Validated Learning: The ‘Build, Measure, Learn’ loop
In the previous post I introduced the lean startup methodology by Eric Ries. He defines two phases in a startup. There is a Customer Discovery and Customer Validation phase. The first fase is all about proving problem/solution fit. It starts of with exploring the customer segments, validating there is a real problem and deciding on a solution. The second fase starts with validating this solution offer and optimize this into something that a lot of people will actually use, and more important, pay for. If you can also grow the number of those users rapidly, you are ready to scale.
The first phase is all about validating if there is a customer that has a problem at all. Whereas the second phase is concerned with optimizing your solution offer into one that a lot of people will actually pay for to use. Although the experiments are likely to be different depending on the fase that your startup is in, the goal is the same.
The goal is to learn.
As with any new idea, the boxes in your businessmodel or lean canvas are filled with a lot of assumptions and therefor a lot of options. It is all about finding the few that really matter. Test if assumptions are true before building upon it. Not every assumption needs to be tested though. You start of by identifying the riskiest assumptions on the canvas. The one that, if not true, will have your whole idea come tumble down like a house of cards. From there you learn, update that information into your canvas and decide what is most critical to learn next.
Traditional product or service development cycles, look something like this; decide on a product, do research, build the product, put it out there and see how people react to it. The time between the idea and putting something out there is a rather long one. In this period, typically nothing is learned at all. Only when the physical product or service finally hits the market and people interact with it, something happens. By than it is usually to late to fix problems is the product is not working, all the while spending time and money. The lean startup methodology aims to increase learning and decrease risks. The goal is to learn and iterate all the way to something that works.
Business Science
But how do we learn? For every assumption that needs to be tested, you design an experiment to test this. Applying a scientific method. Business science that is, a ‘pseudo’ scientific approach optimized for time and money.
The scientific approach lies in the fact that every ‘experiment’ that you run, whether this is a customer interview, an MVP or an A/B test, should validate an hypotheses. The criteria for this hypothesis to fail or succeed should be decided upon by the team and written down before the experiment. The criteria should be based on business value. (Although there are no hard numbers here, it helps to count back from the bigger picture. i.e. What is the amount of people that need to say yes if you look at it from a market segment perspective and the percentage you need to have a viable business.) Build an experiment, Learn, update the information from the experiment on your canvas and make the next step or decision.
Startups exist not to make stuff, make money, or serve customers. They exist to learn how to build a sustainable business. This learning can be validated scientifically, by running experiments that allow us to test each element of our vision -Eric Ries
The startup experience is really a series of experiments. Every single experiment is build to learn. Your Business Model or Lean Canvas is not filled in at once or only once, but is something that you iterate upon. It makes sense to look at your canvas as the product of your startup. Your goal is to learn and iterate this canvas into a working and scalable business model. Experiment by experiment.
