English
5min
Module 1: The Lean Startup Mindset
Module 2: Defining Your Vision
Module 3: The Build-Measure-Learn Feedback Loop
Module 4: The Minimum Viable Product (MVP)
Module 5: Validating with Customers
Module 6: Pivoting or Persevering
7/23 Lessons
Content
Assignement
The Build-Measure-Learn (BML) loop is a continuous process that forms the heart of the Lean Startup methodology. Its purpose is to guide you in systematically testing your hypotheses. It's an iterative cycle that helps you turn ideas into products, measure customer reactions to those products, and learn whether to pivot or persevere.
1. The Loop Explained
Step 1: BUILD
The first step is to build a "Minimum Viable Product (MVP)". As we'll discuss in more detail in the next module, an MVP is not a half-finished product. It's the version of a new product that allows you to collect the maximum amount of validated learning with the least amount of effort. Your goal here is not to build a masterpiece, but to build a learning tool that can test your Leap-of-Faith Assumption.
If your assumption is that "customers will want to order eco-friendly meal kits online," your MVP isn't a complex website with a full payment system. It could be a simple landing page with a sign-up form and a few photos of your planned meals. You're building just enough to test your core assumption.
Step 2: MEASURE
The second step is to "measure the results" of your MVP. This is where you collect data to see how customers are actually behaving. This is not the time for "vanity metrics" (like the total number of website visits), but for actionable metrics that directly relate to your hypothesis.
For our meal kit landing page, an actionable metric would be the number of people who enter their email to sign up for early access. A less useful metric would be how many people simply saw the page. You need to focus on metrics that prove your customers are taking an action that validates your assumption.
Step 3: LEARN
The third and most crucial step is to "learn from the data". This is where you analyze the results of your experiment and decide what to do next. The data you collected will tell you if your hypothesis was right or wrong.
