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The Value of Attention is Nuanced by the Size of the Brand

Karen Nelson-Field, Ph.D.CEO, Amplified Intelligence

This presentation discussed the importance of nuance and interaction effects and how understanding interaction effects are critical in building products. There were four use cases—campaign strategy, planning, verification, buying. Two sets of data—inward and outward facing—looked at tag-based data through tags, outward facing—device based, panel data, gaze tracking, pose estimation, etc. One is observed while the other is human. Both are valuable. Each set has limitations. Looking at actual humans has a scale issue, whereas impression data has limited ability to predict behavior. Human behavior is complex. It is also varied by platforms. Metrics without ground truth misses out on this. Three types of human-attention were measured: active attention (looking directly at an ad), passive attention (eyes not directly on ad), non-attention (eyes not on screen, not on ad). Attention outcomes and attention are not always related. Underneath how attention data works there is a hierarchy of attention—the way ad units and scroll speeds and other interaction effects all mediate with each other. It is not as simple as saying look at this ad unit and we will get this amount of attention. If products don’t include these factors they fail. Amplified Intelligence built a large-scale validation model for interaction effects and “choice” using Pepsi. They employed logistic regression using Maximum Likelihood Estimation (MLE), analyzing observations and tested critical factors—brand size and attention type, to demonstrate strong predictive accuracy with CV accuracy. They found significant interaction effects, particularly brand size and attention type as key influencers of consumer brand choice. Key findings:
  1. Passive and active attention work differently. Passive attention works harder for bigger brands, while active attention works harder for smaller brands. Put differently, small brands need active attention to get more brand choice outcomes.
  2. Attention switching (focus) mediates outcomes. The nature of viewing behavior mediates outcomes. Not just attention yes or no, and what level, but about behavior across time. This is why time-in-view fundamentally fails even though it is considered one of the critical measures of attention. Humans are constantly switching between attention and non-attention. There’s attention decay—how quickly attention diminishes (sustained attention x time). There’s attention volume—the number of people attentive (attentive reach x time).
  3. Eyes on brand attention is vital for outcomes. If the brand is not at the point when people are looking (or hearing), this impacts outcomes. When the brand is missing, we fill in the blanks, but the next generation of buyers are being “untrained.”
Implications:
  1. Human attention is nuanced, complicated, making it difficult to rely merely on aggregated non-human metrics for accuracy. We must constantly train these models, just like GenAI, to ensure that all these nuances are fit into the model. A human first approach is critical.
  2. Outcomes cannot predict attention. Attention can predict outcomes but not the other way around.
  3. Attention strategies should be tailored to campaign requirements (not binary quality or more/less time). Overtime attention performance segments will start to think about other AI.
Key takeaways:
  • Human attention is nuanced. This makes it difficult to rely only on aggregated non-human metrics for accuracy.
  • A human-first approach is critical.
  • Outcomes cannot predict attention.
  • Attention strategies should be tailored to campaign requirements.

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