Every time someone opens Facebook or Instagram, Meta's advertising system faces an extraordinary challenge: selecting which ads to show from a pool of billions of active ad candidates. The system responsible for this initial selection is called Andromeda, and understanding how it works has become essential knowledge for any advertiser serious about Meta Ads performance. Yet despite its critical role in determining whether your ads reach the right people, Andromeda remains one of the least understood aspects of Meta's advertising ecosystem.
This guide explains what Andromeda is, how it has evolved, and most importantly, how you can create campaigns that work with the algorithm rather than against it. Whether you're running Advantage+ campaigns or traditional manual setups, understanding Andromeda fundamentally changes how you approach Meta Ads strategy.
What Is Andromeda and Why Does It Matter?
Andromeda is Meta's ad retrieval system—the first stage in determining which ads appear in your feed. When you scroll through Facebook or Instagram, the platform must decide in milliseconds which ads from millions of active campaigns are most relevant to show you. Andromeda handles this initial filtering, narrowing down the candidate pool from billions of possibilities to a much smaller set that then goes through more detailed ranking.
Think of Andromeda as the first gatekeeper in a two-stage process. Stage one (Andromeda) quickly identifies ads that could be relevant based on various signals. Stage two (the ranking model) then evaluates these candidates more thoroughly to determine final ad placement and pricing. Without an efficient retrieval system like Andromeda, evaluating every possible ad for every impression would be computationally impossible.
For advertisers, this means your ads must first pass through Andromeda's relevance filter before they ever get a chance to compete in the auction. An ad that Andromeda doesn't surface for consideration will never be shown, regardless of how much you bid or how large your budget is. Understanding what signals Andromeda uses to determine relevance is therefore critical for campaign success.
How Andromeda Evolved from Earlier Systems
Meta's ad delivery system has undergone several major evolutionary stages. Early versions relied heavily on explicit targeting parameters set by advertisers—demographics, interests, behaviors, and custom audiences. The algorithm's job was primarily to match ads to users who fit within these defined parameters. This approach worked but had limitations: it required advertisers to know exactly who their customers were and couldn't discover relevant audiences outside defined criteria.
The introduction of machine learning changed this fundamentally. Instead of just matching targeting criteria, the system began predicting which users would engage with or convert from specific ads. This shift enabled features like Lookalike Audiences, where the algorithm finds users similar to your existing customers even when you can't explicitly define what makes them similar.
Key evolutionary milestones
Understanding the progression helps contextualize why Andromeda works the way it does today and where it's headed in the future.
- 2012-2015: Rule-based matching - Ads matched to users based on explicit targeting parameters with limited predictive capability
- 2016-2019: Machine learning integration - Introduction of predictive models that learned from conversion data to find likely converters
- 2020-2022: Multimodal understanding - Systems began processing ad creative content (images, video, text) to understand ad meaning
- 2023-2024: Andromeda deployment - Unified retrieval system processing billions of candidates with deep content understanding
- 2025-2026: Enhanced AI integration - Andromeda now works seamlessly with Advantage+ features, using generative AI signals for matching
The most significant recent change is Andromeda's ability to understand ad content at a semantic level. Rather than just processing metadata and targeting signals, the system now "comprehends" what your ad is about—what product you're selling, what benefits you're highlighting, what emotions your creative evokes. This understanding enables matching that doesn't depend on explicit targeting parameters, which is why broad targeting and Advantage+ campaigns have become increasingly effective.
How Andromeda Processes Ad Ranking
Andromeda's ranking process involves several interconnected components working together in real time. When an ad impression becomes available, the system must evaluate relevance across multiple dimensions simultaneously while maintaining response times measured in milliseconds. Understanding this process helps explain why certain campaign strategies succeed and others fail.
The process begins with what engineers call "candidate generation." Andromeda doesn't evaluate every ad in Meta's system for every impression—that would be computationally impossible. Instead, it uses embedding models to represent both users and ads as mathematical vectors in a high-dimensional space. Ads that are "close" to a user in this space are retrieved as potential candidates.
The three-stage evaluation process
Each ad impression triggers a sophisticated evaluation pipeline that balances speed with accuracy.
