WEBVTT

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- [Instructor] In the
last couple of lessons,

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we identified our target audience,

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recruited them, and conducted interviews.

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In this lesson, we'll
discuss how to debrief

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and synthesize user interviews

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into clear, valuable insights.

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This is a critical step

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in the process of refining user value,

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but it's also the easiest to overlook.

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Even if you've done all the
previous steps correctly,

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you can still stumble at the final hurdle.

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Unless you synthesize those
interviews into clear takeaways

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that you can use to refine the user value,

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all the time you've spent so
far will have been wasted.

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There are three types of problems PMs face

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when they don't synthesize
their interviews

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into clear takeaways.

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First, they might either validate

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or invalidate hypotheses based

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on a small subset of memorable answers

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rather than analyzing all
interviews collectively.

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Let's look at an example from Blue Apron.

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A meal kit delivery app,
Blue Apron used to require

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at least a week's notice
to skip an upcoming order.

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Otherwise, the user would
automatically be billed

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for the following week's meal kit.

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During user interviews,

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one user spent a long time
emphasizing their belief

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that there should be a longer
cancellation window available.

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Their strong feelings might bias

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the Blue Apron PM towards
prioritizing this.

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However, if they were the
only user bringing it up,

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it might not be worth pursuing.

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The second problem is that

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they might generalize
learnings that only apply

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to a subset of the population.

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For instance, GrubHub might
have heard three users say

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that their service is too expensive.

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Based on that, they might
tell the pricing team

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that they should reduce prices,

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but if they debriefed and synthesized

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the information correctly,

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they would see that only users

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under 25 and living in
college towns felt this way.

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This means there's an opportunity

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to provide differential pricing
for this audience segment

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or even define them as an anti-audience

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and focus on working
professionals over 25 instead.

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The third problem is that
they might miss patterns

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that would allow them to
reach deeper insights.

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For example, a PM at Peloton

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might interview two different audiences.

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Users who have purchased their hardware

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and users that only use the app.

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If users across both audiences said

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they wanted strong integration features

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with fitness-tracking apps
like Strava or Fitbit,

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then it's the PM's job to take a step back

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and identify this as a common theme

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regardless of audience segment.

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A PM who doesn't do
this synthesis may miss

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the opportunity to
incorporate this learning

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into their design and development work.

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This is why it's important to invest time

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in translating raw insights

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into a synthesized user value map.

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There are two phases to this process,

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debriefing and synthesis.

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Debriefing happens immediately
after each interview,

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which allows you to identify
the most important insights

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while they're still fresh in your mind.

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It also provides an opportunity

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for you to switch up your approach

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to the next interview based
on what you've learned so far.

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Synthesizing happens at the end

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of the entire interview campaign.

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This step allows you to look
across all your interviews,

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identify patterns, and answer
the initial questions you had

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from your hypotheses when
you started the exercise.

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Let's break down each

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of these processes
starting with debriefing.

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After every interview,

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you should spend a few
minutes writing down

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what you just heard.

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This allows you to begin
identifying new insights

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as well as incorporate lessons learned

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from the previous interview
into the next one,

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ensuring each interview is
more valuable than the last.

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Debriefing has four steps.

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Document the user profile,

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document observations from the interview,

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extrapolate insights
based on observations,

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and evaluate the interview process.

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You can reference the debrief tab

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of the refined user value template

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to follow along as we
walk through this process.

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Let's start with documenting
the user profile.

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It's important to verify
who you've spoken to

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and what attributes they have
from your target audience.

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This will help you group responses
based on user attributes,

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which will help you
identify patterns later on.

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Next, you'll document
observations from each interview.

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These observations should be
about the user problem itself

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and how the user experiences that problem.

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Above all, you want to
make sure you understand

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what problem each user faced.

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This means asking if the
problem was different,

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how was it different?

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What alternatives did they
use to solve the problem?

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Why did they use those alternatives?

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What goals are they trying to achieve

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by solving the problem?

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Answering these four
questions allows you to verify

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if the observation either
supports the hypothesis,

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contradicts the hypothesis, or
introduces a new hypothesis.

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Next, you'll want to
extrapolate insights based

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on your observations.

