State the hypothesis

Describe the audience, proposed intervention and expected customer behaviour. Avoid vague goals such as improve awareness when the real decision concerns purchase intent. The hypothesis should make it possible for a reasonable result to challenge the team’s original belief.

Choose the decision measure

Select a primary outcome and a few guardrails, such as qualified enquiries, activation quality or support demand. Define the observation period and the audience included. If the experiment cannot distinguish the outcomes relevant to the decision, redesign it before committing more resources.

Record what changes next

Say what the team will do if the result is encouraging, weak or inconclusive. Keep an experiment log that includes unsuccessful tests and changes made during delivery. Without that record, organisations tend to remember attractive results and repeat expensive mistakes. Include a short description of what must remain unchanged during the experiment. If pricing, audience and follow-up process all shift midway, the final result may still be interesting, but it will answer a different question from the one originally proposed.

Imagine this

Illustrative operating example

A hypothetical test asks whether a workflow-based landing page generates more qualified corporate enquiries than a general feature page. The team measures qualification quality and sales acceptance as well as form submissions, preventing a misleading victory based on volume alone.

Make it practical.

Use this checklist as you read. Checks are not stored.

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