1 00:00:01,000 --> 00:00:05,400 At this point, we've brainstormed around all the components that we need to start assembling our 2 00:00:05,400 --> 00:00:09,700 hypotheses. So as a reminder, this is what our hypothesis looks like. This is what our 3 00:00:09,700 --> 00:00:13,900 hypothesis statement looks like again. It's We believe We don't know. We 4 00:00:13,900 --> 00:00:17,700 believe that doing this building this particular feature for these 5 00:00:17,700 --> 00:00:21,900 people for Mike, for Jane, for Jane and Mike 6 00:00:22,300 --> 00:00:24,400 will achieve this outcome. 7 00:00:25,600 --> 00:00:29,900 And we'll know this is true. When we see this Market feedback, what is 8 00:00:29,900 --> 00:00:33,800 the evidence that we're looking for, that indicates that we've changed their 9 00:00:33,800 --> 00:00:37,700 behavior? And so what I'd like you to do next is to start 10 00:00:38,100 --> 00:00:42,500 extracting hypotheses from these assumptions and we take an intermediary 11 00:00:42,500 --> 00:00:46,900 step before we jump right into filling this out. And the way we do this is that will 12 00:00:46,900 --> 00:00:50,800 take one of these full-size easel pads and will create this 13 00:00:50,800 --> 00:00:54,900 chart. The sideways chart is horizontal chart that has four 14 00:00:55,200 --> 00:00:59,900 Homes in it. And the First Column is called We Believe and that's 15 00:00:59,900 --> 00:01:02,400 where we put in all of our features. 16 00:01:03,800 --> 00:01:05,600 we believe that these features, 17 00:01:06,600 --> 00:01:07,300 Four. 18 00:01:08,600 --> 00:01:12,700 Mike Jane micro Jane will achieve some kind of outcome 19 00:01:12,800 --> 00:01:16,600 and this is what we're going to measure to prove whether that outcome is 20 00:01:16,600 --> 00:01:20,800 happening or not. Now what we're doing here is we're going to map our post, it 21 00:01:20,800 --> 00:01:24,700 notes to each other in the stripes to start to get a 22 00:01:24,700 --> 00:01:28,300 sense of how these ideas come together as 23 00:01:28,300 --> 00:01:30,700 hypotheses. Now a couple things to keep in mind 24 00:01:32,100 --> 00:01:36,800 There does not have to be a 1 to 1 to 1 mapping here. You could have 25 00:01:36,800 --> 00:01:40,400 several features for one Persona that drive one 26 00:01:40,400 --> 00:01:44,600 outcome, or you could have one feature for both, personas that drive 27 00:01:44,600 --> 00:01:45,800 multiple outcomes. 28 00:01:47,600 --> 00:01:51,800 So, what we're trying to do is we're trying to make sense out of all of these themes, 29 00:01:52,100 --> 00:01:56,500 and to start laying things out into these Stripes from which we will 30 00:01:56,500 --> 00:02:00,900 ultimately extract our hypotheses. Okay? So what we're going to 31 00:02:00,900 --> 00:02:04,800 do next is we're going to create this chart together as a team. You guys can 32 00:02:04,800 --> 00:02:08,900 pull your posted notes in line, or you can rewrite them. It's up to you, we're going 33 00:02:08,900 --> 00:02:12,800 to create these Stripes. That map features two personas 34 00:02:13,100 --> 00:02:14,900 to outcomes and measures 35 00:02:16,100 --> 00:02:16,800 That's our goal. 36 00:02:18,500 --> 00:02:22,900 So as a team, what do you believe to be true? First pick the easy ones 37 00:02:22,900 --> 00:02:26,900 first. You can have to stand up and grab the stuff or your rewrite. It it's up 38 00:02:26,900 --> 00:02:30,900 to you. The easiest way I've seen to do this is to actually just grab the Post-it notes 39 00:02:31,100 --> 00:02:35,500 and move them over into these Stripes but that's again it's up to you whether you want to rewrite them 40 00:02:35,800 --> 00:02:39,900 or or or not. So we 41 00:02:39,900 --> 00:02:43,800 would be taking stuff from these two to prove. 42 00:02:45,100 --> 00:02:46,700 One of those features. 