Of the three jobs that are uniquely specifically important to digital product managers, the first one is identifying product market fit hypotheses and there's a reason that one is first. If you don't do that well and you bring bad ideas or ideas that just aren't going to work no matter what with the user, of which there are many, many features fail, many products fail, then you're going to go here and you can do everything else perfectly released, even have great instrumentation on seeing what happens and you're still going to end up with a big economic zero. One of the oldest maximums of computer science I think is garbage in, garbage out. That applies to this. That's why this is the number one job of the product manager. Of all of them, I would say this is the single most important. What does that specifically mean though, and how do we do it? Well, I like to use this double diamond framework for this work around continuous design. This is an important area of practice for the individual product manager. The idea is that we separate out problem versus solution. That's important because the problems, whether those are habits, desires, jobs, and solutions, they have a very interesting relationship in tech and in digital, which is that the underlying problems or jobs are extremely durable. Underlying needs, behaviors, whether they're for the individual person, a family, a company, they actually frame the way that we're going to frame them. They don't change very much, whereas technology affords new and interesting ways to deliver solutions on those things. That's one of the things that make this a really robust way to think about this idea of product market fit. To bring our hypothesis-driven approach to this a little more specifically, I like to unpack this into over here and write problem. We have a persona hypothesis or who is this person and can we walk up to somebody on the street and say, we're going to ask them a couple of factual questions and that will tell us, yes, they are this buyer or no, they are not. Otherwise, all our work around continuous design, let alone the stuff we released, is just going to be diffused or results are going to be all over the place because a grandparent versus a teenager versus all the other people that are out there, they're very different. People have a lot going on, they have a lot of differences. Then we have this job to be done hypothesis. What is on this person's a-list? For example, if we asked a grownup who happens to be a parent, how many times they cook dinner for their family every week, and they say, well, greater than two, then maybe there are target audience for this food shopping, food prep service that we're thinking of launching, for example. If somebody says less than two, then they're not, and so it's very factual. Then we move on and we have this demand hypothesis. What's nice about this from the standpoint of marrying design and testing is that this allows us to think about all ideas about product market fit, about demand basically in very specific terms, which are that we have a hypothesis that this certain person exists. They have these certain things that are on their a-list regardless of whether they're using our product or not. The demand hypothesis is, well, we have an alternative, a proposition for them that's better enough at doing this job than the current alternatives where they're going to buy our product, use our site, whatever it is we're trying to have happen. Then we move on and we'll look at how we deliver a solution that creates a minimum of friction for them to get that. When we're able to do that reliably for a specific segment, that's when we would say, well, we have product market. We have a durable basis of demand. Back to our iTunes example. Here's what this might look like. We have Miguel, the music lover. This example, the iTunes example is admittedly a little bit dated, but let's say we're in the waning days of buying one song for $0.99, which is how that iTunes Store used to work. Spotify, we'll pretend is out doing design research on how people might want to experience digital music a little bit differently and how they might want to buy it differently. We have the screening question. What's the last thing you bought in the iTunes Store? We have a threshold where if they bought something more than three months ago or they don't know, then they're not Miguel to music lover or our target persona. If they can name the thing they bought and it's within three months, then yes, they are. Right or wrong, that's very specific and testable, and that's what's important here. The alternative right now is that Miguel is buying things on the iTunes Store since at the time they had a huge amount of market penetration, a huge amount of share in that area. Then our proposition is, if we do this certain thing, offer a monthly subscription with access to a big catalog, then we think users will subscribe in this way of framing our proposition as this testable syllogism. If we do this, then the user will do this other thing. That's a great way to bring ideas to your team backstop by a view of the underlying problem, job, habit, desire that you're delivering on and the prevailing alternatives. When we say bring strong, well-researched specific product market fit hypotheses to your team, this is what that might look like, and this is how we're going to unpack that as we go through the course.