- Embedding-based retrieval: Andromeda uses learned embeddings to quickly identify ads that share semantic similarity with the user's interests and behavior patterns
- Lightweight scoring: Retrieved candidates receive initial scores based on predicted engagement and conversion probability using simplified models
- Deep ranking: Top candidates from the lightweight stage go through more sophisticated neural network evaluation for final ranking
This architecture explains why creative quality matters so much. Your ad's embedding— its mathematical representation in Andromeda's vector space—is derived from your creative content, your landing page, your conversion data, and user interactions with your ads. Poor creative generates poor embeddings, which means Andromeda is less likely to retrieve your ad for relevant users.
Key Signals Andromeda Uses for Optimization
Understanding which signals Andromeda prioritizes helps you focus optimization efforts where they matter most. While Meta doesn't publish the exact weights for each signal, extensive testing and Meta's public documentation reveal the categories that have the strongest impact on ad delivery.
Andromeda ranking signal categories
| Signal Category | What It Measures | Advertiser Control Level |
|---|---|---|
| Creative quality | Visual appeal, message clarity, format appropriateness | High - directly controlled |
| Engagement history | Past user interactions with similar ads and content | Medium - influenced by creative |
| Conversion signals | Pixel events, CAPI data, purchase history | High - tracking setup quality |
| Content understanding | Semantic analysis of ad text, images, and video | High - content you create |
| User-ad relevance | Match between ad content and user interests | Medium - product/market fit |
| Account history | Historical performance of your ad account | Medium - built over time |
| Bid and budget | Your willingness to pay for impressions | High - directly set |
| Landing page quality | Page load speed, content relevance, user experience | High - your website |
Notice that most high-impact signals are within your control. Creative quality, conversion tracking accuracy, and landing page experience are all elements you can directly improve. This contrasts with the common misconception that algorithmic success depends on cracking some secret code or manipulating hidden variables.
Impact on Ad Delivery and Performance
Andromeda's design has significant implications for how campaigns perform. The system fundamentally rewards advertisers who focus on quality and relevance over those who try to manipulate delivery through narrow targeting or budget manipulation. Understanding these dynamics helps explain performance patterns many advertisers find confusing.
One major impact is that targeting breadth often outperforms targeting precision. When you define a narrow audience, you're essentially telling Andromeda to ignore potentially valuable candidates. The algorithm's content understanding capabilities mean it can identify relevant users you'd never think to target explicitly. This is why broad targeting strategies and Advantage+ campaigns frequently outperform highly segmented approaches.
How Andromeda affects campaign performance patterns
Several performance patterns become understandable once you recognize Andromeda's role in ad delivery.
- Learning phase volatility: Initial performance swings reflect Andromeda building embeddings and testing retrieval patterns for your new ads
- Creative refresh impact: New creative creates new embeddings, potentially unlocking audience segments Andromeda didn't previously associate with your ads
- Scaling challenges: Rapid budget increases can push Andromeda to retrieve ads for less relevant users faster than it can optimize
- Audience overlap effects: Multiple campaigns targeting similar users create embedding competition within your own account
- Seasonal shifts: User behavior changes (holidays, events) alter engagement patterns Andromeda uses for retrieval
The learning phase deserves particular attention. During this period, Andromeda is actively learning which users respond to your specific ads. The more conversion data it receives, the better it becomes at retrieval. This is why Meta's recommendation of 50+ conversions for stable performance directly relates to Andromeda's learning requirements.
Andromeda and Creative Quality
Perhaps no aspect of Meta Ads has been more transformed by Andromeda than creative strategy. Because the algorithm now deeply understands ad content, creative quality has become the primary lever advertisers have for influencing delivery. This represents a fundamental shift from the targeting-centric approach that dominated Meta advertising for years.
Andromeda processes creative through multiple neural networks that extract meaning from text, analyze visual elements, and even understand video content frame by frame. This processing generates the embeddings that determine which users see your ads. High-quality, clear creative generates embeddings that match well with relevant users. Confusing or low-quality creative generates poor embeddings that make effective retrieval difficult.
