Investigate a metric change systematically: segment decomposition, the mix-shift trap, timing correlation, and the verdict with confidence level.
Investigate this movement: {{metric_change}} (the metric, when it moved, by how much, what data I can pull). Run the sequence: (1) Is it real? (measurement/tracking change, definitional shift, data lag — the boring cause first, always), (2) Decompose: by segment, channel, product, geography — a flat average often hides one segment cratering (and check the MIX-SHIFT trap: every segment stable but the blend changed), (3) Timing correlation: what changed when it moved (releases, campaigns, pricing, seasonality vs last year, external events), (4) The verdict: ranked explanations with confidence and the evidence for each, plus the one query/check that would settle it. No cause found honestly beats a plausible story that's wrong.
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