How can marketers predict the impact of applying recommendations on their optimization score?
Correct Answer
Each recommendation shows its impact on the optimization score as a percentage.
Why is this the correct answer?
Marketers can predict the impact of applying recommendations on their optimization score because each recommendation shows its impact on the optimization score as a percentage. In the Google Ads Recommendations page, every suggestion — whether it's adding new keywords, enabling auto-bidding, or adding responsive search ads — displays the estimated percentage point increase to the optimization score if applied. This allows marketers to prioritise the highest-impact recommendations and make informed decisions before implementing any changes.
Why are the other options incorrect?
Leverage trusted, third-party tools to analyze and optimize their campaigns.
Third-party tools may provide campaign analysis but do not have access to Google's internal optimization score calculations — they cannot predict the score impact of specific recommendations.
Use Google AI to forecast the increase in conversions as a result of recommendations.
Google AI does forecast conversion impacts for some recommendations, but the specific mechanism for predicting optimization score impact is the percentage shown directly on each recommendation card.
Cross reference the settings of comparable campaigns with higher optimization scores.
Cross-referencing comparable campaigns does not reveal what specific score impact applying a recommendation will have — the direct percentage shown on each recommendation is the right approach.
Real-World Example
A campaign manager reviews the Recommendations page and sees three suggestions: adding broad match keywords (+8%), enabling Target CPA (+12%), and adding responsive search ads (+5%). By checking each recommendation's score impact, they prioritise enabling Target CPA first — the highest-impact change — moving their score from 64% to 76% with a single action.