Wires, Instagram, TikTok – one by one, platforms release tools that allow users to clearly tell the algorithm what they want to see. Topic slides. Interest is changing. Content preference menus. The framework says: users are ultimately in control.
The problem with that framework is that users have always been in control. They didn’t need a menu to practice.
Every time someone watches a video all the way to the end instead of swiping, every time they re-watch a clip or stay on a post a beat longer than usual, they are showing something much clearer and more faithful than any selection toggle can capture. The algorithm already knows you’re still thinking about Avatar: The Last Airbender. You know because you watched that fan go down at 1 a.m. three Tuesdays ago, not because you told him you “love animation.”
That’s the key difference here. The obvious preference is coarse. Behavioral symptoms are granular. A title slide that says you like music doesn’t mean you’ll stop scrolling for a medley of ATLA characters singing songs from Hamilton. Behavioral observation does.
What is this really about
So why do platforms build these features at all?
The honest answer is that user-controlled algorithms are better politics than products. In an era of heightened scrutiny around data privacy, algorithmic transparency, and content moderation, giving users a visual dial to respond to is a logical response to public pressure. It shows accountability. It gives administrators something to point to.
For new users, there is also a real use case. Someone who joins the cold field, with no viewing history and no moral cues to learn, benefits from knowing “I care about cooking and I don’t want football.” An algorithm has to start somewhere, and the obvious preference is a quick cold start rather than waiting for behavior to accumulate.
But with the large number of users who have been on these platforms for years? The dial changes very slowly. Their feed is already a detailed picture of their viewing habits, built from thousands of tiny signals that the platform has been reading since day one.
What does this mean for marketers?
This is where what the media is saying becomes clear. If the signal that determines the delivery of content is primarily behavior rather than advertising, then an effective creative strategy is one that achieves behavioral engagement, not one that resembles a stated profit category.
That means that the role of the public broadcaster has not changed with this announcement. The task is to produce art in all formats with enough messages that viewing behavior can tell which version is relevant to which person. A user who watches your product video twice is showing something different than a user who skips it after two seconds, and both signals are more useful than knowing that a brand is “trendy.”
The implication of media planning is straightforward: separate the creative, test formats against each other, and let the actual data of the platform’s delivery tell you what works. The best version of your ad is not the one you would have chosen in the first place. That’s what user behavior chooses over time.
Bottom line
User-controlled algorithms are a logical feature for new people on the platform and a logical response to the political platform is wandering. For advertisers, they don’t change anything. The behavioral signal that drives delivery and performance is still the one that your creativity either gets or doesn’t get. Create content that’s worth sitting on, use enough variation that the algorithm has something to learn from, and trust viewing data over declared preferences. That was the playbook, and it still is.
