This article is the second part of a series of posts that explore how AI-powered synthetic attention data opens up the possibility of greater creative effectiveness at scale. They are foundational to our contribution to the WARC "Guide to Attention".
Our first article deals with how the AI (artificial intelligence) revolution has caused the amount of creative brands can produce to balloon, leading to challenges in measuring ad effectiveness.
The potential of scale from AI-driven attention measurement explains why synthetic attention data, (in these cases, from Realeyes) has integrated quickly into creative analytics systems like CreativeX and Vidmob, as well as leading GenAI creative production tools like GRIP.TOOLS. In Vidmob’s case, the company is evolving its creative analytics engine to incorporate additional metrics like synthetic attention to bring creative measurement and sales outcomes together.
These moves are intended to improve decision-making of high volume, fast-moving paid social content that is validated to drive sales.
Example: Grip Platform with Synthetic Attention
Synthetic attention scoring for ads has begun to make connect into DSPs (demand-side providers) and SSPs (supply-side providers), enabling better ad selection, delivery and sequencing.
Another global CPG advertiser is integrating synthetic attention into a multi-market marketing mix modelling (MMM) study incorporating thousands of ads into a two-year historical view of sales performance.
Example: Vidmob Platform Creative Analytics with Synthetic Attention
Synthetic Attention Complements (not replaces) Human Ground Truth
First-party human testing remains extremely important, as it is the foundation of understanding human response. Separately, AI models need human training data to do anything.
Big brand advertisers like The Coca-Cola Company and Kellanova (formerly Kellogg’s), for example, have invested billions of dollars developing distinct brand assets, validated with human measurement, to drive effectiveness. AI-powered scoring and recommendation systems must embrace this institutional brand knowledge.
Deploying creative performance scores requires new creative coding taxonomies
Advertisers must embrace a new mindset that incorporates the fact they now can use AI to test every ad they produce. Succeeding requires infrastructure, so advertisers also need to adopt more sophisticated and disciplined creative taxonomies and IDs, to orchestrate creative performance data with other critical levers, like platform, ad format, audience, impressions, budgeting and other business outcome data.
Basically, ad creatives must hold the same targeting and management protocols as any other campaign parameter. Today, most large advertisers don’t even know where all their creatives exist within their agency networks, tools, media platforms, and digital asset management tools – even conventional cloud folders like Microsoft SharePoint or Google Drive. They must address this creative data chaos.
One such solution is the IAB’s new universal ad creative ID framework (ACIF), developed in partnership with XR (formerly known as “Extreme Reach”). According to the IAB Tech Lab, this will help standardize operating procedures for ad registries across the globe to be used across platforms and channels. It will help address challenges like: frequency capping, competitive separation, cross-platform reporting, campaign reconciliation and campaign ROI reporting.
Synthetic attention (a sales performance proxy signal) combined with precise identifiers can supercharge ad creatives to transform both viewer experience and campaign outcomes.
In our third piece, we will explore provide guidance on what advertisers should do in this exciting new world, powered by synthetic attention data.