.

In other words, when we measure the neural engagement and time a subject looks at the picture in an online banner, this can significantly predict the time a user subsequently spends on the website advertised by that banner!

Why? How?

The finding that combining EEG and eye-tracking was particularly predictive for market-level effects is in line with previous research where EEG measures of engagement are predictive (for example to story engagement). Moreover, combining the neural information with attention was already indicated to improve prediction of consumer preference by previous studies (Zhu et al., 2022).

Applications

Thus, this once again highlights the potentials of combining eye tracking and EEG measurements to explain market-level variations. By leveraging the power of neuroscientific measurements, marketers can gain a deeper understanding of consumers' engagement, emotional responses, and preferences towards online banner advertisements. This knowledge can be instrumental in optimizing marketing campaigns, creating more appealing ad content, and ultimately driving higher user engagement and conversion rates.

Moreover, this particular experiment highlights that the picture in a banner is essential to capture consumers' attention. By strategically placing attention-grabbing images, you can increase the likelihood of attracting and holding the viewer's attention. For example using faces or gaze cues towards the call-to-action may draw attention and steer it towards the intended direction. Moreover, attention is caught with images containing high contrast, strong colors, or dynamic action shots. Use these in the images, and most probable you’ll win on attention and engagement metrics.

Conclusion

A combination of eye-tracking, EEG and behavioral measurements better explained the market-level efficiency of banner advertisements than each measurement alone. Overall, the study confirms that neural signals of a relatively small number of individuals can forecast aggregate behavior at the population level. Use this as a potential to leverage the engagement to your website!