Ads Meta

Meta Ads Deep Analysis

Process

1. Collect Meta Ads data (Ads Manager export, Events Manager screenshot, EMQ scores)
2. Read ads-shared/references/meta-audit.md for full 46-check audit
3. Read ads-shared/references/benchmarks.md for Meta-specific benchmarks
4. Read ads-shared/references/scoring-system.md for weighted scoring
5. Evaluate all applicable checks as PASS, WARNING, or FAIL
6. Calculate Meta Ads Health Score (0-100)
7. Generate findings report with action plan

What to Analyze

Pixel / CAPI Health (30% weight)

• Meta Pixel installed and firing on all pages
• Conversions API (CAPI) active (30-40% data loss without it post-iOS 14.5)
• Event deduplication configured (event_id matching, ≥90% dedup rate)
• Event Match Quality (EMQ) ≥8.0 for Purchase event
• All standard events configured (ViewContent, AddToCart, Purchase, Lead)
• Custom conversions created for non-standard events
• Aggregated Event Measurement (AEM) configured for iOS
• Domain verification completed
• Server-side events include customer_information parameters
• Pixel fires with correct currency and value parameters

Creative (30% weight)

• ≥3 creative formats active (image, video, carousel, collection)
• ≥5 creatives per ad set (Meta recommendation)
• Creative fatigue detection: CTR drop >20% over 14 days = FAIL
• Video creative: 15s max for Stories/Reels, 30s max for Feed
• UGC/testimonial creative tested
• Dynamic Creative Optimization (DCO) tested
• Ad copy: headline under 40 chars, primary text under 125 chars
• Creative refresh cadence: every 2-4 weeks for high-spend

Account Structure (20% weight)

• Campaign Budget Optimization (CBO) vs Ad Set Budget (ABO) intentional
• Campaign consolidation: ≤5 active campaigns per objective type
• Learning phase health: 50%)
• Budget per ad set: ≥5x target CPA (minimum for learning phase exit)
• Ad set audience overlap 5)
• Retargeting frequency (7-day): 12)
• Custom Audiences: website visitors, customer lists, engagement
• Lookalike Audiences: multiple seed sizes tested (1%, 3%, 5%)
• Advantage+ Audience tested vs manual targeting
• Interest targeting: broad enough for algorithm optimization
• Exclusions: purchasers excluded from prospecting, overlap managed
• Location targeting reviewed for relevance

Advantage+ Assessment

If Advantage+ features are in use:

• ASC (Shopping Campaigns): catalog connected, existing customer cap set
• Advantage+ Audience: performance vs manual audience compared
• Advantage+ Creative: enhancements enabled (text, brightness, music)
• Advantage+ Placements: enabled (let Meta optimize placement mix)
• Budget allocation: Advantage+ campaigns getting fair test budget

Special Ad Categories

If ads are in restricted categories:

• Special Ad Category declared before campaign creation
• Targeting restrictions verified (no ZIP, age 18-65+ only, no Lookalike)
• Creative compliance with category-specific policies
• Read ads-shared/references/compliance.md for full requirements

EMQ Optimization Guide

| EMQ Score | Status | Action | |-----------|--------|--------| | 8.0-10.0 | Excellent | Maintain current setup | | 6.0-7.9 | Good | Add more customer_information parameters | | 4.0-5.9 | Fair | Implement CAPI, improve data quality | | 50% | | Budget per ad set | ≥5x CPA | 2-5x CPA | 20%)

• Quick Wins sorted by impact
• Advantage+ adoption recommendations

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: citedy
• Source: citedy/adclaw
• License: Apache-2.0
• Homepage: https://pypi.org/project/adclaw/

Install and usage instructions live in the source repository linked above.