After the Andromeda update, Meta reduced the importance of manual targeting, but many advertisers haven’t adjusted their approach. That disconnect is where performance drops, budgets burn, and teams keep optimising the wrong levers.
In this blog, we’ll explain why content became the targeting layer and how to build campaigns that stop burning budget on outdated controls.
Contents
What is Meta Andromeda?
Meta Andromeda is Meta’s modern ad delivery system built on machine learning. It decides which ad to show to each user by analysing creative signals and behaviour data, instead of relying on advertiser-defined audiences or manual targeting rules.
The Old Way vs The New Way of Meta Ad Targeting
| Before Andromeda | After Andromeda |
| You told Meta who to target, and then the algorithm found those audiences and showed your ad | You show Meta what your ad is about, then the algorithm finds people who match the intent, problem, and relevance embedded in your creative |
Why Meta Replaced Manual Targeting With Automation
Scale sits at the core of Meta’s revenue model, which makes automation unavoidable. To support that scale, the platform has to move away from manual inputs that slow delivery and toward machine learning systems that can process and act on data far beyond human capacity. Models trained on billions of interactions are more effective at identifying relevant users than interest stacks, as long as they receive clear creative signals. That shift explains why manual targeting has lost priority. The platform now relies on content-rich inputs rather than audience controls to drive delivery.
This is where content-led targeting takes shape, at the level of how the algorithm interprets creative.
How Meta Uses Content-Led Targeting to Decide Ad Delivery
Meta no longer thinks of your ad as a message that needs an audience attached to it. It thinks of your ad as data. This means, every creative asset you publish signals the system what the:
- What the ad is about
- Who the ad is relevant to
- When the ad should be shown
At a technical level, the platform reads your ads as structured input. It starts with visual analysis, breaking images and videos down into interface screens, workflow steps, cursor movement, layouts, and on-screen text. That context then feeds into language analysis, where headlines, primary facebook ad copy, and CTAs are evaluated for intent and meaning. From this, the system identifies friction points, promised outcomes, urgency, and specific use cases. For example, a line like “Reduce manual CRM updates by 40% in two weeks” gives the algorithm far clearer direction than a broad promise such as “Simplify your sales process.”
Finally, performance data closes the loop. Engagement patterns show which users respond to which combinations of visuals and copy, allowing the system to learn over time that certain creatives consistently resonate with people who display similar behaviours across the platform.
This is how content-led targeting works without manual audience inputs.
Below are two clear examples of how this plays out:
- A skincare ad that focuses on dry skin relief reaches people who watch skincare videos, search hydration solutions, and interact with similar content. No interest targeting required.
- A SaaS demo that shows slow CRM workflows reaches users who engage with productivity tools and competitor content. No software audience selection required.
Okay, so creative is the new targeting. Now, what does that mean for how you actually run campaigns?
The Three Vs of Content-Led Targeting in Meta Ads
Once creative becomes the targeting mechanism, the goal is no longer to find the right audience combination. The goal is to give the system enough high-quality creative inputs to learn who responds to what.
This approach is built around three core inputs:
- Volume: One or two ads are not enough for the system to learn. It needs multiple creative inputs to identify patterns and make better delivery decisions. That’s why, five to ten creative variations consistently outperform a small set of ads with tightly defined targeting.
- Variety: Different angles attract different user profiles. For example, a problem-focused message connects with people who are already aware of the pain, a benefit-focused message appeals to users who care most about outcomes, and a testimonial resonates with those looking for proof. Together, these angles act as audience discovery signals rather than copy experiments. And no, you don’t need separate ad sets for each group – the system separates and delivers based on how users respond.
- Velocity: Creative performance declines once the system has extracted all usable signals from an asset. At that point, learning slows and delivery weakens. This is fatigue. For most accounts, fresh creatives are needed every seven to fourteen days to keep signals active. That makes planning content in advance more important than refining targeting options. In this model, a creative calendar replaces an audience map.
Here’s how this approach translates into actual campaign execution.
First, use broad or Advantage+ audiences to remove unnecessary constraints. From there, run a single campaign with five to ten creative variations so the system has enough inputs to learn. Let delivery optimise based on engagement and conversion signals instead of manual controls. As results come in, replace weaker assets with new angles every one to two weeks. And most importantly, don’t forget to track performance at the creative level, where CTR and CPA by ad matter more than audience breakdowns
The Takeaway
Meta’s ad system has changed, which means campaign strategy has to change with it. Andromeda now reads creative as data (visuals, copy, hooks, and CTAs) and uses those signals to decide who should see an ad. The issue is that many brands are still optimizing the wrong levers. They keep refining audiences while the algorithm is evaluating the creative. That mismatch is where performance and ROAS drops. Content has become the targeting layer, and the sooner campaigns are built around that reality, the sooner results begin to stabilize and scale. At Brandshark, a digital marketing agency in Bangalore, we’ve helped SaaS companies, D2C brands, and B2B service businesses build creative systems that match how Meta actually works today. If you want to apply this approach to your Meta campaigns, contact our team today.
Frequently Asked Questions
1. What changed in Meta ads after the Andromeda update?
Meta no longer prioritises targeting settings. Your ad creative now tells the algorithm who should see the ad.
2. Why is content more important than targeting in Meta ads now?
Content matters more because Meta’s algorithm reads creatives as data and uses them to decide who should see your ads.
3. How many creative variations should I run in one Meta campaign?
Run 5 to 10 creative variations in one campaign so Meta has enough signals to learn and optimise delivery.
4. What metrics should I track if audiences no longer matter?
Track creative-level metrics like CTR, CPA, conversion rate, and engagement by ad.
