Ad Campaign Analyzer
Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Takes CSV or pasted data, runs statistical analysis, and produces a diagnostic report with action items.
by Athina AInpx gooseworks install --claude # Then in your agent: /gooseworks <prompt> --skill ad-campaign-analyzer
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"
What's included
Links
Aeo
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Landing Page Intel
Extract competitor and customer intelligence from any company's landing page HTML. Discovers tech stack, analytics tools, ad pixels, customer logos, SEO metadata, CTAs, hidden elements, and more. No API keys required.
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