Over one-third (36%) of US buy-side ad decision-makers said they would focus somewhat or significantly more on attention metrics in 2023, according to a November 2022 survey from the Interactive Advertising Bureau (IAB).
Why attention metrics? Advertisers don’t just want to know that their ad is being placed in front of consumers, they want to know if it's being noticed and if the message is getting through.
With attention metrics, advertisers can measure how long a person views an ad, what actions they took during that period of time, and how they felt or thought about it both while it was happening and after.
How do they work? There are three approaches to measuring attention metrics, according to Angelina Eng, vice president of measurement, addressability, and data center at the IAB, who spoke on a recent Meet the Analyst Webinar.
1. Biometric data
This includes neurological-physiological tracking or scanning, facial recognition, brain waves, heart rate, blood pressure, and more—all of which typically require some sort of device for data collection
While biometric data can measure attention at a much deeper level than other types of data, techniques for observing and analyzing these physiological indicators of attention are somewhat controversial, according to our Attention Metrics 2023 report.
- Biometric data can present a privacy risk in states with data privacy laws that classify it as “sensitive” data, which means it is subject to additional protections.
- Some companies can get around this by sourcing the data from opt-in panels, but these panels do not have the scale to truly be a proxy for attention. Plus, extrapolating data from a sample often introduces biases.
2. Data signals
These signals can be sent either directly from the publisher or captured by a device, said Eng.
Some of the most common data signals include dwell time, scroll speed, cursor location, and completion rates. But since there is no standardization of attention metrics, a number of different conclusions can be made depending on how things are weighted by machine-learning models.
In addition, tracking this kind of data across mediums is challenging because attention metrics vary from one format to another.
3. Cognitive and emotional data
This type of data considers if an ad has affected a user’s mindset or impacted consideration. Brands can collect this type of data through customer feedback surveys and brand lift studies that ask about brand consideration, brand awareness and sentiment. It can also be measured via biometric methods.
The bottom line: Attention metrics are gaining popularity as marketers seek out more ways to measure ad performance. But lack of standardization and controversial data collection methods present barriers to adoption. We’d encourage marketers to give attention metrics a try, but as a complement to other metrics like viewability.
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