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Google Ads · Status at source date: Newsletter archive

Google Ads experiments add auto-apply setting, enabled by default

Written by Paid Media Collective
IN BRIEF

Bob Meijer has flagged that Google Ads experiments now include an auto-apply setting: enabled by default: that automatically applies experiment results to live campaigns. The setting offers configurable confidence thresholds: directional results (default), or statistical significance at 80%, 85%, or 95%.

Original newsletter image: Google Ads experiments add auto-apply setting, enabled by default
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What changed

A guard rail prevents auto-application if any chosen success metric performs significantly worse in the treatment arm. Meijer notes that with a two-metric cap on success metrics, auto-apply may optimise for those metrics while allowing others to deteriorate undetected.

THE COLLECTIVE PERSPECTIVE

Why it matters for advertisers

For you, this means checking your experiment settings immediately: if auto-apply is active on any running experiment, switching to manual review is advisable until you have reviewed the full data picture across all metrics.

Perspective from the original PMC newsletter.

Sources & contributor credit

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Original newsletter text and archive evidence

Google Ads experiments add auto-apply setting, enabled by default

Bob Meijer has flagged that Google Ads experiments now include an auto-apply setting: enabled by default: that automatically applies experiment results to live campaigns. The setting offers configurable confidence thresholds: directional results (default), or statistical significance at 80%, 85%, or 95%. A guard rail prevents auto-application if any chosen success metric performs significantly worse in the treatment arm. Meijer notes that with a two-metric cap on success metrics, auto-apply may optimise for those metrics while allowing others to deteriorate undetected.

For you, this means checking your experiment settings immediately: if auto-apply is active on any running experiment, switching to manual review is advisable until you have reviewed the full data picture across all metrics.

Source captured . No explicit first-contributor label was provided for this update.

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