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 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) Platform(s) — Google / Meta / LinkedIn / All Time period — What date range does this cover? Monthly budget — Total ad spend in this period Primary goal — What conversion are you optimizing for? (Demos / Trials / Purchases / Leads) Target metrics — Do you have target CPA or ROAS? (If not, we'll benchmark) Any known changes? — Did you change creative, budget, or targeting during this period? Channels currently running — Google Ads, Meta Ads, LinkedIn Ads, Twitter/X Ads, TikTok Ads, other Funnel data (if available): Lead → MQL rate MQL → SQL rate SQL → Close rate Average deal size Channels you're considering but haven't tried — Want to test new channels? 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]