Move from a funnel problem to hypothesis, experiment, measurement, and next growth decision.
A measurable experiment loop with hypothesis, baseline, test design, outcome, and decision.
You can decide which growth lever should be scaled, adapted, or dropped.
Document experiment results, derive the next hypothesis, and refine tracking or segmentation.
Where does the customer funnel lose people, and which stage should improve?
Pirate Metrics locate whether the issue is in Acquisition, Activation, Retention, Revenue, or Referral.
The affected funnel phase is then analyzed based on data.
At which step and for which cohort does the transition rate fall most?
Funnel Analysis shows baseline, drop-offs, and segments before forming a hypothesis.
The notable drop-offs are translated into a testable experiment hypothesis.
Which uncertain growth assumption will we test, and what result changes our next decision?
The Growth Experiment defines hypothesis, target group, treatment, and success criterion before implementation.
The experiment design is then measured and monitored.
Does B produce the pre-defined meaningful effect over A without breaching guardrails?
A/B testing separates measured effect from chance, seasonality, or intuition.
The result decides whether to scale, iterate, or stop the growth lever.
Artifacts stay collapsed until you actually need them.
Short plan for hypothesis, test design, success criteria, and decision.
# Experiment Plan
## Hypothesis
We believe that ...
## Target audience
Who are we testing for?
## Test design
What exactly will participants or users do?
## Success criterion
We count the test as positive if ...
## Risks and limits
What can the test not prove?
## Decision afterward
If positive: ...
If negative: ...
## Owner and date
...