This question evaluates a data scientist's competency in diagnostic analytics, causal reasoning, and metric-driven root-cause analysis for ad monetization, including interpreting bid CPM, inventory, fill-rate, and seasonality signals.

You are a data scientist supporting a large ads marketplace. Last week, global ads revenue declined sharply. Potential drivers include auction dynamics (e.g., bid CPM shifts), supply/inventory changes, fill-rate issues, seasonality, and recent UX/ML/product changes. Your task is to triage data sources, isolate the drivers, and brief executives with actionable next steps.
Outline a practical, step-by-step approach to diagnose root causes of the total ads revenue decline. Incorporate how you would use metrics such as bid CPM, inventory, fill-rate, and seasonality in your investigation.
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