Creative elements that influence Andromeda
Focus your creative development on elements that directly impact how Andromeda understands and matches your ads.
- Visual clarity: Clear product shots and uncluttered compositions generate better visual embeddings than busy, confusing images
- Message specificity: Concrete benefits and clear value propositions create stronger text embeddings than vague or generic copy
- Format-message alignment: Using the right format for your message (video for demonstrations, carousel for multiple products) improves content understanding
- Consistency signals: When your ad creative, landing page, and product all align, Andromeda's confidence in relevance predictions increases
- Engagement optimization: Creative that generates genuine engagement provides positive signals that reinforce Andromeda's retrieval decisions
The consistency element is particularly important and often overlooked. If your ad promises one thing but your landing page delivers something different, the resulting poor engagement signals teach Andromeda that retrieving your ad leads to bad user experiences. Over time, this degrades your ad's embedding quality and reduces delivery to relevant users.
How Andromeda Handles Audience Targeting
Traditional audience targeting hasn't disappeared with Andromeda, but its role has fundamentally changed. Rather than being the primary driver of ad delivery, targeting now serves as a constraint or suggestion that Andromeda works within. Understanding this shift is crucial for developing effective targeting strategies in 2026.
When you define a target audience, you're telling Andromeda to only consider users who meet certain criteria. The algorithm still uses its content understanding and embedding matching to find the most relevant users within that constraint. With broad or no targeting, Andromeda has maximum flexibility to apply its matching capabilities across the entire user base.
Targeting approaches and Andromeda interaction
| Targeting Approach | How Andromeda Responds | Best Use Case |
|---|---|---|
| Broad (open targeting) | Maximum algorithmic freedom; relies entirely on content matching | Established products with strong conversion data |
| Interest-based | Constrains candidate pool; Andromeda optimizes within interests | New products or limited conversion history |
| Lookalike audiences | Uses your conversion data as training signal for Andromeda's matching | Scaling proven audiences to new segments |
| Custom audiences | Restricts to known users; useful for retargeting or exclusions | Retargeting, customer win-back, exclusions |
| Advantage+ targeting | Starts with your suggestions but allows algorithmic expansion | Balanced approach with learning guidance |
The trend is unmistakable: broader targeting consistently outperforms narrow targeting for advertisers with sufficient conversion data. This isn't because narrow targeting is inherently bad—it's because Andromeda's content understanding capabilities make it better at finding relevant users than most advertisers are at defining them.
Machine Learning Advances in 2026
Andromeda continues to evolve with significant 2026 updates that impact how advertisers should think about their campaigns. Meta's ongoing investment in AI capabilities means the system becomes more sophisticated each year, generally favoring advertisers who focus on fundamentals over those who rely on tactical tricks.
The most significant 2026 advancement is improved multimodal understanding. Andromeda now processes creative with greater nuance, understanding not just what elements are present but how they work together to convey meaning. A product demonstration video isn't just tagged as "video with product"—the system understands what features are being demonstrated and can match this understanding to users interested in those specific capabilities.
Key 2026 Andromeda enhancements
- Cross-format learning: Performance insights from one ad format now inform predictions for other formats more effectively
- Contextual awareness: Better understanding of when and where users are most receptive to different types of messages
- Privacy-preserving signals: Enhanced ability to optimize without relying on individual user tracking
- Creative-conversion correlation: Deeper understanding of which creative elements drive actual conversions, not just engagement
- Intent prediction improvements: Better forecasting of purchase intent based on behavior patterns
These advances have practical implications. Cross-format learning means testing in one format can benefit your other formats. Privacy-preserving signals indicate that first-party data and conversion tracking become even more valuable as Andromeda relies less on individual user signals. For more on attribution in this evolving landscape, see our Marketing Attribution Guide.
Optimizing for Andromeda's Preferences
While you can't directly control Andromeda, you can create conditions that help the algorithm work in your favor. Optimization for Andromeda isn't about gaming the system—it's about providing the signals and quality inputs the algorithm needs to effectively match your ads with relevant users.
Andromeda optimization checklist
Work through these elements systematically to improve how Andromeda processes and delivers your campaigns.
