The playbook
Growth Experiment Planning: Worked Examples
The following growth experiment planning examples illustrate how to move from an observed problem to a practical decision. They are hypothetical operating scenarios, not customer results or performance benchmarks. The central question is whether the work helps improve experiments producing usable decisions without losing the context needed to judge it.
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.
Example 1: establish a usable starting point
A team begins working on growth experiment planning but cannot consistently describe hypothesis and mechanism for the same item. The immediate task is state the problem hypothesis and expected mechanism. The team records the current state, identifies the source of each value, and resolves conflicting information before making a broader change.
The useful output is a client or workflow record that another person can interpret. This example does not require assuming an uplift. Success at this stage means the next decision can be made using a shared set of facts, with missing information explicitly identified.
Example 2: correct a specific failure
In a second situation, the team observes running many tests without defining what would change your mind. It proposes the correction: specify what result leads to expansion revision or stopping. Before applying that correction everywhere, the owner checks whether the same cause is present in other affected items. A common symptom alone does not prove that all items need the same repair.
| Decision | Illustrative reasoning |
|---|---|
| Observation | Running many tests without defining what would change your mind |
| Likely correction | Specify what result leads to expansion revision or stopping |
| Verification task | Choose a measurable outcome and a reasonable comparison |
| Result to inspect | Record the decision rule and operational constraints |
After the change, the owner repeats the relevant task and records what happened. If the condition remains, the team returns to the evidence rather than declaring the process complete. The example illustrates a repair cycle; it does not claim a particular commercial result.
Example 3: extend a process after it works
A third team already has reliable records for a small scope and wants to expand. It compares the next group with the first: are hypothesis, mechanism, measure defined in the same way, and does the intended outcome remain experiments producing usable decisions? Differences are written down before the earlier process is reused.
Expansion is appropriate when the assumptions still hold and an owner can review exceptions. Where the new group has different needs, the team adapts the process and tests again. Reusing the structure is useful; reusing a conclusion without checking its conditions is much less reliable.
Apply the examples to your own work
- Write the current problem in observable terms using hypothesis.
- Choose which of the three examples matches the stage of the work.
- Complete this action first: state the problem hypothesis and expected mechanism.
- Keep the resulting evidence and review experiments producing usable decisions on a consistent basis.
Follow one customer journey across the involved teams and systems. A correct step can still create a poor experience when its output is not usable by the next team. Verify the transitions as carefully as the individual tasks.
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