Use case - launch measurement
Stop shipping blind - measure the launch in week one
Launches get judged on anecdote and Slack sentiment instead of data, so a genuinely bad feature can survive on good vibes and a genuinely good one can get killed because nobody could prove it worked.
In short
We define the one metric a launch needs to move and instrument the events behind it before the feature ships, so the first week produces a real answer instead of a shrug. Most 'we don't know if the launch worked' problems are actually 'we forgot to instrument it' problems.
The situation
A feature ships, two weeks pass, and when leadership asks how it's doing the honest answer is 'we're not totally sure yet.'
What we implement
We define the launch's success metric and its supporting events before code ships, then build the dashboard that reads them from day one - what's called instrumentation-first launch measurement - so the answer exists the moment someone asks for it.
What you get
- A specific, agreed answer to 'is the launch working' within the first week, not the next quarterly review
- A go/no-go decision made from data instead of the loudest opinion in standup
- A record of what actually happened, so the next launch doesn't repeat the same guess
Illustrative
A team that historically judged launches by support-ticket volume alone might find, once the real usage metric is instrumented, that a quiet feature with zero complaints is also barely used - a very different verdict than 'no news is good news' would have given. (Illustrative scenario - not a measured result.)
Related: Amplitude Experiment: web vs. feature flags · Product analytics metrics, defined · Amplitude implementation sprint
Frequently asked questions
What if we've already shipped and only realize now that we didn't instrument it?+
Instrument it immediately - you'll have a partial week of data rather than none, and every week forward from that point is real signal. What you lose is the ability to compare against a true pre-launch baseline, which is the argument for instrumenting before the next launch, not a reason to skip it now.
How do we pick the one metric that matters for a launch?+
Ask what the feature was built to change, not what's easiest to measure - adoption, a specific action rate, or a downstream retention effect are the usual candidates. If the team can't agree on one metric before shipping, that disagreement is worth resolving before the launch, not after.
Other use cases
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