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ABO (Ad Set Budget Optimization) allocates fixed budgets to individual ad sets, giving you complete spend control but requiring hands-on management and high creative hit rates to be cost-effective. CBO (Campaign Budget Optimization), also known as Advantage+ campaign budget, lets Meta distribute budget across ad sets based on predicted performance. Many advertisers benefit from CBO with minimum ad set spend modifications. Elite media buyers use both: ABO for controlled testing, then scale winners into CBO campaigns using post ID duplication.
The ABO vs CBO debate has been raging for years, and most of the advice you'll find online misses a critical nuance. Advertisers typically pick one strategy and stick with it, or they flip-flop without a clear framework for when each approach makes sense.
Here's the uncomfortable truth most content glosses over: ABO is an elite operator's strategy. It works beautifully when your creative hit rate is high and you have time for hands-on management. But if your hit rate isn't stellar, your ABO losses compound fast and your scaling campaigns have to cover those losses.
This guide cuts through the noise with a practitioner-driven framework. You'll learn when ABO actually makes sense (hint: it's more specific than "when you want control"), why CBO with minimum spend is the happy medium for most advertisers, and how top media buyers use both strategies together for testing and scaling.
ABO means setting your budget at the ad set level rather than the campaign level. Each ad set gets exactly what you allocate, regardless of performance.
If you create a campaign with five ad sets and give each one $50/day, each ad set spends $50/day. The winner doesn't get more. The loser doesn't get cut. Every ad set gets its full allocation.
How it works in practice:
Naming note: Unlike CBO, which is also known as "Advantage+ campaign budget," ABO is still called Ad Set Budget Optimization in Ads Manager. When you toggle off Advantage+ campaign budget at the campaign level, you're using ABO.
The key characteristic of ABO is forced spend distribution. If you want every creative concept or audience to get equal testing budget, ABO guarantees that happens. CBO cannot make this guarantee.
CBO, also known as Advantage+ campaign budget in Ads Manager, sets your budget at the campaign level. Meta's algorithm decides how to distribute that budget across your ad sets based on predicted performance.
If you create a campaign with five ad sets and a $250/day campaign budget, Meta might spend $150 on Ad Set A, $60 on Ad Set B, $30 on Ad Set C, and almost nothing on D and E. The algorithm chases what it predicts will perform best.
How it works in practice:
The Pareto effect is real. In CBO campaigns, you'll often see 20% of your ad sets getting 80% of the spend. One case study showed a CBO campaign spending $90 on one ad set and $10 on another on Day 1, essentially giving no fair read to the second ad set.
This is by design. CBO optimizes for efficiency by finding what works and doubling down. It's not trying to test everything equally.
| Factor | ABO | CBO (Advantage+ campaign budget) |
|---|---|---|
| Budget Level | Ad Set | Campaign |
| Control | Full manual control | Algorithm-driven |
| Spend Distribution | Equal across ad sets | Performance-based (often 80/20) |
| Management Required | High (hands-on daily) | Lower (check weekly) |
| Best For | Testing with high hit rate | Scaling proven winners |
| Risk Profile | Losses compound if hit rate is low | May starve promising ad sets prematurely |
| Learning Phase | Each ad set learns independently | Can concentrate budget to exit learning faster |
| Scaling | Manual budget increases (risk learning reset) | Can increase campaign budget freely |
The fundamental tradeoff: ABO gives you control at the cost of efficiency. CBO gives you efficiency at the cost of control.
Neither is inherently better. As consultant Levi Steede puts it: "Most advertisers treat ABO vs CBO like picking a favorite sports team. It's not about which one is 'better.' It's about using the right tool for the right situation."
ABO isn't for everyone. It's specifically for advertisers who meet certain criteria. Let's be honest about what those are.
This is the most important factor, and most content ignores it entirely.
Hit rate means the percentage of your ads that work. If you launch 10 ads and 7-8 of them hit your target metrics, you have a high hit rate. If only 2-3 work, you have a low hit rate.
ABO works best when your hit rate is already high. Why? Because ABO spends equally on everything. If you have 10 ad sets and 7 are winners, you're getting efficient spend on 70% of your budget. But if only 2 are winners, you're wasting 80% of your budget on losers that CBO would have cut automatically.
As Levi Steede explains: "ABO lets you push spend directly into specific winners... but if you're not confident in your hit rate yet, use CBO."
