You’re a marketing executive at an airline company and have been asked to plan your company’s online advertising budget on a monthly basis. You decided to use Google Ads’ Performance Planner to help accomplish this task. What are two advantages Performance Planner offers you? Select 2 Correct Responses
Select all correct answers
Correct Answers
Performance Planner leverages machine learning for forecasting.
Performance Planner forecasting is powered by billions of Google searches conducted each week.
Why is this the correct answer?
Two features of Google Ads Performance Planner that are most useful for monthly budget planning at an airline company are that it leverages machine learning for forecasting and that its forecasting is powered by billions of Google searches conducted each week. Performance Planner uses Google's AI and vast search data to model how different budget levels and campaign settings will affect performance outcomes — giving the marketing executive accurate, data-backed projections for setting monthly advertising budgets with confidence.
Why are the other options incorrect?
Performance Planner integrates with other budgeting software, such as QuickBooks.
Performance Planner does not integrate with third-party budgeting software like QuickBooks — it is a native Google Ads forecasting tool that operates independently of external financial systems.
Performance Planner will help you identify funds from other operational budgets to allocate to marketing.
Identifying funds from other operational budgets is not a Performance Planner feature — the tool forecasts advertising outcomes within Google Ads, not across broader company budget categories.
Real-World Example
An airline's marketing executive uses Performance Planner before setting the Q4 Google Ads budget. The tool models that increasing spend by £12,000 per month on 'flights to New York' campaigns will drive an additional 840 ticket sales based on current search demand. Acting on this forecast, the executive secures the additional budget — and actual results come within 11% of the projection, validating the machine learning-driven forecast.