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Lean Startup Machine learnings  

Eric Ries, already mentioned earlier today, also posted this retrospective on Lean Startup Machine, a Boston-based "build an MVP in 48 hours" event.

He makes a few really important points:

For example, one team managed to put together a very decent looking minimum viable product, in the form of a landing page with a “click to signup button” that basically did nothing but collect data about who was clicking. And their MVP even had a reasonably high click rate. But is a 25% click rate validation of the idea? That depends on who’s clicking and why. Do they understand the product? Are they eager to try it, or were they just enticed by the shiny button? Unfortunately, the team had no way of answering these questions. They weren’t even collecting contact information from these first customers. They were just counting clicks.

(...)

This is a classic startup fallacy: “ship it and see what happens.” Whenever you use this plan, you are guaranteed to succeed – at seeing what happens. Unfortunately, if you cannot fail, you cannot learn.

One way to organise the MVP-building process to ensure it does answer real questions is, of course, Hypothesis-Driven Development, covered here before.

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