Attention Prediction Models
Attention Prediction Models use machine learning to estimate how much visual attention a user is likely to give an ad based on placement, size, format, and content. They analyze historical engagement data, scroll depth, dwell time, and eye-tracking benchmarks to forecast performance before an impression is served.
For example, a model might determine that a mid-article native placement generates 25% more attention than a sidebar banner, prompting the DSP to increase bids for that zone.
Attention-based optimization shifts the focus from viewability to measurable human engagement, aligning ad delivery with real user focus and brand impact.
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