{"slug":"ad-campaign-analyzer","title":"ad-campaign-analyzer","summary":"Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift rec","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-09-11T17:23:13.56533Z","repo":{"url":"https://github.com/gooseworks-ai/goose-skills","stars":1229,"forks":211,"license":"MIT","updatedAt":"2026-10-01T15:06:43Z"},"bodyHtml":"
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name: ad-campaign-analyzer\ndescription: >\nAnalyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's\nworking, what's wasting budget, and specific cut/scale/test recommendations. Runs\nstatistical analysis, funnel diagnostics, and multi-channel budget reallocation\nwith specific dollar-amount shift recommendations and scenario modeling.\ntags: [ads]

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Ad Campaign Analyzer

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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.

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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).

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When to Use

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Phase 0: Intake

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  1. Campaign data — One of:\n\n
    2. \n
  2. Platform(s) — Google / Meta / LinkedIn / All
    3. \n
  3. Time period — What date range does this cover?
    4. \n
  4. Monthly budget — Total ad spend in this period
    5. \n
  5. Primary goal — What conversion are you optimizing for? (Demos / Trials / Purchases / Leads)
    6. \n
  6. Target metrics — Do you have target CPA or ROAS? (If not, we'll benchmark)
    7. \n
  7. Any known changes? — Did you change creative, budget, or targeting during this period?
    8. \n
  8. Channels currently running — Google Ads, Meta Ads, LinkedIn Ads, Twitter/X Ads, TikTok Ads, other
    9. \n
  9. Funnel data (if available):\n\n
    10. \n
  10. Channels you're considering but haven't tried — Want to test new channels?
    11. \n
  11. Constraints — Minimum spend on any channel? Platform you must stay on?
    12. \n
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Phase 1: Data Ingestion & Normalization

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Accepted Data Formats

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
SourceKey Columns Expected
Google AdsCampaign, Ad Group, Keyword, Impressions, Clicks, CTR, CPC, Conversions, Conv Rate, Cost, Conv Value
Meta AdsCampaign, Ad Set, Ad, Impressions, Reach, Clicks, CTR, CPC, Conversions, Cost Per Result, Amount Spent, ROAS
LinkedIn AdsCampaign, Impressions, Clicks, CTR, CPC, Conversions, Cost, Leads
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Normalize all data into a standard analysis format:

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DimensionImpressionsClicksCTRCPCConversionsConv RateCPASpendRevenue/Value
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Multi-Channel Normalization

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

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

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Funnel-Adjusted CAC (If Funnel Data Available)

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Channel CAC = CPA ÷ (MQL rate × SQL rate × Close rate)\n
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This reveals which channels produce leads that actually close, not just convert.

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Phase 2: Performance Diagnostics

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2A: Campaign-Level Health Check

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For each campaign:

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MetricValueBenchmarkStatus
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]
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2B: Budget Waste Detection

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Identify spend that produced no or negative return:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Waste TypeSignalAction
Zero-conversion keywords/adsSpend > $[X] with 0 conversionsPause or add negatives
High CPA outliersCPA > 3x targetPause or restructure
Low CTR adsCTR < 50% of campaign averageReplace creative
Broad match bleedSearch terms report showing irrelevant clicksAdd negative keywords
Audience overlapSame users hit by multiple campaignsExclude audiences
Dayparting wasteConversions cluster at certain hours; spend is 24/7Set ad schedule
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2C: Winner Identification

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Find what's actually working:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Winner TypeSignalAction
Top-performing keywordsLowest CPA, highest conv rateIncrease bid, add variants
Winning adsHighest CTR + conv rate comboScale spend, clone for other groups
Best audiencesLowest CPA segmentIncrease budget allocation
Best timesPeak conversion hours/daysConcentrate budget
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2D: Statistical Significance Check

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For any A/B test (ad variants, audiences, landing pages):

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Test: [Variant A] vs [Variant B]\nMetric: [Conv Rate / CTR / CPA]\nVariant A: [X%] (n=[sample_size])\nVariant B: [Y%] (n=[sample_size])\nConfidence level: [X%]\nVerdict: [Statistically significant / Not enough data / Too close to call]\nRecommended action: [Pick winner / Continue test / Increase budget to reach significance]\n
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Minimum sample: 100 clicks per variant for CTR tests, 30 conversions per variant for CPA tests.

