--- name: meta-ads-lead-gen-analysis description: "[Didoo AI] Specialized analysis module for Meta lead generation campaigns. Use when CPL is elevated, lead quality is unclear, or you need to diagnose why leads aren't converting downstream. For general campaign analysis, use meta-ads-analysis." homepage: https://didoo.ai/blog --- ## Required Credentials | Credential | Where to Get | Used For | OAuth Scope | |-----------|-------------|---------|-------------| | META_ACCESS_TOKEN | Meta Developer Console → Graph API Explorer → Generate Token | Fetching campaign and form integration data | `ads_read` (read-only) | | META_AD_ACCOUNT_ID | Ads Manager URL: `adsmanager.facebook.com/act_XXXXXXXXX` | Identifying which account to query | — | ## When to Use When running lead generation campaigns and standard e-commerce analysis logic doesn't apply. Specifically for: - CPL is higher than expected or target - Lead volume is healthy but conversion rate to closed deals is low (quality problem) - LPV rate looks fine but form submit rate is unknown - CAPI may not be sending offline lead data back to Meta - Not sure if the problem is the ad, the audience, or the form itself Lead gen has fundamentally different metrics than e-commerce — standard CVR and LPV benchmarks don't apply. Use this skill instead of applying e-commerce logic to lead gen campaigns. --- ## Key Differences from E-commerce Analysis | Metric | E-commerce Benchmark | Lead Gen Benchmark | Why It Differs | |---|---|---|---| | LPV Rate | > 70% is healthy | > 50% is healthy | Many leads browse without intent to submit | | Form Submit Rate | N/A | > 20% is healthy | Form friction is the primary drop-off point | | CPL | Varies by industry | The primary metric | What you're actually paying for | | CVR (post-click) | Purchase rate | Lead-to-qualified rate | Meta can't see what happens after form submit | | Attribution | 1-day or 7-day click | 7-day click usually | Longer window for consideration | --- ## Step 1: Gather Lead Gen Specific Data Pull these metrics at the campaign and adset level: - Spend, impressions, CTR - LPV Rate (Landing Page View rate = landing page views / link clicks) - Form Submit Rate (if available — requires CAPI or form integration) - CPL (cost per lead) - Frequency - Lead volume vs. prior period Also ask the user: - What does a "good lead" look like for their business? - What's their lead-to-close rate historically? - Are they using CAPI to send offline lead data back to Meta? --- ## Step 2: CAPI Verification — Critical for Lead Gen Without CAPI sending offline lead data back to Meta: - Meta is optimizing for form submissions, not qualified leads - CPL shown may be "form submit cost" not "actual lead cost" - The algorithm can't learn which leads actually convert **How to verify CAPI is connected:** 1. Go to Meta Events Manager → select your pixel 2. Check "About CAPI" — status should be green/active 3. Ask: are offline leads (calls, CRM-qualified leads) being sent back via CAPI? If CAPI is NOT connected for offline leads: - This is a priority fix — it will lower CPL over 2–3 weeks - Recommend a CRM integration or Zapier/Make workflow - Or use Meta's Lead Forms (form submission = conversion automatically tracked) --- ## Step 3: Audience Targeting Analysis for Lead Gen ### Cold Audiences (Interest-Based Targeting) - Interest targeting works for cold audiences - Frequency builds fast — limit budget to avoid rapid fatigue - Test narrower interest layers (2–3 stacked interests, not broad categories) ### Custom Audiences (Retargeting and Lookalikes) - Website visitors (pixel): typically highest conversion rate - Email lists (matched audiences): strong for existing customer re-engagement - Lookalikes (LAL): based on converter audiences, quality depends on seed list quality - Test LAL layers 1–3% first — wider LAL = lower quality but more volume --- ## Step 4: Format Output **Lead Gen Health Summary:** - Spend / CPL / Lead Volume - LPV Rate vs. benchmark (> 50% healthy for lead gen) - Form Submit Rate (if available) - CAPI Status (connected / not connected) ### SECTION 1: CAPI Gap (if applicable) State whether offline lead data is being sent to Meta. Impact: algorithm is optimizing for quantity, not quality. ### SECTION 2: Audience Diagnosis Diagnose targeting based on frequency and CPL — is the audience too broad or well-matched? ### SECTION 3: Funnel Stage Identify the CPL bottleneck stage: - LPV < 50% → Ad-to-Form disconnect - LPV ≥ 50% but low leads → Form friction (check form fields) - LPV and CVR OK but CPL high → Audience mismatch or CAPI not connected > **LP Landing Page diagnosis:** The full LP disconnect diagnostic (Step 4a–4d) lives in **meta-ads-recommendation → Step 4**. This skill identifies the funnel stage; recommendation prescribes the fix. --- ## Session Context — What This Skill Writes After completing analysis, store the following in session context: | Key | Description | Example | |-----|-------------|---------| | lp_diagnosis | Funnel stage causing the CPL problem | "Form friction — submit rate 12%, below 20% benchmark" | | capi_status | Whether offline leads are being sent to Meta | "Not connected — offline lead data not flowing" | | cpl_breakdown | Which stage is the bottleneck | "LPV 65% OK; Form submit 11% is the bottleneck" | | recommended_fix_priority | Ranked fix order | "1. CAPI 2. Form fields 3. Audience" | > meta-ads-recommendation reads these keys to produce the lead gen action plan. --- ## Key Collision Resolution > ⚠️ **Key priority:** `meta-ads-analysis` writes `lp_diagnosis_general`; this skill writes `lp_diagnosis` (the lead-gen-specific version). `meta-ads-recommendation` is configured to prefer `lp_diagnosis` when available — so running both skills in sequence does NOT cause overwriting. The two keys coexist. --- ## Rules - Apply lead gen benchmarks, not e-commerce benchmarks - Always verify CAPI status before diagnosing CPL issues — missing CAPI is the most common cause of misleading CPL data - Do not recommend creative changes if form friction is the bottleneck - Do not recommend audience changes if LPV rate indicates a page problem - This is analysis only — recommendations route to meta-ads-recommendation --- Source attribution: elias-didoo Original project: https://github.com/elias-didoo/didoo-marketing-suite/blob/70c89f2304d4360f553768e2b2626c4b319caa58/meta-ads-skills/analysis/meta-ads-lead-gen-analysis/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice # MIT License Copyright (c) 2026 Didoo AI Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. --- ## Trademark Notice "Didoo AI" and the Didoo AI logo are trademarks of Didoo AI. This license does not grant permission to use the "Didoo AI" name, trademark, or product name for commercial or promotional purposes without prior written consent. Distributing skills or derivatives claiming to be "official" Didoo AI integrations is prohibited. # Mega AI Hub resource metadata Creator / publisher: elias-didoo Original project: https://github.com/elias-didoo/didoo-marketing-suite/blob/70c89f2304d4360f553768e2b2626c4b319caa58/meta-ads-skills/analysis/meta-ads-lead-gen-analysis/SKILL.md Attribution basis: Source repository record Fields of work: Marketing Roles: Advertising Specialist, Growth Marketer, Marketing Analyst, Product Marketer, SEO Specialist Specialties: API, Frontend, Testing Tools: Zapier Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: ed95fe9c2ed6bd329470c3326c289e6f9ec79a9cddeea7102fc09511dd87d7ca