Disruptive by Design: Uncovering Game-Changing Insights (with Larry Marine)

Ever wonder how certain products feel inevitable the moment they appear—rearranging entire markets overnight? In this episode of The Design Psychologist, Thomas sits down with UX pioneer Larry Marine to unpack the mechanics of truly disruptive research—the kind that yields insights so fundamental they can’t be unseen.

Most teams unknowingly skip a handful of critical research steps, blinding themselves to the knowledge that changes everything. Larry shows us how treating users, tasks, and entire processes as flows of knowledge reframes both what you look for and what you ultimately build. Along the way we probe why familiar tools like personas sometimes help—and sometimes hurt—and how principles from cognitive science give sharper edges to every question we ask. 

🔍 You’ll learn 

• What makes research “disruptive.” Why some methods surface game-changing insights while standard approaches miss them.
• The critical steps most teams skip. How a small shift early on can rewrite both your findings and your final design.
• Knowledge-centric mapping. Viewing users and processes through the lens of knowledge—revealing needs that action-based models overlook.
• Where personas really belong. When they clarify design decisions and when they get in the way.
• Cognitive science in practice. Concrete ways to align products with how people actually think and behave.
• A self-audit toolkit. Practical prompts to evaluate (and radically improve) your current research workflow. 

Whether you’re launching a start-up or steering a mature product team, this conversation arms you with a sharper lens and actionable tools to uncover deeper, more market-shaking insights—before someone else does.

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Chapters

• 0:04 Game-Changing Insights from Research
• 1:29 The Power of Disruptive Research
• 4:44 Observational Insights and User Behavior
• 4:51 The ProFlowers Success Story
• 9:44 Understanding User Needs and Market Gaps
• 10:58 Bridging Knowledge Gaps in Design
• 12:10 The Role of Cognitive Science in UX
• 17:35 Methodical Paths to Disruption
• 26:37 Navigating User Research Challenges
• 33:18 From Personas to Knowledge Profiles
• 39:51 Ensuring Continuity in UX Deliverables
• 42:32 Prioritizing Tasks in UX Strategy
• 44:56 Designing for Knowledge Dependency
• 51:34 Complex Scenarios in User Experience
• 56:23 Crafting a Clear UX Strategy
• 1:00:40 Rethinking Research for Disruption

Transcript

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How can we find game-changing insights from our research?

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We're talking about the kind of insights that lead to viral apps or services

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that seem so obvious after they become the new normal.

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If we set out to create something groundbreaking, how can we capture the right

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knowledge at the right time, which would be sometime early on in the project?

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In other words, what method can we follow to find the revolutionary answer we're looking for?

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What if I told you that most teams are skipping certain particular steps that

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could have changed both what and how they designed?

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Today, I'm excited to share a conversation I had with Larry Marine,

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who I'll introduce momentarily.

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Together, we explore questions like, what is disruptive research?

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And how does it reveal insights that get missed by more commonplace methods?

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What happens when we view users and processes in terms of knowledge?

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And where do personas fit into all this?

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And how can cognitive science help us build products and services that support

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how people think and behave?

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By the end of this episode, you'll have a clearer lens for evaluating your own

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research practices, and you'll have a set of tools that you can use to uncover

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insights that are deeper and for my entrepreneurial folks,

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more disruptive to your marketplace.

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So let's have a listen as I introduce and speak with today's guest.

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Welcome to the Design Psychologist podcast. I am your host, Thomas Watkins,

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and I have a really special guest today.

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I'm really excited to have Larry Marine, who is a veteran UX researcher.

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He started back in the day as a cognitive scientist and a human factors professional,

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and he's accumulated over three decades of success stories spanning across a very wide scope

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of applications, military, all types of B2B, all types of B2C.

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He is a methodologist who is really good at distilling down these steps and

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the reason why you're doing those steps when doing a methodology.

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We're going to talk about some of those today.

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And he works with his distinguished colleague, Debbie Levitt.

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Larry Marine is the author of some very

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influential blog articles including task analysis

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the key ux design step

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everyone skips that's when i first discovered him he wrote that back in 2014

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it's all over the internet and i discovered uh larry through that work and he

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is also the author of disruptive research do you also have your copy there okay

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you do you have to hold it in front of you because you're for people on video

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are holding up copies of the book.

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Yes, this is my current favorite book.

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I highly, very much would recommend all UX researchers or people interested

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in UX research, or even strategy should be reading this book.

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And we'll talk about that today.

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Larry Marine, thank you for being on the show today.

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Oh, thanks for having me. Glad to be here. Awesome. Awesome. Glad to have you.

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So I laid out some of your background there.

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Is there anything you'd like to add to that to help give people context of,

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you know, what kind of perspective you're going to be coming from today?

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So I think one of the things that a lot of people don't know is I was in the

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first graduating class of cognitive scientists in the world.

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And Don Norman was my professor. I had him for like five or six classes.

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So I'm what you call

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original gangster that's right he is

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an og yeah and

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that was in 88 correct uh that's when

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i started that program i graduated in 90 nice nice

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and so yeah that's during the time that really you know if you wanted to give

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a sort of a birth date to what we call modern usability i think it would be

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somewhere around there that's one of the some of the major books were starting

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to hit the market That's when Don Norman published his book,

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Design of Everyday Things Actually,

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back then it was called Psychology of Everyday Things And it got put in the

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wrong bookshelf at the stores So he changed the name It reflected it's more

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about design than it is psychology Although it really gets into understanding

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the human from the psychology perspective Rather than just,

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oh, they're a user But what does that really mean?

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Exactly. So, and I'm glad you pointed that out. That's a good piece of history.

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I think that's interesting.

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And so let's start off by getting into the use case.

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You've got lots of use cases out there, but one of the ones that people will

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mainly come across from you is the ProFlowers example. That's my best story.

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That's the best story. And I think it's a nice beachhead. It is easy to understand,

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like anybody can understand the use case.

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And I think it highlights this phenomenon that it's easy to think that something

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is obvious now, after it's been created, after something's been around for a

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while, after things are a certain way, it's easy to hear, oh,

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of course you would do it that way.

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But there is no of course, it has to be, it has to, people have to arrive at

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this. So tell us a little bit about the ProFlowers example, anything you want

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to say about it for folks who haven't heard about it.

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Yeah, so it was interesting.

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I like that perspective of, oh, it seems natural now.

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But when we did that, that was back in 97, nobody had done a website,

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a flower website, like the way we did for ProFlowers.

