ad-campaign-analyzer

# 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] |