You’re a marketing executive who has been charged with planning an online advertising budget each month and you decided to use Google Ads’ Performance Planner to assist your efforts. What are two advantages Performance Planner offers you? Select 2 Correct Responses
Select all correct answers
Correct Answers
Performance Planner relies on machine learning for forecasting purposes.
Performance Planner forecasting is driven by billions of Google searches conducted every week.
Why is this the correct answer?
Two advantages of using Google Ads Performance Planner for monthly budget planning are that it relies on machine learning for forecasting purposes, and that its forecasting is driven by billions of Google searches conducted every week. Performance Planner uses Google's vast search data and machine learning to model how changes in spend will affect campaign performance — giving marketing executives accurate, data-driven budget forecasts. This allows planners to optimise budget allocation across campaigns and identify the spend level that will best achieve their goals before committing the budget.
Why are the other options incorrect?
Performance Planner can help you locate funds from other operational budgets so they can be allotted to marketing.
Performance Planner cannot locate or reallocate operational budgets from other departments — it forecasts the impact of different advertising spend levels within Google Ads, not across broader business budgets.
Performance Planner can integrate with additional budgeting software like QuickBooks.
Performance Planner does not integrate with third-party budgeting software such as QuickBooks — it is a Google Ads native tool for campaign budget forecasting only.
Real-World Example
A marketing executive at a SaaS company uses Performance Planner to plan Q3 budgets across five campaigns. The tool models that increasing total spend by 15% and reallocating £2,000 from brand campaigns to non-brand will increase total conversions by 23%. The executive acts on the recommendation — and at month-end, actual results come within 8% of the forecast, validating the machine learning model's accuracy.