This week, we're going to really dive into testing and analyzing the demand or value hypothesis. The foundation for this work is a good strong persona hypothesis and job to be done or problem hypothesis like the one we have here, linked to Trent the Technician. You can see the relationship, we're saying basically that if we do these things over here, this proposition is going to be better enough than this alternative at doing this job that Trent is going to come use this tool and it'll improve his outcomes. A real question is, how do we decouple the demand or the value part of that from the rest of our work and test it using the tools of Lean Startup? This is a set of analytical tools that are specifically designed to test motivation. So the basic idea here is that we've got motivation, we've got usability. This fog curve is a great way to think about the relationship between these things. It turns out that the tools to isolate and test these are actually pretty different. Sometimes, all we can do is observe from afar. When we're observing customers out on the Internet using our product, all we can observe is action, did they use it or did they not use it? We don't know to what extent necessarily that was a function of motivation versus ability or usability. But what we're going to start with here is ways that we can isolate each of these and test them separately because that gives us specific better conclusions that we can act on and we can always pair those with future analytics where we're just looking at the phenomenon of the customer behavior. If you've heard of Lean Startup, this is the core process. We have this idea of build, measure, or learn. Really starting with learn and forming hypotheses. Building stuff to test them, and then measuring the outcomes through experiments and iterating. I like to unpack this into the scientific method, the scientific process where we start with an idea, we make that idea testable, and we design experiments to test this. That's really where the practice of Lean Startup comes into play and is important. There are a lot of ideas about how to use MVP or minimum viable products, which ideally are just product proxies, things that we don't actually have to build out that are real products to test this demand of value hypothesis. We run our experiments and then we get a definitive result of pivot or persevere moment. So what we're going to look at is really the detail of how we would organize structure such an experiment and some experiment vehicles that are popular for pairing with different situations to test motivation, tests the demand hypothesis.