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And the big difference was people

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were designing it based on what they knew so florists were

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designing florist websites and what do florists

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do they build bouquets well when we

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went out and did observational research in the brick

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and mortars instead of watching people build you know

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try to use a flower shop online we went out and watched people to use a flower

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shop in a brick and mortar and what we learned was you know just observing the

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guys would walk in and most of the people buying flowers at the time were guys

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buying flowers for women guys would walk in.

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And they'd look at the display case clerk would come up says can I help you

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says yeah I need some flowers oh sure that's why you're in a flower shop I get

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that now what do you think the next question was that the clerk asked it wasn't

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what flowers do you need it was why do you need them.

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Right it was not about the flowers it was about

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the occasion or the reason and so we designed

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the pro flowers around that so instead of organizing

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the flowers by the flower types chrysanthemums wrote you know florida roses

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we organized the bouquets around the occasion that they were appropriate for

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not every occasion works for every or uh not every bouquet works for every occasion You know,

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you don't want to give flowers that are a sympathy bouquet to somebody when

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they're on their 30th birthday. That's sending the wrong message.

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But the thing is, like I said, no other websites were organized that way at all.

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And it wasn't until ProFlowers came out that all of the other websites changed.

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So here's a little side story that you probably never heard before.

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And it's in the book but um when

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we designed pro flowers we gave the design

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spec basically it was like this screen

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has to have this this screen has to have that but you can change the graphics

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if you want they gave this to a contract house out of chicago and valentine's

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came and a couple weeks later we get a call back from this the management of

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pro flowers saying we want our money back.

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What this is yeah the website failed miserably

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so i looked at it pulled it

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up on the website looked at my spec and go whoa so i

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had them come in and i walked through this is

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what they does they delivered to you but

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this is what i designed for you and so

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they called the developers and said what up and

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they said well you know didn't look like any of the other websites

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so we made some changes and those

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changes failed terribly and so they

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they told them rebuild it the

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way we told you to build it and it's not going to

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cost us any money otherwise we'll sue you for

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everything that we paid you well they rebuilt it and launched it on mother's

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day now at that time the average website was converting it around one to two

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percent pro flowers on the day that they launched It started converting,

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meaning convert a visitor to a buyer at 26%.

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And it stayed that way every month for 20 years.

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So that was a good A-B test of like, this is the way the developers wanted to do it.

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This is the way we wanted to do it. And which way do you think won?

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Obviously, you know, ProFlowers sold for almost a half a billion dollars here

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a few years ago. For a flower shop.

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That's incredible. And so not only was it a game changer for the company,

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it was an industry changer.

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And in fact, you talk about that

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a lot in the book about these kinds of industry changes and standards.

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Yeah, because a lot of people don't do the user research.

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They don't understand the users well enough to really look into what the problem

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is that they're trying to solve. What happens, I see a lot, is one competitor

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starts off with something and says, we have this green widget.

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Everybody else makes green widgets after that.

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Well, nobody ever thought to investigate, is that really the problem?

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So they end up solving the wrong problem very well.

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And if you go out and do the research and you look at beyond just the use of that one product,

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If you start looking at the whole spectrum of the user's experience or the customer's

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experience or the employee's experience from beginning to end,

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you find that there's a whole lot missing.

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So there's this one product I worked on that the company, my client,

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was focused on the fifth step of a five-step process.

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And what they had was a report that came out and people were complaining about the report and all that.

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Well, I went out and watched the entire five-step process. It was an all-night affair.

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It was for blood screening labs. It turns out that they weren't complaining about the report.

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They were complaining about they didn't trust the whole process.

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And so what I designed was as a management process or a management tool.

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Checked up on the five steps of the process to make sure no data was lost,

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no data was confused or anything like that.

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And so the end result was the lab directors, when they got that final report

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at the end of the night that, oh yeah, everything was fine, all of this blood

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is okay to release to the hospitals,

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they felt confident, they trusted the results.

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And that was what the real problem was. It wasn't the format of the report.

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It was the trust that everything went smoothly all night.

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And you wouldn't know that if you just looked at a list of feature requests

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or, you know, requirements and just went by that.

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Right. And so when you think about user psychology and a lot of people ask customers,

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well, what's your problem? What do you need?

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You know, psychologically, that's difficult for people to answer.

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And what they do instead of describing the problem is they describe a solution

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that they think captures the problem.

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And you're left to reverse engineer that.

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But if you don't understand psychology and understand that people don't describe

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their problems as well as they describe a potential solution, you would miss that.

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And you would try to basically build what they told you. But they weren't telling

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you everything that they know.

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Yeah. So now your book gets a lot into the psychology, specifically knowledge.

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Knowledge is a very central concept in your book.

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And I love that. Um, yeah, that's the big differentiator.

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You know, uh, I focus a lot on task analysis, but you know, task analysis has been around a while.

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Uh, Joanne Hackers and Jenny Reddish wrote a book about task analysis and I've

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referred that quite a bit.

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It's a good book, but now what I've done is taken task analysis and added in

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that, that knowledge component.

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Because when you think about it, what you're trying to solve with,

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with respect to your design is how do you impart your knowledge into the product

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or design such that the user doesn't have to think about things that they may not know.

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Good example, the pro flowers example. Guys don't know anything about flowers.

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They don't know what the right flowers or bouquets are for forgetting their

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wife's birthday, right?

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And so when instead of organizing it by flowers,

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we organized that by the occasion all the

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person had to do was recognize the occasion oh yeah there's birthday i'll look

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at the birthday bouquet oh my wife likes purple i'll take that purple bouquet

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they knew that they were getting the right bouquet for the right occasion because

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pro flowers imparted their knowledge of what was right into the design.

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And I think a lot of the reason why a lot of companies get it wrong is because

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they're just living in their own situation.

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So like, you know, you might be if you're a flower shop doing it wrong on the

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web, you'll say like, OK, well, here are the roses that we sell here, the tulips.

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Here are the things. And they're thinking almost in terms of their inventory.

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Well, the user shouldn't have to think on those terms.

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Right, right. You should be thinking of that for them.

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Your job is to identify what knowledge they already have going into a task and

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what knowledge they're going to need in order to solve that problem, that task.

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And then you have to bridge that

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gap by imparting your knowledge into the design in some form or another.

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And there's really four basic ways to do it.

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There's examples, templates, intelligent defaults, and best practices.

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And so so like apple great

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example of a best practice approach when the mp3s first came out they were all

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the rage and but they were pretty clunky to use first you had to go to mp3.com

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to be able to use to find your music download it create a directory on your computer.

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Go to music match to create a playlist of the songs that you had take that playlist

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and then upload it into the device with its proprietary software,

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and then you finally played it.

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Think about that. That was pretty technical, especially when,

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what, 25 years ago when they came out.

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That was a pretty technical task, and the average mainstream user couldn't do it.

