Uber introduces platform-specific attention metric in partnership with Adelaide and Kantar

Uber is introducing a new platform-specific attention measurement model for its advertising inventory, developed in collaboration with Adelaide and Kantar. The metric, known as Custom AU for Uber Advertising, is designed to evaluate how effectively ads within Uber’s ecosystem capture user attention and influence brand outcomes.

The approach builds on the growing shift in the advertising industry away from traditional viewability measures — which only confirm whether an ad appeared on screen for a brief period — toward metrics that attempt to quantify actual engagement and impact. Attention measurement incorporates signals such as how long an ad remains in view, placement on screen, surrounding content, and user behavior during exposure.

The model draws on Uber’s first-party data, including where and how ads are displayed across ride environments and Uber Eats ordering surfaces. Kantar’s brand lift studies are incorporated to connect attention signals with measurable campaign outcomes, while Adelaide provides the predictive framework to score media placements.

According to the companies, early use of the metric shows that Uber’s formats — including in-ride screens and Uber Eats checkout placements — outperform industry benchmarks for similar video and display environments. Uber’s advertising contexts often involve users waiting on ride or delivery progress, which can create longer viewing windows than standard mobile web or banner placements.

For Adelaide, this marks the first large-scale implementation of a customized version of its AU metric, allowing a media platform to use first-party signals to shape its own attention scoring model. For Kantar, it represents an example of integrating brand lift data directly into predictive attention measurement rather than treating the two evaluations separately.

Uber is initially offering access to the Custom AU metric to selected premium advertising partners. The company indicates the model will expand to more advertisers over time as part of broader measurement and delivery tools across its advertising business.

The development comes as attention metrics gain momentum across the industry, though standardization remains a point of debate. While interest grows in using attention as a performance measure, platforms and publishers differ in how they define and calculate attention, creating variation in comparability across media environments.