A product hypothesis turns an assumption into an outcome you can check. It states what you believe, what you expect to happen and how you will measure whether it happened.
This Designli Academy lesson compares a specific signup experiment with a vague statement about a new design. It also explains why learning about an unfamiliar opportunity can be a useful outcome in its own right.
What you'll learn.
- State the change you want to make and the outcome you expect it to produce.
- Choose a measurable result and a period in which to check it.
- Use the result to learn about the opportunity, even when you have not captured all of its value.
Turn the assumption into an expected outcome.
Start with what you believe to be true. Then say what you think will happen if you act on that belief and how you will measure success. Those parts make the hypothesis useful as a guide for a product experiment.
An assumption alone leaves the expected result unstated. A testable hypothesis makes that result explicit enough for you to compare it with what actually happens.
Give the experiment a metric and a deadline.
Carlos gives a hypothetical signup experiment: reducing a signup form from five fields to two is expected to increase the completion rate from 40% to 60% over the next two weeks.
This is an example of how to write a hypothesis, not a reported improvement or a promised result. It names the proposed change, the current completion rate, the expected completion rate and the period in which the team will measure it.
The completion rate is the metric the team can follow. At the end of the agreed period, the expected outcome can be compared with the observed result.
Make success more specific than liking a design.
The weaker example is a belief that users will like a new onboarding design. That statement does not explain how liking it will be measured or what outcome would count as success.
To apply the lesson to your own work, check whether someone else could read the hypothesis and understand what result you intend to measure. If the result is still open to interpretation, clarify it before using the statement to guide the experiment.
Use the test to make unfamiliar ground clearer.
Carlos compares exploring an opportunity with uncovering a new area of a video-game map. Revealing the area is useful even before you have captured everything within it, because you can see more clearly what you need to do next.
The same idea applies to a product experiment. A measurable outcome gives you a way to learn about the assumption and improve your understanding of the opportunity.
Before building the next experiment, write down the assumption, expected outcome and measure of success. Give the test a clear period, then use what it shows to make the next decision.