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And actually, MP3 sales were starting to flat, and they weren't selling that

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many more MP3s because you had to be a techie to do that.

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So Apple looked at it, and they came out with the iPod, but it wasn't the iPod

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that made the difference.

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It was marrying the iPod with iTunes in the Apple Store that made all the difference.

00:16:24.749 --> 00:16:29.549
Because you would go to iTunes, find your music, and it would automatically

00:16:29.549 --> 00:16:35.749
download into the device when you went to charge it out.

00:16:35.909 --> 00:16:40.329
It would automatically upload the music in there for you. and it created four

00:16:40.329 --> 00:16:43.529
generic playlists, album, artist, genre, and decade.

00:16:43.969 --> 00:16:46.589
And then you could make your own if you wanted, but you didn't have to.

00:16:47.109 --> 00:16:52.829
So they basically used their knowledge of what a best practice approach was.

00:16:52.929 --> 00:16:57.249
So all you had to do is find your music, the system took care of the rest,

00:16:57.409 --> 00:16:59.829
and then play the music. It was that simple.

00:17:00.269 --> 00:17:07.889
And that's when the iPod went from, I think they sold 650,000 units of MP3s

00:17:07.889 --> 00:17:12.709
worldwide and the iPod in their third year sold like 20 million units.

00:17:13.787 --> 00:17:19.767
It was disruptive. And the, you know, it's the person who's using it wants a

00:17:19.767 --> 00:17:21.287
song. They want to listen to music.

00:17:21.547 --> 00:17:25.907
They don't want to figure out what kind of file and then they're downloading

00:17:25.907 --> 00:17:27.287
it and they're storing it.

00:17:27.667 --> 00:17:31.387
That's not. And so the Apple did a good job of meeting people where they are

00:17:31.387 --> 00:17:35.587
and achieved disruption like that. Well said, yes.

00:17:36.327 --> 00:17:41.787
Yeah. And so in your book, you talk about, I mean, disruptive is in the title disruptive research.

00:17:42.227 --> 00:17:48.627
And I find that you're getting at a concept that is a often romanticized concept.

00:17:49.407 --> 00:17:53.907
And, you know, innovators, they say, well, we're disrupting and we want to,

00:17:53.947 --> 00:17:55.447
you know, innovate and disrupt.

00:17:56.027 --> 00:18:00.047
And but they don't talk about how to achieve it. And you kind of get the idea

00:18:00.047 --> 00:18:05.307
that it just comes from, you know, an innovator business person and a turtleneck

00:18:05.307 --> 00:18:08.467
comes out on stage and they say, I've come up with a great idea.

00:18:10.287 --> 00:18:13.287
And then people try to repeat this.

00:18:13.487 --> 00:18:18.027
And a lot of founders, I'm sure you know a lot of founders, I do as well,

00:18:18.247 --> 00:18:21.887
where they're trying to come up with the next idea and you can tell they want

00:18:21.887 --> 00:18:24.967
this kind of a groundbreaking thing and it's hard to achieve.

00:18:25.507 --> 00:18:33.807
You and your book get into actually methodically how to arrive at the key insights

00:18:33.807 --> 00:18:37.487
that could lead to a disruptive invention.

00:18:37.927 --> 00:18:41.407
So do you want to unpack that a little bit for us? This, this idea of getting

00:18:41.407 --> 00:18:44.107
to those, those unmet needs?

00:18:44.427 --> 00:18:48.367
Yeah, I was actually thinking about that when I was telling the last story.

00:18:48.747 --> 00:18:52.807
So yeah, there is a method and that's what I learned from Donna Norman,

00:18:53.067 --> 00:18:57.767
by continually breaking things down into smaller chunks as you go along.

00:18:58.087 --> 00:19:02.267
You start out with the big picture. You do some, some research on the users

00:19:02.267 --> 00:19:05.827
or the problem domain. Then you go out and you watch the users.

00:19:06.367 --> 00:19:11.227
And by watching, I mean standing off to the side, don't interrupt them, just watch them.

00:19:12.207 --> 00:19:15.587
And then you start to take that information and figure out, okay,

00:19:15.667 --> 00:19:19.847
what was the process they went through? What was the problem they were trying to solve?

00:19:20.227 --> 00:19:23.447
And so that's where the task analysis comes in.

00:19:23.767 --> 00:19:29.387
And when you capture this notion about the users, you know, it's not the persona

00:19:29.387 --> 00:19:34.787
type information. It's more about what can you observe about their knowledge set?

00:19:35.465 --> 00:19:39.465
You know, because people are going to act based on what they think they know.

00:19:40.105 --> 00:19:44.645
And so you can observe behaviors and start to attribute some cognitive phenomenon

00:19:44.645 --> 00:19:46.605
to the observed behaviors.

00:19:46.745 --> 00:19:51.045
So you kind of capture that like that person didn't seem to know what they were doing.

00:19:51.065 --> 00:19:54.525
They looked at that thing three times before they made a choice.

00:19:54.685 --> 00:19:56.645
So obviously that's not very clear.

00:19:57.085 --> 00:20:00.645
So these are the kind of things you look for in the user. And then the task

00:20:00.645 --> 00:20:06.685
analysis breaks down what steps were they going through. And so you don't do it all at once.

00:20:06.865 --> 00:20:10.165
You break it down into, these are the five main steps they went through.

00:20:10.625 --> 00:20:13.905
And then you take each of those and break that down and break that down until

00:20:13.905 --> 00:20:16.305
you get to the atomic level, if you will.

00:20:16.965 --> 00:20:21.285
But you don't stop there. What you've done is just identified what the world

00:20:21.285 --> 00:20:23.145
is right now, what exists.

00:20:24.565 --> 00:20:29.005
Well, obviously something's missing, but you don't know what that is.

00:20:29.185 --> 00:20:33.465
And so you start to optimize the task flow. What if we could do this for the

00:20:33.465 --> 00:20:37.865
user? What if the system automatically generated that artifact for the user?

00:20:38.085 --> 00:20:44.465
What if we streamlined the task flow? What if we optimized their task?

00:20:45.325 --> 00:20:49.925
And you're always looking at, you're trying to achieve the desired outcome.

00:20:50.185 --> 00:20:55.565
The desired outcome, like you said, isn't to do all of these things,

00:20:55.665 --> 00:20:56.985
create directories and all that.

00:20:57.025 --> 00:21:01.925
It's to listen to music. Like as Ted Levitt from the Harvard Business School

00:21:01.925 --> 00:21:05.285
said, people don't want a quarter-inch drill. They want a quarter-inch hole.

00:21:07.485 --> 00:21:14.165
So the process in the book follows that pattern. Understand the users.

