Andromeda Update and Its Marketing Impact

If your usual tactics like splitting audiences into dozens of tiny segments or making near-identical creative tweaks no longer deliver clear results, there’s a reason. Meta rebuilt the ad retrieval layer that decides which ads are shown, and the system now rewards distinct creative concepts and clean conversion signals.

You can adapt by testing clearly different creative concepts with unique messages, formats, and value propositions. Simplify delivery so budgets focus on meaningful tests with fewer ad sets, one learning objective per test, and larger budgets per test. 

Strengthen event signals by using server-side or enhanced conversions, deduping events, and consistent conversion windows, so creative performance ties directly to real outcomes.

This blog will break down how Andromeda’s new ad retrieval impacts creative testing and campaign setup, offer actionable tips to boost signal quality, and provide a concise checklist to ready your creatives and tracking for more predictable, scalable results.

What Is Andromeda in Meta Ads?

Andromeda is Meta’s new ad retrieval system. It uses modern machine learning to pick which ad variation to show each person. Old systems used hand-made rules, tight audience splits, and step-by-step filters to decide which ad to show. Andromeda replaces much of that with learned signals and an indexing layer that finds better matches between creative and people at scale. This means the system quickly searches many creative options and ranks them by likelihood of success rather than applying fixed audience rules.

So the change isn’t just technical. It affects how performance behaves in real campaigns, which is why recent results and rollout timing matter.

Why This Matters in 2025

Meta began public rollouts in late 2024 and expanded Andromeda across 2025. Meta’s engineering team reported about a 6% lift in retrieval recall and around an 8% improvement in ad quality across selected segments.

This boost strengthened the performance of Advantage+ automation and Meta’s creative GenAI tools, helping advertisers see more accurate matches between creative and audience intent.

Note: Treat the 8% figure as an observed performance uplift from Meta’s own tests rather than a universal benchmark, but it signals that creative variety and alignment with automation now directly influence campaign success.

To understand that impact clearly, it helps to look at how the system now picks ads in real time.

How Ads Are Chosen in Andromeda

The system now favors many creative options and matches them to subgroups of people rather than relying on dozens of narrowly defined ad sets. It works more like a learning engine that looks across all available creatives and finds the right one for each person in real time.

When someone opens a Meta surface, the system pulls from a pool of available creatives. It runs them through a personalized ad retrieval layer, powered by Meta’s MTIA chips and NVIDIA’s Grace Hopper GPU platform. These models compare learned signals from people’s interactions, predict which ads are most relevant, and return the top ad candidates almost instantly.

Example: 

In practice, this means you can now run one campaign with one broad ad block and feed the system around 8–12 creative variations. The model automatically learns which version connects best with each type of person, often outperforming setups that rely on many micro-targeted ad sets.

This shift introduces new patterns in how campaigns work. The points below break down the main changes and what they mean in practice.

Key Features of the Meta Andromeda Update

If you’ve been adjusting to Meta’s recent changes, this update reshapes how your ads are built, tested, and delivered. Here are the key features that define the Andromeda Update and what they mean for your campaigns:

1. Smarter Ad Retrieval

Think of Andromeda as a smarter selector that scans many versions of your creatives and chooses which might fit a given person. It does this before the later ranking and auction steps. That means the match between an individual and a specific creative happens earlier and more often. 

Result: The system rewards clear, distinct creative options because it can test them against different people in more combinations than older systems could. 

How does this affect you in practice:

• If your ads are near-duplicates, Andromeda is less likely to treat them as useful options.

• If you give it distinct concepts, formats, and messages, the engine has a better chance of finding a good match for each person.

2. Creative-First Optimization

Andromeda shifts the main lever from tiny audience tweaks to the creative library you provide. That means your creative choices act like the new targeting. To take advantage:

What to prioritize:

• Build meaningful variation, not small edits. Offer different visual styles, different opening hooks, different customer pain points, and different calls to action.

• Include multiple formats: short video (10–15s), medium video (30s), longer demo (45–60s), static images, and UGC-style clips. Each format helps the engine test context and attention spans.

Aim to provide 6–12 distinct creative concepts per campaign block, so Andromeda offers real variety to match different people. Industry testing shows the old “limit of 6” is no longer a hard rule; some advertisers run many more (even dozens) with good results, but focus on quality over just quantity.

Examples of distinct hooks:

• Visual change: product-in-use video vs. product-on-white-image.

• Different pain points: “save time” vs. “save money” vs. “look professional.”

• Alternate CTA: “Try free” vs. “Get a demo” vs. “Read reviews.”

Each of these is a different signal for the engine to test with different people.

Focus your work on creating distinct ad concepts rather than minor A/B text tweaks. The engine rewards distinct ideas that fit different people. 

3. Simplified Campaign Structure

Andromeda performs best when you give it broad room to work. That means fewer, more open campaign blocks with many ad variations.

