How to Structure Your Facebook Ad Account for Apps (2025)

Most app marketers structure their Facebook ad accounts around whatever seems logical in the moment—broad audiences in one campaign, lookalikes in another, maybe a test campaign for new creatives.

This approach works until you try to scale. Then you realize your account structure either helps you move quickly or creates friction at every decision point.

A well-structured account makes it obvious which campaigns to increase budget on, which audiences are tapped out, and where your next growth opportunity lives. Poor structure forces you to dig through reports for answers that should be immediately visible.

Here's how to build an account architecture that supports systematic testing and efficient scaling.

The Core Framework: Testing, Scaling, Retargeting

Your account structure should reflect three distinct operational modes, each with different goals and optimization approaches.

Testing Campaigns (ABO)

These campaigns validate new audiences, creatives, and offers. Use Ad Set Budget Optimization to control spend allocation and ensure each test receives adequate budget.

Structure:

• Campaign 1: Testing - Broad Audiences - [Geo]
• Campaign 2: Testing - Lookalike Audiences - [Geo]
• Campaign 3: Testing - Interest Targeting - [Geo]

Budget 20-30% of your total spend to testing campaigns. Each ad set gets a fixed budget (typically $10-$20/day) so you can measure performance on equal footing.

Run tests for 7-14 days before making decisions. Shorter testing periods don't account for day-of-week variance and lead to false positives.

Scaling Campaigns (CBO)

Once you identify winning audiences from testing, migrate them to CBO campaigns where Facebook automatically allocates budget to the best performers.

Structure:

• Campaign 1: Scaling - Prospecting - [Geo] - CBO
• Campaign 2: Scaling - LAL 1-3% - [Geo] - CBO

These campaigns should contain only validated audiences. If an ad set hasn't proven efficiency in testing, it doesn't belong in your scaling campaigns.

Budget 60-70% of your spend to scaling campaigns. Increase budgets gradually (10-20% every 3-5 days) to avoid shocking the algorithm and triggering re-learning.

Retargeting Campaigns

Separate campaigns for users who installed but haven't converted, or who engaged with your ads but didn't install.

Structure:

• Campaign 1: Retargeting - App Installers - [Optimization Event]
• Campaign 2: Retargeting - Video Views / Engagers

Budget 10-15% of total spend here. These audiences are smaller and warmer, typically delivering lower CPIs and higher conversion rates than prospecting.

Ad Set Organization

Within each campaign, organize ad sets by a single variable so you can isolate what's working.

By Audience Type

Don't mix broad targeting, lookalikes, and interest-based audiences in the same campaign. Each has different performance characteristics and scale potential.

Example - Testing Campaign:

• Ad Set 1: Broad - US - 25-44 - iOS
• Ad Set 2: Broad - US - 25-44 - Android
• Ad Set 3: LAL 1% - Purchasers - US - iOS
• Ad Set 4: LAL 1% - Purchasers - US - Android

This structure makes it immediately clear whether broad or lookalike audiences perform better, and whether iOS or Android delivers more efficiently.

By Geography

When testing multiple geos, separate them into different ad sets or campaigns. Grouping multiple countries together obscures which markets are profitable.

Start with your core market (typically US, UK, or your largest existing user base), prove efficiency, then expand.

By Platform (iOS vs Android)

For most apps, separate iOS and Android into different ad sets or campaigns. The economics often differ significantly:

• iOS users typically have higher LTV but face attribution limitations from SKAdNetwork
• Android delivers clearer attribution but may have lower monetization depending on your app category

Separating platforms makes it easier to set appropriate CPI targets and evaluate true profitability per platform.

Naming Conventions

Consistent naming eliminates confusion and makes bulk editing and reporting significantly faster.

Use this format: [Type][Geo][Audience][Platform][OptEvent]

Campaign Examples:

• TEST_US_Broad_iOS_Installs
• SCALE_US_LAL1-3_iOS_Purchase
• RETARGET_US_Installers_Subscribe

Ad Set Examples:

• Broad_25-44_iOS
• LAL1_Purchasers_iOS
• Interest_Fitness_25-34_Android

Ad Examples:

• Video_15s_V1_Jan2025
• Carousel_FeatureHighlight_V2

This system lets you filter by campaign type, geo, or platform instantly in Ads Manager, and makes performance patterns obvious when reviewing reports.