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The goal of this next step is to translate

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the surface level observations
from the last step

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into something more
meaningful and specific

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to your project and its objectives.

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You can translate observations

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into insights for three
categories, problem takeaways,

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severity takeaways, and
alternative takeaways.

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After you've debriefed the
content of the interview,

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you should evaluate the
interview process itself

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and determine how to proceed.

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This helps you improve your interviews,

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so that you get deeper

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and more meaningful insights each time.

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If you don't take the time

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to evaluate your interview process

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and adjust if necessary,

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you're likely to make the same mistakes,

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face similar challenges across interviews,

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and encounter diminishing returns.

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After completing an interview,

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you can decide to do one of three things.

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One, don't change anything.

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This normally happens when
you're early on in your research

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and haven't yet reached
saturation on your questions.

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You don't want to change
anything right now

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if things are going well
because there's likely more

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to be learned from your existing plan.

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Two, you can adjust your questions.

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This can happen when
you've reached saturation

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on a particular question,

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meaning you're hearing the same thing over

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and over again from users and
aren't learning anything new.

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Three, you can stop interviewing.

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Maybe you're hearing the
same things consistently,

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and you don't think you're
going to hear anything

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remarkably different or gain new insights

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from continuing to have
additional conversations.

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For example, if you've
heard from four participants

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that they have the exact
same concern about something,

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participants five and six aren't likely

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to yield new insight.

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Let's go through a debrief exercise

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using some illustrative responses

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from users that the PM at
Gusto might have interviewed.

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Check out the visual here

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if you need a reminder of the
initial user value hypothesis

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the PM defined with their manager.

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Pause the video and review
these interview notes

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from a conversation with Brandy,

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a CPO at a growing startup.

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Then answer these three questions.

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How do these responses compare

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to the initial problem hypothesis?

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What might you extrapolate
from these responses?

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How, if at all, would you
change your interview approach?

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Let's start by answering
the first question.

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Brandy's responses indicate

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that Gusto was right to hypothesize

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that growing businesses have a ton

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of setup to do when
onboarding new employees

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and that it can be easy to make mistakes.

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The team can also extrapolate
a few key takeaways

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from Brandy's responses.

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A problem takeaway is
that the mistakes made

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include data entry errors
as well as missed deadlines,

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a problem the team hadn't
previously highlighted

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in the user value map.

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In terms of severity,
Brandy's experience of missing

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a deadline which resulted

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in an employee not having benefits

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for the first month of work shows us

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that this type of mistake
can be particularly severe.

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This might indicate a high willingness

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to pay for a robust solution.

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In evaluating their interview approach,

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the team might choose to
probe for deadline tracking

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in future interviews to understand how big

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of a problem this is and for whom.

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This will enable them to
determine whether or not

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this is an issue worth
prioritizing in feature design.

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Now, let's answer the same three questions

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using an interview Gusto
conducted with Joseph,

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the Founder and CEO of
a seed stage startup.

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How do these responses compare

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to the initial problem hypothesis?

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What might you extrapolate
from these responses?

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How, if at all, would you
change your interview approach?

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Let's start with observations.

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Joseph's interview
surfaced a new hypothesis.

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Storing sensitive employee data is painful

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and an important aspect
of an onboarding tool.

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We can extrapolate problem

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and severity takeaways
from these observations.

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The problem may be larger

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in scope than the team
initially anticipated

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including not only data entry,
but also secure data storage

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and work authorization verification.

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Joseph's work authorization mistake

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in particular indicates
that the consequences

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of these onboarding
mistakes may be more severe

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than originally expected.

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This is a high-severity problem.

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In terms of evaluating
the interview approach,

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one adjustment the team
would likely make is

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to start asking questions

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about securely storing employee data

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and managing work authorization

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after hearing about these
problems from Joseph.

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After you've completed and
debriefed all of your interviews,

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you need to synthesize
your learnings across

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all interviews to effectively answer

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the questions you set out to answer.

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If you don't synthesize across interviews,

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you could end up generalizing
anecdotal evidence

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from your most vocal participants

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as opposed to identifying
trends and patterns.

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You could also find yourself
cherry picking responses

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to fit your narrative or hypotheses

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as opposed to learning
new things from your users

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even if they disprove what
you believed initially.

