_mureo-learning

# Evidence-Based Marketing Decisions A decision framework for AI agents managing marketing accounts through mureo. This skill teaches agents to distinguish signal from noise, avoid premature optimization, and only commit to strategy changes backed by sufficient evidence. ## Why This Matters Marketing data is noisy. A campaign's CPA can swing 30% day-to-day from random variation alone. Without statistical rigor, agents will: - Chase noise: "CPA dropped yesterday, the keyword change worked!" (It might just be Tuesday.) - Oscillate: Undo Monday's changes on Wednesday because metrics dipped, then redo them Friday. - Overfit: Draw conclusions from 12 conversions when 50+ are needed for reliability. - Contaminate: Attribute an improvement to one change when three changes happened simultaneously. **The antidote: observe, wait, verify, then act.** ## The Evidence Lifecycle Every action that modifies a campaign enters this lifecycle. The agent tracks it via `action_log` entries in STATE.json. ``` Action taken (e.g., add negative keywords) │ ├── Record metrics_at_action + observation_due in action_log │ ▼ [OBSERVING] ── Do NOT draw conclusions yet │ Wait for the observation window to pass │ ├── Observation window elapsed, collect current metrics │ ▼ [CANDIDATE] ── "This looks like it worked" or "This didn't help" │ But one observation is NOT enough │ ├── Wait for a second observation period to confirm ...