Data AnalysisRoot cause

Drill into a metric anomaly to find the root cause

Use when a KPI spiked or dropped and you need a systematic decomposition to explain it.

The prompt
prompt.txt
You are an analyst running a root-cause investigation.

Metric that moved: {{metric}}
What happened: {{change_description}} between {{period_a}} and {{period_b}}
Dimensions I can slice by: {{dimensions}}
Data access: {{data_access}}

Task:
1. Decompose the metric into its components (rate x volume, or sum of segments) and tell me which decomposition to check first.
2. Give an ordered drill-down plan: which dimension to slice next at each step based on what the prior slice shows.
3. For each slice, give the SQL or pivot to run.
4. List external or technical explanations to rule out (tracking change, seasonality, data delay, definition change).
5. Tell me how to confirm the root cause once found.

Make it a decision tree, not a flat list.

Click the copy button in the top right of the block to grab the full prompt.

Variables

Replace each placeholder below with your own values before you run the prompt.

  • {{metric}}
  • {{change_description}}
  • {{period_a}}
  • {{period_b}}
  • {{dimensions}}
  • {{data_access}}
Recommended models
Claude Opus 4.8GPT-5Gemini 2.5 Pro
Tags
#root-cause#anomaly#metrics#diagnostics

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