Maximum Performance Ads has been introduced as an efficient way to manage Google Ads across multiple Google-owned channels, making campaign management simpler for PPC advertisers.
Combine all Google Ads channel options into one campaign and use Google AI (Gemini) to optimize budgets and ad serving across all channels based on performance.
At first glance, I’ve seen great results from our clients’ top performing campaigns, but it definitely made me wonder which channels performed best.
Imagine the learnings you could get from rapid testing and AI data collection applied to your Google ads.
It could easily help you adjust and improve channel-specific campaigns and even use Google Ads learnings in other advertising platforms.
Exploring peak performance data
Digging into Performance Max data is easier said than done. Google is careful to provide only certain statistics about campaigns.
For example, in Google Ads (and when pulling data from Google Ads via the API), advertisers cannot analyze performance data, such as conversions or cost per conversion, by channel. And why not?
I think Google is using Performance Max for two main purposes.
First, Google will likely realize that certain advertising channels, such as Google Discover, act more like a brand interaction than a bottom-of-the-funnel (BOFU) interaction.
Since marketers are primarily measured by performance such as clicks and conversions, I suspect advertisers weren’t biting on these ad formats.
Performance Max allows Google a conduit to force advertisers to use channels they may not have originally chosen under the guise of optimized overall performance.
However, marketers benefit from AI-optimized ad serving, but at a cost. We are unable to remove top performing channels, which is affecting overall conversion performance.
Therefore, if Google Discover is negatively impacting performance, Google will either stop showing your top performing ads there or continue to show your ad on Google Discover to increase usage of that ad channel at the expense of a optimal click and conversion performance for your campaign?
Unfortunately, advertisers can’t adjust the top performing channels and have relatively little visibility into ad channel performance.
And therein lies the conundrum with Performance Max. Love it for the budget optimization or hate it for the lack of performance control.
If you also have a love/hate relationship with Performance Max, let me share with you some ways you can tailor Performance Max to your marketing needs using Google Ads and GA4 data, despite data limitations.
As Menachem Ani shares in his 2022 article, focus your effort on guiding the AI machine.
Build larger campaigns and learn from them
When we first started testing Performance Max for a fine wine retailer we work with, advised by other Performance Max experts, we created campaigns for very specific wine varieties such as Champagne, Merlot, White Zinfandel, etc. However, we didn’t. find it necessarily successful.
While some wine varieties performed well, less popular varieties struggled. Even after getting a high CTR, they often didn’t generate conversions or revenue.
We changed our strategy and decided to go a little wider. Using this strategy, we determined which varieties performed well using revenue data. Those that performed well earned their own top performing campaign.
When testing Performance Max, consider starting with a broader category and then learning from your data to narrow it down.
You can get this information from GA4 using a scans report (or pull the data using the GA4 API) and review item sales by campaign.
This will tell you the specific categories that are performing well using Performance Max and could probably support your specific Performance Max campaign.
Dig deeper: How to combine GA4 and Google Ads for powerful paid search results
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Create multiple asset groups per campaign with a specific focus
Think of asset groups like you would ad groups in search campaigns. In search ad groups, it’s a best practice to group common keywords into an ad group. Do the same with asset groups and asset focus.
We recently ran two groups of assets in a test in a top performing campaign for the same retailer. Resource groups had the same ad copy and extensions, but different images and videos per group.
Group A contained images and videos with beautiful scenery of vineyards, delicious glasses of wine, or a group of friends celebrating with wine. Group B included images of specific wine bottles from our inventory, using a variety of brands based on our most popular wines in that category.
Although Google doesn’t make it intuitive, you can compare the results of two asset groups. Asset groups in top performing campaigns are somewhat similar to ad groups in search campaigns, but there is limited reporting in Google Ads about asset groups.
To see how one asset group is performing against another in a top performing campaign, navigate to the campaign in Google Ads and instead of the default asset group summary view, switch to the of table:
The table view provides more detail on performance by asset group, and you can also customize the columns, which allow you to view conversion data by asset group:
Use different destination URLs for each resource group
However, seeing the performance of the asset group in GA4 is a bit more difficult. Unlike ad groups, GA4 does not pass resource group information to GA4. So if you want to gather this data in GA4, how do you go about it?
The easiest way is to add a tracking parameter to the destination URL of the resource group. For example, you can create a separate destination URL for each resource group using a parameter, such as utm_creative_format, and then capture that parameter in a custom dimension in GA4.
This would allow you to break down the actual sales and conversion data by asset group in GA4 in addition to the campaign.
Ad strength is not always indicative of potential success
In peak performance resource groups, there are usually more resource items (such as images, videos, etc.). increase the strength of ads. However, Google’s definition of ad strength does not always indicate likely success.
In the example below, both resource groups have identical resources, except for visual creatives. The only differences between the two groups are:
The focus of the creative asset: The “Good” asset group focuses on product scenes, while the “Average” asset group focuses on actual product images.
Types of creative assets: The “Good” resource group contains the maximum number of images and five videos, while the “Average” resource group contains the maximum number of images and no videos.
We tested this same asset group across all campaigns over 30 days, using a scenic asset group versus a product image asset group.
In all cases, the product image asset group that includes product bottles outperformed the scenic asset group every time, even though Google Ad Strength’s defined method implies that the scenic asset group will have better performance
The scenic asset group often got more clicks and a higher CTR, but never generated revenue during this test period; only the product image asset group did.
While ad strength as a metric may seem useful, use actual conversion data from
Set your ROAS high and then increase
If you set your ROAS target at 200%, you will get around 200%. But what if you set it higher?
One of the strengths of Performance Max is its ability to maximize the budget to achieve the desired ROAS. Push it to the max and use Performance Max for what it’s good at.
Check your results in GA4 and Google Ads to see how the change affects performance.
Beware of default channel grouping between networks
Top performing campaigns appear as “cross-network” in GA4’s default channel groupings. It can be tempting to group all campaigns into this aggregated grouping to evaluate performance.
However, because Performance Max is AI-assisted, combining new campaigns with existing ones won’t provide the true clarity that individual campaign-level reports do. Avoid the temptation to evaluate peak performance at the aggregate level in GA4.
The views expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.
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