The rise of automation across hands-on-keyboard platforms like Google and Meta has introduced a double-edged sword in media strategy. Without question, there are innovations that have made the process “easier” for marketers: The days of juggling campaigns with exact-match or broad-match keyword lists are increasingly in the rear-view mirror, thanks to Google’s Performance Max, which uses your campaign goals and powerful machine learning to fully automate the creation of one multichannel campaign.
You are not able to choose any keywords but it can show across all match types, and will generate ads across search, shopping, display, Gmail, YouTube, Discover Page, and Maps. Any ingredients you can provide, including video and images, will be used to generate live ads. Likewise, Meta has introduced Advantage Plus, an AI feature that will automate copy and keywords for your ads on Facebook, as well as optimize creative, and – if you allow it – slightly alter images, to optimize your campaign goals.
These platform giants certainly know how to appeal to their target audiences, and their new developments have been well-received. Campaigns are, undoubtedly, easier to run, and the new offerings also enable smaller businesses to scale up and be more competitive.
There is, however, a dark side, as the optimizations may actually be less than “optimal.” The automation gives a marketer less hands-on control and, if the campaign is not performing up to expectations, it’s very difficult to see which elements are working and which are not, because the reporting is rather simplistic.
Marketers who want to be more judicious about spending will find that setting up exclusions in these multimodal campaign types is more complicated. If you know your consumers don’t convert well from YouTube or display ads and want to exclude those media, for example, there is no definitive way to do so within Performance Max. There are potential workarounds with placement exclusions, negative keywords or other steps, but these are not infallible and not recommended by Google. The advertiser is then left to hand over all optimization power to Google and simply trust it knows best.
Marketers might wonder how to regain control of their online campaigns if they’re not going well, but many media platforms seem to take the position that it simply will go well, as powerful machine learning will continue to collect data and learn to reach an advertiser’s goal. For those with aggressive efficiency goals, the inability to exclude non-converting channels or perform analysis into wasted spend areas is a turnoff.
When the backbone of your media strategy is automation, is there really even a role for the strategist? For now, it’s a gray area. Today’s suite of automated AI-powered features has accompanied a lack of transparency into the signals and learnings that are being used to improve performance. The hope is that the reporting capability advances soon to enable and enhance strategic management, rather than prohibit or replace it.