The measurement of attention metrics typically employs three main approaches, as explained by Angelina Eng,  at the IAB:

1. Biometric data: This approach involves tracking neurological-physiological signals like facial recognition, brain waves, heart rate, and blood pressure to measure attention levels in-depth. However, concerns regarding privacy risks and the need for special devices for data collection pose challenges to this method.

2. Data signals: This method involves the capture of signals from publishers or devices, such as dwell time, scroll speed, cursor position, and completion rates. The lack of attention metric standardization and differences in metrics across platforms can lead to varying interpretations.

3. Cognitive and emotional data: This approach examines how an ad impacts a user’s mindset and consideration. Brands gather such data through surveys on brand consideration, awareness, sentiment, and biometric methods.

While attention metrics are becoming increasingly popular for measuring ad effectiveness, challenges such as lack of standardization and controversial data collection methods hinder universal adoption. Marketers are encouraged to incorporate attention metrics alongside other metrics like viewability to gain a comprehensive understanding of ad performance.