name: "ad-campaign-analyzer" display_name: "广告活动分析" display_name_en: "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." description_zh: "将广告活动数据转化为预算调整、扩量和实验建议。" description_en: "Turn ad campaign data into concrete cut, scale, reallocation, and testing decisions." category: "business" version: "0.1.0" author: "GooseWorks" 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: "v1.0" 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)

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"