I would start by identifying proxy metrics that correlate with the outcome we care about. For a feature like a new onboarding flow, I might look at activation rate, time-to-first-action, and seven-day retention. I would track these in an A/B test and check for statistically significant lift. If the proxies move in the right direction, that is a strong signal the feature is working. I would also segment by cohort to make sure the gains are not driven by a single user group.
Before I pick any metric, I want to walk the causal chain — what behavior does this feature change, and how does that behavior connect to a customer outcome we actually care about? For a new onboarding flow, the chain might be: feature reduces friction → users reach first meaningful action faster → they form a habit → thirty-day retention improves. I would propose time-to-first-meaningful-action and thirty-day retention as my leading and lagging proxies. But here is the part I think is often skipped: I would then actively try to break my own framework. What if retention improves but users are doing the action mechanically without real value? I would pair the quantitative signal with a small qualitative check — session recordings or a targeted survey — to validate the proxy is measuring engagement and not just motion. I would document that assumption explicitly before shipping the measurement plan, so the team knows what would cause us to revisit it.