Recommended structure (example):

• One campaign per objective (for example: installs, purchases).

• Inside each campaign: a single broad ad block (audience-wide) with Advantage+ placements turned on.

• Inside that ad block: many creative variations and a small set of sensible exclusions (for existing customers or excluded countries).

Why does this help the system learn faster?

• Broad ad blocks let Andromeda test more creatives against a wider set of people.

• Putting variety at the ad level (not by splitting audiences across many ad blocks) concentrates budget and speeds up learning. 

Less structure at the ad-block level, more variety inside that block. This helps the engine test real options and spend the budget where the data shows real promise.

4. Data-Driven Improvement

Good conversion signals still matter a lot. Andromeda can match creatives to people more often, but it needs reliable event data to learn what actually drives value.

The engine uses conversions and events to learn which creative-person matches produce real outcomes. Missing or messy events slow down that learning and hide what works.

Simple data hygiene steps you can apply:

• Map events clearly: name the key events you care about (install, purchase, subscription) and map them consistently across platforms.

• Use server-side events (Conversions API) alongside the pixel. This fills gaps when browser-based signals fail, helping preserve attribution.

• Deduplicate and match event IDs: ensure server events and pixel hits are tied to the same IDs so Meta can combine them correctly.

• Keep naming and parameter rules consistent so the learning system reads signals consistently from campaign to campaign.

Make the data you send clear and reliable. Cleaner inputs speed up learning and raise the chance that the engine finds the right creative for the right person. 

Andromeda makes your creative library the most important lever. Give it different, meaningful ad options and tidy, reliable event data. Then simplify the campaign structure so the system can test and learn quickly.

Once these pieces are in place, the most significant factor is how clearly your creative library signals different ideas to the system.

Why It Matters for Creative Performance and Testing

The engine performs better when you give it a set of distinct, meaningful creative choices and clear, reliable signals that show which creative actually led to a conversion. If creatives are too similar or your conversion events are noisy, the system cannot learn which messages work for which people.

Practical Checklist You Can Apply Right Now

Follow these steps in the order shown. They are practical, low-friction actions that create cleaner signals and better tests:

• Make clearly distinct creatives: Produce versions that tell distinct short stories or show different use cases. For example: one ad shows a quick problem→solution, another uses a real user voice (UGC-style), and another highlights a key feature. The engine rewards real variety over tiny edits.

• Keep the landing flow the same for test variants: Use the same page or the same funnel steps for creatives you are testing. If you change both the creative and the landing flow at once, you won’t know which change caused the result. Clean conversion paths give the system better signals to learn from.

• Tag and track each creative with a distinct ID: Add a creative ID parameter to your tracking and analytics. That lets you tie conversions back to the precise creative shown and compare performance without guesswork. Good tagging speeds up valuable insights.

• Give new candidates enough time, then retire true losers: Avoid killing a new idea too early. Let each creative reach a reasonable number of conversions or a set time window before you judge it. After that, stop creatives that clearly underperform and replace them with fresh, distinct candidates.

Note: If you want one platform that automatically tags creative elements across images, video, and playables and links those tags back to campaign metrics (IPM, CTR, ROAS), Segwise does that using multimodal AI. It also monitors creative fatigue and issues alerts so you can refresh or pause ads before performance slides, connects with MMPs and ad networks to bring tag-level data into your reporting, and offers a free Meta ad tracker that shows up to five competitors on a 7-day rolling dashboard for quick competitor signals.

Once your creative inputs and conversion signals are clear, the engine has what it needs to optimize effectively. 

With those basics handled, you can see how the update changes everyday campaign behavior. These are the most direct effects you’ll notice.

Also Read: Creative Testing Strategies for Mobile UA Campaigns in 2025

5 Major Impacts of Meta’s Andromeda Update on Ad Performance

Meta’s Andromeda Update reshaped how ads are chosen and shown. If you run paid user growth and creative tests, this change affects your day-to-day work. The tips below explain five clear impacts and what you can do next.

1. Creative Diversity Now Drives Reach and Cost

When you upload a range of distinct ads with different headlines, hooks, lengths, and thumbnails, the engine can match a specific creative to a specific person. That matching reduces costs by letting the system pick the best fit rather than showing the same creative to everyone. Variety improves how often the right person sees the right message, and that usually cuts your cost per outcome.

2. Simpler Campaign Setups Reduce Budget Cannibalization

Fewer ad blocks, with more creative options, help the delivery system spend money in one place rather than fighting itself. If you split the budget across many overlapping campaigns or small audience segments, your ads may compete with each other. 

Consolidating into fewer campaign/ad-set blocks and loading them with multiple creative options reduces that internal competition. Consolidation speeds up learning and helps budget flow to the best-performing creative faster. Try grouping similar goals into a single delivery block and let the system pick which creative wins.

