For years, running ads on Meta platforms followed a predictable pattern. Advertisers spent most of their time choosing audiences. Age, gender, interests, behaviors, and lookalikes were treated as the most important part of campaign setup. Many believed that if targeting was perfect, performance would automatically follow.

That approach is slowly changing.

Meta’s newer delivery system, often referred to by marketers as the Andromeda update, reflects a major shift in how ads are evaluated and distributed. Instead of relying heavily on manual audience selection, the platform now depends more on machine learning to decide who should see an ad. The system studies user behavior patterns, engagement signals, and response history at a large scale. Because of this, targeting settings have become less powerful than they once were.

The biggest implication is simple. The creative itself now matters more than the audience selection.

Earlier, advertisers could run average creatives and still get acceptable results if targeting was precise. Today the platform does much of the targeting automatically. The algorithm observes which users stop scrolling, watch a video longer, click, save, or interact. Based on these signals, it expands delivery toward people who show similar behavior. This means the ad content becomes the main input that guides performance.

In practical terms, Meta is no longer asking, “Who do you want to reach?”
It is asking, “What kind of users react to this content?”

This change explains why many advertisers feel confused recently. They launch a campaign with detailed interest targeting but results remain unstable. The problem is not always the audience selection. Often the creative is not giving the algorithm enough useful signals to learn from.

A strong creative now performs two roles. First, it communicates a message to the user. Second, it trains the system. Every watch, click, or pause helps the platform understand what type of person finds the ad relevant. Weak creatives send unclear signals, so delivery struggles to stabilize.

As a result, testing creatives is increasingly more crucial than improving targeting. The system finds responsive users more quickly with the use of various hooks, graphics, copy >

This also changes how beginners should approach digital marketing campaigns. Instead of spending hours inside audience settings, more effort should go into understanding attention. What makes someone stop scrolling? What makes them curious? What makes them trust a brand quickly? These questions now influence performance more than detailed interest stacks.

Another noticeable shift is broader targeting recommendations. Many campaigns now work better when audiences are less restricted. When advertisers limit targeting too much, they prevent the algorithm from learning. Giving the system room to explore allows it to discover patterns that humans cannot easily predict.

This does not mean targeting is useless. Location, language, and basic relevance still matter. However, they act as boundaries rather than the main driver of success. The primary driver has moved to creative quality and engagement signals.

For advertisers, the lesson is clear. Campaign performance is no longer mainly a media buying skill. It is becoming a communication skill. Understanding people, behavior, and attention is now central to advertising success.

The platform is gradually moving from audience selection to response detection. Those who adapt by improving creative thinking, messaging clarity, and testing frequency will benefit. Those who rely only on targeting settings may continue to see inconsistent results.

In simple terms, the algorithm now finds the audience.

It's your responsibility to make it something worth displaying.