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Phase 3: Funnel Analysis

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Click → Conversion Path

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Impressions: [N] (100%)\n     ↓ CTR: [X%]\nClicks: [N] ([X%] of impressions)\n     ↓ Landing page → Conversion: [X%]\nConversions: [N] ([X%] of clicks)\n     ↓ Conversion → Revenue: $[X] avg\nRevenue: $[N]\n
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Funnel Drop-Off Diagnosis

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Drop-Off PointRateBenchmarkLikely CauseFix
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]
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Phase 4: Budget Reallocation

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

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4A: Channel Efficiency Ranking

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
RankChannelCPAFunnel-Adj CACShare of SpendShare of ConversionsEfficiency Index
1[Channel]$[X]$[X][X%][X%][Conv share ÷ Spend share]
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Efficiency Index:

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4B: Marginal Return Analysis

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

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
ChannelCurrent CPAImpression Share / Saturation SignalMarginal Return Estimate
Google Search$[X][X%] impression share — room to growLikely positive
Meta$[X]Frequency [X] — audience may be saturatedDiminishing
LinkedIn$[X]Low volume — limited targeting poolCeiling soon
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4C: Funnel Stage Coverage

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Funnel StageChannels Covering ItCurrent SpendGap?
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]
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4D: Budget Shift Recommendations

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ChannelCurrent SpendRecommended SpendChangeReasoning
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]$0Budget-neutral reallocation
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4E: Scenario Modeling

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

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

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

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Phase 5: Output Format

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# Ad Campaign Analysis — [Product/Client] — [DATE]\n\nPeriod: [Date range]\nTotal spend: $[X]\nPlatform(s): [Google / Meta / LinkedIn]\nPrimary goal: [Conversions / Revenue / Leads]\n\n---\n\n## Executive Summary\n\n[3-5 sentences: Overall performance verdict, biggest win, biggest problem, top recommendation including any reallocation moves]\n\n---\n\n## Performance Dashboard\n\n| Campaign | Spend | Impressions | Clicks | CTR | CPC | Conversions | CPA | ROAS | Verdict |\n|----------|-------|------------|--------|-----|-----|-------------|-----|------|---------|\n| [Name] | $[X] | [N] | [N] | [X%] | $[X] | [N] | $[X] | [X] | [Scale/Optimize/Pause] |\n\n---\n\n## Budget Waste Report\n\n**Total estimated waste: $[X] ([X%] of total spend)**\n\n### Wasted on zero-conversion items: $[X]\n[List of keywords/ads/audiences with spend but no conversions]\n\n### Wasted on high-CPA items: $[X]\n[List of items with CPA > 3x target]\n\n### Recommended saves: $[X]/month\n[Specific items to pause]\n\n---\n\n## Winners to Scale\n\n### Top Keywords/Audiences\n| Item | CPA | Conv Rate | Current Spend | Recommended Spend |\n|------|-----|----------|--------------|-------------------|\n\n### Top Ads\n| Ad | CTR | Conv Rate | Why It Works |\n|----|-----|----------|-------------|\n\n---\n\n## A/B Test Results\n\n### [Test Name]\n- Variant A: [Metric] (n=[N])\n- Variant B: [Metric] (n=[N])\n- Confidence: [X%]\n- **Verdict:** [Winner / Continue / Inconclusive]\n\n---\n\n## Budget Reallocation\n\n### Current vs Recommended Allocation\n\n| Channel | Current | Recommended | Change | Why |\n|---------|---------|------------|--------|-----|\n| [Channel] | $[X] | $[Y] | [+/-$Z] | [1-line reason] |\n\n**Projected impact:**\n- Conversions: [N] → [N] (+[X%])\n- Blended CPA: $[X] → $[Y] (-[X%])\n\n### Funnel Stage Coverage\n[Coverage map with gaps identified]\n\n### New Channel Recommendations\n\n#### [Channel Name]\n- **Why test:** [Reasoning]\n- **Recommended test budget:** $[X]/mo for [X weeks]\n- **Success criteria:** CPA < $[X]\n- **Competitors using it:** [Yes/No — who]\n\n---\n\n## Action Plan\n\n### Immediate (This Week)\n- [ ] **Pause:** [Specific items — keywords, ads, audiences]\n- [ ] **Scale:** [Specific items — increase budget/bids]\n- [ ] **Add negatives:** [Specific keywords from search terms]\n- [ ] **Reallocate:** [Specific dollar shifts between channels]\n\n### This Month\n- [ ] **Test:** [New ad angles / audiences / landing pages]\n- [ ] **Restructure:** [Ad groups that need splitting or merging]\n- [ ] **Optimize:** [Bid strategy changes]\n- [ ] **Monitor reallocation:** Track CPA shifts on scaled channels, watch for diminishing returns\n\n### Next Month\n- [ ] **Expand:** [New campaigns / channels to test]\n- [ ] **Re-evaluate:** [Run this analysis again with new data, adjust allocations based on actual results]\n
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Save to campaign-analysis-[YYYY-MM-DD].md in the current working directory (or user-specified path).

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Cost

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ComponentCost
Data analysisFree (LLM reasoning)
Statistical calculationsFree
TotalFree
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Tools Required

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Trigger Phrases

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