Leading and Lagging Indicators: How to measure Product OKRs

Product teams that chase only lagging metrics such as revenue, churn, or quarterly growth end up reacting too late, because those numbers only change after the work is done. The article argues that the real power of OKRs comes from using leading indicators that can be measured continuously and that directly reflect the actions a team can take today. By checking these metrics every week or two, teams can steer their work before the outcomes solidify. The core idea is to treat leading indicators as early warning signals that predict the eventual lagging results. It shows how a product team at a fictional company called Postgorilla uses "average quarterly upgrade revenue" as a lagging goal, then backs it with leading metrics like "number of actively used integrations per user" and "time spent on integration installation" to guide day-to-day decisions. The piece also explains why the same metric can be leading for one team and lagging for another, depending on who can influence it directly. It walks through a set of guiding questions-look for metrics that are directly linkable to team actions, predict future success, and change frequently-to help product leaders identify their own leading indicators. Finally, it recommends working backward from a desired lagging outcome, reverse-engineering the behaviors that drive it, and continuously validating those leading metrics against real product impact. This approach lets technical leaders keep their OKRs responsive, reduce wasted effort, and make more data-driven decisions throughout a goal cycle.