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We'll introduce a five-step process

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to synthesize your learnings.

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To guide us through this process,

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we'll refer to the synthesis tab

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of the refined user value template.

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The first step is clustering based

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on the problems faced by users.

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This helps you see the frequency

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of different problems that users faced

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and whether those problems were similar

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or different to those you hypothesized.

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Generally, you want to take a closer look

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at the problems most frequently mentioned

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by users and determine the
root of those problems.

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This helps you add additional nuance

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to the problem description

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and the reasons behind the problem.

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After you've identified
the problems to focus on,

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you can look for patterns
across user profiles.

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This helps you understand

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if only certain user
profiles experience a problem

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or if all groups of users
experience a similar problem.

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You can ask yourself questions like,

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did different user profiles
experience different problems

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or have different goals?

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Do different user profiles
experience the problem

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at different frequencies?

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Do different user profiles
experience the problem

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at different levels of severity?

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After you've established
user problem combinations,

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you can identify patterns across
alternatives and severity.

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This helps you understand
which problems are more severe

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across user groups and how well

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the alternatives they use are working.

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You can ask yourself questions such as,

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are some problems more severe

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than others across
different user profiles?

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What causes the difference
or delta in severity?

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Do some problems have more
or better alternatives?

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Let's return to our Gusto example

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to practice clustering users
and identifying patterns

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based on the illustrative
responses we shared.

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In Joseph and Brandy's interviews,

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we heard about data entry mistakes,

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missed timelines, and data
storage security concerns.

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Brandy and Joseph represent
two different user profiles.

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Brandy already has HR systems,
while Joseph does not.

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These problems show up in different ways

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for these two types of users.

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For those with existing HR
management solutions like Brandy,

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the most severe problems are data entry

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and missed timelines.

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Secure data storage
isn't much of a challenge

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because the existing tools
provide this functionality.

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Meanwhile, users like Joseph
who don't yet have HR solutions

00:12:04.020 --> 00:12:06.240
struggle primarily with missed timelines

00:12:06.240 --> 00:12:08.250
and secure data storage.

00:12:08.250 --> 00:12:10.110
These problems are high severity

00:12:10.110 --> 00:12:13.050
and the existing manual
approach doesn't offer solutions

00:12:13.050 --> 00:12:14.283
to these challenges.

00:12:15.600 --> 00:12:18.060
Now, that you've identified
key problem clusters,

00:12:18.060 --> 00:12:19.920
profiles and patterns,

00:12:19.920 --> 00:12:23.100
the next step is to complete
your user value map.

00:12:23.100 --> 00:12:25.590
This map should capture
the refined hypotheses

00:12:25.590 --> 00:12:28.680
for how users will get
value from your feature.

00:12:28.680 --> 00:12:30.390
Start with the hypotheses you gathered

00:12:30.390 --> 00:12:33.540
in your manager briefing and
make updates to each based

00:12:33.540 --> 00:12:36.693
on the insights you extrapolated
from your user interviews.

00:12:37.620 --> 00:12:40.740
Let's complete the user
value map for Gusto.

00:12:40.740 --> 00:12:43.110
As a reminder, here are the user profile

00:12:43.110 --> 00:12:45.660
and user problem hypotheses we landed on

00:12:45.660 --> 00:12:47.550
in our manager briefing.

00:12:47.550 --> 00:12:49.470
Pause the video here.

00:12:49.470 --> 00:12:51.870
What changes would you
make to these hypotheses

00:12:51.870 --> 00:12:54.690
based on your insights
from user interviews?

00:12:54.690 --> 00:12:56.370
What additional detail would you add

00:12:56.370 --> 00:12:58.473
to help your team hone in on a solution?

00:13:01.230 --> 00:13:03.540
Let's start by refining our user profile

00:13:03.540 --> 00:13:06.060
and user problem hypotheses.

00:13:06.060 --> 00:13:08.880
The biggest change we need
to account for here is adding

00:13:08.880 --> 00:13:12.000
in the user problem around
secure data storage.

00:13:12.000 --> 00:13:14.730
We can also add some more
information about the severity

00:13:14.730 --> 00:13:18.450
of this problem and the
alternatives that exist today.

