ad-campaign-analyzer

name	ad-campaign-analyzer
description	Use this skill when the user shares ad campaign performance data and asks what to cut, scale, or test. Trigger for prompts like "analyze my ad campaigns", "where am I wasting ad spend", "reallocate my ad budget", "which ads are actually working", or "ROAS analysis". Do not trigger for campaign planning or creative generation without performance data.
license	MIT
compatibility	Cross-platform. Pure reasoning skill over user-provided campaign exports (CSV, paste, or screenshot from Google, Meta, or LinkedIn) — no external tools, network calls, or API keys.
metadata	{"version":"1.0","author":"GooseWorks","source":"https://github.com/gooseworks-ai/goose-skills"}

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)
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?
8. Channels currently running — Google Ads, Meta Ads, LinkedIn Ads, Twitter/X Ads, TikTok Ads, other
9. Funnel data (if available):
   • Lead → MQL rate
   • MQL → SQL rate
   • SQL → Close rate
   • Average deal size
10. Channels you're considering but haven't tried — Want to test new channels?
11. Constraints — Minimum spend on any channel? Platform you must stay on?

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

Multi-Channel Normalization

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

Channel	Monthly Spend	Impressions	Clicks	CTR	CPC	Conversions	Conv Rate	CPA	ROAS	CAC*
Google Search	$[X]	[N]	[N]	[X%]	$[X]	[N]	[X%]	$[X]	[X]	$[X]
Google Display	...
Meta (FB/IG)	...
LinkedIn	...
[Other]	...
Total	$[X]					[N]		$[X] avg	[X] avg	$[X] avg

*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:

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: Budget Reallocation

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

4A: Channel Efficiency Ranking

Rank	Channel	CPA	Funnel-Adj CAC	Share of Spend	Share of Conversions	Efficiency Index
1	[Channel]	$[X]	$[X]	[X%]	[X%]	[Conv share ÷ Spend share]

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:

Channel	Current CPA	Impression Share / Saturation Signal	Marginal Return Estimate
Google Search	$[X]	[X%] impression share — room to grow	Likely positive
Meta	$[X]	Frequency [X] — audience may be saturated	Diminishing
LinkedIn	$[X]	Low volume — limited targeting pool	Ceiling soon

4C: Funnel Stage Coverage

Funnel Stage	Channels Covering It	Current Spend	Gap?
Awareness (top)	[Meta Display, YouTube]	$[X]	[Yes/No]
Consideration (mid)	[Google Search, Meta retargeting]	$[X]	[Yes/No]
Decision (bottom)	[Google Brand, Google Search]	$[X]	[Yes/No]
Retargeting	[Meta, Google Display]	$[X]	[Yes/No]

4D: Budget Shift Recommendations

Channel	Current Spend	Recommended Spend	Change	Reasoning
Google Search	$[X]	$[Y]	+$[Z]	[Lowest CPA, room to scale]
Meta	$[X]	$[Y]	-$[Z]	[Audience saturation, frequency too high]
LinkedIn	$[X]	$[Y]	$0	[Maintain — niche but valuable]
[New channel]	$0	$[Y]	+$[Y]	[Test budget — competitors succeeding here]
Total	$[X]	$[X]	$0	Budget-neutral reallocation

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

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"
• "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"