SBIR Phase I: Automated Display Optimization Based on Attention Predictions

Eye-Predict, Los Angeles CA

Investigators

Abstract

This Small Business Innovation Research (SBIR) Phase I project will establish the feasibility of optimizing the effectiveness of commercial displays using a predictive model of human attention deployment. Traditional solutions for optimizing displays require human data. For example, during A/B testing, the target audience is exposed to alternate versions of a display (comparables) and responses such as clicks are used to determine effectiveness. A key shortcoming of traditional optimization solutions is that the number of comparables that can be tested is severely limited by the available audience. These shortcomings will be eliminated if display optimization could be performed based on computer analyses rather than human data. The Phase I research will establish the extent to which computer-generated attention scores can predict catalog selections as measured by mouse clicks. It is anticipated that catalog items with high attention scores will be selected more frequently than catalog items with low and average attention scores. Billions of dollars are spent annually on optimizing commercial displays using techniques that require human data, such as A/B and multivariate testing, contextual and behavioral targeting, consumer research, etc. If successful, this SBIR project will result in a software-as-a-service solution for optimizing commercial displays based on attention predictions. This proposed innovation will enable large scale display optimizations that are practically impossible to perform based on human data. Another key advantage of automated optimization is that it could be performed before exposing audiences to ineffective displays. As a result, the proposed innovation will increase revenue gains from commercial displays. This innovative solution could be applied to a variety of online and offline displays, including catalogs, shelf plans, and graphic ads. Beyond display optimization, attention models could lead to important scientific and technological advances, commercial applications, and health benefits.