The compounding problem: If your hit rate is low, ABO losses add up fast. Every underperforming ad set burns through its full allocation. Your scaling campaigns have to generate enough profit to cover all that testing waste. This is the hidden cost of ABO that most guides never mention.
ABO makes sense when you have confidence in your entire funnel. Your creative process is proven. Your landing pages convert. Your audiences are dialed in.
In this scenario, you know your ads will perform. You just need data on all of them. ABO ensures every concept gets a fair test with equal spend.
Depesh Mandalia notes: "ABOs work well because you can control them far better... If your ABO is performing well, it means you've put the right inputs in - good product, good targeting, good funnel."
Sometimes CBO's optimization works against you. The algorithm might identify an early leader based on CTR or engagement, then starve other ad sets that could have been long-term winners with more data.
If you have 5 creative concepts and need genuine performance data on all 5, ABO is the only way to guarantee it. CBO might spend 90% on one ad set before the others even get a chance.
Barry Hott, a well-known Facebook ads strategist, prefers ABO for creative testing specifically for this reason: "I prefer to do that with manual budgets... The reason I like to control the budget is to get an even read on everything. I'm consciously choosing to perform a little worse [overall] but to have a better understanding of what is working."
ABO requires active management. You need clear rules for:
If you're not checking campaigns daily and making decisions based on fresh data, ABO will waste money. Set-and-forget doesn't work here.
Here's an uncomfortable truth: for the majority of advertisers, CBO is the better default choice. Here's why.
CBO's algorithm makes budget allocation decisions faster than any human can. It processes signals across all your ad sets simultaneously and shifts spend toward predicted winners in real-time.
If you don't have time to monitor campaigns daily, CBO handles optimization automatically. It's the "set and check" approach versus ABO's "set and manage" requirement.
Once you've validated which creatives and audiences work, CBO becomes the efficient choice. You already know what performs. Now you want Meta to allocate a larger budget optimally across your proven winners.
This is where CBO shines. You're not testing. You're maximizing results from known performers.
If you're not confident that most of your ads will work, CBO provides a safety net. The algorithm will quickly cut spend to losers and concentrate budget on anything showing promise.
As Marin Istvanic notes: if your "hit rate... is very low," running many ABO tests would be "very inefficient spend as not many ads would turn out [winners]."
With ABO, increasing an ad set's budget by too much can reset learning phase. Meta recommends keeping budget increases under 20% at a time.
With CBO, you can increase the campaign budget freely. The algorithm distributes the additional spend across ad sets without triggering learning resets. This makes CBO more stable for aggressive scaling.
Here's the approach that works for most advertisers but rarely gets covered in depth. It combines CBO's efficiency with ABO's testing fairness.
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The minimum spend forces every ad set to get initial budget, similar to ABO. But once you remove it, CBO takes over and allocates based on performance data.
Remove the minimum spend after approximately one week.
This is crucial. If you keep forcing spend indefinitely, you're essentially running ABO with extra steps. You're paying for CBO but negating its optimization.
After a week, Meta has enough data to make informed allocation decisions. Removing the minimums lets the algorithm do what it does best: concentrate budget on winners.
You get initial testing fairness (every ad set gets minimum spend to gather data) plus long-term optimization (CBO concentrates budget on proven performers).
It's the best of both worlds for advertisers who:
Meta itself discourages overusing spend limits. Jon Loomer notes: "Some advertisers use ad set spend limits... in fear Meta will overspend on one ad set... but in most cases, this defeats the whole purpose [of CBO]... If you don't trust the algorithm to distribute budget optimally between ad sets, you should probably just use ad set budgets instead."
Use minimums sparingly and temporarily. If you find yourself constantly constraining CBO, you might actually want ABO.
The most sophisticated advertisers don't choose ABO or CBO. They use both strategically in a workflow that maximizes testing clarity and scaling efficiency.
Use ABO campaigns for initial creative testing:
The goal isn't efficiency. The goal is data clarity. You want to know definitively which creatives work before scaling.
Once you've identified winners in ABO testing, move them to CBO campaigns for scaling:
Here's the step-by-step process elite media buyers use:
This workflow separates testing from scaling. ABO handles the messy, expensive work of finding winners. CBO handles the efficient work of maximizing those winners.
When you duplicate an ad normally, you create a new post and lose all engagement. Your winning ad with 500 likes starts at zero in the new campaign.
Post ID duplication preserves that social proof by using the same underlying Facebook post. Tools like Ads Uploader include this in their Duplicator panel, making it easy to scale winners while maintaining accumulated engagement.
