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.
Best use case
ad-campaign-analyzer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using ad-campaign-analyzer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ad-campaign-analyzer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ad-campaign-analyzer Compares
| Feature / Agent | ad-campaign-analyzer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
1. **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)
2. **Platform(s)** — Google / Meta / LinkedIn / All
3. **Time period** — What date range does this cover?
4. **Monthly budget** — Total ad spend in this period
5. **Primary goal** — What conversion are you optimizing for? (Demos / Trials / Purchases / Leads)
6. **Target metrics** — Do you have target CPA or ROAS? (If not, we'll benchmark)
7. **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
```markdown
# 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"Related Skills
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