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. Then it goes further: it compares channels on equal terms, finds where you're over-spending vs under-spending relative to results, and produces a concrete budget reallocation plan.

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. It also solves the allocation problem — most startups either spread budget too thin across channels (no channel gets enough to learn) or dump everything into one channel (missing cheaper opportunities elsewhere).

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"
• "How should I split my ad budget?"
• "Should I spend more on Google or Meta?"
• "Reallocate my ad spend across channels"
• "Where am I getting the best return?"
• "I have $X/month for ads — how should I distribute it?"

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)

1. Platform(s) — Google / Meta / LinkedIn / All
2. Time period — What date range does this cover?
3. Monthly budget — Total ad spend in this period
4. Primary goal — What conversion are you optimizing for? (Demos / Trials / Purchases / Leads)
5. Target metrics — Do you have target CPA or ROAS? (If not, we'll benchmark)
6. Any known changes? — Did you change creative, budget, or targeting during this period?
7. Channels currently running — Google Ads, Meta Ads, LinkedIn Ads, Twitter/X Ads, TikTok Ads, other
8. Funnel data (if available):

• Lead → MQL rate
• MQL → SQL rate
• SQL → Close rate
• Average deal size

1. Channels you're considering but haven't tried — Want to test new channels?
2. Constraints — Minimum spend on any channel? Platform you must stay on?

Phase 1: Data Ingestion & Normalization

Accepted Data Formats

Normalize all data into a standard analysis format:

Multi-Channel Normalization

When data spans multiple channels, also produce a channel-level rollup:

*CAC = Full customer acquisition cost if funnel data provided (CPA × close-rate adjustment)

Funnel-Adjusted CAC (If Funnel Data Available)

Channel CAC = CPA ÷ (MQL rate × SQL rate × Close rate)

This reveals which channels produce leads that actually close, not just convert.

Phase 2: Performance Diagnostics

2A: Campaign-Level Health Check

For each campaign:

2B: Budget Waste Detection

Identify spend that produced no or negative return:

2C: Winner Identification

Find what's actually working:

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

Phase 4: Budget Reallocation

When data spans multiple channels, perform cross-channel budget optimization.

4A: Channel Efficiency Ranking

Efficiency Index:

• > 1.0 = Under-invested (getting more than its share of conversions)
• = 1.0 = Proportional (fair share)
• < 1.0 = Over-invested (getting less than its share)

4B: Marginal Return Analysis

For each channel, estimate if additional spend would yield proportional returns:

4C: Funnel Stage Coverage

4D: Budget Shift Recommendations

4E: Scenario Modeling

Scenario 1: Conservative shift (+/- 20%)

• Expected conversions: [N] (currently [N]) = [X%] improvement
• Expected blended CPA: $[X] (currently $[X])
• Risk: Low

Scenario 2: Aggressive shift (+/- 40%)

• Expected conversions: [N] = [X%] improvement
• Expected blended CPA: $[X]
• Risk: Medium — less data on scaled channels

Scenario 3: Budget increase to $[Y]/mo

• Recommended allocation: [table]
• Expected conversions: [N]
• New channels to test: [list]

Phase 5: 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 including any reallocation moves]

---

## 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]

---

## Budget Reallocation

### Current vs Recommended Allocation

| Channel | Current | Recommended | Change | Why |
|---------|---------|------------|--------|-----|
| [Channel] | $[X] | $[Y] | [+/-$Z] | [1-line reason] |

**Projected impact:**
- Conversions: [N] → [N] (+[X%])
- Blended CPA: $[X] → $[Y] (-[X%])

### Funnel Stage Coverage
[Coverage map with gaps identified]

### New Channel Recommendations

#### [Channel Name]
- **Why test:** [Reasoning]
- **Recommended test budget:** $[X]/mo for [X weeks]
- **Success criteria:** CPA < $[X]
- **Competitors using it:** [Yes/No — who]

---

## Action Plan

### Immediate (This Week)
- [ ] **Pause:** [Specific items — keywords, ads, audiences]
- [ ] **Scale:** [Specific items — increase budget/bids]
- [ ] **Add negatives:** [Specific keywords from search terms]
- [ ] **Reallocate:** [Specific dollar shifts between channels]

### This Month
- [ ] **Test:** [New ad angles / audiences / landing pages]
- [ ] **Restructure:** [Ad groups that need splitting or merging]
- [ ] **Optimize:** [Bid strategy changes]
- [ ] **Monitor reallocation:** Track CPA shifts on scaled channels, watch for diminishing returns

### Next Month
- [ ] **Expand:** [New campaigns / channels to test]
- [ ] **Re-evaluate:** [Run this analysis again with new data, adjust allocations based on actual results]

Save to campaign-analysis-[YYYY-MM-DD].md in the current working directory (or user-specified path).

Cost

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"
• "How should I allocate my ad budget?"
• "Should I spend more on Google or Meta?"
• "Reallocate my ad spend"
• "Where am I getting the best ROAS?"
• "Optimize my multi-channel ad budget"