At this point, we've brainstormed around all the components that we need to start assembling our
hypotheses. So as a reminder, this is what our hypothesis looks like. This is what our
hypothesis statement looks like again. It's We believe We don't know. We
believe that doing this building this particular feature for these
people for Mike, for Jane, for Jane and Mike
will achieve this outcome.
And we'll know this is true. When we see this Market feedback, what is
the evidence that we're looking for, that indicates that we've changed their
behavior? And so what I'd like you to do next is to start
extracting hypotheses from these assumptions and we take an intermediary
step before we jump right into filling this out. And the way we do this is that will
take one of these full-size easel pads and will create this
chart. The sideways chart is horizontal chart that has four
Homes in it. And the First Column is called We Believe and that's
where we put in all of our features.
we believe that these features,
Four.
Mike Jane micro Jane will achieve some kind of outcome
and this is what we're going to measure to prove whether that outcome is
happening or not. Now what we're doing here is we're going to map our post, it
notes to each other in the stripes to start to get a
sense of how these ideas come together as
hypotheses. Now a couple things to keep in mind
There does not have to be a 1 to 1 to 1 mapping here. You could have
several features for one Persona that drive one
outcome, or you could have one feature for both, personas that drive
multiple outcomes.
So, what we're trying to do is we're trying to make sense out of all of these themes,
and to start laying things out into these Stripes from which we will
ultimately extract our hypotheses. Okay? So what we're going to
do next is we're going to create this chart together as a team. You guys can
pull your posted notes in line, or you can rewrite them. It's up to you, we're going
to create these Stripes. That map features two personas
to outcomes and measures
That's our goal.
So as a team, what do you believe to be true? First pick the easy ones
first. You can have to stand up and grab the stuff or your rewrite. It it's up
to you. The easiest way I've seen to do this is to actually just grab the Post-it notes
and move them over into these Stripes but that's again it's up to you whether you want to rewrite them
or or or not. So we
would be taking stuff from these two to prove.
One of those features.
Right. I'm sorry. Features for specific. Let's talk about which who
were building the feature for, and then let's see what
outcomes we can achieve based on those features. What outcomes do you think those features
will drive? And what's interesting is you may run into a situation where feature you've
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
so you can put will improve communication here. And then how are we going to measure that,
right? Usage of this feature?
Who knows?
So one thing I've experienced before with trying to create
measurable outcomes, Associated features is there, is sometimes a
looking for my keys under the lamp light effect. Yeah, which is where were more likely
to build and emphasize features that are easier to measure. Yeah.
Is there a way to deal with that? Is that okay?
You should work as a so as an organization, you need to work to clearly identify.
What success means to you as a business and enter
Translate that down to the team level. The team has a clear sense of priorities about what's important
to the business and what they should be working on to move forward. So even if those
outcomes aren't easy to achieve the team should be working towards
what's important. So as a team you might want to get together initially intake,
take the output of the outcome exercise. And
prioritize those outcomes to say what are the most important outcomes that we'd like to work
on as a business, which ones do we think will absolutely spell success for us
and focus on ways to improve?
Achieve those. First course it's not always the most important, sometimes it's the easiest to measure.
So, what if some of the most important features of the most difficult to measure?
I don't think that changes the need to focus on them or
prioritize them. You're just going to have to get creative in how to measure them or
what what other things you can measure that would indicate that
that features of that, that metric is moving in the right direction. Yeah.
And it gets difficult especially as you kind of dig deeper into going to be to be situations
where you're removed from the
I'd for from the from the End customer or they're difficult to
find or their behaviors can't be tracked. Digitally, there are
situations where you're going to have to get creative about how to measure those things.
Okay. All right, the last step in this exercise. So we've got our stripes. The last
step in this exercise is to translate our Stripes
into actual hypotheses. And so what I'd like you to do is a team is use that
second at second large easel pad and pull out.
Two, three, two, three or maybe four hypotheses and actually just
translate that chart
Into.
Three to four hypothesis statements that literally read. We believe
that doing this for these people will achieve these outcomes.
So you want to extract because you've got a thumb based on this because you've got it, you've got,
you know, five different features here, six different features here. So let's, let's pick
one. Let's get let's get granular and let's get specific and right,
three or four hypothesis statements based on these
Stripes. Let's extract the hypotheses out of these stripes and
use the syntax.
I believe that doing this for Mike for Mike
and Jane will achieve a specific outcome. Okay so as a team you want to
write let's say three of those.
Okay, so you can have to work together as a team to do this based on this.
Very good and nicely done. And it's, it's not an easy thing to think about, right?
We tend to fall very easily into the feature
world, where it's like, oh well, you know what would be cool? Let's put this in the product and let's build
that. And really what we're forcing ourselves to think about, is what we're trying to
achieve with the feature and who we're targeting and how that helps us as a business, that
helps them as a customer. And once we've developed this list and you
can see, look, we have a fairly finite list with a short amount of time overall to
generate this list but if you're
You through this as a team and you're spending a half day or a day. Maybe a couple of days going
through these exercises, you have the ability and the positive real possibility
of generating dozens of hypotheses about what to
build and how to measure its success and who it's for. And so,
what you need to do is you move forward, is you need to decide how you're
going to progress and to do. So, you
need to be able to test your riskiest assumptions. First,
you have to decide as a team.
Team what which of these hypotheses are the riskiest
to us and start their first. Because if those things cause you to fail
right away, you want to learn that as quickly as possible.
And so as a team you want to get together and decide which of these hypotheses you'd like to test
first and then decide how you're going to test them. And what's really interesting is that as you start to
test and validate these assumptions, you'll learn more about the other
hypotheses and you'll be able to adjust them moving forward.
So we've declared a series of assumptions around who we believe we're building a product for what outcomes
we'd like to achieve and what features We Believe will
drive those outcomes, we've written hypotheses, from those.
And so the last thing that we're going to last assumption that we're going to declare
is, what should this product look like? Well how should it work? Or how
should it flow, what should the workflow look like? And we'll do that
with an exercise called Design Studio.