Modernize Your MMM With Attention Metrics

Marketing mix models were built for a simpler media landscape. Today, media quality can vary dramatically across and within channels. When MMM treats those impressions as equal, it introduces noise that makes it harder to separate media’s impact from other performance drivers.

Adelaide AU adds an outcome-trained quality signal to MMM, weighting media inputs by quality rather than volume alone. This improves model fit, sharpens ROI reads and forecasts, and produces more actionable investment recommendations.

Use this guide as a practical starting point for incorporating attention metrics into new or existing models and aligning teams around better inputs and more useful outputs.

Inside the Guide:

• Why treating all impressions equally can distort MMM insights
• How AU adds a media quality dimension to marketing mix models
• 3 ways attention metrics make MMM more precise and actionable
• Real examples of AU-enhanced MMM improving accuracy and ROI