Ad Campaign Analyzer — diagnose what to cut, scale, and test — Claude Skill
Un Skill de Claude para Claude Code por Gooseworks — ejecutar /ad-campaign-analyzer en Claude·Actualizado el 10 abr 2026
Diagnose ad campaign performance and produce action items
- Accepts CSV, paste, or screenshot data from Google/Meta/LinkedIn
- Detects budget waste, zero-conversion items, and high-CPA outliers
- Identifies winners with sample-size and significance checks
- Diagnoses funnel drop-offs from impression to revenue
- Outputs a prioritized action plan: pause, scale, test
Para quién es
Qué hace
Replace dashboard staring with a diagnosis: what is working, what is wasting budget, and what to do this week.
When you take over an existing ad account, get a structured audit before you touch a single bid.
Generate an executive summary with the top recommendation before your weekly leadership review.
Cómo funciona
Take CSV, paste, or screenshot of campaign data as input
Normalize across platforms into apples-to-apples metrics
Run diagnostic: budget waste, winners, A/B significance, funnel drop-offs
Score each campaign: scale, optimize, or pause
Output a prioritized action plan with specific items per timeframe
Métricas que mejora
Funciona con
¿Quieres usar Ad Campaign Analyzer?
Elige cómo empezar.
Instala y ejecuta este skill localmente en tu computadora.
Abre una terminal en tu computadora y pega este comando:
Esto descarga el skill con todos sus archivos en tu computadora:
Añade -g al final para tenerlo disponible en todos tus proyectos.
Inicia Claude Code, luego escribe el comando:
Ad Campaign Analyzer
Take raw campaign performance data and turn it into clear decisions. This skill doesn't just summarize metrics — it diagnoses problems, identifies winners, checks statistical significance, and tells you exactly what to cut, scale, and test next.
Core principle: Most startup founders check their ad dashboard, see a ROAS number, and either panic or celebrate. This skill gives you the nuanced analysis a paid media specialist would: what's actually significant, what's noise, and where your next dollar should go.
When to Use
- "Analyze my Google Ads performance"
- "Which ads should I kill?"
- "Is this campaign working?"
- "Where am I wasting ad spend?"
- "Optimize my Meta Ads"
Phase 0: Intake
- Campaign data — One of:
- CSV export from Google Ads / Meta Ads Manager / LinkedIn Campaign Manager
- Pasted performance table
- Screenshots of dashboard (we'll extract the data)
- Platform(s) — Google / Meta / LinkedIn / All
- Time period — What date range does this cover?
- Monthly budget — Total ad spend in this period
- Primary goal — What conversion are you optimizing for? (Demos / Trials / Purchases / Leads)
- Target metrics — Do you have target CPA or ROAS? (If not, we'll benchmark)
- Any known changes? — Did you change creative, budget, or targeting during this period?
Phase 1: Data Ingestion & Normalization
Accepted Data Formats
| Source | Key Columns Expected |
|---|---|
| Google Ads | Campaign, Ad Group, Keyword, Impressions, Clicks, CTR, CPC, Conversions, Conv Rate, Cost, Conv Value |
| Meta Ads | Campaign, Ad Set, Ad, Impressions, Reach, Clicks, CTR, CPC, Conversions, Cost Per Result, Amount Spent, ROAS |
| LinkedIn Ads | Campaign, Impressions, Clicks, CTR, CPC, Conversions, Cost, Leads |
Normalize all data into a standard analysis format:
| Dimension | Impressions | Clicks | CTR | CPC | Conversions | Conv Rate | CPA | Spend | Revenue/Value |
|---|
Phase 2: Performance Diagnostics
2A: Campaign-Level Health Check
For each campaign:
| Metric | Value | Benchmark | Status |
|---|---|---|---|
| CTR | [X%] | [Industry avg] | [Good/Okay/Poor] |
| CPC | $[X] | [Category avg] | [Good/Okay/Poor] |
| Conv Rate | [X%] | [Benchmark] | [Good/Okay/Poor] |
| CPA | $[X] | [Target or benchmark] | [Good/Okay/Poor] |
| ROAS | [X] | [Target or benchmark] | [Good/Okay/Poor] |
| Impression Share | [X%] | [>60% ideal] | [Good/Okay/Poor] |
2B: Budget Waste Detection
Identify spend that produced no or negative return:
| Waste Type | Signal | Action |
|---|---|---|
| Zero-conversion keywords/ads | Spend > $[X] with 0 conversions | Pause or add negatives |
| High CPA outliers | CPA > 3x target | Pause or restructure |
| Low CTR ads | CTR < 50% of campaign average | Replace creative |
| Broad match bleed | Search terms report showing irrelevant clicks | Add negative keywords |
| Audience overlap | Same users hit by multiple campaigns | Exclude audiences |
| Dayparting waste | Conversions cluster at certain hours; spend is 24/7 | Set ad schedule |
2C: Winner Identification
Find what's actually working:
| Winner Type | Signal | Action |
|---|---|---|
| Top-performing keywords | Lowest CPA, highest conv rate | Increase bid, add variants |
| Winning ads | Highest CTR + conv rate combo | Scale spend, clone for other groups |
| Best audiences | Lowest CPA segment | Increase budget allocation |
| Best times | Peak conversion hours/days | Concentrate budget |
2D: Statistical Significance Check
For any A/B test (ad variants, audiences, landing pages):
Test: [Variant A] vs [Variant B]
Metric: [Conv Rate / CTR / CPA]
Variant A: [X%] (n=[sample_size])
Variant B: [Y%] (n=[sample_size])
Confidence level: [X%]
Verdict: [Statistically significant / Not enough data / Too close to call]
Recommended action: [Pick winner / Continue test / Increase budget to reach significance]
Minimum sample: 100 clicks per variant for CTR tests, 30 conversions per variant for CPA tests.