Even experienced advertisers make these errors with ABO and CBO. Here's what to watch for.
If most of your ads fail, ABO just spreads losses evenly. You're guaranteeing spend on losers that CBO would have cut.
Solution: Be honest about your hit rate. If it's low, use CBO and focus on improving your creative process. Or use the CBO with minimum spend approach to get testing fairness with less waste.
Setting minimum spends and never removing them defeats CBO's purpose. You're paying for algorithmic optimization but preventing it from working.
Solution: Use minimum spends only for initial testing (about one week), then remove them. If you can't bring yourself to remove them, you probably want ABO.
Randomly switching between ABO and CBO based on gut feeling leads to inconsistent data and wasted budget.
Solution: Define your rules before launching:
Having clear rules removes emotion from decisions.
CBO optimizes delivery. It can't turn losers into winners. If all your creatives are weak, CBO will just pick the least bad one and spend your budget on mediocrity.
Solution: CBO is for scaling good ads, not salvaging bad ones. Focus on creative quality first.
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If you have 10 ad sets in a CBO campaign with a $50 total budget, each ad set gets too little spend to generate meaningful data. Meta recommends 3-5 ad sets for most CBO campaigns.
Solution: Consolidate. Fewer ad sets with more budget each performs better than many ad sets with fragmented budget.
Constantly tweaking budgets, pausing/unpausing ad sets, or editing targeting resets learning and prevents optimization.
Solution: Let campaigns run at least a week before making major changes. The algorithm needs stable conditions to learn.
Pro tip: Name your ad sets with the budget included (e.g., "Broad - Creative A - $50/day") for quick reference.
Budget formula: Campaign daily budget = (Target CPA × 50 conversions) ÷ 7 days. This gives the algorithm enough budget to potentially exit learning phase within a week.
Neither is universally better. CBO is better for scaling proven winners with less management overhead. ABO is better for controlled creative testing when you have a high hit rate. Most advertisers benefit from CBO with minimum spend for the first week, then letting the algorithm optimize. The best approach is using both: ABO for testing, CBO for scaling.
Budget your campaign so each ad set can potentially generate 50 conversions per week. Formula: (Target CPA × 50) ÷ 7 = minimum daily campaign budget. For example, with a $25 CPA target, you need at least $178/week ($25/day) to give the algorithm enough data to exit learning. With multiple ad sets, multiply accordingly.
CBO doesn't inherently spend more. It spends the same campaign budget but allocates it differently. CBO concentrates spend on predicted winners, which can feel like "overspending" on certain ad sets. But the total spend matches your campaign budget. The difference is efficiency of allocation, not total expenditure.
Technically yes, but it's not recommended. Changing budget structure mid-campaign resets learning and can hurt performance. Better approach: create a new CBO campaign and move winning ads (using post ID duplication to preserve social proof) rather than converting an existing ABO campaign.
Meta added "Advantage+ campaign budget" as an alternative name for Campaign Budget Optimization as part of their broader "Advantage+" product naming convention. The functionality is identical. When you see "Advantage+ campaign budget" toggled on at campaign level, that's CBO.
Track your creative testing results over time. If 6-7 out of 10 new ads hit your target metrics, your hit rate is high enough for ABO to be efficient. If only 2-3 out of 10 work, CBO with minimum spend is a better choice. There's no universal threshold, but anything below 50% hit rate makes ABO expensive.
Advantage+ Shopping (now Advantage+ Sales) campaigns use CBO under the hood but add automated audience expansion and creative optimization. They've become popular for e-commerce, with Meta reporting 70% YoY adoption growth. The ABO vs CBO choice doesn't apply to Advantage+ campaigns because budget allocation is fully automated. Many advertisers run Advantage+ alongside traditional ABO/CBO campaigns.
The ABO vs CBO debate isn't about finding the "right" answer. It's about matching your strategy to your situation.
Key takeaways:
The "best" strategy depends on your hit rate, your available time for management, and whether you're testing or scaling. Not on what some guru says works for their clients.
For teams testing creative at scale, bulk upload tools like Ads Uploader eliminate the manual overhead of launching multiple ad variations. Whether you're setting up ABO test campaigns with 50 creative variations or moving winners into CBO scaling campaigns via post ID duplication, automation handles the tedious work so you can focus on strategy.
Chris is the founder of Ads Uploader, helping marketing teams and agencies save hours on Meta Ads automation. After years of watching teams waste time on repetitive ad uploads, he built the tool he wished existed.
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