Meta continues to be the most popular social platform for selling products and services. Small, medium, and large businesses spend hundreds of dollars every day to reach their target audience and generate sales. To do this as efficiently as possible and achieve a high return on ad spend (ROAS), it is essential to understand how Meta’s algorithm works, what changes have been made, and what steps to take to achieve the best results.
Before 2024–2025, targeting in Meta was based on users’ interests, behaviors, demographics, and other parameters. Advertisers themselves decided which users would see their ads, and the effectiveness depended on choosing the right audience. The more precise and narrow the audience, the better the ad performed.
For example, a travel agency’s targeter could select interests like “travel, vacation, Hotels.com,” narrow the audience by “frequent travelers” behavior, and obtain an excellent target audience. However, the rise of artificial intelligence (AI) and machine learning models in digital marketing completely changed the game.
Machine learning is a branch of AI that studies algorithms capable of improving themselves autonomously using data and experience. These algorithms build models based on collected data and can make decisions or predictions without direct programming.
Machine learning is used every day across almost every industry: analyzing traffic, suggesting optimal routes, filtering spam emails, recommending music on YouTube according to user tastes, assessing creditworthiness, helping autopilots stay in lane, and much more. Now, let’s see how it helps Meta personalize ads and increase effectiveness.
Andromeda is Meta’s machine learning system designed to ensure maximum alignment between the user and the ad. It helps users discover products and services that interest them, while helping advertisers achieve goals like increasing brand awareness or boosting sales.
Imagine you want to buy new sneakers and visit several websites to check models. Then you see relevant posts on Facebook, follow pages of well-known sports brands, and like some posts.
Meta’s machine learning system, Andromeda:
After this, you will see sneaker ads that match your interests as closely as possible.
Data collection, comparison, analysis, and prediction — this is how machine learning works. This is only the first stage of Andromeda, where thousands of ads are selected from millions, and then more complex models predict which ads best match users’ needs.
In short, machine learning models analyze data about both the advertiser and users (interests, behavior, past interactions, ad attributes), filter ads, and show users those most likely to interest them. This all happens quickly, in real time, so that ads remain relevant. For this, Meta uses the NVIDIA Grace Hopper Superchip — a “brain” capable of thinking and analyzing massive amounts of information simultaneously.
Andromeda predicts which ads are most relevant to the user, while Advantage+ automates and optimizes their delivery. Opportunities for advertisers include:
Audience creation automation — Advantage+ finds the right audience based on Andromeda’s work without manual selection.
Budget optimization — the system decides how to spend the ad budget for maximum effectiveness.
Dynamic placement — the system chooses the optimal placements for each ad across Meta platforms (Facebook, Instagram, Messenger, etc.).
Ad creative creation — using AI, Advantage+ helps create or optimize ad creatives: adding music, varying CTAs, improving visuals, and enhancing brightness.
Advantage+ destination allows ads to be optimized for different destinations (e.g., website or app). In other words, Andromeda predicts who will be interested in an offer, and Advantage+ ensures the ad reaches the right audience at the right time and in the best way.
Meta officially announced the launch of Andromeda in December 2024, after which the system underwent several updates. Familiarizing yourself with them and adapting will help you get the most out of your ads.
Before: the more precise the targeting, the better the results.
With Andromeda: the more and higher-quality creatives you have, the better the results.
For years, targeters developed strategies to guide users from product awareness to purchase. Retargeting, lookalike audiences, and narrow targeting are gradually becoming secondary, as Andromeda itself determines the target audience. The wider the audience, the more data Meta can analyze and the more accurately it can personalize ads. Therefore, it is best to give the system freedom and focus on the creatives.
Create as Much High-Quality Content as Possible
People are different and have different motivations. One buys an iPhone for reelmaking, another chooses it for speed and performance, a third just wants to impress friends. Creatives should reflect different problems and solutions. One user may be ready to buy immediately, another needs to get familiar with the brand. Even within a single campaign and a single ad group, having creatives for awareness, sales, and audience problem-solving allows Andromeda to effectively personalize ads, because Meta is smart enough to show the right ads to the right people. The more high-quality content — the more effective your ads.
After updates, many targeters notice an increase in CPM (cost per 1000 impressions) or a decrease in CTR (click-through rate). But it is important to remember: the advertiser is Meta’s client, and if the platform’s solutions are not beneficial, advertisers will stop running ads. The game remains the same; only the rules have slightly changed.
Create quality content that highlights problems and solutions, or trust professionals to handle it. Sparsis’ Facebook and Instagram marketing services can help you achieve this.
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