The playbook
Growth Experiment Planning: Metrics and Measurement
Measure growth experiment planning by defining experiments producing usable decisions precisely enough that two people can reproduce the result from the same records. Keep the reporting unit, date basis, and scope visible. Without those definitions, a change in the report may reflect a changed calculation rather than a changed customer outcome.
Put the task in context
State the mechanism you expect to change, such as clearer qualification improving lead suitability. Choose a comparison and an outcome tied to that mechanism. A result that contradicts the hypothesis should change the plan rather than be explained away automatically.
Create a metric definition
| Definition field | What it must explain |
|---|---|
| Measure | experiments producing usable decisions |
| Unit | What one client or workflow record or completed outcome represents |
| Included records | The scope identified by hypothesis and mechanism |
| Date basis | The event date or period used to include a record |
| Exclusions | Tests, duplicates, or other records outside the agreed definition |
| Source owner | business owner |
Separate delivery checks from outcome measures
A delivery check asks whether the team completed a specific task, such as state the problem hypothesis and expected mechanism. An outcome measure asks what happened for the user or business afterward. Keep both visible. Completing the task is useful evidence of execution, but it does not by itself prove that the broader outcome improved.
Worked arithmetic example
Suppose an illustrative review includes 80 eligible records, and 60 satisfy the explicitly defined completion condition. The completion share is 60 ÷ 80 × 100 = 75%. This is hypothetical arithmetic, not an industry benchmark or a predicted improvement. It describes only the population and condition you defined.
For growth experiment planning, explain the condition before applying that calculation. If the question is whether the team can record the decision rule and operational constraints, a record counts only when the evidence demonstrates that condition. Do not replace missing outcomes with successful ones just to complete a report.
Compare like with like
Keep the scope, date basis, and exclusions consistent between observations. If mechanism changes meaning between periods, note the change and rebuild a comparable view where possible. A rate can also move because the composition of the underlying group changed, even when individual performance did not.
Investigate an unexpected result
- Check whether the underlying hypothesis records are complete.
- Look for this known failure: running many tests without defining what would change your mind.
- Verify that the process still includes: choose a measurable outcome and a reasonable comparison.
- Reconcile a sample with the original record before changing the operating plan.
Use the report to choose an action
If the evidence supports a correction, specify what result leads to expansion revision or stopping. Record when the change occurred and what should be checked next. Keep uncertainty visible when the sample is small or the observation period is incomplete. The purpose of measurement is a better decision about experiments producing usable decisions, not a favorable-looking number.
Prefer a small set of shared definitions across marketing, sales, and delivery. If a qualified lead or accepted deliverable means something different to each team, the disagreement will reappear in reports and client conversations.
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