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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
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add github/awesome-copilot --skill ad-campaign-analyzer -a cursor -y
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.
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.
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).
| 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 |
|---|
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) | ... | |||||||||
| ... | ||||||||||
| [Other] | ... | |||||||||
| Total | $[X] | [N] | $[X] avg | [X] avg | $[X] avg |
*CAC = Full customer acquisition cost if funnel data provided (CPA × close-rate adjustment)
Channel CAC = CPA ÷ (MQL rate × SQL rate × Close rate)
This reveals which channels produce leads that actually close, not just convert.
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] |
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 |
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 |
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.
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]
| 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] |
When data spans multiple channels, perform cross-channel budget optimization.
| 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:
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 |
| $[X] | Low volume — limited targeting pool | Ceiling soon |
| 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] |
| 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] |
| $[X] | $[Y] | $0 | [Maintain — niche but valuable] | |
| [New channel] | $0 | $[Y] | +$[Y] | [Test budget — competitors succeeding here] |
| Total | $[X] | $[X] | $0 | Budget-neutral reallocation |
Scenario 1: Conservative shift (+/- 20%)
Scenario 2: Aggressive shift (+/- 40%)
Scenario 3: Budget increase to $[Y]/mo
# 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).
| Component | Cost |
|---|---|
| Data analysis | Free (LLM reasoning) |
| Statistical calculations | Free |
| Total | Free |