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 actu