/ad-lead-quality-analyzer

name: ad-lead-quality-analyzer
description: For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
tags: [ads]

Ad Lead Quality Analyzer

Meta optimizes for whatever conversion event you fire. For lead-gen and participant-recruitment campaigns that's almost always "signup" — but a signup is worthless if the lead never qualifies, never completes the requested action, or never gets paid out. The lowest-CPA campaign is often the one bringing in the *worst* leads.

This skill joins what the ad platform knows (spend, signups) with what your own product knows (downstream funnel) and replaces vanity CPA with **true CAC per qualified lead**. It then classifies every creative into actionable buckets so you stop scaling the wrong winners.

**Core principle:** The ad platform's CPA is a half-truth. Real optimization needs both halves of the funnel — pre-signup (the platform has it) and post-signup (you have it). Until they're joined, you're flying blind.

When to Use

• "Which ads are bringing in real leads vs. junk?"
• "True CAC per qualified contributor / customer / participant"
• "Why is my lowest-CPA campaign performing worst downstream?"
• "Audit lead quality across creatives / audiences / placements"
• "Should I trust Meta's CPA when scaling?"
• "Find the creatives that look like winners but aren't"

Pipeline Pattern Assumptions (Read First)

This skill is opinionated about **what** to measure (true CAC per qualified lead, with cohort maturation, with vanity scoring) and agnostic about **how** the data is sourced.

It assumes one of three standard attribution patterns:

| Pattern | Setup | Join Key | |---|---|---| | **A. UTM-only** *(most common)* | UTM params captured on signup form, stored on lead/user record. Downstream events joined by user_id inside your DB. | `utm_content` (typically the ad ID) on both sides, or `fbclid` | | **B. UTM + CAPI send-back** *(best)* | Same as A, plus your app fires Conversions API events back to Meta when downstream stages hit. Meta then optimizes for quality, not signups. | `event_id` / `external_id` | | **C. Meta Lead Ads + CRM sync** | Meta-hosted lead form, `lead_id` syncs to CRM/DB, joined there. | `lead_id` |

If none of these patterns is wired up, the skill switches to **`tracking-gap` mode** — it produces a fix-the-tracking report instead of an analysis.

Phase 0: Discovery Interview

6 short questions. Don't proceed until each is answered (default = "I don't know — let's find out").

1. **Where do downstream events live?** (Postgres / MySQL / Airtable / custom internal admin / spreadsheet / "no idea") 2. **Can the agent query that source directly?** (DB credentials / API endpoint / CSV export / "needs a person to pull it") 3. **Does the signup form capture `utm_*` params or `fbclid`?** ("I don't know" → inspect the signup form's HTML / network requests) 4. **Is the app sending CAPI events back to Meta** for any downstream stage? (None / signup-only / signup + qualification / full funnel) 5. **What is a "qualified lead"?** (Default: ≥1 unit of value-producing action completed within 14 days of signup. Examples: first purchase; demo attended; subscription activated; trial converted; first task completed and paid out) 6. **Cost basis per qualified lead?** (Flat payout, variable, tiered by quality, or N/A — needed to compute margin)

Output of Phase 0: a one-paragraph **Pipeline Brief** stating the assumed pattern (A/B/C), the join key, the qualification definition, and any unknowns.

Phase 1: Tracking Validation (Gating Step)

Pull a sample of 10–20 recent signups from the downstream source. For each, check:

• Is `utm_source` / `utm_campaign` / `utm_content` present? (Or `fbclid`? Or `lead_id`?)
• Does the join key resolve back to a specific Meta ad?
• Are there orphan signups (in your DB but no Meta join key)?
• Are there orphan Meta signups (in Meta but no matching DB record)?

**Coverage thresholds:**

| Coverage | Action | |---|---| | ≥80% joinable | Proceed to Phase 2 (`analysis` mode) | | 50–80% joinable | Proceed with explicit confidence caveat on every finding | | <50% joinable | Switch to **`tracking-gap` mode**. Skip Phases 2–6. Output the gap report. |

Output of Phase 1: a **Data Quality Report** with coverage %, sample of orphan records, and exact field-level findings.

Phase 2: Build the Per-Creative Funnel

For every ad / ad set / campaign with statistical volume (default ≥30 signups in the window), construct:

| Stage | Count | Conv. from prev. | What a drop here means | |---|---|---|---| | Impressions | n | — | — | | Link Clicks | n | CTR | Hook / placement issue | | Signups | n | Click → Signup | LP / form friction (use `ad-to-landing-page-auditor`) | | Qualified action started | n | Signup → Started | **Vanity signups** — wrong promise in the ad | | Qualified action approved | n | Started → Approved | Wrong audience or fraud | | Payout / value event | n | Approved → Paid | The "real" conversion | | Repeat action (configurable window) | n | Retention | One-and-done quality |

The skill should pull Meta-side data via the existing Meta Marketing API connection (MCP, native API, or pasted CSV) and downstream-side data via whichever source Phase 0 identified.

Phase 3: Compute True CAC

Per creative / ad set / campaign:

• **Platform CPA** = spend ÷ signups *(what Meta reports)*
• **True CAC** = spend ÷ qualified leads *(what actually matters)*
• **Quality Multiplier** = True CAC ÷ Platform CPA *(how badly the platform is misleading you per ad — higher = worse vanity problem)*
• **Margin per qualified lead** = (cost-basis or LTV-equivalent value) − True CAC

Phase 4: Score and Classify Each Creative

Compute three quality scores per creative with sufficient volume:

• **Vanity score** = 1 − (Started ÷ Signups)