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Define a north-star metric and 3–5 input metrics that ladder up to it. Align squads to inputs they can influence directly.
Set up clean event tracking early. Name events consistently, avoid over-tracking, and validate data before shipping experiments.
Prioritize experiments by expected impact and confidence. Keep cycles short; a medium-quality test this week beats a perfect test next month.
Build guardrails: monitor activation, retention, and support tickets during experiments to catch negative side effects quickly.
Operationalize learnings. Successful experiments become playbooks; failed ones feed a ‘what we learned’ archive to avoid repeat mistakes.
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