You’re a marketing executive at a clean energy company and have been asked to come up with your company’s online advertising budget on a monthly basis. You decide to leverage Google Ads’ Performance Planner to help you achieve your aims. What are two benefits you’ll get from Performance Planner? Select 2 Correct Responses
Select all correct answers
Correct Answers
Its forecasting that is driven by billions of weekly Google searches
Its reliance on machine learning for forecasting purposes
Why is this the correct answer?
Two features of Google Ads Performance Planner that are most useful for monthly budget planning at a clean energy company are its forecasting driven by billions of weekly Google searches, and its reliance on machine learning for forecasting purposes. Performance Planner ingests Google's massive search dataset and applies machine learning to project how campaign performance will change at different spend levels — enabling precise, data-backed budget decisions. This gives the marketing executive confidence that budget recommendations are grounded in real search behaviour rather than guesswork.
Why are the other options incorrect?
A way to identify operational budget to reallocate to marketing
Identifying operational budget to reallocate from other departments is not a feature of Performance Planner — it forecasts advertising outcomes within Google Ads, not across a company's wider budget categories.
Its integration with other budgeting software, such as QuickBooks
Integration with budgeting software like QuickBooks is not a Performance Planner feature — the tool operates within Google Ads and does not connect to external financial systems.
Real-World Example
A clean energy company's marketing executive uses Performance Planner before setting the monthly Google Ads budget. The tool models that shifting £3,500 from awareness campaigns to solar installation keywords will increase qualified leads by 18%. Acting on this insight, the team reallocates the budget and achieves a 21% increase in leads — demonstrating the value of machine learning-driven forecasting for monthly planning.