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:
2. CSV export from Google Ads / Meta Ads Manager / LinkedIn Campaign Manager
3. Pasted performance table
4. Screenshots of dashboard (we'll extract the data)
5. Platform(s) — Google / Meta / LinkedIn / All
6. Time period — What date range does this cover?
7. Monthly budget — Total ad spend in this period
8. Primary goal — What conversion are you optimizing for? (Demos / Trials / Purchases / Leads)
9. Target metrics — Do you have target CPA or ROAS? (If not, we'll benchmark)
10. Any known changes? — Did you change creative, budget, or targeting during this period?
11. Channels currently running — Google Ads, Meta Ads, LinkedIn Ads, Twitter/X Ads, TikTok Ads, other
12. Funnel data (if available):
13. Lead → MQL rate
14. MQL → SQL rate
15. SQL → Close rate
16. Average deal size
17. Channels you're considering but haven't tried — Want to test new channels?
18. 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 | | Lin