App campaigns rely on creative rotation powered by machine learning to choose the right creative for the right inventory, the right user, and the right moment. How should you approach assets to achieve strong results?
Upload fewer creatives than previous app campaigns.
Provide identical videos with minor differences.
Provide identical videos cut to different lengths.
Use diverse creatives in content, theme, length, and orientation.
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App campaigns rely on creative rotation powered by machine learning to choose the right creative for the right inventory, the right user, and the right moment. How should you approach assets for an app campaign to achieve strong results?
Upload fewer creatives than previous app campaigns.
Provide identical videos with minor differences.
Provide identical videos cut to different lengths.
Use diverse creatives in content, theme, length, and orientation.
Explanation:
The correct approach to achieve strong results in an app campaign, leveraging creative rotation powered by machine learning, is to ‘Use diverse creatives in content, theme, length, and orientation.’ App campaigns rely on machine learning algorithms to dynamically select the most relevant and engaging creatives for each user, context, and moment. By providing a diverse range of creatives in terms of content, theme, length, and orientation, advertisers increase the likelihood of having a suitable creative available for various inventory types, user preferences, and viewing contexts. This diversity ensures that the campaign can effectively cater to different audience segments and capture their attention across a wide range of placements and devices. Additionally, diverse creatives allow for better testing and optimization, enabling the machine learning system to learn and adapt based on performance data, ultimately driving stronger results in terms of user acquisition, engagement, and app installs. Therefore, adopting a strategy of using diverse creatives is crucial for maximizing the effectiveness of app campaigns and leveraging machine learning to its full potential.