A digital marketing manager has chosen to upgrade keywords to broad match. Which of these behaviors should they incorporate into their routine optimization cadence in order to guide machine learning?

Add more phrase and exact match keywords to improve reach.

Eliminate negative keywords that could block relevant traffic.

Ignore suggestions to add relevant new keywords.

Add Customer Match lists with data that is more than 90 days old.

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Explanation: The correct answer option is ‘Eliminate negative keywords that could block relevant traffic.’ When upgrading keywords to broad match, it’s essential for the digital marketing manager to incorporate the practice of eliminating negative keywords into their routine optimization cadence. Broad match keywords allow for a wider range of search queries to trigger ads, which can lead to increased visibility and potential traffic. However, this broader reach also comes with the risk of irrelevant or low-quality clicks if not properly managed. By regularly reviewing and refining the list of negative keywords, the manager ensures that irrelevant searches are filtered out, guiding the machine learning algorithm to focus on delivering ads to more relevant audiences. This proactive approach not only improves the efficiency of ad spend but also helps to optimize campaign performance over time by providing clearer signals to the machine learning system about the types of users and queries that are most valuable to the business. Therefore, incorporating the elimination of negative keywords into the optimization routine is crucial for guiding machine learning effectively when upgrading keywords to broad match.

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