Trusted by world-class UX and Conversion teams at:
Instantly see what your users will
see by analyzing attention.
EyeQuant predicts which parts of your design are most and least eye-catching, so you can make sure that your most important content is visible right away.
Get instant design metrics on
visual clarity and excitingness.
EyeQuant rates how clear and exciting your designs are,
so you can benchmark and identify subpar designs.
Find visual design flaws
Validate and improve designs pre-launch
Justify decisions and win support for ideas
Analyze and take a detailed look at best-in-class and competitors to learn from their designs
Inspect your winning (and losing) test designs to learn what works and not
Agree on what should be prioritized in the design and use EyeQuant to make sure you’ll hit those goals
EyeQuant’s researchers and scientific advisors are pioneers in the field of computational neuroscience, and the team’s work in relation to visual processes and modeling has been published in numerous peer-reviewed academic journals. Our predictive design analysis is built through a rigorous process of data collection, statistical analysis and machine learning.
We invented, developed and patented EyeQuant with brain researchers at the California Institute of Technology, the University of Southern California and the University of Osnabrück, Germany.
Co-Founder and Scientific Director
1. We collect data on how people see and perceive websites
In order to build predictions about how users will react to a specific design, we first need to have a detailed understanding of how people actually see the web. To do this, we’ve conducted user studies and experiments with thousands of participants. We use eye-tracking equipment (in Osnabrück, Germany) to measure exactly how users look at websites, and we ask users to rate and compare website designs by asking questions like “which of these 2 designs feels cleaner and more organised?”
2. Statistical analysis and machine learning
After gathering millions of data points in our research, we use statistical analysis to find correlations between specific design characteristics and user behaviours. For example, we know that (all else equal), colour contrast tends to catch users’ eyes, and we know that the number of edges in a design can help us predict whether or not a design is too cluttered. Once we have a list of design characteristics that affect behaviour, we use these as variables in predictive models. We use machine learning to determine the optimal weight and combinations of these design characteristics. By comparing predictions with real user data, we can evaluate the accuracy of our models.
3. The EyeQuant Web Service
Once we’ve got an accurate predictive model, we offer cloud-based access via the EyeQuant web app. When you upload a design, EyeQuant takes just seconds to return results. During that time, EyeQuant identifies important design characteristics, and assigns scores for around 50 different predictive variables to each pixel on the page to get a clear picture of how users will react to this specific design.