I thought it might be useful to do kind of a speed round of pairing situations, where a company has a value hypothesis of some sort with the MVP vehicles that they actually use. So I'm going to put forward a situation to give you a second to think about it and then tell you about what they did. The first example is Dropbox. When Dropbox first came out, there were a bunch of these peer-to-peer file sharing systems where I could share files to several people, there were personal backup systems, but they were all kind of crummy. And Dropbox, their hypothesis as well if we built this so it was really integral to the file system and well executed, then people would use it and pay for it and so forth. And so, the problem was, that was going to be a lot of engineering. And, when they went to the VCs, venture capitalist, to get funding for all this engineering and said, this is what we want to do, what they heard was, look, there's a lot of competitors and they're doing poorly so this doesn't seem like something we want to invest in. So they thought, how can we build an MVP to test this? And their persona was Tom the Techie, so their early adopters who they were very clear about with somebody who does a lot of work in digital, like science fiction and math and stuff and they kind of catered the the solution to that. And the problem scenario job to be done is it's hard to share files between a network of collaborators. It's also nice to have your files be portable is another problem scenario or job to be done that you might know. And the alternatives were there were backup services, there were manual like file systems an engineer would set up and then there were a few alternatives out there that we're all kind of kind of hard to use, frankly. And so, their value hypothesis was, if we created something the file really transparent, it was really awesome, then the users would use it adopted and this category would take off and we'd be at the crest of it. Any ideas on an MVP that might help test this and get the kind of answers that they want which is over all evidence of demand for what they have? And how could you do that in around a week with a minimum of resources? That's the question? The answer of Dropbox was that they created a smoke test where they built a video which was kind of a demo of their solution. And, they put it online and they advertise it on Hacker News and a few places where they knew they would find Tom the Techie. And, they got a ton of signups, were able to raise money, got traction and that was evidence of demand. So just those email signups, just the the amount of attention that this MVP got was enough for them to kind of answer their value hypothesis to go to the next step. Let's take a look at Zappos. And in 1999, they observe that there was this problem scenario around finding the right shoe for somebody who wanted a particular type of shoe but lived in a small city and couldn't buy it. And, consumers were still at this point in the very early stages of being able to be comfortable buying things online. So, basically we have this persona Sam the shoe-hound, he knows what she wants and he knows what size he wants it in. He doesn't have a place to get it. And, that is the problem scenario or job to be done here. The alternatives are that he or she may be does mail-order through a catalog or they just wait until they're taking a trip to a big city to do the shopping. And our value hypothesis is that if we make this accessible online then send the shoe-hound will buy the show online. Again, this was back quite a few years ago before that was an obvious thing that you could just take for granted and not test. Any ideas on what they might do and a good MVP to test this? Well, they created what you might call kind of a mixed smoke test Wizard of Oz MVP. And so basically what they did, they photograph shoes, put them on the Internet on web page. They had to deal with a shoe store and they went and every single time somebody want to shoot, they lost money. This is a reminder because especially taking account of their time. They went to the shoe store, they package up, they mail it to this person just to see how this whole thing would work and validate, if there was demand and where it came from. Remember, the MVP is a learning vehicle, a test vehicle, not kind of a prototype of your actual operational execution. So this was a great MVP that answer the questions they had and Zappos lived happily all thereafter and continues to prosper. Sprig, was a startup that was looking at kind of food delivery, super hot space now, but this was a few years ago. And really what they kind of wanted to provide was for the person that goes to the Whole Foods deli, we'll call her Paula the Professional. They have this job to be done if wanting a nice healthy dinner at a reasonable price. Probably the alternatives that they can go to the store like a Whole Foods or get takeout, but it's really expensive. And their hypothesis was, well, if we offer a nice healthy meal to Paula like like she'd order a cab, on Uber or Ride, then she'll use it and she'd reuse the service. So you have any idea about what they did? because this doesn't require a big complicated infrastructure to do. Well, what they did was that they put together a temporary kitchen and delivery organization, they emailed their circle of friends about this. They used Eventbrite as a way to buy the meal and then they also they looked at who ordered it and whether the email was forwarded, whether the buyer came from their sort of first set of contacts or secondary or tertiary contacts. And, so this was sort of a concierge MVP, and they learn a lot about the user experience, their ability to execute on this and also who came for the service and why. Paul Howe and Associates, this is a talk I saw at the Lean Startup Circle. They were funded for to just basically run a series of MVPs and see if they could find something really awesome these guys, I think could start a couple of other companies. And they wanted to do was, I'm not really clear frank to be honest with you on who the person was, but the job to be done was that I have all this stuff around and I want to know how much it's worth. Because maybe I want to be able to sell it or I'm just curious, this I guess sort of implicitly I would say because I'm sort of formulating this in backwards engineering in a bit. This was the thing that they kind of believed was interesting. And right now this person might go through their credit card statements or their receipts. And their hypothesis was, if we could give people service where they could quickly automatically know how much all their stuff is worth then they would use it, pay for it or something. Not really sure of the revenue model, again, just because it was early stage. What do you think is a good MVP vehicle for them? Well, what they did was they got a few signups from people, they got their passwords to their credit card site with with agreement from them. And then they just went in, dumped all the data and hand created the reports that they would give to the users and they observed whether they looked at these things. What they did with the information? Did they repeat visit them? In this case, they got a negative. Nobody cared, nobody was that interested, but that was good. The alternative of building software machine learning and all the algorithms and infrastructure. They would need to do this would have taken months or maybe a year or two. And instead, they were able to eliminate this idea and give themselves a shot at doing something else. And that's really the point of this type of testing. Another example is there was a photo social startup I was working with and they had a bunch of ideas but few resources. One of their ideas was, hey, there's this person that loves to post up online and they want to just post something that people are going to like. The underlying job to be done I think is to just get attention from other people online, get likes. And, their idea was well, if we build a tool where they can create special dispositions of their photos that a couple different ideas, then people would use this service to make their photos cooler the sort of pre-Instagram, and this would this would become a great startup. And so I said, hey, why don't you just hand create whatever your idea about this thing, this tool you're going to build? Why don't you hand create the output and see if anybody likes it and gives you the likes that you believe or the proposition for you on social media? They tried it out. It did not, none of them were really compelling. So again, that was a win because they spend a minimum of resources doing this and they were able to move on to the next thing. So, those are some ideas on how you might pair MVPs with value hypotheses. And some really interesting, I think very creative, very scrappy, very astute ideas about how you can test your value hypothesis and get the right kind of answer that you need in a very small amount of time. Most of these executions took around a week and that's what's really exciting about Lean Startup and its ability to give you more shots of getting a win.