00:13:18.450 --> 00:13:21.390
The updated map might say,
"Through conversations

00:13:21.390 --> 00:13:23.610
with potential customers like Joseph,

00:13:23.610 --> 00:13:25.470
the team realized that
this feature could help

00:13:25.470 --> 00:13:28.380
both current Gusto users and non-users."

00:13:28.380 --> 00:13:30.000
The team was also able to hone in

00:13:30.000 --> 00:13:32.340
on specific individuals
that would benefit most

00:13:32.340 --> 00:13:34.440
from this feature, namely founders

00:13:34.440 --> 00:13:37.590
and other executives who
are growing their teams.

00:13:37.590 --> 00:13:40.530
For user problem, the team
validated the pain point

00:13:40.530 --> 00:13:42.210
around making mistakes

00:13:42.210 --> 00:13:45.570
and got more specific about
the common types of mistakes,

00:13:45.570 --> 00:13:48.750
data entry errors, and missed deadlines.

00:13:48.750 --> 00:13:50.910
They also identified a new problem,

00:13:50.910 --> 00:13:53.730
concerns around personal data storage.

00:13:53.730 --> 00:13:56.790
Across all of these problems
in multiple user types,

00:13:56.790 --> 00:13:58.890
the team found that severity was high

00:13:58.890 --> 00:14:01.800
and existing alternatives were limited.

00:14:01.800 --> 00:14:05.790
After refining the user profile
and user problem hypotheses,

00:14:05.790 --> 00:14:08.940
the team decided to slightly
update their user goal.

00:14:08.940 --> 00:14:11.250
Instead of just focusing on efficiency,

00:14:11.250 --> 00:14:12.900
the feature should create a secure

00:14:12.900 --> 00:14:15.300
and efficient onboarding experience.

00:14:15.300 --> 00:14:17.850
The final step of
synthesis is to reevaluate

00:14:17.850 --> 00:14:20.730
the project based on your conclusions.

00:14:20.730 --> 00:14:23.490
If the conclusions you reached
in the last step support

00:14:23.490 --> 00:14:26.490
or add value to your
user value hypotheses,

00:14:26.490 --> 00:14:28.800
then you should continue
exploring the problem

00:14:28.800 --> 00:14:31.293
and move on to refining
the business value.

00:14:32.280 --> 00:14:35.370
However, if the conclusions
contradict your hypotheses

00:14:35.370 --> 00:14:37.260
or disprove key assumptions,

00:14:37.260 --> 00:14:39.690
you should reevaluate if
this is the right problem

00:14:39.690 --> 00:14:42.840
to be working on, or if
other problem hypotheses

00:14:42.840 --> 00:14:46.230
might be more relevant and
impactful for the business.

00:14:46.230 --> 00:14:47.580
If this is the case,

00:14:47.580 --> 00:14:50.160
you can bring a few
options to your manager.

00:14:50.160 --> 00:14:51.780
You can conduct another short round

00:14:51.780 --> 00:14:54.540
of interviews to prove a new hypothesis.

00:14:54.540 --> 00:14:56.760
You can stop working on
the problem altogether

00:14:56.760 --> 00:14:58.710
and move to a different project

00:14:58.710 --> 00:15:02.133
or you can proceed with a
refined view of a new problem.

00:15:03.300 --> 00:15:05.370
When bringing these
options to your manager,

00:15:05.370 --> 00:15:08.250
you should have a recommendation
on how you want to proceed

00:15:08.250 --> 00:15:10.890
and why you want to proceed that way.

00:15:10.890 --> 00:15:14.070
For the Gusto example, the
conclusions reached indicate

00:15:14.070 --> 00:15:17.100
that this feature has the
opportunity to deliver real value

00:15:17.100 --> 00:15:18.990
to the target user.

00:15:18.990 --> 00:15:22.440
Since their user value hypotheses
were largely supported,

00:15:22.440 --> 00:15:25.530
all science point to
continuing the project.

00:15:25.530 --> 00:15:27.810
This completes our interview process,

00:15:27.810 --> 00:15:31.890
which helped us validate our
initial user value hypothesis.

00:15:31.890 --> 00:15:33.120
In the next section,

00:15:33.120 --> 00:15:36.273
we'll explore how to validate
our business value hypotheses.