00:21:14.865 --> 00:21:18.845
Understand the task. Optimize the task. And once you've optimized the task,

00:21:19.065 --> 00:21:22.725
then you can start to see a strategy for that product.

00:21:22.905 --> 00:21:27.945
Like the strategy for ProFlowers was ProFlowers doesn't sell flowers. They sell occasions.

00:21:29.045 --> 00:21:32.345
And then you can start to design from

00:21:32.345 --> 00:21:35.265
that strategy because everything has to has to

00:21:35.265 --> 00:21:38.525
fit that strategy has to fit what the desired outcome

00:21:38.525 --> 00:21:44.605
is and then it fits into that that optimized task flow and the cool thing about

00:21:44.605 --> 00:21:49.505
the optimized task flow is you identify the different artifacts that the users

00:21:49.505 --> 00:21:54.425
are going to use like they have to use uh this screwdriver and this socket set

00:21:54.425 --> 00:21:55.645
things like that, right?

00:21:56.185 --> 00:22:00.305
You also start to identify what things the system can do for the user.

00:22:00.485 --> 00:22:03.405
Because when you optimize it, what you're trying to do is to get the system

00:22:03.405 --> 00:22:05.745
to do more of the steps for the user.

00:22:05.885 --> 00:22:11.025
And the user only has to acknowledge some or make a final decision somewhere.

00:22:11.705 --> 00:22:16.985
So what you end up with is, I use different color stickies. The yellow is for

00:22:16.985 --> 00:22:18.705
the system. The green is for the user.

00:22:19.105 --> 00:22:24.025
You end up with a few greens. And then all you All you have to do is design screens for the greens.

00:22:25.292 --> 00:22:29.392
Yeah. So in for in for the audience, you know, picture a bunch of sticky notes.

00:22:29.592 --> 00:22:33.872
And I think one of the takeaways that I take from this whole process before

00:22:33.872 --> 00:22:41.252
I dive into the sticky note process is that you have the human being in the environment.

00:22:41.252 --> 00:22:46.312
One of the examples I give is, you know, an app like Uber Eats or something

00:22:46.312 --> 00:22:50.272
like that, right, where it's a human looking for food.

00:22:50.432 --> 00:22:54.092
And humans have been looking for food for thousands of years.

00:22:54.772 --> 00:22:58.172
And we have whatever ways we used to do it.

00:22:58.172 --> 00:23:05.752
But the information architecture of the screen has to deliver the right information

00:23:05.752 --> 00:23:10.192
in the right ways to just so you're only thinking about the food that you want.

00:23:10.292 --> 00:23:12.312
You're not thinking about the screen and navigation.

00:23:12.872 --> 00:23:16.412
You're seeing, OK, those are that's a restaurant. I like the food from there.

00:23:16.612 --> 00:23:19.452
And then you open as OK, here's the menu. what type of

00:23:19.452 --> 00:23:22.952
thing and it pulls your thinking

00:23:22.952 --> 00:23:26.072
out of the interface and it

00:23:26.072 --> 00:23:29.432
puts your thinking onto the content that

00:23:29.432 --> 00:23:32.612
you actually are trying to to work with and

00:23:32.612 --> 00:23:35.372
as you mentioned in your book and i've heard others say it as well is

00:23:35.372 --> 00:23:37.992
that good ux is invisible so if you

00:23:37.992 --> 00:23:41.032
do it right you're really stripping away

00:23:41.032 --> 00:23:44.552
everything and you're you're trying to get the most direct path of

00:23:44.552 --> 00:23:47.612
the human being doing their work right it's

00:23:47.612 --> 00:23:50.772
almost intuitive like that's the way it should flow

00:23:50.772 --> 00:23:54.092
and that's what their expectations are that's what

00:23:54.092 --> 00:23:56.892
that's how you make it invisible they don't have to think about

00:23:56.892 --> 00:24:01.072
that navigation it just happens exactly and

00:24:01.072 --> 00:24:06.212
so so one methodological nuance i want to get your opinion about is um you mentioned

00:24:06.212 --> 00:24:13.752
um user uh the researchers doing research show up at the environment where people

00:24:13.752 --> 00:24:19.012
are doing their task and starting with naturalistic observation,

00:24:19.012 --> 00:24:21.072
you're not interfering.

00:24:21.072 --> 00:24:24.232
You're just watching, observing, recording.

00:24:24.632 --> 00:24:32.452
And then in your view, when is it time to do contextual inquiry or when is it

00:24:32.452 --> 00:24:35.332
time to bring in the subject matter expert to say, okay,

00:24:35.632 --> 00:24:40.732
from your expertise, what are the tasks, what order and how should those kinds of things interplay.

00:24:41.677 --> 00:24:45.637
Yeah, that's good that you mentioned that. So the subject matter expert is somebody

00:24:45.637 --> 00:24:50.157
that you might want to talk with early on so you can get a kind of a sense of the domain.

00:24:50.637 --> 00:24:54.437
Now, the problem with subject matter experts is they've been doing this a long

00:24:54.437 --> 00:24:57.137
time, and they're stuck in their ways.

00:24:57.617 --> 00:25:00.877
I mean, that's an oversimplification.

00:25:01.357 --> 00:25:04.557
But generally speaking, their way is the way.

00:25:04.797 --> 00:25:09.357
Well, in reality, it never is. There's always an improvement you could make somewhere.

00:25:09.557 --> 00:25:14.077
But at least start with them. And you bring them in after you've got some data,

00:25:14.077 --> 00:25:19.917
you know, to do a validity check, a reality check. Am I going down the right direction?

00:25:20.257 --> 00:25:25.597
You know, it's amazing. A lot of times you'll find something that they have

00:25:25.597 --> 00:25:29.577
internalized for so long that they don't even realize it is there anymore.

00:25:29.997 --> 00:25:33.197
And so it's kind of a surprise when you say, well, what about this?

00:25:33.337 --> 00:25:34.957
Oh, yeah, I forgot about that.

00:25:35.137 --> 00:25:39.677
Because it's so endemic to the way they do things that they no longer give it any thought.

00:25:40.197 --> 00:25:44.137
So you start with the subject matter expert and any, you know,

00:25:44.217 --> 00:25:46.317
buddy in the company that has any insights.

00:25:46.477 --> 00:25:50.257
And I like to go to the customer support people and talk to them.

00:25:50.377 --> 00:25:51.677
And I don't ask, you know, what's

00:25:51.677 --> 00:25:55.037
the most difficult part of the interface that people complain about?

00:25:55.117 --> 00:25:59.957
I ask them, what problems are people trying to solve? What are they trying to

00:25:59.957 --> 00:26:01.937
do that they're struggling with?