Phase 3: Funnel Analysis
Click → Conversion Path
Impressions: [N] (100%)
↓ CTR: [X%]
Clicks: [N] ([X%] of impressions)
↓ Landing page → Conversion: [X%]
Conversions: [N] ([X%] of clicks)
↓ Conversion → Revenue: $[X] avg
Revenue: $[N]
Funnel Drop-Off Diagnosis
| Drop-Off Point | Rate | Benchmark | Likely Cause | Fix |
|---|---|---|---|---|
| Impression → Click | [CTR%] | [Benchmark] | [Ad relevance / targeting] | [Copy/targeting change] |
| Click → Conversion | [Conv%] | [Benchmark] | [Landing page / offer / audience mismatch] | [LP optimization] |
| Conversion → Revenue | [Close%] | [Benchmark] | [Lead quality / sales process] | [Qualification criteria] |
Phase 4: Output Format
# Ad Campaign Analysis — [Product/Client] — [DATE]
Period: [Date range]
Total spend: $[X]
Platform(s): [Google / Meta / LinkedIn]
Primary goal: [Conversions / Revenue / Leads]
---
## Executive Summary
[3-5 sentences: Overall performance verdict, biggest win, biggest problem, top recommendation]
---
## Performance Dashboard
| Campaign | Spend | Impressions | Clicks | CTR | CPC | Conversions | CPA | ROAS | Verdict |
|----------|-------|------------|--------|-----|-----|-------------|-----|------|---------|
| [Name] | $[X] | [N] | [N] | [X%] | $[X] | [N] | $[X] | [X] | [Scale/Optimize/Pause] |
---
## Budget Waste Report
**Total estimated waste: $[X] ([X%] of total spend)**
### Wasted on zero-conversion items: $[X]
[List of keywords/ads/audiences with spend but no conversions]
### Wasted on high-CPA items: $[X]
[List of items with CPA > 3x target]
### Recommended saves: $[X]/month
[Specific items to pause]
---
## Winners to Scale
### Top Keywords/Audiences
| Item | CPA | Conv Rate | Current Spend | Recommended Spend |
|------|-----|----------|--------------|-------------------|
### Top Ads
| Ad | CTR | Conv Rate | Why It Works |
|----|-----|----------|-------------|
---
## A/B Test Results
### [Test Name]
- Variant A: [Metric] (n=[N])
- Variant B: [Metric] (n=[N])
- Confidence: [X%]
- **Verdict:** [Winner / Continue / Inconclusive]
---
## Action Plan
### Immediate (This Week)
- [ ] **Pause:** [Specific items — keywords, ads, audiences]
- [ ] **Scale:** [Specific items — increase budget/bids]
- [ ] **Add negatives:** [Specific keywords from search terms]
### This Month
- [ ] **Test:** [New ad angles / audiences / landing pages]
- [ ] **Restructure:** [Ad groups that need splitting or merging]
- [ ] **Optimize:** [Bid strategy changes]
### Next Month
- [ ] **Expand:** [New campaigns / channels to test]
- [ ] **Review:** [Run this analysis again]
Save to clients/<client-name>/ads/campaign-analysis-[YYYY-MM-DD].md.
Cost
| Component | Cost |
|---|---|
| Data analysis | Free (LLM reasoning) |
| Statistical calculations | Free |
| Total | Free |
Tools Required
- No external tools needed — pure reasoning skill
- User provides campaign data as CSV, paste, or screenshot
Trigger Phrases
- "Analyze my ad campaign performance"
- "Which ads should I pause?"
- "Where am I wasting ad budget?"
- "Is my Google Ads campaign working?"
- "Optimize my Meta Ads spend"