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How Google Ads Performance Planner Ties Performance to Marketing ROI

4 minute read
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By Pierre DeBois
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Marketers need solutions that can quickly identify the metrics with the greatest influence on ROI. Google Ads Performance Planner aims to solve that issue.

Digital ads require extensive planning to ensure the campaign persuades customers to click without going over the marketing budget. But many of the tools now available for marketers to follow the progress of ad campaigns explore basic metrics that may not play a significant role in ROI. Marketers need solutions which can quickly display the metrics that have the greatest influence on ROI.

Google has gradually updated its advertising scheduler, Performance Planner, for its Google Ads service to make ROI-influencing decisions easier for marketers. Introduced in 2019, the planner predicts the best spend opportunities for Google Ad campaigns relative to conversion objectives. Those conversions can be used to identify which campaigns are yielding returns or have the potential to see improved returns with changes. 

Since its introduction, Performance Planner has received some refinements. Google included support for shopping campaigns last fall, a timely addition given the increase in online retail during the past year.

How Performance Planner Works

The planner works by incorporating conversion data, monitoring for incremental volume changes according to the conversion types for the campaigns in a given Google Ads account. 

To access the planner, click the Tools "wrench" icon in your Google Ad account menu at the top of the screen. The performance planner selection appears in the dropdown menu. Once selected, it takes you to the main screen where you can create a plan by clicking the "To create a plan" button.

You then select the campaigns to be included in your plan, adding date ranges to establish the forecast period and key metrics you want. Conversion types are the same ones marketers select in the Google Ads performance reports — clicks, conversions or conversion value. Forecasts are based on different inputs, such as spend levels and Cost Per Action (CPA). Machine learning treats inputs as forecast signals for the most efficient spend opportunity. The forecasts also consider inputs from 7-10 days prior to your current date as a signal for machine learning projections. Marketers can also add a target value for spend, conversion, or Average CPA to help manage the prediction.

Learning Opportunities

google ads

Once you make your plan changes, the performance planner estimates the potential campaign performance. To see your results click the 'Create a Plan" button. A forecast graph page appears, where you'll have a choice between two types of visualizations. You can have a forecast graph which displays how changes adjust with your changes. The graph on the page is adjustable, revealing how a minor change in an input leads to a changed forecast outcome. The second option is a campaign comparison bar graph, showing the difference of your key metrics.

A great feature of the performance planner is that it allows you to save a plan as a draft for review later.  

Once running, the performance planner updates the forecast every 24 hours, keeping results stay in step with weekly campaign updates.

There are minimum campaign requirements for using the forecast graph. Search ads must be running at least 72 hours, generating at least three clicks and/or one conversion in a seven day period. Shopping campaigns have a slightly different threshold: they must receive 100 conversions within seven days with a minimum spend of $10 in the last 10 days. They must be active every day.

Related Article: How Analysis Hub in Google Analytics GA4 Eases Ad-Hoc Analysis Workflows

Google's Performance Planner: Linking Ad Performance to ROI

Many digital ad metrics focus on ad performance, but they do not link the performance to metrics that can be incorporated easily into ROIs. The performance planner makes that link, so marketers can better identify opportunities that are most likely an influence on a campaign's ROI.

About the Author

Pierre DeBois

Pierre DeBois is the founder and CEO of Zimana, an analytics services firm that helps organizations achieve improvements in marketing, website development, and business operations. Zimana has provided analysis services using Google Analytics, R Programming, Python, JavaScript and other technologies where data and metrics abide. Connect with Pierre DeBois:

Main image: Bruno Figueiredo