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Your MMM Has a Media Quality Blind Spot
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When MMM ignores media quality, cheap reach can look like a smart investment.
Marketing mix models were built for a simpler media landscape. Today, media quality varies dramatically—even within the same channel. When models ignore those differences, they over-credit low-quality impressions and misread what’s influencing performance.
Attention metrics add the missing dimension. Adelaide AU scores each placement on its likelihood of capturing attention and driving outcomes, giving MMM the quality input it needs for sharper forecasts and more actionable recommendations.
A Google-Nielsen study found that placement-level ROAS can vary by up to 48% within channels that MMMs typically treat as uniform.
Inside the guide:
Attention data is already in your stack. This guide shows how to use it in MMM.
Advertisers can incorporate attention data and norms into new or existing models without new platforms or data pipelines.
Depending on your objectives, AU can support MMM as a predictor, value multiplier, or verifier.
Use this guide as a practical starting point for adding a media quality layer to MMM and aligning stakeholders around better inputs.
Who this playbook is for:
