Ad Campaign Analyzer

Overview

Take raw campaign performance data and turn it into testable decisions. Normalize the inputs, distinguish descriptive results from causal evidence, quantify uncertainty when the data supports it, and propose bounded budget experiments.

Core principle: Use verified measurements and explicit uncertainty to prioritize tests. This guide supports analysis, not a guarantee of optimal allocation or specialist performance.

When to Use This Skill

• "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, ask for an approved, dated benchmark source; never invent one.
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?