00:26:02.637 --> 00:26:08.097
So you get, you try to stay solution agnostic, you know, early on.

00:26:08.097 --> 00:26:13.337
So then you do some observational research, and you might even call it contextual

00:26:13.337 --> 00:26:18.377
inquiry because I like to do a post-observation interview with people if possible.

00:26:18.617 --> 00:26:24.377
Sometimes you can't because, you know, they're not available or you can't talk

00:26:24.377 --> 00:26:25.697
to them for whatever reason.

00:26:25.997 --> 00:26:31.917
But when possible, yes, do a post-observation interview. That's contextual inquiry right there.

00:26:32.217 --> 00:26:38.077
But you can't just ask people what they're, oh, no, not again.

00:26:38.097 --> 00:26:45.457
Again oh it looks like we're yeah i can still hear you for the technical technical

00:26:45.457 --> 00:26:51.177
difficulties just uh for the audience right there um yeah give you a second

00:26:51.177 --> 00:26:55.677
on there yeah can you can you still hear my light keeps uh turning,

00:26:56.567 --> 00:27:01.207
Yes. All right. Can you hear me? Excellent. Yes, I can. I think we're back.

00:27:03.427 --> 00:27:08.107
So one of the things you talk about in your book is when you're doing the research,

00:27:08.347 --> 00:27:10.887
things to do and things not to do.

00:27:11.307 --> 00:27:19.227
Right. So one of the things is you're you're working on a project and the workers,

00:27:19.467 --> 00:27:23.027
the users, if you're using B2B software, are using a current solution.

00:27:23.027 --> 00:27:25.727
And they have a tool that they've been

00:27:25.727 --> 00:27:28.967
using for God knows how long and everybody knows

00:27:28.967 --> 00:27:32.627
this tool and they've learned the the workarounds

00:27:32.627 --> 00:27:35.467
and they've got a cheat sheet at their desk of how to deal with

00:27:35.467 --> 00:27:38.207
all the quirky things and um and we've

00:27:38.207 --> 00:27:42.927
all seen that and so but one of the things that you point out is that if you

00:27:42.927 --> 00:27:49.527
research the tool or people using that tool you're missing an opportunity to

00:27:49.527 --> 00:27:53.847
learn just about the task itself because the person is kind of winding their

00:27:53.847 --> 00:27:55.487
way through the, you know,

00:27:55.587 --> 00:27:59.467
the peculiarities of the tool, but you want to learn about the task.

00:27:59.807 --> 00:28:03.447
What are some things you would recommend for situations where,

00:28:03.707 --> 00:28:05.867
well, that concept overall,

00:28:06.207 --> 00:28:11.467
but then following up, what, you know, what if it's kind of a quirky,

00:28:11.727 --> 00:28:17.687
weird, specific business task that it's hard to get a use case of them?

00:28:17.787 --> 00:28:21.367
What are some strategies that people can do when they're thinking about,

00:28:21.707 --> 00:28:26.147
you know, I want to research the task itself and not get so wrapped up in the tool?

00:28:26.647 --> 00:28:33.767
Yeah, that's the science part of it and where the art comes into the science.

00:28:34.067 --> 00:28:40.247
Because yes, you know you have to be somewhat agnostic, but you have to take

00:28:40.247 --> 00:28:44.907
that step back and say, what they're doing with that tool is dictated by the

00:28:44.907 --> 00:28:46.507
design of that tool right now.

00:28:46.847 --> 00:28:51.567
Is that really the task or is the task something more generic?

00:28:51.967 --> 00:28:56.767
So take the step back, try to be solution agnostic.

00:28:57.207 --> 00:29:00.547
Otherwise, all you end up doing is automating current frustrations.

00:29:01.617 --> 00:29:04.837
You know, if people are doing the wrong thing and you fine tune that,

00:29:04.997 --> 00:29:06.177
they're still doing the wrong thing.

00:29:07.137 --> 00:29:12.077
And so I've always looked at it like every, and to be honest,

00:29:12.117 --> 00:29:17.377
I've had over 250 projects in my 35 years of doing this.

00:29:18.157 --> 00:29:23.537
And every time the original solution was solving the wrong problem.

00:29:24.057 --> 00:29:27.697
So you have to take that step back and say, what is the real problem?

00:29:27.697 --> 00:29:32.237
And you start with, what is the problem that they're trying to solve?

00:29:32.797 --> 00:29:34.677
What is the desired outcome?

00:29:35.677 --> 00:29:39.757
So, for instance, dashboards. You know, dashboards are all the rage,

00:29:39.817 --> 00:29:43.297
you know, because they're pretty, you know, graphics and everything up there.

00:29:43.597 --> 00:29:47.137
But when it comes down to it, what are people trying to get out of a dashboard?

00:29:47.697 --> 00:29:50.897
They're trying to figure out what to do next. next and so

00:29:50.897 --> 00:29:53.697
a really good dashboard and i've got an example in

00:29:53.697 --> 00:29:56.617
the book a really good dashboard tells the user

00:29:56.617 --> 00:29:59.657
what they need to do not what's you know

00:29:59.657 --> 00:30:02.597
what their sales were last month or you know

00:30:02.597 --> 00:30:06.097
who's got the most sales or you know which advertising

00:30:06.097 --> 00:30:09.217
is working no they want to know what to

00:30:09.217 --> 00:30:12.137
do next and so they're taking

00:30:12.137 --> 00:30:14.777
that step back you say people are doing this so they

00:30:14.777 --> 00:30:17.797
can figure out what to do next a dashboard

00:30:17.797 --> 00:30:21.137
only gets you halfway yeah larry

00:30:21.137 --> 00:30:24.097
i really appreciated it in chapter 11 when uh

00:30:24.097 --> 00:30:29.037
you said uh why dashboards suck and there's a section on it and you talk about

00:30:29.037 --> 00:30:33.177
some of the things and i run across it all the time i'm a data viz guy myself

00:30:33.177 --> 00:30:37.917
and i work on a lot of dashboards and i follow the steven few approach to um

00:30:37.917 --> 00:30:42.277
data visualization um but there's all kinds of things that drive me nuts.

00:30:42.397 --> 00:30:47.117
There's the thing I like to call it the donut stamp, where you just take a number,

00:30:47.117 --> 00:30:49.717
and then you just have a donut graph around it.

00:30:49.837 --> 00:30:53.097
And then you just stamp a bunch of those at the top of the screen.

00:30:53.577 --> 00:30:57.117
And you call it a dashboard and then wonder why people aren't using it.

00:30:57.237 --> 00:30:59.797
People don't engage with it. It doesn't solve problems.

