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  Let's recap what we learned about experimenting and how we can use that to establish a product market fit.
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                      Using this framework of exploring your
business model using a business model
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                      canvas, and conducting a series of
experiments to validate your assumptions,
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                      you will soon end up with a business model
that's scalable and repeatable.
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                      Let's recap what we've learned so far.
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                      After we develop the first iteration of
your business model, we need to.
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                      Conduct experiments to test each
assumption we built in the business model.
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                      Our experiments should engage with
customers and
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                      test their understanding of the model.
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                      Gain a better understanding of our
customers.
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                      Implement the insights gained from
conducting each experiment.
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                      The reason we conduct experiments is so
that we validate our assumptions.
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                      Validating our assumptions is an important
activity,
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                      because validating your ideas before you
build can save you a lot of time and
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                      money by avoiding a situation, where you
build a product that no one wants.
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                      Validating your assumptions gets you
connected with your customers.
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                      Start by validating your problem.
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                      If this test passes, validate the size of
the market, and then the product.
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                      Your experiments should be simple, passed
fail tests that enable you to
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                      quickly gain feedback and move on to
validated learning.
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                      Finally, all experiments should happen
through customer interaction.
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                      When conducting experiments, we are going
to use a system developed by Eric Reese,
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                      called the Build, Measure, Learn Feedback
Loop.
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                      In the Build stage, we start by asking
what metric would confirm our hypothesis?
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                      Once we have our metric, we design the
experiment to gain data on that metric,
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                      by developing our MVP or minimum viable
product.
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                      The MVP is a version of our product that
enables us to
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                      complete a single iteration of the Build,
Measure, Learn,
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                      Loop with a minimum amount of effort and
the least amount of development time.
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                      Once the MVP is done,
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                      we move onto the Measure phase, where we
analyze whether product development
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                      efforts in the Build phase actually
translated to meaningful progress.
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                      After the Measure phase it's the Learn
phase, where we take the insights that we
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                      have gained from this experiment and apply
that to our product or service.
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                      If the test fails, we discard those
assumptions and keep experimenting.
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                      If the experiment validates our
assumption, we persevere and
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                      continue to the next experiment.
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                      If not, we discard the assumption.
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                      If our riskiest assumptions fail, that is
the market, problem, or
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                      product assumptions, then we need to
pivot.
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                      A pivot is a major change to your business
model that's designed to
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                      test a new set of hypotheses and
assumptions.
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                      With each pivot, we discard our previous
business model and start from the top.
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                      We have a new business idea that we need
to explore using the canvas, and
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                      a new set of assumptions that we need to
validate with experiments.
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                      Remember, the point of all this is to find
a business model
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                      that is scalable and repeatable
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