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Attention metrics seek to accurately measure the attention of the person interacting with an experience.ย
Attention metrics have became popular through a resurgence in the use of them on digital advertising. The intent there is to measure how impactful, the environment, the placement and the creative itself is.ย
There also is a field of attention measurement that extends to websites and web apps to capture how users interact.ย
Advertising use of attention metricsย
These can be broken down into the value chain of how an ad is served.
- Attention on the page the ad is served on.
- Attention for the placement.
- Attention for the creative.
- Attention for the click on the destination page.
The varied uses of attention is why there are different methodologies, and definitions.
Modeled attention
Modeled attention is where the provider has built a model, to help translate one datapoint to another.
It is often used to predict attention, using other metrics on page.
For example, converting viewability & placement data to a prediction on the eye tracking data.
Very simply, this works by completing a manual study with real world eye tracking data from a panel. This data is then compared to the other contextual factors, to build a model. Then rather than running an eye tracking study on every campaign, advertisers can use the model.
Similarly this can be used for creative attention, or page attention.
Example attention metrics:
- Attention, measured in seconds, how long someone pays attention to a page.
- APM, attentive seconds per thousand. This seeks to show how much eye attention the ad placement gets on average.
- AU, attention unit attempts to normalize attention across different types of media, so they are more comparable.
Importantly each metric attempts to get to the same idea, attention in different parts of the value chain.ย
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