In advertising, what does it mean to be eye-catching and why is catching the eye not enough to maximize conversion rates and lead generation?
Marketers and designers want to create eye-catching visuals that strongly appeal to their consumers, creating more effective advertisements.
What is the science behind being “eye-catching”?
Consumers are overwhelmed by the volume of content they are exposed to everyday, on a constantly growing number of media channels. Over 50% of all visual content is not seen. That is a primary driver to create eye-catching content in order to maximize the ROI of product and marketing investments.
Cognitive neuroscience has proven two things, first, that even if I catch the consumers direct eye attention, I do not necessarily cause them to remember the content; secondly, if the content is strong, the consumers will remember it, even if there is not direct eye contact.
These two kinds of attention are refered to by scientist as overt and covert. Catching the eye is about overt attention as the position of the eye can be openly seen by an external observer. Covert attention is not visible by looking at the positioning of the eyes. It is about what is processed by the neurons in the brain based on the direct and peripherical input from the eyes. Overt attention is like raw data and covert attention is processed data.
Instant Attention Map (Overt Attention)
Lasting Recall Map (Covert Attention)
Covert attention is the trigger to influence consumer behaviors.
As an advertiser, “eye-catching” is too superficial a goal, high cognitive impact is the real objective. A high cognitive impact causes the brain to process and remember the content effectively.
Overt attention has historically been measured using eye-tracking because it’s the easiest way. Covert attention was rarely measured because it required costly complex neuroscience methods (fMRI, EEG, etc.).
Hippoc Neuro-AI software provides accurate measurements of cognitive impact on all types of visual content (email, website, ads, billboard, TV, etc.).
Hippoc Software uses deep learning models trained on cognitive research data consisting of more than 300 thousands consumers exposed to millions of images and videos using standardized neuroscience tests to measure their cognitive reactions.
Hippoc AI models were validated with MIT databases and obtained over 94% prediction accuracy for attention and over 96% accuracy for memory prediction.
Here are some examples demonstrating the difference between what people are looking at (overt attention) and what they remember afterwards (covert attention).