How Meta Andromeda Ads are Reshaping Ad Targeting

Meta—owner of social media and communication platforms like WhatsApp, Facebook, and Instagram—has long been recognized as a trendsetter in the tech world. With the rise of artificial intelligence (AI) and machine learning (ML), it’s no surprise that Meta is seeking to capitalize on these new tools. One innovation for performance marketers to watch is Meta Andromeda ads, a proprietary ML system used to fine-tune ad recommendations.

While Andromeda undoubtedly holds promise based on early usage, performance marketers who want to harness the power of paid social AI in 2025 will need to adapt to make the most of it. Below, we outline how Andromeda works and provide tips on optimizing AI ad targeting on Meta platforms.

What Is Meta’s Andromeda System?

Meta’s Andromeda promises better campaign performance by more accurately targeting advertising content according to users’ interests. It’s a tantalizing prospect for any organization that engages in paid social media advertising.

Although Meta has already used targeting to fine-tune its paid ad delivery in the past, Andromeda makes the process even more precise, upgrading earlier ad targeting methods by leveraging deep neural networks to maximize ad performance. Already, Meta is reporting that Andromeda has led to an 8% increase in ad quality¹.

The Mechanics of AI-Powered Ad Selection

Meta’s reach is enormous, and advertisers on the tech behemoth’s platforms may find their ads among millions vying for consumers’ attention. Relevance is a challenge—the high volume of ad candidates makes it difficult to accurately match the right content to the right consumer. High user volumes can also cause delays, resulting in a frustrating user experience and making ads less effective¹.

The ads recommendation system starts with retrieval, in which the system narrows down tens of millions of ad candidates to a few thousand based on relevance to the user. Springboarding off this retrieval stage, the system then predicts ad value for target audiences before deciding which ads to show users².

Meta AI personalization via Andromeda more effectively narrows ad selection from millions to thousands. Andromeda makes ad delivery more relevant and efficient with updates like:

• Deep neural network: Meta has created a deep neural network (DNN), a type of artificial neural network designed to mimic the human brain’s ability to learn, process information, and make decisions. DNNs have many layers, allowing them to learn complex data patterns³. Meta’s DNN runs on an NVIDIA Grace Hopper Superchip, allowing it to quickly and effectively learn from users’ ad interactions¹.
• Hierarchical indexing: Hierarchical indexing refers to a method of structuring relationships between groups of data⁴. Andromeda’s hierarchical indexing builds on Meta’s Advantage+ suite of AI tools, which are used to automate audience targeting as well as budget allocation and bid adjustments¹. Since hierarchical indexing is better at identifying relationships between data sets, automated targeting and spend adjustments are set to improve.
• Streamlined systems: Meta’s Andromeda helps cut system complexity and enhance efficiency by reducing reliance on rule-based logic. Users can create automated rules in Meta’s ad manager for various reasons, such as pausing a low-performing ad when it hits a predefined metric⁵. Andromeda optimizes performance by reducing reliance on such rule-based logic, making ad retrieval more efficient.

With these technical features, Meta Andromeda ad targeting can monitor user behavior and interest signals (like clicks) to determine the most relevant ads to show. More timely, precise targeting can be beneficial in combating ad fatigue—the phenomenon where users tune out ads because they’re subjected to so much digital noise. This reduces engagement and makes it harder for brands to connect with audiences⁶.

Creative diversity is essential to cutting through that digital noise and providing fresh content that catches audiences’ attention—if the content makes it in front of the target audience in the first place. And that’s where Andromeda can help.

Why It Matters for Performance Marketers

Higher ad relevance not only reduces ad fatigue—it also supports better click-through and conversion rates, meaning fewer wasted impressions and better return on ad spend (ROAS). Even before Andromeda, businesses that utilized the Advantage+ AI targeting suite saw a reported 22% increase in ROAS².

With Andromeda, Meta promises faster feedback loops. Performance marketers can more quickly and accurately determine what is and isn’t working for users. This information can then be used to support creative iteration, allowing marketers to craft ads that are more likely to resonate with target audiences.

