Standardizing vs customizing metrics

Standardized metrics provide consistency across experiments, making results comparable and easier to analyze over time. These predefined metrics, like conversion rate, engagement rate, or retention, ensure that teams speak the same language when discussing experimental outcomes. However, they might not capture the unique aspects of every feature or user flow.

Customized metrics address specific hypotheses that standard metrics can't evaluate effectively. For example, measuring "time to first value" for an onboarding experiment or "collaborative edits per session" for a team feature provides more relevant insights than generic engagement metrics. These tailored measurements often detect meaningful changes that standardized metrics might miss.

Most effective experimentation programs use both approaches: a core set of standardized metrics tracked across all experiments, supplemented by custom metrics for specific test contexts. This maintains organizational alignment while allowing teams to measure what matters most for their particular experiments.