Attention Analytics
Attention, the hallmark of consumer intent.
Attention Analytics: The practice of using real-time consumer behavior signals to understand interest & improve advertising’s relevance & performance.
AEROS Attention Analytics is fundamentally different from many of the binary marketing metrics on the market. At Yieldmo we fluidly measure, model and act on all aspects of attention a person gives an ad.
Attention Metrics
Attention Metrics
Yieldmo uses impression data to enrich the data used to train our machine learning models to achieve best performance. In particular, we are able to use granular data like consumer signals, contextual signals, and creative signals in conjunction with Attention Signals.
Yieldmo Attention Signals range from granular data like pixels in view to baseline information such as quartiles of videos reached. We identified more than 20 important signals that we categorize into Baseline Signals and Gestural Signals (proprietary to our technology).
Pinger Technology captures all of these signals and we create a set of Attention Metrics.
We factor in a set of what we consider baseline attention metrics, including but not limited to Reach, Ad Frequency, CTR, VCR, Video Play Rate, Time in View, and others.
From the gestural attention signals, we determine a set of metrics that are helpful in evaluating a level of attention on every impression. Some examples of important proprietary gestural metrics include (but are not limited to): scroll impression, scroll rate, and scroll intensity.
Unique AI Models
Once we’ve measured available signals from the particular ad medium (exposure, gestural information, ad and device capabilities), our AI models learn what is “attentive” relative to the norm. These models are frequently tailored to the particular advertiser, channel, device, format, etc.
A simplified summary of how we calculate attention might look something like this:
How many + for how long + in what sequence + in what context + integrity of the gesture + all other relevant factors = the unique algorithms that contribute to raising attention and KPI goals
The various factors that go into our understanding of attention are the deep data that we collect, the way we collect it, and the way we then use the data to predict future behavior.
Real-time Media Optimization
We use this data to provide unparalleled insights to our partners. We not only share detailed reporting with information not found elsewhere, but we also actively optimize their campaign in real-time.
Each model is unique in that we optimize to the advertiser’s KPI of choice as well as downstream KPIs, taking the guesswork out of the equation.