Strategic Considerations for Brands

Brands that want to keep up in the age of AI innovations like Andromeda have to adapt. What worked a year ago isn’t going to work today. Currently, there are a few key steps brands can take to make sure their messages aren’t getting lost among the millions of ads on social media:

• Use content modules to maximize creative variety: Creative variety is more important than ever. Lean into modular content, which helps streamline and automate content creation by assembling assets into pre-approved content blocks or modules. These modules can then be deployed for diverse creative purposes⁷.
• Lean into signal-rich assets: Signal-rich assets are essential to learning more about target audiences. A buying signal, like clicking on an ad or visiting a brand’s product page numerous times in a short period, can indicate interest. With Andromeda, collecting info about signals is faster and easier⁸.
• Utilize AI-friendly formats: With AI increasingly integral to paid social media advertising, it’s critical to use AI-friendly content formats. Examples include reels-first approaches and dynamic product ads (DPAs), which display ads featuring specific products to users based on data like their shopping cart contents or browsing history.

More generally, brands need to think about adapting audience strategies. In a signal-rich world, testing becomes even more important. A/B testing to see what is and isn’t converting is one example. Socium’s creative and media teams help brands with adaptive testing frameworks like these, providing fast, informative results to help boost ad impact.

What Brands Can Learn From Socium’s Clients

Socium is already helping clients harness the power of Meta Andromeda ad targeting, and case studies show how impactful Andromeda can be. For example, home humidification and hydration brand Canopy came to Socium for help with customer acquisition and domain authority. Using data-informed audience testing, Socium was able to boost new customer acquisition by 176% year over year.

That’s not the only success story. VINCE, a luxury apparel and accessories brand based in Los Angeles, turned to Socium for help boosting brand recognition and revenue. Socium leveraged lookalike audiences (LALs) for customer acquisition and adopted a full-funnel direct response approach on Meta. Combined with other efforts, these initiatives led to a 127% increase in orders and a 183% increase in revenue via paid social within a single quarter.

Socium achieves these kinds of results using a holistic approach tailored to each client’s needs. Methods include dynamic creative testing, adaptive media buying, or signal-optimized strategies, among others. The techniques used are personalized according to the client’s goals for their paid social AI in 2025.

Final Takeaways

As AI and LM technologies improve, ad targeting and paid social ads in general will continue to evolve, likely at a rapid pace. Performance marketers who want to future-proof their Meta ad campaigns with AI at the core need to stay on top of the latest updates—or risk getting left behind. Socium Media’s dedicated paid social team helps our clients adapt accordingly. Discover how we can help you keep up in the AI era.

Sources:

1. (March 27, 2027). AI Innovations in Meta’s Ad Ranking Driving Advertiser Performance. Facebook.com. Retrieved May 23, 2025, from https://www.facebook.com/business/news/ai-innovation-in-metas-ads-ranking-driving-advertiser-performance
2. (December 2, 2024). Meta Andromeda: Supercharging Advantage+ automation with the next-gen personalized ads retrieval engine. Engineering.Facebook.com. Retrieved May 23, 2025, from https://engineering.fb.com/2024/12/02/production-engineering/meta-andromeda-advantage-automation-next-gen-personalized-ads-retrieval-engine/ 
3. Deep Neural Network. Science Direct. Retrieved May 23, 2025, from https://www.sciencedirect.com/topics/computer-science/deep-neural-network 
4. What is Hierarchical Indexing? Integrate.io. Retrieved May 23, 2025, from https://www.integrate.io/glossary/what-is-hierarchical-indexing/ 
5. About Automated Rules in Meta Ads Manager. Facebook.com. Retrieved May 23, 2025, from https://www.facebook.com/business/help/1694779440789213?id=295312871325091 
6. Gluzman, I. (September 28, 2023). Ad Fatigue and Creative Diversity: Navigating the Digital Advertising Landscape. ThingOrTwo.com. Retrieved May 23, 2025, from https://thingortwo.com/2023/09/28/ad-fatigue-and-creative-diversity/ 
7. What is Modular Content & Why it’s Necessary Today. Aprimo.com. Retrieved May 23, 2025, from https://www.aprimo.com/what-is-modular-content-amp-why-its-necessary-today 
8. Ehlers, K. (November 15, 2023). Optimizing Signal Marketing In Meta Ads. Forbes. Retrieved May 23, 2025, from https://www.forbes.com/councils/forbesagencycouncil/2023/11/15/optimizing-signal-marketing-in-meta-ads/