Avoiding Common Structural Mistakes

Too Many Ad Sets Per Campaign

More than 7-8 ad sets in a single campaign fragments your budget. Each ad set needs enough volume to exit the learning phase (10 conversion events within 7 days). With limited budget spread across too many ad sets, none receive adequate delivery.

Start with 3-5 ad sets. Only add more once existing ad sets are consistently exiting learning.

Mixing Testing and Scaling

Running brand-new, unvalidated ad sets in the same campaign as proven winners creates volatility. CBO may shift budget to the new ad set during its learning phase, destabilizing your profitable delivery.

Keep testing isolated until you have 7-14 days of data confirming an ad set meets your efficiency targets.

No Clear Graduation Path

Define clear criteria for moving ad sets from testing to scaling:

• Exited learning phase
• CPI at or below target for 7+ days
• Minimum 50 installs to validate performance

Without defined criteria, decisions become subjective and you'll either scale too early (wasting budget) or too late (missing growth opportunities).

Campaign Count Recommendations

Your account size should reflect your budget and operational capacity.

$1,000-$5,000/month: 3-5 campaigns

• 2 testing campaigns
• 1 scaling campaign
• 1-2 retargeting campaigns

$5,000-$20,000/month: 6-10 campaigns

• 3-4 testing campaigns (different geos, audiences)
• 2-3 scaling campaigns
• 1-2 retargeting campaigns
• 1 creative testing campaign

$20,000+/month: 10-15 campaigns

• 5-6 testing campaigns (multiple geos, audience types)
• 4-5 scaling campaigns (segmented by performance tier)
• 2-3 retargeting campaigns
• 1-2 creative testing campaigns

More campaigns than this creates management overhead without improving performance. Fewer campaigns limits your ability to test systematically.

Performance Monitoring

Set up custom columns in Ads Manager to track the metrics that matter for your structure:

Testing Campaigns:

• CPI
• Install volume
• Learning phase status
• Days since launch

Scaling Campaigns:

• CPI
• Daily spend
• 7-day ROAS (if tracking revenue events)
• Frequency

Retargeting Campaigns:

• CPI vs prospecting CPI
• Conversion rate (install to in-app event)
• ROAS

Check these daily for scaling campaigns, 2-3x per week for testing campaigns. Avoid checking hourly—day-to-day variance is normal and doesn't require action.

Account Structure Evolution

Your structure should evolve as your account matures.

Month 1-2: Simple structure focused on validating core audiences and establishing baseline CPIs.

Month 3-6: Expand testing campaigns to new geos and audience types. Graduate winning ad sets to dedicated scaling campaigns.

Month 6+: Introduce segmentation by performance tier (high-efficiency scaling, moderate-efficiency testing expansion, retargeting optimization).

Don't start with maximum complexity. Build structure as you prove what works and identify specific needs your current setup doesn't address.

FAQs

Should I use one campaign or multiple campaigns for app installs?

Use multiple campaigns organized by purpose: testing campaigns (ABO) for validation, scaling campaigns (CBO) for growth, and retargeting campaigns for re-engagement. This separation enables clear measurement and prevents testing from disrupting scaled performance.

How many ad sets should I have per campaign?

Start with 3-5 ad sets per campaign. This provides enough variation for Facebook's algorithm to optimize while concentrating budget for faster learning. More than 7-8 ad sets typically fragments budget and extends the learning phase.

Should I separate iOS and Android into different campaigns?

Yes, especially when attribution and economics differ between platforms. iOS campaigns using SKAdNetwork have different measurement capabilities than Android campaigns, and separating them makes optimization and profitability analysis clearer.

When should I create a new campaign vs add ad sets to existing campaigns?

Create new campaigns when testing a fundamentally different strategy (new geo, new optimization event, different budget approach). Add ad sets to existing campaigns when testing variations within a proven strategy (new audience within same geo and optimization event).

How often should I restructure my account?

Review structure quarterly. Make adjustments when you identify clear friction points—difficulty finding data, campaigns that no longer serve a purpose, or new testing needs that don't fit existing campaigns. Avoid restructuring for its own sake.

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Account structure isn't static. Start with clear separation between testing, scaling, and retargeting, then refine based on what your specific growth patterns require.