What does a Responsive Display Ad use in its machine-learning model to determine the optimal combination of assets for your ad slot?

Predictions built from files exported from your CRM.

Predictions built from ad portfolio data, aggregated across benchmark businesses.

Predictions built from your performance history.

Predictions built from performance data across your industry.


Choose an option to see if it’s correct. Check the explanation below. Learn Smarter, not Harder.

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Explanation: What does a Responsive Display Ad use in its machine-learning model to determine the optimal combination of assets for your ad slot?


Explanation: The correct answer is **Predictions built from your performance history**. Responsive Display Ads utilize a machine-learning model that analyzes your past performance data to predict the optimal combination of assets for each ad slot. By leveraging historical performance metrics such as click-through rates, conversions, and engagement levels, the algorithm identifies patterns and trends to determine which assets are most likely to resonate with your target audience and drive desirable outcomes. This data-driven approach allows Google Ads to dynamically adjust the ad creative elements, including headlines, images, and descriptions, based on what has proven effective in the past. Consequently, the ads presented to users are tailored to maximize engagement and achieve your advertising objectives, resulting in improved campaign performance and better return on investment. Utilizing your own performance history ensures that the ad recommendations are customized to your specific business needs and audience preferences, leading to more relevant and impactful advertising experiences.

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