- Conversion tracking audit: Verify both Pixel and Conversions API are accurately capturing all conversion events
- Creative quality assessment: Ensure creative is clear, professional, and accurately represents your product/offer
- Landing page alignment: Confirm your landing page delivers what your ad promises
- Targeting evaluation: Consider whether narrow targeting is helping or constraining Andromeda
- Data volume check: Verify you're generating enough conversions for stable algorithmic learning
- Account health review: Address any policy violations or quality warnings affecting account standing
- Creative diversity: Provide multiple creative variations so Andromeda can test and optimize
Conversion tracking deserves special emphasis. Andromeda's effectiveness depends heavily on receiving accurate conversion signals. If your tracking undercounts conversions due to poor CAPI implementation or pixel issues, Andromeda learns from incomplete data and makes suboptimal retrieval decisions. Investing in robust tracking infrastructure directly improves algorithmic performance.
Common Misconceptions About the Algorithm
Misinformation about how Meta's algorithm works leads many advertisers to waste time on ineffective tactics or avoid strategies that would actually improve performance. Clearing up these misconceptions helps focus your efforts on what actually matters.
Myths versus reality
Understanding what Andromeda actually does—and doesn't do—prevents costly mistakes in your advertising strategy.
- Myth: Posting times significantly impact ad delivery. Reality: Andromeda optimizes delivery timing automatically based on when users are most likely to engage and convert.
- Myth: Editing ads resets the algorithm. Reality: Minor edits to text or destination URLs typically don't trigger full relearning; significant creative changes may.
- Myth: Higher bids guarantee more delivery. Reality: Andromeda prioritizes relevance; a highly relevant ad with a lower bid often outperforms a poorly matched ad with higher bids.
- Myth: Narrow targeting is always more efficient. Reality: Andromeda's content matching often finds better audiences than human-defined targeting parameters.
- Myth: New accounts are disadvantaged. Reality: Andromeda evaluates ads on their own merits; strong creative and clear conversion signals matter more than account age.
- Myth: Algorithm changes are designed to increase ad costs. Reality: Updates focus on improving user experience and advertiser efficiency; better matching benefits everyone.
The targeting misconception is particularly damaging. Many advertisers spend enormous effort building complex audience segments when they would achieve better results with broad targeting and excellent creative. Andromeda's content understanding capabilities mean the algorithm often knows better than you do who will respond to your ads.
The Future of Meta's AI-Driven Ad Delivery
Andromeda represents Meta's current approach to ad delivery, but the system continues to evolve. Understanding the trajectory helps advertisers prepare for coming changes and position their strategies accordingly. Meta's investments in AI suggest several likely developments that will shape advertising on the platform.
The clearest trend is toward greater automation with less advertiser input. Features like Advantage+ Shopping Campaigns minimize manual configuration while letting the algorithm make more decisions. This trend will likely continue, with creative generation becoming the next frontier—Meta's generative AI tools already offer automatic creative variations, and this capability will expand.
Anticipated developments in Meta's ad systems
- Expanded generative AI integration: AI-generated creative variations that Andromeda can test and optimize automatically
- Predictive campaign planning: Tools that forecast performance based on creative concepts before you spend budget
- Cross-platform optimization: Unified optimization across Meta properties with even more seamless delivery
- Privacy-first personalization: More sophisticated matching that works without individual user tracking
- Outcome-based everything: Shift from impression-based to outcome-based optimization at every level
For advertisers, these trends suggest focusing on strategy and creative rather than technical campaign management. The value you provide will increasingly come from understanding your customers, developing compelling offers, and creating authentic brand experiences—activities the algorithm can't automate. Technical optimization work will continue to be absorbed by Meta's AI systems.
Andromeda has fundamentally changed what it means to succeed with Meta Ads. The advertisers who thrive are those who embrace the algorithmic shift, focusing on creative quality, conversion tracking accuracy, and genuine relevance rather than trying to manipulate a system designed to resist manipulation. Understanding Andromeda doesn't give you a shortcut—it gives you clarity about where to invest your effort for maximum impact.
