Not sure what we’re talking about? We’ve got you: keep reading for our breakdown of what the learning phase is, why it matters, and best practices for navigating it, including when and how often to make changes.
What Is the Meta Learning Phase?
When you launch a new campaign, ad set, or make a significant edit, Meta’s delivery system enters what’s called the learning phase. During this time, the algorithm is experimenting: testing different audience pockets, placements, and creative combinations, to figure out how to best deliver your ads to achieve the lowest cost per optimization event (e.g., purchase, lead, or landing page view).
Meta generally requires around 50 optimization events per ad set per week to exit the learning phase. Until that threshold is reached, performance tends to be more volatile, and results aren’t always indicative of long-term efficiency.
Why the Learning Phase Matters
- Performance volatility: Costs may spike or dip as the system learns. This is normal, but it’s why results in the first few days shouldn’t be judged too harshly.
- Algorithm stability: Exiting the learning phase means the delivery system has enough data to optimize efficiently, usually leading to more stable CPAs or ROAS.
- Scalability: If your campaigns never exit learning due to frequent changes or low conversion volume, scaling becomes much harder.
Best Practices for Managing the Learning Phase
Group Optimizations Simultaneously
When making edits, it’s tempting to tweak one setting today and another tomorrow. But every significant edit (changing budgets by more than ~20%, swapping out creatives, altering audiences) resets the learning phase. To minimize disruption, group your planned changes and implement them all at once. This way, the algorithm only has to re-learn once instead of multiple times in quick succession.
Allow Enough Time Before Making Large Changes
After launching a new campaign or ad set, resist the urge to optimize too quickly. Some good rules of thumb to follow:
Wait at least 3–5 days (depending on spend and volume) before making major edits:
- If you’re running high-budget, high-volume campaigns (e.g., $500/day+ optimizing for purchases), you may see 50+ conversion events within just a few days. In this case, you can confidently evaluate and adjust after 3 days.
- For mid-budget campaigns ($100–$300/day), it often takes closer to 5–7 days to build enough conversion volume to make data-backed decisions.
- For low-budget or niche campaigns (under $100/day or very specific audiences), you may need to wait 7–10 days before judging performance, since conversions trickle in more slowly.
Assess whether the ad set is trending toward 50 conversion events per week. If not, consider whether you need a larger budget, broader targeting, or an easier optimization event (e.g., “Add to Cart” instead of “Purchase”) to build volume.
Patience pays off here. Early changes often prevent the algorithm from gathering the data it needs to optimize effectively.
Higher Budgets Help Exit Learning Faster
Because exiting learning requires ~50 optimization events per week, budget size directly impacts how quickly you get there. A higher budget allows your campaign to generate more impressions and optimization events in a shorter time, giving Meta’s system the data it needs to stabilize.
For example:
- An ad set with a $20/day budget optimizing for purchases may take weeks to hit 50 conversions.
- The same ad set at $200/day could exit learning within days, provided the audience and creative are strong enough to drive volume.
This doesn’t mean you should always overspend upfront, but when testing new campaigns or scaling proven ones, allocating a higher budget early can help the algorithm find efficiency faster and save you from weeks of unstable results.
Scale Gradually
If performance looks strong and you’re ready to scale, avoid dramatic budget jumps that can throw the campaign back into instability. A safe approach is to increase budgets by no more than 20–30% every few days. For more aggressive scaling, consider duplicating your ad set instead of heavily editing the existing one.
Use the Learning Limited Indicator as a Guide
In Ads Manager, you’ll sometimes see the “Learning Limited” warning. This means the ad set isn’t getting enough optimization events to exit learning. While this isn’t always a dealbreaker, it’s a signal that you may need to:
- Consolidate ad sets (to pool conversions together).
- Expand your audience size.
- Simplify your account structure so budgets aren’t spread too thin.
The learning phase isn’t just a technicality in Meta’s delivery system: it’s a window where your decisions can either accelerate performance or stall optimization. By grouping edits, waiting the right amount of time before making changes, leveraging higher budgets for faster learnings, scaling carefully, and watching for “Learning Limited,” you give your campaigns the best chance to stabilize and perform efficiently.
In short: trust the process, let the system learn, and make thoughtful adjustments.