43 00:02:48,000 --> 00:02:52,900 Right. I'm sorry. Features for specific. Let's talk about which who 44 00:02:52,900 --> 00:02:56,400 were building the feature for, and then let's see what 45 00:02:56,400 --> 00:03:00,800 outcomes we can achieve based on those features. What outcomes do you think those features 46 00:03:00,800 --> 00:03:04,800 will drive? And what's interesting is you may run into a situation where feature you've 47 00:03:04,800 --> 00:03:08,800 come up with doesn't make sense for either one of the personas and that's terrific. We don't have to work on that 48 00:03:08,800 --> 00:03:12,900 so you can put will improve communication here. And then how are we going to measure that, 49 00:03:12,900 --> 00:03:14,100 right? Usage of this feature? 50 00:03:16,900 --> 00:03:17,400 Who knows? 51 00:03:20,500 --> 00:03:24,800 So one thing I've experienced before with trying to create 52 00:03:25,700 --> 00:03:29,200 measurable outcomes, Associated features is there, is sometimes a 53 00:03:29,900 --> 00:03:33,800 looking for my keys under the lamp light effect. Yeah, which is where were more likely 54 00:03:33,800 --> 00:03:36,900 to build and emphasize features that are easier to measure. Yeah. 55 00:03:37,900 --> 00:03:40,400 Is there a way to deal with that? Is that okay? 56 00:03:42,300 --> 00:03:46,700 You should work as a so as an organization, you need to work to clearly identify. 57 00:03:46,700 --> 00:03:49,500 What success means to you as a business and enter 58 00:03:49,600 --> 00:03:53,900 Translate that down to the team level. The team has a clear sense of priorities about what's important 59 00:03:53,900 --> 00:03:57,800 to the business and what they should be working on to move forward. So even if those 60 00:03:57,800 --> 00:04:01,200 outcomes aren't easy to achieve the team should be working towards 61 00:04:01,200 --> 00:04:05,600 what's important. So as a team you might want to get together initially intake, 62 00:04:05,600 --> 00:04:09,800 take the output of the outcome exercise. And 63 00:04:09,800 --> 00:04:13,800 prioritize those outcomes to say what are the most important outcomes that we'd like to work 64 00:04:13,800 --> 00:04:17,800 on as a business, which ones do we think will absolutely spell success for us 65 00:04:17,800 --> 00:04:19,600 and focus on ways to improve? 66 00:04:19,700 --> 00:04:23,700 Achieve those. First course it's not always the most important, sometimes it's the easiest to measure. 67 00:04:23,700 --> 00:04:26,700 So, what if some of the most important features of the most difficult to measure? 68 00:04:27,000 --> 00:04:31,800 I don't think that changes the need to focus on them or 69 00:04:31,800 --> 00:04:35,800 prioritize them. You're just going to have to get creative in how to measure them or 70 00:04:35,800 --> 00:04:39,600 what what other things you can measure that would indicate that 71 00:04:39,600 --> 00:04:43,900 that features of that, that metric is moving in the right direction. Yeah. 72 00:04:43,900 --> 00:04:47,300 And it gets difficult especially as you kind of dig deeper into going to be to be situations 73 00:04:47,300 --> 00:04:49,400 where you're removed from the 74 00:04:49,700 --> 00:04:53,400 I'd for from the from the End customer or they're difficult to 75 00:04:53,400 --> 00:04:57,900 find or their behaviors can't be tracked. Digitally, there are 76 00:04:57,900 --> 00:05:01,100 situations where you're going to have to get creative about how to measure those things. 77 00:05:02,600 --> 00:05:06,900 Okay. All right, the last step in this exercise. So we've got our stripes. The last 78 00:05:06,900 --> 00:05:09,700 step in this exercise is to translate our Stripes 79 00:05:10,200 --> 00:05:14,700 into actual hypotheses. And so what I'd like you to do is a team is use that 80 00:05:14,700 --> 00:05:18,300 second at second large easel pad and pull out. 81 00:05:18,300 --> 00:05:22,700 Two, three, two, three or maybe four hypotheses and actually just 82 00:05:22,700 --> 00:05:24,200 translate that chart 83 00:05:25,300 --> 00:05:26,100 Into. 84 00:05:28,300 --> 00:05:32,600 Three to four hypothesis statements that literally read. We believe 85 00:05:32,600 --> 00:05:36,900 that doing this for these people will achieve these outcomes. 