"How are you tracking real attention?"
Readefined tends to bring out people’s curiousity :)
"What are you measuring?"
Without giving away our secret sauce, we’re tracking all the factors you usually get separately in different tracking dashboards (time, depth, heatmapping, etc.), and push them through a complex algorithm that is based on learned human behaviour patterns.
This allows Readefined to save users a lot of time and headaches by giving straight insights, in contrast to typical dashboards that force you to try to make sense of all the data yourself.
(No, we do not use eye-tracking, for a variety of reasons, including our belief in building technology that doesn’t encroach on people’s privacy.)
"Behaviour patterns"?
Yes. People tend to absorb information in particular ways, depending on their individual style and the nature of the content. These factors can be determined in a variety of ways, including learning individual reader styles and the tendencies of entire audiences. Readefined dives deep into this territory, too, including applying machine learning to continually improve how attention is assessed.
"So is this just for written content?"
Reading is our bread and butter, and where we can extract the highest degree of certainty regarding the measurement of attention. That said, Readefined is also quite sophisticated when it comes to measuring attention on video, podcasts, and images, too! For example, Readefined understands if an image is aesthetic, or if it contains information, and takes this into account.
"How accurate is it?"
Readefined is the most accurate way of checking for attention outside of actual eye tracking technology. Did you notice the tracker on the right side of the page? That gives you a sense of the tracking that happens behind the scenes for anyone who uses Readefined - and this simplified demo doesn't even include the added accuracy of our detailed content-aware and audience-based machine learning! Need more proof? How’s this: