/ad-campaign-analyzer

name: ad-campaign-analyzer
description: >
  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 recommendations and scenario modeling.
tags: [ads]

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