You think you’re in control when scrolling through your social media feeds, but think again. Social media platforms, like TikTok, Facebook, Instagram, X and many more have become testing grounds for constant marketing experiments, where users unknowingly participate in targeted campaigns, according to research from the UBC Sauder School of Business. Due to the algorithms powered by AI and machine learning, it’s difficult to predict which content will appear in front of which users and why.
According to the report, marketers lack the ability to know with certainty if one ad performs better than another, given the absence of “random assignment.” As a result, important messages can be unintentionally excluded from certain groups, and algorithms are so precise that they can target individuals on a personal level.
Despite focussing on Facebook and Google, the UBC study “On the Persistent Mischaracterisation of Google and Facebook A/B Tests: How to Conduct and Report Online Platform Studies” claims that all of the major social media platforms, from Instagram to TikTok, use comparable techniques.
As per a release, for the study, the researchers examined all known published, peer-reviewed studies of the use of A/B testing by Facebook and Google – that is, when different consumers are shown different ads to determine which are most effective – and uncovered significant flaws.
UBC Sauder Associate Professors and study co-authors Dr Yann Cornil and Dr. David Hardisty say that at any given moment, billions of social media users are being tested to see what they click on, and most importantly for marketers, what they buy. From that, one would think advertisers could tell which messages are effective and which aren’t – but it turns out it isn’t nearly that simple.
By using Facebook’s A/B test tool, researchers can access a massive audience and observe real behaviour – and because the participants are unaware they’re part of an experiment, their responses are considered more genuine and reliable.
The problem is that highly complex algorithms decide which consumers will be shown different content and ads; and as a result it’s impossible for anyone – even those who created the algorithms – to fully understand why specific consumers have been targeted by an ad, and to determine why some of them decided to click on the ad. According to Dr Cornil, it comes down to a lack of something called “random assignment” – for example, when experimenters randomly present two different ads to selected groups.
“You can’t say that whatever changes you made in your ad are causing an increase in click behaviour, because within each ad there’s going to be an algorithm that will select the participants most likely to click on it. If the algorithms are different, it means that there’s no real random assignment,” he says. “It also means we cannot say for sure that an ad generated a higher click-through rate because creatively it’s a better ad. It might be because it’s associated with a better algorithm.”