00:31:00.257 --> 00:31:05.337
There is research to be done there too, not just, oh, we've got data, let's visualize it.

00:31:06.029 --> 00:31:09.969
You know, think about it. When you say we have data, let's visualize it.

00:31:10.069 --> 00:31:11.589
You're starting from the data perspective.

00:31:11.949 --> 00:31:17.229
If you start from what the desired outcome is, you're starting from the user's

00:31:17.229 --> 00:31:22.429
endpoint perspective and working backwards instead of from the data moving forward.

00:31:23.129 --> 00:31:30.609
So, and what you'll find is that if you work backwards, what the people want

00:31:30.609 --> 00:31:36.189
is different than what the data wants. and sometimes you won't even have the right data.

00:31:37.289 --> 00:31:40.629
So data visualization is about, we have this data, let's make it pretty.

00:31:40.969 --> 00:31:44.209
But if you start from the desired outcome, you realize, wow,

00:31:44.309 --> 00:31:48.689
we don't have the data to do this. We need to find that somehow.

00:31:49.309 --> 00:31:55.949
So there's one product I've worked on had to do with how do you incentivize

00:31:55.949 --> 00:31:59.089
salespeople to do a better job at their job?

00:31:59.289 --> 00:32:04.629
Well, turns out that sales managers all have different formulas for their product,

00:32:04.809 --> 00:32:06.969
their domain, you know, things like that.

00:32:07.089 --> 00:32:11.409
And if you start from saying, the user needs to know what to do,

00:32:11.489 --> 00:32:14.949
because what's the salesperson most important consideration?

00:32:15.509 --> 00:32:20.389
How am I going to make my bonus? Because they're compensated on how well they perform.

00:32:20.889 --> 00:32:27.329
So the formula, if you follow a sales formula, right, and you deviate from the

00:32:27.329 --> 00:32:31.509
formula, it can tell you, you're not visiting, you know, this customer often

00:32:31.509 --> 00:32:35.189
enough. You need to visit them some more. That's an actionable state.

00:32:35.789 --> 00:32:41.349
And so that means you're going to design to that actionable state to inform

00:32:41.349 --> 00:32:46.449
the user they need to go visit these three customers that they haven't visited

00:32:46.449 --> 00:32:50.209
for a while because those sales are starting to come down. Go visit them.

00:32:51.169 --> 00:32:55.329
Well, if you start from that and you work backwards, you realize we don't have

00:32:55.329 --> 00:32:57.069
all the data that the formula needs.

00:32:57.249 --> 00:33:00.849
So we need to put in another hook to find that data and plug it in.

00:33:01.829 --> 00:33:05.389
And that's better than a dashboard. Yeah, absolutely.

00:33:05.829 --> 00:33:09.509
And, you know, returning to, you know, sort of the knowledge being the central

00:33:09.509 --> 00:33:18.969
piece, one of the things that I love is this contrast between personas and knowledge profiles.

00:33:19.089 --> 00:33:22.529
And I think the knowledge profile is still kind of a persona,

00:33:22.689 --> 00:33:26.969
but a better persona focused on the right things and not like this person is

00:33:26.969 --> 00:33:31.549
married and has drives a minivan and has three kids and stuff that's irrelevant.

00:33:32.309 --> 00:33:38.469
To the building of the product, but a knowledge profile. Can you talk about that?

00:33:39.130 --> 00:33:42.430
Sure. Thanks for asking about that. That's one of my favorites, too.

00:33:42.870 --> 00:33:48.730
So I found this persona that somebody had put together and posted it up on the web.

00:33:48.970 --> 00:33:51.730
And her name is Diane. She's an HR specialist.

00:33:52.190 --> 00:33:56.290
And one of the things they have on there is she likes designer gin.

00:33:57.310 --> 00:34:01.690
How is that going to help you sell an HR software to this woman?

00:34:02.630 --> 00:34:08.670
You know, it likes designer what? Gin, you know, the drink gin.

00:34:11.570 --> 00:34:14.350
You know that's that's worse than the ones that say oh

00:34:14.350 --> 00:34:17.590
yeah has a dog a minivan and three kids

00:34:17.590 --> 00:34:20.590
yeah but how is that going to help you sell refrigerators you

00:34:20.590 --> 00:34:23.910
know so the problem

00:34:23.910 --> 00:34:27.570
with personas is that they're far too vague now what's interesting a little

00:34:27.570 --> 00:34:34.370
backstory there when alan cooper was first writing um his book about face uh

00:34:34.370 --> 00:34:40.750
he called a number of us people that had graduated from Don Norman's program, I being one of them,

00:34:40.990 --> 00:34:46.130
to talk about this notion of the user. And he crafted the term persona.

00:34:46.390 --> 00:34:52.350
I actually think Don Norman did. But anyway, he crafted this image of a persona

00:34:52.350 --> 00:34:54.510
in his book, but he got it wrong.

00:34:55.070 --> 00:35:00.350
Because we were all talking about the persona is based on what it is they're

00:35:00.350 --> 00:35:02.450
doing, not just this general person.

00:35:03.110 --> 00:35:08.330
A persona is not a person. is it was supposed to represent now what I call hats.

00:35:08.930 --> 00:35:13.490
So for instance, when you do a different task, you're an expert at that task.

00:35:13.570 --> 00:35:16.950
But when you do another task, you're a novice, you change hats.

00:35:17.250 --> 00:35:18.710
Your knowledge base is different.

00:35:19.290 --> 00:35:27.150
So you may be an expert chart maker in Excel, but when you turn around and do

00:35:27.150 --> 00:35:30.510
a pivot table, which you've never done, are you still an expert?

00:35:30.750 --> 00:35:33.050
No, you're a novice. You've changed hats.

00:35:34.170 --> 00:35:37.430
So the knowledge profile user

00:35:37.430 --> 00:35:40.530
knowledge profile is all about understanding what task

00:35:40.530 --> 00:35:44.570
is it that somebody is performing what problem are they trying to solve what's

00:35:44.570 --> 00:35:49.210
their desired outcome and what do they know going into that task and then what

00:35:49.210 --> 00:35:53.930
will they need to know in order to succeed but they won't know because that's

00:35:53.930 --> 00:35:58.450
the knowledge gap that you have to design a bridge across.

00:35:58.950 --> 00:36:03.790
Your goal is to bridge that knowledge gap for the user with your design.

00:36:04.940 --> 00:36:08.660
And it's going to change for each task that the user is going through.

00:36:08.920 --> 00:36:12.900
So, like I said, they may be an expert in one sense, but a novice in another.

00:36:13.100 --> 00:36:15.960
How do you, you know, design for that?

