Leading vs lagging indicators

Leading indicators are predictive metrics that signal future outcomes before they happen. For example, feature adoption rates, user engagement frequency, customer satisfaction scores, free trial conversion momentum, and onboarding completion percentages. These metrics act as early warnings. When engagement patterns shift, teams can step in before revenue takes a hit. For instance, a drop in session frequency often signals churn risk, giving product teams time to act before users leave.

Lagging indicators, on the other hand, reflect past performance after the results are in. For example, monthly recurring revenue, customer lifetime value, churn rate, market share, and profit margins. While they confirm what has already happened, they don’t provide much time to react.

A strong analytics approach includes both. Leading indicators help teams stay ahead, while lagging indicators validate the impact of past decisions. Together, they give a clearer view of product health. Leading indicators are also useful when you need to move fast. For example, if your goal is to improve month 2 retention, you don’t have to wait two months to learn if you’re on track. You can use day 1 retention or activation rate as a proxy and course-correct in real time.