First Edition | September 2024
Times Internet for Marketers
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Machine learning in ad-buying algorithms is challenging the belief that high viewability guarantees high quality. AI often reveals that lower-viewability placements can outperform when bought strategically, especially for lower-funnel goals. New attention standards are being developed to help advertisers better measure and compare attention across platforms, though there’s concern that simplifying attention could limit campaign performance.
The danger of overreliance on attention: Viewability was adopted to ensure ads were seen and to prevent fraud. Unlike viewability, there's no widespread issue with "inattention fraud" that a high-attention checkbox could solve. Attention involves more than just placement; it includes message, creativity, and viewer mindset. Treating attention as a mere checkbox could unfairly limit supply and inflate costs, while performance should also be assessed with other metrics like CPA or ROAS.
The complexity of attention: Defining a "viewable" impression is simple. However, measuring attention is more complex, involving methods that vary in accuracy and invasiveness. Unlike viewability, attention metrics are harder to standardize, and achieving consistency would require trade-offs between complexity and data scale. Eye-tracking, often seen as invasive, may not become universal, leading to reliance on estimates like linear gross rating points. This underscores the challenges in capturing audience interest and mindset, despite advancements in programmatic technology.
Letting data decide: Advertisers should treat attention scores as one of many dynamic inputs in their media mix, rather than a binary metric. Sometimes the ad's creative or user targeting may matter more than the attention score itself. Embracing the complexity of attention metrics allows advertisers to test various solutions without relying on standardized segments. Instead, integrating attention as part of AI-driven buying algorithms can help optimize campaigns more effectively.
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