00:36:16.820 --> 00:36:20.600
Yeah. And so with the, I think you mentioned it in the book,

00:36:20.800 --> 00:36:26.160
with Alan Cooper's work, because he introduced the idea to, I think,

00:36:26.220 --> 00:36:28.980
many people in the business world, their first time hearing about personas was

00:36:28.980 --> 00:36:30.280
inmates are running the asylum.

00:36:30.280 --> 00:36:35.980
And then he clarified it a little bit more in About Face in that chapter that he's got.

00:36:36.000 --> 00:36:38.920
About Face was his first book.

00:36:39.800 --> 00:36:44.780
Inmates Are Running the Asylum was his second book. Was it a different edition?

00:36:45.460 --> 00:36:48.700
I'm thinking of the About Face published in 2005 or something.

00:36:49.760 --> 00:36:53.140
Maybe there was multiple editions or something like that. I thought,

00:36:53.200 --> 00:36:56.140
you know, I had a copy of it here and I don't think so.

00:36:56.420 --> 00:37:02.020
Sure, sure. I thought about face came first, and inmates came second. I could be wrong.

00:37:02.740 --> 00:37:07.620
Okay. But during that time period, you seemed to allude to it,

00:37:07.680 --> 00:37:13.440
that practitioners were sort of talking to each other and challenging each other

00:37:13.440 --> 00:37:18.600
during that time to try to get it right. Because was that the case?

00:37:19.380 --> 00:37:24.660
Well, a lot of us were out of Don Norman's class or one of his graduate students.

00:37:24.660 --> 00:37:28.820
And we all kind of understood personas from the way Don told it.

00:37:28.900 --> 00:37:31.300
And it was task oriented. It was task related.

00:37:31.820 --> 00:37:37.900
So I learned task oriented analysis and design from, you know, that program.

00:37:38.520 --> 00:37:40.820
And everything was about the task.

00:37:42.080 --> 00:37:46.420
Excellent. And so I think that one of the things that really proves the value

00:37:46.420 --> 00:37:51.280
of the things that you talk about in your book is that the ability.

00:37:52.899 --> 00:37:56.399
Deliverables are more tied to each other. And you talk about this,

00:37:56.479 --> 00:38:00.539
there's a section where, you know, when we think about user research,

00:38:00.699 --> 00:38:02.939
right, there's all these deliverables we have.

00:38:03.099 --> 00:38:07.099
And as we go further and further into UX future, we're expected to make them

00:38:07.099 --> 00:38:10.159
more beautiful and not necessarily more valid.

00:38:10.499 --> 00:38:13.919
Because I talk to people who are, you know, young practitioners,

00:38:13.919 --> 00:38:17.859
and they say, oh, I'm working on my portfolio. Oh, what do you do? I'm a researcher.

00:38:18.299 --> 00:38:21.479
It's like, okay, well, can I see your portfolio? And they have these beautiful

00:38:21.479 --> 00:38:25.719
setups and you get the idea there's much more focus, especially coming out of

00:38:25.719 --> 00:38:32.339
these bootcamps on how to make wonderful journey maps and these beautiful, yeah, exactly.

00:38:33.299 --> 00:38:38.319
But you point out that a lot of this stuff doesn't get used, which is sad.

00:38:40.239 --> 00:38:43.219
And it goes a little bit by the wayside.

00:38:43.219 --> 00:38:47.379
But when you're talking about your you

00:38:47.379 --> 00:38:50.839
know sort of reconstruction um of these

00:38:50.839 --> 00:38:53.859
deliverables and um kind of

00:38:53.859 --> 00:38:56.999
advocating a way to do it it becomes really clear

00:38:56.999 --> 00:39:02.059
and you show it multiple times that each deliverable is directly tied to the

00:39:02.059 --> 00:39:07.039
next they all stay relevant one of them doesn't fall out of relevance they stay

00:39:07.039 --> 00:39:12.199
linked and they stay useful right because we've all heard that personas once

00:39:12.199 --> 00:39:15.459
they're done they They look great, have nice graphics on them and everything.

00:39:15.619 --> 00:39:18.879
And they go on a shelf and never to see the light of day again.

00:39:19.279 --> 00:39:23.579
So yes, a really good process should be a connected process.

00:39:23.979 --> 00:39:28.259
One step should inform the next, should inform the next, should inform the next.

00:39:28.459 --> 00:39:34.039
And that's what the book talks about and describes and displays and demonstrates

00:39:34.039 --> 00:39:36.879
is this is all a continuous flow.

00:39:37.379 --> 00:39:41.359
And the reason you do that is so that everybody can follow along.

00:39:41.359 --> 00:39:44.499
Why were these design decisions made this way?

00:39:44.879 --> 00:39:49.499
It's because the strategy was this, and the strategy came from the optimized

00:39:49.499 --> 00:39:55.759
task flow, which came from the task analysis, which came from the user observations.

00:39:56.819 --> 00:40:04.599
And so you get away from this opinion war that typically happens in research

00:40:04.599 --> 00:40:08.099
and design. Well, I think this, I saw this, you know, this, that, and another.

00:40:08.759 --> 00:40:12.659
And a lot of research reports are just that, they're reports,

00:40:12.859 --> 00:40:17.079
but they don't describe how to apply that to the next step.

00:40:18.199 --> 00:40:24.479
And it's because people learn these disconnected steps and they don't realize

00:40:24.479 --> 00:40:27.039
that there should be a connection between everything.

00:40:27.459 --> 00:40:30.959
One thing should inform the next, it should flow all the way down to the,

00:40:31.139 --> 00:40:33.219
actually to the usability testing.

00:40:33.219 --> 00:40:38.079
Because the task analysis that you do up front, the task optimization,

00:40:38.739 --> 00:40:46.179
should inform what the tasks are in the usability tests at the very end of the design process.

00:40:47.647 --> 00:40:51.867
Yeah, absolutely. And on this topic of artifact continuity, I love that phrase

00:40:51.867 --> 00:40:55.347
that you got in the book. You also address the jobs to be done.

00:40:55.527 --> 00:40:56.907
And I think that's really important

00:40:56.907 --> 00:41:01.727
because it's become popular in the business world, jobs to be done.

00:41:01.867 --> 00:41:06.187
And jobs to be done is probably better than the older business ways of doing things.

00:41:06.327 --> 00:41:13.847
But could you contrast for us where jobs to be done is maybe good or where it

00:41:13.847 --> 00:41:15.227
maybe misses some things?

00:41:16.027 --> 00:41:20.967
Well, you know, jobs to be done is kind of like a task analysis light.

00:41:21.687 --> 00:41:25.787
It's not as specific as a task analysis. Now, with task analysis,

00:41:25.807 --> 00:41:28.447
you're breaking down all the tasks and jobs to be done.

