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What are two ways to achieve creative excellence in Google App campaigns? (Choose two)

• Follow best practices for uploading a diverse and comprehensive asset mix.
• Invest a lot of time in reviewing the assets being used by similar businesses.
• Introduce one asset type at a time, to eventually identify which type works best.
• Improve and optimise the asset mix based on insights from the asset report.

Explanation:

Creative excellence in Google App campaigns can be achieved through two key strategies. Firstly, following best practices for uploading a diverse and comprehensive asset mix is essential. This involves incorporating a variety of high-quality assets such as images, videos, and text that effectively showcase the app’s features and benefits. By diversifying the asset mix, advertisers increase the chances of capturing the attention of different audience segments and delivering engaging ads that resonate with users. Secondly, continuously improving and optimizing the asset mix based on insights from the asset report is crucial. Regularly analyzing the performance data of different assets allows advertisers to identify which creatives are driving the most conversions, clicks, or other desired actions. Armed with this information, advertisers can refine their creative strategy, allocate budget more effectively, and invest resources in producing assets that yield the best results. Overall, by prioritizing these two approaches, advertisers can enhance the quality and effectiveness of their creative assets in Google App campaigns, ultimately driving better campaign performance and achieving their marketing objectives.

What are two ways that marketers can benefit from machine learning in Google Ads? (Choose two)

• Machine learning produces results that align with defined campaign goals.
• Machine learning saves time by analysing hundreds of signals automatically.
• Machine learning sets campaign bids and budgets automatically.
• Machine learning evolves campaign goals automatically, without input.

Explanation:

Marketers can benefit from machine learning in Google Ads in two primary ways: firstly, machine learning produces results that align with defined campaign goals, and secondly, machine learning saves time by analyzing hundreds of signals automatically. The first option is correct because machine learning algorithms in Google Ads are designed to optimize campaign performance based on predefined objectives such as maximizing conversions, increasing website traffic, or improving return on ad spend. By continuously analyzing data and adjusting campaign parameters, machine learning ensures that ad campaigns are tailored to meet specific goals, leading to more efficient and effective outcomes. The second option is also correct because machine learning automates the process of analyzing vast amounts of data and identifying patterns or trends that would be impractical or impossible for human marketers to analyze manually. By automating tasks such as audience segmentation, keyword selection, and bidding optimization, machine learning frees up marketers’ time to focus on strategic decision-making and creative tasks, ultimately improving campaign performance and driving better results. Therefore, these two options highlight the tangible benefits that marketers can derive from leveraging machine learning capabilities within Google Ads to optimize campaign performance and streamline workflow processes.

What are two ways that a marketer can provide good data to a Google App campaign, powered by machine learning? (Choose two)

• Select events done by more than 5% but fewer than 50% of users.
• Ensure that events are being measured accurately.
• Select your most valuable events, regardless of how often they occur.
• Select events done by at least 95% of users.

Explanation:

Two ways that a marketer can provide good data to a Google App campaign, powered by machine learning, are by selecting events done by more than 5% but fewer than 50% of users and by ensuring that events are being measured accurately. Firstly, selecting events with a moderate level of occurrence, as indicated by being done by more than 5% but fewer than 50% of users, allows machine learning algorithms to identify meaningful patterns and trends without being overwhelmed by excessively common or rare events. This ensures that the campaign receives a diverse dataset that accurately represents user behavior without skewing the model’s understanding of user engagement. Secondly, ensuring the accurate measurement of events is crucial for providing reliable data to the machine learning system. Accurate event tracking ensures that the campaign receives precise information about user interactions, which is essential for optimizing targeting, bidding, and ad creative placement based on real user behavior. By selecting these two options, marketers can enhance the effectiveness of their Google App campaigns by providing quality data that enables machine learning algorithms to make informed decisions and drive better results.

What are two ways in which a marketer can use data insights to better understand campaign performance? (Choose two)

• Use the asset report to identify and add more top-performing assets.
• Evaluate all paid channels and distribute investment equally.
• Include view-through-conversion reporting to understand the full value of the ads shown.
• Track users across individual sites and apps visited.

Explanation:

Two ways in which a marketer can use data insights to better understand campaign performance are by utilizing the asset report to identify and add more top-performing assets and by including view-through-conversion reporting to understand the full value of the ads shown. Firstly, using the asset report allows marketers to analyze the performance of various campaign assets such as images, videos, or copywriting elements. By identifying the top-performing assets, marketers can discern what resonates best with their audience and allocate resources more effectively by creating similar content or iterating on successful elements. This approach optimizes campaign performance by focusing resources on what works best. Secondly, incorporating view-through-conversion reporting provides insights beyond just direct clicks on ads. It tracks conversions from users who were exposed to an ad but didn’t interact with it immediately, providing a more comprehensive understanding of the ad’s impact. This is crucial because many users may not click on an ad but can still be influenced by it, leading to conversions later on. By including view-through-conversion reporting, marketers can accurately gauge the effectiveness of their campaigns and make informed decisions about where to allocate their advertising budget for maximum ROI. Therefore, these two options are correct as they offer valuable insights into campaign performance and aid in optimizing marketing strategies for better results.