Ads Meta

Meta Ads Deep Analysis

Andromeda AI Engine

Meta's Andromeda AI engine (Oct 2025) filters ads using 10,000x more complex models. Creative diversity is now the #1 performance lever. Ads with Similarity Score >60% get retrieval suppression. The algorithm clusters near-identical creatives and limits their delivery. 100 minor variations perform no better than 10. Prioritize genuinely distinct concepts, angles, and formats over volume of similar variants.

Process

1. Collect Meta Ads data (Ads Manager export, Events Manager screenshot, EMQ scores)
2. Read ads/references/meta-audit.md for full 50-check audit
3. Read ads/references/benchmarks.md for Meta-specific benchmarks
4. Read ads/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: 1-3 campaigns total recommended
• 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:

• Advantage+ Sales 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/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

Threads Placement

Threads placement GA Jan 2026, 400M+ MAU. Lower CPMs than Feed/Stories. Currently ~0.04% of total spend. Emerging channel. Evaluate:

• Is Threads placement enabled in Advantage+ Placements?
• Monitor CPM and engagement vs other placements
• Early-mover advantage for brands with active Threads presence

Source & license

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

• Author: Maxymize
• Source: Maxymize/maxym-ai-ads
• License: MIT
• Homepage: https://github.com/Maxymize/maxym-ai-ads/blob/main/docs/BLUEPRINT-ZERO-TO-SALES.md

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