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

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"

ad-campaign-analyzer

NPX Install

Tags

SKILL.md Content