Predictive eye tracking uses algorithms and simulates human vision and predict where users will focus their attention on your design. It allows you to measure the visual impact of your designs such as digital ads, website, packaging, etc. Predictive eye tracking models are able to accurately interpret how a typical user would scan any ad/webpage when they first arrive, typically what people will automatically see or miss in the first 3-5 seconds.
What’s the difference between mouse or click heat maps and predictive eye-tracking heat maps?
There are various platforms like hotjar, vwo and inspectlet which enable CRO, CXO, and UI/UX designers to generate mouse tracking or click heat maps of their websites.
The attention heat maps results from predictive eye-tracking shouldn’t look the same as your mouse tracking or click heat maps as both serve different purposes. Predictive eye tracking is for pre-testing your designs before you go live and mouse or click heat maps are post-release testing and understanding what and how users and interacting with the website. So predictive analysis should complement your other tools, not replace them.
When a user visits your website or sees your digital ad you don’t have much time to communicate with him through your designs. You have only 3-5 seconds to make your first impression count and help users to see the most important part of your design within that timeframe. The way you use user’s limited attention results in bouncing rate and retention rate.
Why use predictive eye tracking?
Your design should be aligned with your business goals and must know a few things which are crucial. The important statistics for the marketing funnel is how many users are visiting and how many of them are actually clicking on CTA (call to action) button. But is it seen at all in your web design? Is the image on the website is grabbing the user’s attention without leading to any particular conversion like clicking on the CTA button?
Predicting what and how users will notice your webpage or ad in the initial few seconds will help you to design much smarter designs. It helps to boost the ad effectiveness and user experience of the website and ultimately resulting in increased business ROI.
“It takes only 1/10th of a second to form a first impression about a person. Websites are no different. It takes about 50 milliseconds (ms) (that’s 0.05 seconds) for users to form an opinion about your website that determines whether they’ll stay or leave”. Peep Laja, founder of CXL the most influential CRO expert in the world
3-5 seconds. That's about all the time you get for your ad, website, or any other visual communication designs to be noticed or lost in the crowd. What if you could predict what people are likely to see in those critical seconds? And use that knowledge to make your designs to optimize and stand out?
Dhiti analyses your designs, using algorithms that simulate what people see during the first 3-5 seconds of viewing and can analyze almost any visual like print ads, digital ads, websites, packaging and store shelf layout, signage, billboards, and more. It helps you increase client confidence, simplify approvals, and gain consensus on visual priorities.
How does it work?
Many people are hesitant or skeptical about relying on predictive algorithms to make design decisions as opposed to based n consensus or gut feeling. When they hear about predictive heat maps the question is of course, about reliability. Can you really trust a simulated eye-tracking study enough to make business decisions based on it?
The Core Principle
“As humans, irrespective of our cultures, genders, and age groups, our visual system is wired to look at very much the same aspect of any design/image in the first few seconds of viewing. That’s why it is possible to generate a predictive model of human visual attention.”
The predictive eye-tracking algorithms analyses your designs using artificial neural networks (ANN) algorithm, that simulates what people see during the first 5 seconds of viewing. With image processing and machine learning the model map out the underlying visual perceptual patterns and features in the data like contrast, orientation, layout, size, intensity, color, text, and face detection.
Dhiti's attention prediction model is based on deep learning and can automatically detect visual attention shifts that can be used as a substitution for eye-tracking studies.