UX Playbook

##7 Rapid testing (experimenting)

We’re looking to evaluate which solutions will help us best create customer value in a way that drives business value. In other words, we want to check if our solutions would be capable of nudging us toward our desired outcome. By testing our hypotheses before making significant changes, we reduce the risk of wasting resources on things that nobody will ever use.

Experiments are not necessarily a complicated scientific process that takes a lot of time and money. They are just a way to break down large risky undertakings into small steps to collect evidence and make sure we build the right thing (and in the right way). How can we check if our assumptions are correct? How can we measure this? What research possibilities do we have access to?

Test card

Always use the test card or something similar to scope experiments. What is the assumption (the hypothesis you're trying to prove)? What will you do to verify it? What will you measure? When is your hypothesis proven (how much is 'enough')?

Learning card

After having executed the experiment, the assigned person(s) can use the learnings card to report on the evidence that was gathered.

Examples of experiments

For different types of assumptions, we can come up with different types of experiments. For desirability and usability assumptions for example, we can design experiments that allow us to evaluate customer behaviour, such as prototyping a design, executing a one-question survey, A/B testing, or just calling a couple of customers.

For feasibility assumptions for example, we typically conduct engineering activities that allow us to understand how difficult something might be to build, such as a research spike or creating a technical PoC.

More examples here.

Two-way door decisions

As data from experiments is collected, we can decide what to do next. Remember that opportunity and solution assessments and prioritisation decisions are all two-way door decisions. Once you choose one, you’ll test whether or not you made the right decisions by experimenting. If you learn through experimentation that you didn’t choose the best opportunity or solution, you can always walk back up the tree and choose another. This means that we might learn that it’s safe to continue with one of our existing ideas, or we might learn that we should throw ideas away and generate new ones, or choose a different target opportunity altogether.

For workshop format, see [opportunity-mapping-w-team](../workflow/explore/opportunity-mapping-w-team "mention")

Tools