The BML cycle emphasizes a feedback loop that allows you to create a minimum viable product (MVP), measure its performance with real users, and then learn from the results to iterate and improve the product or service. This approach minimizes wasted effort and resources by focusing on validated learning and quick adaptation to customer needs.​

Tips for use​

Despite what the name of the tool might suggest, it's important not to start with building but rather with learning. By focusing on learning first, you ensure that whatever you create is purposefully designed to validate or invalidate your assumptions, leading to more effective and meaningful outcomes.​

Make a large print of the template to use in a brainstorming session with the (experiment) team. Use sticky notes to add items on the canvas, so you can easily make changes.​

Be inspired by the example of Lille.​

How to use ​

Step 1: Learn​

Start with defining what you need to learn about the market, the user, or the problem. What assumptions are you making about your users or solution? These hypotheses will guide what you build and help you focus your experiments.​

Not all ideas need to be tested at once. Prioritize experiments that will provide the most valuable insights with the least amount of effort. This way, you can quickly validate or invalidate critical assumptions.​

Step 2: Measure​

Before launching your experiment and building your solution, define what success looks like. Identify specific metrics that will tell you whether your hypothesis is valid. These metrics should be actionable and directly tied to the solution you're testing.​

Step 3: Build​

Determine what (if anything) you need to build to measure and learn.​

When planning what to build, aim for the smallest possible version of a feature that can test your hypothesis: a minimal viable product (MVP). This MVP approach ensures you invest minimal resources while still gathering meaningful data.​

Learn, measure and build (again)​

Plan for quick feedback loops by setting up ways to gather user data as soon as your experiment goes live. This could involve analytics tools, surveys, or direct user interviews. Based on the data you collect, be prepared to pivot or iterate on your original plan. If the results don’t align with your expectations, use the insights to adjust your product or hypothesis and re-enter the BML cycle.​

​Document Learnings​

After each experiment, document what you’ve learned and how it will influence the next steps. This helps in making informed decisions and keeps the entire team aligned on what works and what doesn’t.​

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