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Meta Ads deep analysis covering Facebook, Instagram, and Threads advertising in the Andromeda + GEM + Lattice era. Evaluates 50 checks across Pixel/CAPI health, creative diversity and Entity-ID clustering risk, account structure, ASC/AAC defaults for Sales/Leads/App, and audience targeting. Includes Advantage+ assessment and creative-as-targeting scoring. Use when user says Meta Ads, Facebook Ads, Instagram Ads, Threads ads, Advantage+, ASC, AAC, Andromeda, GEM, Lattice, Entity-ID clustering, creative diversity, Sales optimization, Leads optimization, App optimization, or Meta campaign.

Ads & Marketing

What this skill does


# Meta Ads Deep Analysis

## Andromeda + GEM + Lattice (2026)

Meta's delivery stack was rebuilt across three releases:

- **Andromeda** (Oct 2025) — ad-retrieval ranking model with 10,000× more
  model capacity than the previous funnel ([Meta Engineering, Dec 2024](https://engineering.fb.com/2024/12/02/production-engineering/meta-andromeda-advantage-automation-next-gen-personalized-ads-retrieval-engine/)).
  Filters the candidate creative set before the auction layer ever sees it.
- **GEM** (Generative Embedding Model, late 2025) — replaces the feature
  pipeline. Creative *content* embeds directly into the targeting space, which
  is why "creative is the new targeting" is now mechanical truth not slogan.
- **Lattice** (rolled out late 2025 / early 2026) — sequence-aware optimizer
  on top of GEM that uses user-action sequences to rank candidate ads.

Net effect: creative diversity is now the #1 performance lever. Ads with
Similarity Score >60% (per [Confect's measured threshold](https://confect.io/tactics/meta-andromeda-2026))
get retrieval suppression — the algorithm clusters near-identical creatives
and silently limits their delivery. **100 minor variations perform no better than 10
genuinely distinct ones.** Prioritize concept / angle / format diversity over
variant volume.

### Creative-as-targeting scoring rubric

When auditing a creative library against Andromeda's retrieval logic, score
across these 5 axes (each 0-2, total 0-10):

| Axis | 0 (Risk) | 1 (OK) | 2 (Strong) |
|------|----------|--------|------------|
| Concept diversity | Single core message / value prop across all assets | 2 distinct messages | 3+ distinct angles (problem-led, social proof, comparison, …) |
| Format diversity | One format (e.g. all static image) | 2 formats | 3+ (image, video, carousel, collection) |
| Visual diversity | One palette / one model / one composition | 2 distinct visual treatments | 3+ visually distinct treatments |
| Hook diversity (video) | All hooks ≤3s look alike | 2 hook patterns | 3+ hook patterns (UGC POV, question, claim, demo, …) |
| Headline diversity | All headlines paraphrase the same line | 2 headline structures | 3+ structures (number-led, question, claim, comparison) |

Score 8-10 = LOW Entity-ID clustering risk. Score 4-7 = MEDIUM risk (some
suppression likely). Score 0-3 = HIGH risk (significant retrieval ticket loss).

### Entity-ID Clustering Predictor (pre-launch)

Before launch, predict which creatives Meta will cluster. Cluster-mates
share retrieval tickets — only one wins per impression opportunity.

**Predictor heuristics (apply to every pair of creatives in the launch set):**

1. **Visual fingerprint** — same product hero, same model, same backdrop,
   same lighting → **likely cluster**. Different products or different
   visual identities → likely *not* a cluster.
2. **Headline fingerprint** — same first 4 tokens → likely cluster
   (e.g. "Save 30% on" + "Save 30% off" + "Save 30% — limited time").
3. **Body copy fingerprint** — same opening sentence, same CTA verb → likely
   cluster regardless of middle-body differences.
4. **Video hook fingerprint** — same 0-3s shot, same voiceover pattern →
   likely cluster even if the rest of the video diverges.
5. **Format mismatch wins** — if pair is (static + video) AND visual fingerprint
   differs, they are *not* clustered. Crossing format AND visual is a strong
   diversity signal.

**Output**: produce a `creative-cluster-risk.md` deliverable that groups the
launch set into predicted clusters, recommends which creative in each cluster
should ship and which should be cut or rebuilt, and reports the final pre-
launch diversity score (target ≥8/10).

### MAPI v25 ASC/AAC Deprecation Detector

Meta Marketing API v25 deprecates the explicit Advantage Shopping Campaigns
(ASC) and Advantage App Campaigns (AAC) creation paths — those campaign types
are folded into standard Sales / Leads / App objectives where ASC behavior
becomes the *default* configuration. Detection:

- If the account uses MAPI v23 or earlier: ASC/AAC API endpoints will return
  deprecation warnings before the v25 cutover. Capture and flag them.
- If the account uses MAPI v25+: confirm that previously-ASC campaigns have
  been migrated to the new objective-default model with the equivalent
  catalog + budget + existing-customer cap settings preserved.
- If creating new campaigns: use the Sales / Leads / App objective + ASC
  defaults rather than the legacy ASC/AAC endpoints.

### ASC defaults for Sales / Leads / App (2026 behavior)

When Sales / Leads / App objectives are selected, ASC behaviors are now the
default. Audit confirms:

- **Catalog connection** (Sales): product catalog linked and feed health green
- **Existing customer cap** (Sales): set to 10-25% (default may be too high
  for high-LTV brands)
- **Advantage+ Audience** (all three objectives): on by default; only override
  with manual interest stacks for highly restricted categories
- **Advantage+ Creative** (all three): text / brightness / music enhancements
  on by default; if your brand-safety policy requires off, document the
  exception per ad set

## 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: <30% ad sets in "Learning Limited" (FAIL >50%)
- Budget per ad set: ≥5x target CPA (minimum for learning phase exit)
- Ad set audience overlap <30% (Audience Overlap tool)
- Campaign naming conventions consistent and descriptive
- Advantage+ Sales Campaigns active for e-commerce
- Simplified campaign structure: 1-3 campaigns total (fewer, larger ad sets preferred)

### Audience & Targeting (20% weight)
- Prospecting frequency (7-day): <3.0 (WARNING 3-5, FAIL >5)
- Retargeting frequency (7-day): <8.0 (WARNING 8-12, FAIL >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**: 
Files: 1
Size: 10.9 KB
Complexity: 25/100
Category: Ads & Marketing

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