To help you create the most media attention engaging creatives possible, we’ve integrated Viomba’s AI-powered media attention prediction metrics directly into the NEXD platform. This addition gives creative designers instant feedback on how much real media context human attention their ads are likely to generate - even before a campaign goes live.
The goal: help you optimize ad-on-media performance effortlessly, using real human-attention data modeled on Viomba’s benchmarked datasets.
How It Works
AI Generator
The media attention score is calculated automatically for all generated variations.
If you generate additional variations, the system will automatically calculate new scores.
Manual recalculation is not available in the AI Generator.
Media Attention score options:
Low
Average
Great
When you click View Details, you’ll see a breakdown that includes:
Benchmarks
Media Attention score
Viewtime score
(See more details in the Media Attention Metrics section below.)
Regular Ad Creation Flow
Create a creative in the Campaign Manager.
Click “Measure” to calculate the media attention score.
After making creative updates, users may click “Remeasure” to generate a revised score.
Important: Any edits made during an active calculation will not be included. Always wait for the process to complete before making new changes.
Media Attention score options:
Low
Average
Great
Clicking View Details opens a detailed breakdown with benchmarks, media attention metrics, and viewtime scores.
Media Attention Metrics
Media Attention Score (0–100%)
Represents the percentage of impressions that receive active human focus in real media, benchmarked by device and media type.
A higher Media Attention Score means more users are likely to focus on your ad long enough in live media context to generate meaningful attention.
Viewtime (ms)
Measures how long people are likely to view an ad after it has been initially noticed in media.
Longer viewtimes signal stronger human attention and greater potential for memory impact. This helps inform optimal targeting frequency for maximizing campaign effectiveness.
Memory Impact Rule-of-Thumb
Approximate combined viewtime → expected memory duration:
~2.5–3 seconds ≈ 1–2 days of memory impact
~7 seconds ≈ ~15 days of memory impact
~10 seconds ≈ ~30 days of memory impact
Media Attention Score Statuses
An ad’s AI- predicted score is determined by its performance compared to Viomba’s benchmark.
Great → Score is more than +3.01% above the benchmark
Average → Score is within ±3.00% of the benchmark
Low → Score is more than –3.01% below the benchmark
How Viomba Predicts Attention
Viomba’s attention metrics are built on real human eye-tracking data. They use a panel of participants equipped with professional-grade infrared eye-tracking hardware to record where people look in live media domains, what catches their focused gaze fixation, and how long they keep looking at different parts of an ad.
This empirical dataset is then used to train Viomba’s AI model, which can predict human-centric media attention for any new creative without tracking end-users in real time. The AI model analyzes visual features such as layout, contrast, format, device type and element placement to estimate how closely a creative matches real human visual system's gaze fixation patterns.
While the Viomba AI model is trained already on +500M empirical ground truth samples, the provided Media Attention Score and Viewtime predictions reflect very accurately on how people will engage with your ad designs. This gives you a reliable indication of how your each creative is likely to grab visual attention in live media context.