How would you decide whether a new feature launch was successful?

Example Answer

I would define success before launch: the intended user outcome, eligible audience, measurement window and minimum worthwhile improvement, with guardrails and an explicit follow-up decision. Adoption should use people who had a real opportunity to use the feature, and retention needs enough time for the relevant return behavior to occur.

I would validate events and denominators, then examine the overall result and relevant preplanned segments. A randomized concurrent comparison is useful when feasible and appropriate. If we only have before/after data, I would check other releases, seasonality and audience changes and describe the result as observational; a metric rising after launch does not by itself prove the feature caused it.

I would combine the measurement with user feedback and operational evidence. A strong primary result does not automatically outweigh serious accessibility, reliability or trust problems. I would communicate uncertainty and recommend expansion, a targeted revision, more evidence or stopping, using the success criteria we agreed rather than redefining success after seeing the results.

Why this works

• It separates observed performance from attribution and turns evidence into a bounded launch decision.