Google is rolling out a significant update to its AI-driven advertising suite, introducing new ‘Branded Search’ controls specifically for AI Max campaigns. This development aims to provide advertisers with more granular management over how their ads appear in response to queries containing brand-related keywords.
Granular Control for AI-Powered Ads
Historically, AI-driven campaigns have often prioritized automation, sometimes making it difficult for advertisers to restrict their brand presence. With this update, Google is offering a more sophisticated layer of control, allowing marketers to dictate exactly how their ads behave when a user includes a brand name in their search query.
The Three Key Management Options
According to reports and screenshots circulating from industry experts like Thomas Eccel, the new configuration provides users with three distinct options for handling branded traffic:
- Show ads on all relevant searches: This default setting allows the AI to determine the best placement across both branded and unbranded queries.
- Control branded searches with brand inclusions and exclusions: This allows for precise manual intervention, letting advertisers curate which brand terms trigger their ads.
- Show ads only on unbranded searches: This option is designed for campaigns where the primary goal is discovery. By selecting this, advertisers can instruct Google to ignore all known brand names, ensuring their budget is focused entirely on attracting new, non-brand traffic.
Strategic Implications for Advertisers
This update is particularly vital for companies that run separate campaigns for brand protection versus brand expansion. By utilizing the ‘unbranded searches’ feature, marketing teams can effectively prevent their AI Max campaigns from cannibalizing organic traffic or wasting spend on users who have already signaled high brand intent.
As Google continues to push its AI-first advertising strategy, the integration of these manual guardrails represents a positive shift, offering a ‘best of both worlds’ scenario: the efficiency of machine learning combined with the strategic oversight required by enterprise-level marketing teams.