Python Attention Optimiser
The loss of third-party cookies, privacy regulation, and erosion of trust in the standard currencies of measurement have thrown our industry into flux. Viewability has gone some way to address that by measuring whether an ad had a chance to be seen. But as an arbitrary metric, it is merely an opportunity for the browser to load the ad and has no bearing on whether the ad was seen or not by the human eye. Through eye tracking research, we discovered that ads with high viewability (70%+) weren’t being seen, with an average eye gaze of just 0.1 seconds. To solve this we built an automated attention platform, dubbed “Mediahub ARC” (Attentive Reach & Composition). Mediahub ARC matches monthly eye-tracking research panel data, publisher site clutter of competing ads on the page, and dwell-time on screen, with media plan partners through technology that converts eye tracking attention into real time, cookieless optimisation tags. We built Custom Bidding Algorithms programmed to ignore viewability and point towards formats that drove higher rates of attention. The results were astonishing. High attention formats drove 14% true-lift in brand effect and 24% lower cost per acquisition. These outputs have evolved Mediahub’s planning capability, making better choices for ad formats and publishers that deliver attention against uncluttered publisher sites and moving beyond legacy planning decisions focused on reach and penetration alone. Mediahub ARC has paved the way for a cookieless solution that aligns the needs of brands, publishers and users for better advertising experiences.