In 2024–2025, Meta underwent one of the most significant transformations of its advertising system in the platform’s history. This isn’t about new ad formats, cosmetic interface tweaks, or additional automation tools. Meta Andromeda represents a change in the core logic of how the system selects and serves ads to users.

For advertisers and affiliates, this means one thing: many of the usual approaches no longer deliver predictable results. To understand how to move forward, it’s crucial to grasp what exactly has changed and why.

What Meta Andromeda Is

Meta Andromeda is a new ad delivery algorithm built into the Facebook and Instagram ecosystem. Inside Meta, it’s described as a next-generation retrieval engine: a system that first selects relevant ads and only then decides which ones to show.

The key feature of Andromeda is its scale. The algorithm can analyze millions of active ads and match them to a specific user’s behavior in real time. This isn’t just an overlay on the old system—it replaces the underlying logic entirely.

It’s important to note: Andromeda isn’t a separate tool or setting. It’s the foundation on which Advantage+, automated campaigns, and most standard ad scenarios now operate.

How Meta’s Ad System Worked Before

Before Andromeda, ad logic was audience-centric. Advertisers set targeting parameters—interests, age, location, behavioral signals—and the system showed ads within those boundaries.

Optimization happened post-launch: Meta would redistribute budgets between ads based on CTR, conversions, and other metrics. However, it was the audience settings that determined the overall direction of the campaign.

Even when running “broad targeting” campaigns, advertisers were still limiting the algorithm with the initial conditions.

What Changed with Andromeda

With Andromeda, the system no longer starts from the audience. The focus has shifted to the individual user and their behavior.

The algorithm first analyzes the person: how they interact with content, which formats they prefer, what they engage with, and what actions they take after seeing an ad. Only then does it select ads from the overall pool that are most likely to be relevant for that user.

In effect, Meta no longer finds an audience for the ad, it finds the ad for the user.

How Andromeda Works

The algorithm’s operation can be conditionally divided into three stages.

Initial Selection.
From millions of active ads, the system instantly forms a set of potentially relevant options. At this stage, user behavioral signals, creative characteristics, interaction history, and accumulated data from similar profiles are taken into account.

Ranking.
Next, ads are evaluated based on the likelihood of achieving the target action. Meta increasingly focuses less on surface metrics like CTR and more on the quality of engagement: attention retention, depth of view, and repeat actions.

Delivery.
The final ads are shown to the user. This cycle repeats every time the feed updates, allowing the system to continuously refine its selections. This process is fully automated and scalable across Meta’s ecosystem.

The main consequence of implementing Andromeda is a reduction in the role of manual targeting. Creative content comes to the forefront, with its ability to provide clear signals to the algorithm. The system evaluates not just the text or visuals, but the overall pattern of user interaction with the ad.

Moreover, Meta has become much stricter about traffic quality. Clicks without further engagement lose value, and short-term spikes in CTR are no longer considered an indicator of effectiveness.

What this means for affiliates and media buyers

Under the new conditions, creative content effectively acts as targeting. It determines which audience the algorithm will find and scale.

Experience shows that single “perfect” ads perform worse than a set of diverse creatives with different messages and formats. The algorithm needs options and comparative data.

The importance of the learning phase has also increased. Campaigns now require more time for the system to gather enough signals. Early conclusions and abrupt changes often worsen results. Broad audiences, in this context, become a necessary condition for the algorithm to function properly.

How to adapt strategies for Andromeda

Current best practices include:
— Focusing on a variety of creative hypotheses rather than micro-tests
— Planning campaigns over a longer horizon
— Using Meta’s automated solutions where appropriate
— Analyzing not just clicks, but the quality of engagement
— Avoiding excessive restrictions in settings

The specialist’s role shifts from manual management to designing a system where the algorithm can learn efficiently.

Common Mistakes

A frequent misconception is believing that targeting has completely lost its value. In practice, it remains a tool, but it is no longer primary.

Another mistake is relying on mass-producing similar creatives. The algorithm values diversity, not quantity.

It is also important to note that short-term dips after launch do not always signal a problem. In most cases, this is part of the data accumulation phase.

Expert Opinion

Nikolay, media buyer at “True Euphoria.”

Meta has recently rolled out a new algorithm, which prioritizes the quantity, quality, and uniqueness of creatives rather than detailed targeting settings. On one hand, Meta Andromeda is a sophisticated and flexible algorithm that saves time on audience analysis and precise targeting within campaigns. On the other hand, it now requires more effort to develop creatives, copy, new angles, and formats.

Simply duplicating ad sets or changing the background/font will no longer deliver consistently good results. You need to analyze your target audience, their behavioral patterns, and create a package of creatives with different formats and angles to guide the algorithm in the right direction. Quality is also crucial—if the algorithm cannot recognize your creative, it will show it to a broad audience, wasting the budget.

Additionally, it’s important to stay attentive and not rely entirely on the algorithm. Given the moderation specifics in the Nutra vertical, no one has a precise understanding of how it works. Decisions should always be based on metrics and results.

Conclusion

Meta Andromeda marks a shift to a model where key decisions are made by the algorithm based on user behavior and creative quality. For the advertising market, this means moving away from the old logic of “precise targeting” toward a more systemic, strategic approach.