86 00:05:36,900 --> 00:05:40,800 So you want to extract because you've got a thumb based on this because you've got it, you've got, 87 00:05:40,800 --> 00:05:44,800 you know, five different features here, six different features here. So let's, let's pick 88 00:05:44,800 --> 00:05:48,400 one. Let's get let's get granular and let's get specific and right, 89 00:05:48,400 --> 00:05:52,600 three or four hypothesis statements based on these 90 00:05:52,600 --> 00:05:56,600 Stripes. Let's extract the hypotheses out of these stripes and 91 00:05:56,600 --> 00:05:57,800 use the syntax. 92 00:05:58,200 --> 00:06:02,900 I believe that doing this for Mike for Mike 93 00:06:02,900 --> 00:06:06,800 and Jane will achieve a specific outcome. Okay so as a team you want to 94 00:06:06,800 --> 00:06:09,600 write let's say three of those. 95 00:06:12,600 --> 00:06:15,900 Okay, so you can have to work together as a team to do this based on this. 96 00:06:17,500 --> 00:06:21,500 Very good and nicely done. And it's, it's not an easy thing to think about, right? 97 00:06:21,500 --> 00:06:25,500 We tend to fall very easily into the feature 98 00:06:25,500 --> 00:06:29,700 world, where it's like, oh well, you know what would be cool? Let's put this in the product and let's build 99 00:06:29,700 --> 00:06:33,600 that. And really what we're forcing ourselves to think about, is what we're trying to 100 00:06:33,600 --> 00:06:37,800 achieve with the feature and who we're targeting and how that helps us as a business, that 101 00:06:37,800 --> 00:06:41,800 helps them as a customer. And once we've developed this list and you 102 00:06:41,800 --> 00:06:45,600 can see, look, we have a fairly finite list with a short amount of time overall to 103 00:06:45,600 --> 00:06:47,100 generate this list but if you're 104 00:06:47,300 --> 00:06:51,900 You through this as a team and you're spending a half day or a day. Maybe a couple of days going 105 00:06:51,900 --> 00:06:55,900 through these exercises, you have the ability and the positive real possibility 106 00:06:55,900 --> 00:06:59,800 of generating dozens of hypotheses about what to 107 00:06:59,800 --> 00:07:03,900 build and how to measure its success and who it's for. And so, 108 00:07:03,900 --> 00:07:07,700 what you need to do is you move forward, is you need to decide how you're 109 00:07:07,700 --> 00:07:11,900 going to progress and to do. So, you 110 00:07:11,900 --> 00:07:15,500 need to be able to test your riskiest assumptions. First, 111 00:07:15,800 --> 00:07:17,100 you have to decide as a team. 112 00:07:17,300 --> 00:07:21,700 Team what which of these hypotheses are the riskiest 113 00:07:21,700 --> 00:07:25,800 to us and start their first. Because if those things cause you to fail 114 00:07:25,800 --> 00:07:29,100 right away, you want to learn that as quickly as possible. 115 00:07:31,000 --> 00:07:35,700 And so as a team you want to get together and decide which of these hypotheses you'd like to test 116 00:07:35,700 --> 00:07:39,900 first and then decide how you're going to test them. And what's really interesting is that as you start to 117 00:07:39,900 --> 00:07:43,700 test and validate these assumptions, you'll learn more about the other 118 00:07:43,700 --> 00:07:47,200 hypotheses and you'll be able to adjust them moving forward. 119 00:07:49,200 --> 00:07:53,900 So we've declared a series of assumptions around who we believe we're building a product for what outcomes 120 00:07:53,900 --> 00:07:57,800 we'd like to achieve and what features We Believe will 121 00:07:57,800 --> 00:08:01,800 drive those outcomes, we've written hypotheses, from those. 122 00:08:03,300 --> 00:08:07,600 And so the last thing that we're going to last assumption that we're going to declare 123 00:08:08,400 --> 00:08:12,900 is, what should this product look like? Well how should it work? Or how 124 00:08:12,900 --> 00:08:16,900 should it flow, what should the workflow look like? And we'll do that 125 00:08:17,100 --> 00:08:19,100 with an exercise called Design Studio.