The Trust Dilemma: Are Advertising Platforms Acting in Your Best Interests?

Advertising platforms such as Google and Facebook are pushing AI/machine learning to help companies optimize results and increase efficiency. But these platforms consider their AI or machine learning algorithms proprietary, leading the platforms to provide less and less data on who you are targeting, what ads are performing well, and how to optimize. Instead, you’re asked to rely on the platform to do all the optimization for you. But can you trust that the advertising platforms are acting in your best interests?

The Trust Challenge for Advertising Platforms

Evolution of Optimizing Campaign Performance

Years ago, marketers (or their media agencies) built complex campaigns to see performance at a granular level: by geography (DMA or state), creative, audience, and keyword. Yes, this was incredibly labor-intensive and required close monitoring to ensure campaigns were optimized. Then platforms rolled out goal setting, which allowed goals such as awareness or conversion to be set, and the platform optimized to that specific goal. Yet the platform data still allowed the ability to download fairly granular performance data.

Now platforms use that high-level goal setting with AI or machine learning capabilities to optimize the audience and geography targeting, as well as the creative, based on broadly set parameters. Platform data is far less granular, so even experts and media agencies must leave more and more of the decision-making up to the advertising platform. Add in that browsers have moved or are moving away from third-party cookies, tracking individuals is even more difficult and gives advertising platforms more power.

Increasingly clients tell us they are craving more insights from their digital spending. When you’re spending millions on a media channel, you want to know what’s working and what’s not. And increasingly, clients ask if the platforms’ black box approach is in their interests. So, can you trust the advertising platforms?

Revisiting Facebook and Google’s Missteps

History is not on the advertising platforms’ side. Both Facebook and Google, two of the largest advertising platforms, have prominent instances where they either mislead companies about metrics (Facebook) or tried to use their size to snuff out competition (Google).

Remember back in 2015 when Facebook said video was the future? Facebook speculated that within five years, Facebook’s news feed may be mostly video and trotted out stats that showed an impressive number of video views. Turns out Facebook overstated (“miscalculated,” according to Facebook) video views by as much as 900% and subsequently settled a lawsuit, paying $40 million to advertisers.

Google, on the other hand, used its market dominance to push accelerated mobile pages (AMP) with publishers, which required the use of Google’s advertising platform and limited ad space, effectively cutting off third-party advertising platforms from viability with publishers. Currently, Google is facing multiple antitrust lawsuits related to AMP, including one filed in January 2023 by the U.S. Justice Department. The Justice Department alleges that Google used AMP as a land grab to force “parts of the open web into a Google-controlled walled garden, one where Google could dictate more directly how digital advertising space could be sold.”

Obviously, some skepticism of these platforms is warranted. Yes, the platforms want you to be successful so you’ll continue spending money, but they also are in the business of maximizing their profits—not yours. Who would know if your automated bid strategy wasn’t as efficient for some segments or campaigns?

How to Compare Automated AI vs. Manual Optimizations

So, what can you do to compare automated AI optimizations versus manual optimizations?

Conduct an in-market A/B test to compare an AI/machine learning campaign against a manually set up campaign. Select geographies with similar levels of sales and demographics to see if the automated AI optimizations perform better than manually set optimizations. Can your marketing team outsmart and overperform?

AI & Advertising Platforms: Maximizing Results

Overall, the move toward AI/machine learning on advertising platforms, as well as the degradation of third-party cookies, means the digital channel will become more difficult to measure. Can you leave your marketing strategy, measurement, and optimization up to the platforms? Mixed media modeling (MMM) and multi-touch attribution (MTA) can help companies understand how channels are performing, where each channel is on the spending curve, and how to optimize investment to maximize results.