3. Manual Micro-Targeting Is Less Useful

Broad signals plus smart exclusions often beat dozens of tiny segments. The update gives the ad system more power to find likely converters across wide audiences. That makes finely sliced manual segments less effective and slower to learn. Instead of many narrow lists, run broader coverage and exclude groups that should not see the ads (for example, current customers). 

This approach lets the model find pockets of interest you didn’t think to target. Open your targeting up, use exclusions to keep focus, and let the model surface high-value pockets.

4. Data Quality Affects Learning Speed

Clean, consistent event data speeds up stable optimization. The engine learns from the actions you send it. If conversion events are noisy, missing, or inconsistent across partners and tags, the system needs more time and budget to find patterns. Sending clear, matched event signals and keeping conversion definitions stable helps the update learn faster and hold performance as you scale. Audit event tags and simplify conversion rules so the ads learn from clean signals.

5. Creative Supply Matters for Scaling

To scale spend smoothly, you need a large pool of usable creative candidates and regular refreshes. The engine can only test and expand what you give it. If you want to increase the budget without big swings in cost, feed many well-built ad variants across formats (short video, captioned clips, static, carousel). 

Rotate or refresh creatives at a steady cadence so the model always has fresh candidates to choose from. That reduces fatigue and keeps performance more stable as spend rises. Keep the creative pipeline full and rotate thoughtfully so scale doesn’t break performance.

Make these five shifts, then watch which creatives the system favors and scale around those winners.

Even with the gains in automation, a few habits can hold performance back. Here are common patterns to watch for and how to correct them.

Common Mistakes to Avoid with the Andromeda Update

The new system rewards clear, different creative signals and stable learning data. Below are three common traps and short fixes you can apply right away:

1. Relying on Tiny Creative Tweaks Instead of Genuinely Different Ideas:

If you only change small details like a color, a line of copy, or a slightly altered thumbnail, the retrieval engine often treats them as the same creative signal. You need clearly different story angles, formats, or hooks so the system can match the right creative to the right person. 

Practical fix: create at least a few assets that differ in format (short demo, testimonial, UGC-style, and a benefit-led clip) and test them together.

2. Spreading Budget Across Many Tiny Ad Blocks That Fight Each Other:

When you split the budget into lots of narrow ad groups, each block gets too little data, and the system can’t learn which creative truly works. Consolidating the budget across many creatives into a single, wider setup provides clearer signals and faster learning. 

Practical fix: run a broader campaign structure with many distinct creatives under one budget to let the engine test and allocate efficiently.

3. Changing Landing Pages and Creatives at the Same Time, Which Confuses Learning: 

If you swap both destination and ad at once, you won’t know which change moved the needle. The model can’t separate the creative signal from the conversion signal. Keep creative updates and landing-page tests on different cadences so each change can be measured. 

Practical fix: freeze landing pages while you rotate a set of distinct creatives for one testing window, then run a controlled landing-page test after you’ve identified the best-performing creatives. 

Also Read: Implement and Optimize Facebook Ad Tools for 2025

Conclusion

Andromeda makes creative variety and clean event signals the main levers for better delivery. Give the ad system distinct concepts across formats, run broader campaign blocks to focus budget and learning on the best-performing creatives, and keep landing paths stable.

At the same time, you test to see which creative actually drove the outcome. Tagging creatives so each asset can be tied back to conversions, letting new ideas run long enough to collect real signal, and refreshing assets before performance slides are the practical steps that will keep costs lower and scaling smoother.

Segwise helps you prepare and refine creative assets so they perform at their best within Andromeda’s retrieval system. It automatically tags visual, audio, and text elements, consolidates data from ad networks and MMPs, and alerts you when creatives show signs of fatigue.

Starting a free trial lets you see how these insights make your campaigns cleaner, faster to optimize, and more likely to succeed under Andromeda’s system.

FAQs

1. What is the Andromeda Update?

Andromeda Update is Meta’s new ad-retrieval system that uses advanced ML and custom hardware to determine which creative variants are eligible to be shown to a person before the auction step.

2. How does the Andromeda Update change campaign setup?

It favors fewer, broader campaign/ad blocks with many distinct creatives (rather than lots of tiny ad sets), because the system tests creative options across large audiences.

3. Does Andromeda make manual micro-targeting obsolete?

Many advertisers report that very narrow audience slices lose value under Andromeda's broader targeting, while smart exclusions tend to find pockets of value more effectively.

4. What data and tracking changes matter after the Andromeda Update?

Clean, consistent conversion events, and using the server-side Conversions API alongside the pixel help the system learn which creative→conversion matches actually work.

5. How many creatives, and in what formats, should I feed Andromeda?

Best practice is to use several distinct concepts and mixed formats (short + medium videos, images, UGC-style), and many teams recommend roughly 6–12 clear, distinct creative ideas per ad block.