As an experienced marketer on Google’s advertising platform, I’ve run a number of search campaigns, shopping campaigns, and Performance Max (PMax) campaigns over the years.
However, my experience with PMax has been less than satisfactory and I’d like to share my thoughts on why I believe advertisers should exercise healthy caution when considering this type of campaign.
How Does It Work?
PMax campaigns work by feeding insights and performance data into a learning algorithm, which then makes all the decisions on how the ads should be served.
While this may seem like a convenient solution for advertisers, it comes at a cost.
The reporting that Google provides for PMax campaigns is currently not informative or actionable, leaving users in the dark about what is actually happening with their campaigns.
The Loss Of Reporting
One of the biggest issues with PMax campaigns is the loss of search terms reporting, inventory reporting, and asset group reporting. What does that mean for you as an advertiser?
With no way of knowing which search terms are driving traffic, where your ads are being served, or what assets are being used, the levers with which you would make data-driven decisions begin to disappear.
The lack of transparency in this process is a major problem, as advertisers are unable to make informed decisions about their campaigns.
Scattershot Ad Delivery
Another major concern with PMax campaigns is the fact that they serve ads on a wide range of inventory, including the display network, Gmail ads, YouTube, and Discovery, with no choice for the advertiser on where the ads will be served.
This lack of control can result in poor conversion quality, as ads may be served to audiences that are not relevant to the advertiser’s product or service.
Additionally, the dynamic generation of creative assets means that advertisers have no control over the exact look and feel of the ads, which can further reduce conversion quality.
Junk Leads and Wasted Spend
A repeated complaint with PMax campaigns is that they can easily overspend on junk leads or sales. The algorithm-driven nature of PMax campaigns means that the campaign can spend a lot of money on leads or sales that are not of high quality.
This can be damaging for businesses of any size. Small businesses may not have the resources to recover from such a loss, but large businesses will quickly blow through large portions of their monthly budget while Google snaps up traffic indiscriminately.
Performance Tweaks Incur Choppy Learning Phases
Most changes made to PMax campaigns can incur a long and choppy learning phase, as the algorithm relearns.
This can result in a drop in performance, which can be frustrating for advertisers who are trying to make improvements to their campaigns in realtime and downright infuriating when trying to act quickly on evolving trends.
Traffic Leech
Finally, PMax campaigns can steal traffic from existing campaigns, which can be especially dangerous if the campaigns are already doing well.
This can result in a decrease in performance for the existing campaigns, which can be a major problem for advertisers who are relying on these campaigns to drive traffic and sales.
While exact-match search keywords will generally be prioritized over Pmax campaigns, most other aspects will favor the higher ad rank. Shopping campaigns and dynamic display, however, will always prioritize PMax whenever possible.
Conclusion
In conclusion, while PMax campaigns may seem like a convenient solution for advertisers, they come with a number of serious drawbacks. The lack of transparency, control, and actionable reporting makes it difficult for advertisers to make informed decisions about their campaigns.
Until Google provides more informative and actionable reporting, I would recommend against migrating your existing campaigns over to Performance Max.
If you’re considering using this type of campaign, I strongly advise that you weigh the potential benefits against the risks. FTOptimize’s advertising and market consultation experience will ensure that you are making an informed, data-drive decision every time.