Maximum Performance (or PMax) campaigns are gaining traction among search advertisers.
Google’s machine learning resources optimize ad placements across the Google inventory network. This allows advertisers to maximize their efforts and reach their target audiences more efficiently.
However, there are always downsides to taking advantage of these proprietary capabilities, especially when we lose more visibility and levers to pull.
We generally have limited visibility into data for these campaigns, making it difficult to make informed decisions about how to best optimize campaigns and allocate budget.
Additionally, advertisers often need to analyze their aggregated data across all campaign types and platforms. This may require:
Obtaining data using the Google Ads API. Loading it into a larger data store for further manipulation and analysis. Join it with Google Analytics data to create a more complete picture of the user journey.
These are some of the limitations to be aware of when pulling and analyzing PMax performance data inside and outside of the Google Ads interface.
1. Limited granularity of PMax data
PMax campaigns offer limited reporting options than other Google Ads campaigns, which can make it difficult to analyze performance in the way we’re used to.
Typically, Google Ads campaign data can be obtained by accessing the standard report via the API. You can define the level at which you want to segment the data, even down to the keyword level.
Because PMax campaigns use machine learning to determine the best placements to serve ads, there are no ad groups or keywords associated with these campaigns.
Therefore, a standard report generated at any level that is more granular than the campaign will contain several fields that are irrelevant to PMax and will exclude all data from those campaigns entirely instead of simply overriding the irrelevant fields.
To capture your standard and PMax campaigns, you need to call the API multiple times and retrieve two separate data connections that can then be loaded and joined to your data store.
The first should be a standard report at the desired level of granularity, which will not contain PMax campaign data. The second should also be a standard campaign-level report, but this time it should exclude all campaigns that are no PMax to avoid duplicate data.
Also, note that many custom reports and segments can be useful for campaign analysis, such as top performing location.
They cannot be retrieved via the API and can only be viewed in an isolated environment within the Google Ads interface.
Get the daily search newsletter marketers trust.
2. Google Analytics information requires careful browsing
With the release of Google Analytics 4 and the subsequent deprecation of Universal Analytics, advertisers can use website and app data to understand the customer journey and post-click website interaction activity .
Any website traffic generated by PMax campaigns should be carefully watched and analyzed.
For starters, you won’t see PMax data in the default paid search channel grouping, but in a separate grouping called Cross Channel that contains data from both PMax and Smart Shopping campaigns.
Be careful about placing dimension filters that are incompatible with PMax campaigns.
Unlike the API issues mentioned above, where the data will not be displayed, these filters will cause the data to be displayed incorrectly in the GA4 interface and cannot be trusted.
For this reason, obtaining multi-channel statistics that include PMax campaigns within GA4 can be challenging.
In addition, PMax campaigns count engaged view conversions.
This type of conversion is very valuable because it is more specifically tailored to video advertising and the behavior of the user who follows a video ad than other ad types and is a strong indicator of engagement .
Just keep in mind that Google Analytics by default does not count these conversions and will have to be intentionally set up to do so.
3. Traditional analysis methods may not apply
Given the above issues, using Google platforms to generate reports and insights about PMax campaigns in isolation is always an option.
When viewing the data available on the platform, it is essential to consider all the limitations surrounding this data and to know that traditional analysis tactics may not be effective or possible.
For example, while some basic report templates are available on the platform for PMax campaigns, advertisers cannot customize any reports or create custom metrics.
Another factor to consider is that since PMax campaigns are optimized with real-time data, campaign performance should be analyzed closer to real-time and less dependent on historical data and trends, as algorithms are constantly adjusted to maximize optimizations.
This reliance on real-time data also makes traditional A/B testing difficult, especially since we have no control over things like ad placements, formats, creatives, or audiences that we can isolate to test hypotheses.
Instead, you can simply run tests that compare your PMax campaigns to standard Shopping campaigns, or run an uplift experiment that demonstrates how adding a PMax campaign to your existing campaign mix can increase conversion volume.
Other examples of information we lose with PMax campaigns include audience targeting, ad placement, and budget control.
While this is all by design, it can be a tough adjustment for advertisers to lose the ability to say where their dollars are allocated.
They may not have the time or budget to allow the campaign to run long enough to collect enough data to maximize efficiency.
And while PMax optimizes based on audience behavior and ad creatives, they don’t provide detailed data about those behaviors or how individual headlines or images are performing.
The opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.
[ad_2]
Source link
