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The landscape of digital advertising is changing faster than ever, and Meta’s Andromeda update marks a major turning point. Fashion brands—especially DTC and eCommerce-led labels—are entering an era where traditional audience targeting is no longer the core engine of performance. Instead, Meta has moved toward a powerful AI-driven system that decides the best ad for every user in real time.
In 2025, this shift is especially critical because fashion shoppers behave differently from audiences in other industries. They browse visually, compare styles, jump between categories, and expect highly personalized recommendations. The Meta advertising update 2025 is built exactly to solve these challenges, making ads more relevant by understanding user intent at a much deeper level.
In the past, performance on Meta relied heavily on interest targeting, lookalike audiences, and manual campaign segmentation. But Andromeda changes this entirely. The new approach focuses on AI-led ad retrieval, where Meta’s systems automatically determine which ad variation, SKU, or creative should be shown—regardless of what audience the advertiser selected.
For fashion brands with large catalogs and fast-moving trends, this update can dramatically improve product discovery, creative efficiency, and buyer relevance.
To understand the impact of Andromeda, imagine Meta’s ad system as a huge marketplace with millions of advertisers competing to show ads to billions of users. Previously, the system ranked the ads you entered into the auction, and then decided which ones to serve.
The Meta Andromeda retrieval engine transforms this system by adding a new step before ranking itself: retrieval.
Instead of only ranking the ads you choose, Meta now searches through a much wider pool of ad assets—your creatives, catalog items, dynamic variations—and retrieves the most relevant one based on user behavior and predicted intent.
It works like a search engine inside Meta’s ad system. Before, the ads you provided were the only options. Now, the system can analyze all eligible ads and pick the best match for each individual person.
Andromeda interacts closely with Meta’s machine learning models, integrating into:
This integration makes the system smarter with time. The more signals it receives, the better it becomes at connecting shoppers to the right product.
Old Meta system:
You choose audiences → Meta ranks your ads → delivery happens.
New Andromeda system:
Meta chooses the best ad → ranks it → personalizes delivery.
This shift allows Meta to tap into deeper behavioural signals instead of relying only on declared interests or static audience buckets. Andromeda uses learning systems that can interpret signals far beyond what humans or manual targeting can achieve.
For example, a user who viewed a “black boots” video may be more likely to buy a “leather jacket” than another pair of boots—AI recognizes these nuanced correlations automatically.
This is why the Meta AI ads retrieval engine is a breakthrough for fashion commerce.
Personalization on Meta has always been strong, but the Andromeda system takes it several levels deeper.
Instead of simply showing ads based on your chosen interests, Meta now reads a much wider spectrum of signals:
This creates a dynamic user profile that changes daily, reflecting what people are currently interested in—not what they liked months ago.
Andromeda enhances Meta’s understanding of real intent. For example, if a user repeatedly watches haul videos or engages with fashion creators, the system identifies them as high-value prospects. If they check out multiple items from a specific category—like women’s tops, activewear, or streetwear—it automatically starts retrieving ads from those segments.
No manual targeting is needed.
No interest audience selection is needed.
No lookalike models are needed.
The system becomes capable of understanding user behaviour far more deeply than advertisers can.
Fashion is unique because buyers behave in highly visual and emotional ways. Creative cues like fabric movement, styling, model type, and aesthetic all influence interest. Andromeda now reads these micro-signals and decides which creative or product variant to show based on what the user is likely to respond to.
This is a major advantage for:
With AI-driven fashion ad targeting, Meta can now match each shopper to the right style at the right moment.
The Andromeda update isn’t just a technical upgrade—it has very real implications for fashion brand performance.
Because ads are more relevant, click-through rates rise. Instead of serving broad fashion ads to general audiences, Meta now surfaces highly specific product creatives to users who have shown matching intent. This increases the likelihood of clicks and improves conversion quality.
For example, if a user engaged with “streetwear hoodies,” Meta may prioritize hoodie videos, oversized fits, or campaigns featuring casual wear. The system becomes context-aware.
One of the biggest challenges for fashion brands is making sure the right product reaches the right person. For brands with thousands of SKUs, Andromeda simplifies discovery by automatically retrieving the best SKU-level ad.
It can understand correlations between:
This reduces wasted impressions and accelerates catalog turnover.
Manual targeting often breaks at scale because interest segments saturate. Andromeda, by contrast, scales performance based on dynamic user behaviour, not static audience buckets. This means higher budgets can be deployed without the typical ROAS decline.
This leads directly to Meta ad relevance improvements, which can significantly impact a brand’s overall growth velocity.
One of the biggest shifts in Meta’s ad ecosystem is how Andromeda works hand-in-hand with Advantage+. Meta has made it clear: the platform is moving toward fully automated ad systems, where the AI handles most of the decisions that media buyers used to make.
