Metrics fall into two categories based on when they measure relative to outcomes they predict.
Lagging indicators measure what has already happened. Revenue, churn, and customer lifetime value are lagging. By the time a lagging metric changes, the cause is weeks or months in the past. Lagging metrics are accurate but slow. They confirm outcomes; they do not help steer toward them.
Leading indicators measure current activity that predicts future outcomes. Activation rate — the proportion of new users who complete a meaningful first action — predicts retention. Feature engagement — which features users return to — predicts long-term value. These metrics are faster but noisier; they can be misleading if the team misunderstands what they are actually measuring.
Technical decisions primarily affect leading indicators. A deployment that improves time-to-first-meaningful-interaction by 20% will show up in activation rate within days. It may show up in revenue six months later, buried in many other causal factors. Measuring technical investments against lagging indicators introduces an attribution gap that makes technical work invisible to business decision-makers.