Hiroko's manager asks why Hiroko spends time working on her new Google App campaign. The manager believes that machine learning is doing everything. What are three ways in which Hiroko can help guide the machine-learning-powered campaign?

While Google App campaigns heavily rely on machine learning for automation, the advertiser's role is to guide the system for optimal results. The human provides the strategy and inputs, while the machine executes and optimizes.

Analysis of Correct Answer(s)

• Provide a lot of good data: Machine learning thrives on data. A marketer's crucial role is to supply high-quality inputs, including a diverse range of creative assets (text, images, videos) and accurate conversion data. Better data enables the system to learn faster and make more effective decisions about who to show ads to and when.

• Evolve the strategy: The machine automates tactics, but the marketer sets the overarching strategy. This includes making high-level decisions like adjusting budgets based on business priorities, changing the campaign's optimization goal (e.g., from installs to in-app actions), and adapting to market trends. The marketer steers the ship.

• Set boundaries: The advertiser defines the operational framework within which the machine learning algorithm works. This involves setting key constraints such as the daily budget, target bid strategies like target cost-per-action (tCPA) or target return on ad spend (tROAS), and geographic or language targeting.

Analysis of Incorrect Options

• Update campaign settings daily: Frequent changes can disrupt the machine's learning period, preventing it from gathering enough data to optimize effectively. This leads to performance volatility. It's best to allow the system time to learn and stabilize.

• Adjust bids regularly: Bidding in App campaigns is automated. The marketer sets a strategic target (e.g., tCPA), and the system handles the real-time bid adjustments for each auction. Manual bid management is not a part of this campaign type.