Advantage+ Shopping Campaigns (ASC) automatically manage:
With the Andromeda retrieval engine powering the front end of the system, ASC becomes even more accurate. It’s now capable of retrieving the right ad for the right user from a much larger pool of creatives and catalog items.
Fashion brands—especially DTC labels—are seeing stronger performance with ASC because the system can quickly learn patterns such as:
Meta is intentionally pushing advertisers toward this automation-first model because AI performs better when it has full control over variables that were previously assigned manually.
DTC apparel brands should consider Advantage+ as their foundation when:
Manual structures still serve a purpose, but they should now complement—not replace—automation.
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With Andromeda and Advantage+ handling targeting and delivery, creative becomes the main growth lever for fashion brands. Meta’s system is now heavily creative-driven, meaning the algorithm evaluates:
Before Andromeda, advertisers spent more time choosing audiences. Today, the quality and relevance of creative determines audience reach. Meta tests your creatives across many micro-audiences to understand who responds best, and then scales the ones that show strong signals.
Fashion brands should prioritize:
Product-led videos
Short, clear product showcases help the algorithm understand what is being sold and match viewers with the right SKU category.
Lifestyle UGC
UGC is powerful because it mirrors real customer behaviour. Try-ons, reviews, outfit-of-the-day content, and POV shots perform extremely well post-Andromeda.
Collection-based storytelling
Carousels, collection ads, and multi-product videos give the AI more points of reference. This helps Meta retrieve the most relevant SKU for each user.
Creative variation is now essential. Meta’s system thrives when brands upload multiple versions of:
In the Andromeda era, creative performance for fashion brands is the new targeting.
To thrive under the new retrieval-first ad system, fashion brands need to adjust their strategies. Complexity used to be the norm—multiple audiences, split budgets, layered lookalikes. That era is over.
These brands perform best when they:
DTC brands typically benefit the most from Andromeda because they have large catalogs and frequent new arrivals, giving Meta plenty of retrieval signals.
Luxury brands should focus more on:
Luxury customers care more about aesthetic storytelling, so the creative needs to reflect that while still being performance-friendly.
The best fashion brand Meta ads strategy today includes:
Manual audience targeting is now a supportive tool, not the foundation.
Scaling Meta ads used to be complicated because performance would decline once budgets increased. With Andromeda, scaling becomes more stable—as long as your underlying signals are clean.
Meta’s retrieval engine depends heavily on accurate purchase and add-to-cart signals. Any gaps or incorrect event setups will directly weaken performance.
Brands should ensure:
The system grows faster when it sees new creative formats and styles. Creative fatigue sets in quickly for fashion brands; refreshing content regularly keeps discovery high.
Fashion eCommerce relies heavily on catalog accuracy. Meta performs best when product feeds include:
Improving catalog quality is one of the fastest ways to unlock fashion DTC Meta ads optimization and stronger scaling.
The rise of Andromeda has increased the importance of data-driven, creative-focused performance partners. Agencies can no longer rely on audience manipulation—they need deep expertise in:
This is where agencies like Veicolo stand out. Modern performance marketing is no longer “media buying.” It’s a combination of:
Veicolo’s approach to fashion performance marketing aligns perfectly with the Andromeda era, helping brands grow through a blend of creative intelligence and AI-friendly ad setups.
This shift transforms the role of agencies from tactical executors into strategic growth partners.
The biggest mindset shift for fashion brands is understanding that AI is the new targeting. Andromeda rewards brands that give the system:
The brands that embrace this new way of advertising will outperform competitors for the next 3–5 years. The ones who resist it will see rising CPMs, unstable performance, and limited scale.
Meta’s Andromeda retrieval engine is not just an update — it’s the future of fashion eCommerce advertising.
Andromeda improves fashion eCommerce performance by delivering more relevant product ads to shoppers based on real-time behavior. It enhances CTR, strengthens conversion quality, and improves catalog discovery, especially for brands with wide SKU assortments.
Audience targeting exists, but it’s far less important than before. Meta now relies primarily on dynamic behavioural signals and AI-driven retrieval, making broad targeting more effective than narrow interest groups.
Yes. Advantage+ is now one of the strongest tools for scaling, as it is designed to work directly with the retrieval engine. It helps automate distribution, reduce manual errors, and improve personalization.
Product videos, lifestyle UGC, try-on clips, model-fit videos, and collection-based formats perform exceptionally well. These give Meta strong signals to match users with the right product category.
Start with fixing data tracking, improving catalog cleanliness, increasing creative volume, and simplifying campaigns. These steps give Andromeda the strongest foundation to optimize performance.
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