Every experiment needs clear measurement priorities to avoid conflicting signals and misinterpretation of results. Your primary metric is the main measurement that determine if an experiment succeeds or fails, directly tied to your hypothesis and business objectives. Secondary metrics provide additional context but don't determine the experiment's outcome. For instance, if your experiment aims to increase checkout completion, conversion rate would be your primary metric while metrics like product split or cart total/average order value might be secondary. This distinction helps teams make clear decisions even when metrics move in different directions.

Secondary metrics can also be considered health metrics that serve as guardrails and diagnostic tools that help explain why your primary metrics changed. They ensure your experiment doesn't improve one aspect at the expense of others and provide insights into the mechanisms behind observed changes.^[1] This dual approach helps teams understand not just if their experiment worked, but why it worked and what trade-offs occurred.