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pA The Nudge Attention Model
If you’ve spoken to us recently, you’ve probably seen a WHOLE new generation of Nudge tools ✨, anchored around our attention model.
The attention model, enables attention measurement & insights on any given page without the use of tracking code.
Attention is a predictor of downstream objectives like impact, brand lift & conversion. Bring it into your workflow, to improve content upfront, predict success & impact. Audit content, competitive analysis.
For example:
The attention model, enables attention measurement & insights on any given page without the use of tracking code.
Attention is a predictor of downstream objectives like impact, brand lift & conversion. Bring it into your workflow, to improve content upfront, predict success & impact. Audit content, competitive analysis.
For example:
This is being used by advertisers for attention optimization, PR companies for insight & measurement, email companies as extra metrics and agencies for attention measurement & insights and platforms for measurement & insights embedded into the platform.
Whether it’s on branded content, content marketing, earned media, emails or contextual placements, as long as it’s a page with content, we can model it.
Most are starting with our general model, then working towards have their own custom models over time. Custom models bring in and link to historical performance data, leveraging our content model, to understand and synthesize, for future prediction. Think about our data, but enhanced with an intelligence layer, that’s the future of analytics. First party data + predictive models.
For example, a platform with a lot of content or content data, could bring in that historical data, and use the models, to build their own predictive model to predict their metrics. Predict the conversion rate, click rate, brand outcomes, the pickups or ranking. Whatever the objective is. These sorts of things are now possible, using these building blocks.
Here is an example API output for a given url:
[
{
“url”: “https://brandwebsiteorpublisher.com/hello-great-article-on-brand“,
“pAttention”: “48.0”,
“attentionCategory”: “Good”,
“insights”: [
“Revisit the tone of the piece, is it engaging?”,
“Make it easier to read.”,
“Consider reducing industry lingo or repetitiveness of terms.”
]
}
]
Whether it’s on branded content, content marketing, earned media, emails or contextual placements, as long as it’s a page with content, we can model it.
Most are starting with our general model, then working towards have their own custom models over time. Custom models bring in and link to historical performance data, leveraging our content model, to understand and synthesize, for future prediction. Think about our data, but enhanced with an intelligence layer, that’s the future of analytics. First party data + predictive models.
For example, a platform with a lot of content or content data, could bring in that historical data, and use the models, to build their own predictive model to predict their metrics. Predict the conversion rate, click rate, brand outcomes, the pickups or ranking. Whatever the objective is. These sorts of things are now possible, using these building blocks.
Here is an example API output for a given url:
[
{
“url”: “https://brandwebsiteorpublisher.com/hello-great-article-on-brand“,
“pAttention”: “48.0”,
“attentionCategory”: “Good”,
“insights”: [
“Revisit the tone of the piece, is it engaging?”,
“Make it easier to read.”,
“Consider reducing industry lingo or repetitiveness of terms.”
]
}
]
Given the pace of development the website continues to lag the actual progress. This page is aimed to keep up to date, whilst we continue to update product and other areas of the website.
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