00:41:29.027 --> 00:41:34.327
You're starting to do that, but you don't get into identifying what some of

00:41:34.327 --> 00:41:36.267
the issues are, what the artifacts are.

00:41:36.487 --> 00:41:42.027
And there's no real step that they build into that about task optimization.

00:41:42.587 --> 00:41:46.587
How do you reinvent the task? You know, if you're going to have some new,

00:41:46.807 --> 00:41:50.547
say, for an AI, right, you're going to introduce an AI.

00:41:50.747 --> 00:41:54.487
What can you get the AI to do? How does that change the user's task?

00:41:55.067 --> 00:41:58.867
That's not covered in Jobs to be Done. Jobs to be Done is the initial step,

00:41:58.907 --> 00:42:01.807
but they don't follow through with it. And it's much more vague.

00:42:01.947 --> 00:42:04.127
It's not as specific and clear-cut.

00:42:04.607 --> 00:42:06.927
But it's still a worthwhile, you know, practice.

00:42:08.087 --> 00:42:12.367
Yeah. And with these, you know, kind of business methods, another one that I

00:42:12.367 --> 00:42:16.687
liked, and it had a big overlap with UX or perhaps it's UX.

00:42:17.747 --> 00:42:23.847
Well, it's the three different parties, technology, UX, business that play a role into it.

00:42:24.327 --> 00:42:34.307
Priority matrix as a way of being a little bit more objective about how we prioritize.

00:42:34.987 --> 00:42:39.167
I'd like to say that I created that, but I'll be frank and said I stole it from

00:42:39.167 --> 00:42:40.807
Six Sigma's House of Quality.

00:42:41.607 --> 00:42:46.527
But I streamlined it down because House of Quality was far too cumbersome.

00:42:47.707 --> 00:42:53.227
So I actually was a certified product manager for a while, and I learned all

00:42:53.227 --> 00:42:57.247
about Six Sigma and the House of Quality. And it had an application.

00:42:57.987 --> 00:43:04.007
But I streamlined it down to you list the tasks that the user is trying to perform,

00:43:04.127 --> 00:43:06.047
not the features. That was a big difference.

00:43:06.447 --> 00:43:10.987
So you list the tasks that the users are trying to perform and you identify

00:43:10.987 --> 00:43:13.407
what is the priority of that task.

00:43:14.168 --> 00:43:18.568
You know, if they didn't get to print something out, that's a lower priority,

00:43:18.568 --> 00:43:21.828
but they needed this calculation. That's the highest priority.

00:43:23.068 --> 00:43:28.288
So then you look at what's the value of that task to the business,

00:43:28.568 --> 00:43:32.168
you know, to the marketing, to the to the business side of it.

00:43:32.268 --> 00:43:36.808
And so you get a different set of scores along that list of tasks.

00:43:37.328 --> 00:43:42.988
So you may have one task that's a highest priority, a priority one for the user,

00:43:42.988 --> 00:43:46.188
but the business can't figure out how to make money out of it.

00:43:46.228 --> 00:43:48.668
And it's just not cost effective for them.

00:43:48.788 --> 00:43:52.068
So that would give it a three on a three point scale.

00:43:52.528 --> 00:43:57.788
And then the technology column is how feasible is it for us to make this?

00:43:58.008 --> 00:44:03.748
Is this a task that's going to require all new sets of features that we've never done before?

00:44:03.988 --> 00:44:09.088
That's a difficult level of three. or as we already have all of these features,

00:44:09.228 --> 00:44:11.788
we just have to reorganize them. That would be a one.

00:44:12.668 --> 00:44:18.008
So you do that with all of the different tasks and what you find is that a third

00:44:18.008 --> 00:44:20.168
of them are things you should do right now.

00:44:20.628 --> 00:44:25.528
And it's not just based on the user and it's not based on the technical feasibility

00:44:25.528 --> 00:44:28.268
and it's not solely based on the business.

00:44:28.628 --> 00:44:33.628
But that priority matrix is only a guideline because you still need to make

00:44:33.628 --> 00:44:37.248
some accommodations to different perspectives here and there.

00:44:37.508 --> 00:44:42.788
Like we have to do this now because this other thing that we want to launch

00:44:42.788 --> 00:44:44.208
is going to be dependent on it.

00:44:44.308 --> 00:44:49.308
So you have those technical dependencies, business dependencies and user dependencies.

00:44:49.648 --> 00:44:53.188
But at least it gives you a pretty good starting point.

00:44:54.102 --> 00:44:57.002
And I like the constraint that you can only use the number, you have to use

00:44:57.002 --> 00:44:59.782
each number, one, two, and three, an equal number of times.

00:45:00.242 --> 00:45:02.862
So you can't just claim one, one, one, one, one, one. Otherwise,

00:45:03.022 --> 00:45:07.362
everything's the one in marketing, everything's the three in technology. That's right. Exactly.

00:45:08.482 --> 00:45:13.462
So now in your discussion about knowledge-oriented design, there's a concept

00:45:13.462 --> 00:45:21.222
that's truly powerful and it's fairly simple, but it's required knowledge and the knowledge gap.

00:45:21.222 --> 00:45:25.822
And you have this way of kind of, you could almost put it in an equation.

00:45:25.822 --> 00:45:32.622
I don't think it's quite that, you know, maybe explicit, but it's really deliberate

00:45:32.622 --> 00:45:36.822
about getting to what is the knowledge gap and what is the current knowledge.

00:45:36.942 --> 00:45:38.982
Could you talk about that a little bit?

00:45:39.802 --> 00:45:46.402
Yeah. So the example I like to use, and it's in the book, is nurses can be expected

00:45:46.402 --> 00:45:48.482
to have a certain base knowledge.

00:45:48.722 --> 00:45:53.002
They know how to give medications, such as ointments, pills,

00:45:53.222 --> 00:45:56.142
shots, rubs, things like that.

00:45:56.322 --> 00:45:59.602
So you could expect every nurse to know that.

00:45:59.822 --> 00:46:06.222
But a key responsibility for nurses is to avoid any drug interactions that might occur.

00:46:06.502 --> 00:46:11.902
So there's a whole host. There's tens of thousands of potential drug interactions

00:46:11.902 --> 00:46:15.522
where people would react negatively. It could even kill somebody, right?

00:46:16.122 --> 00:46:20.142
Well, you can't expect the nurses to remember all of those.

00:46:20.602 --> 00:46:27.262
So that's the knowledge gap. The knowledge is they know how to administer medications.

00:46:27.622 --> 00:46:31.202
And the required knowledge is to prevent drug interactions.

00:46:31.562 --> 00:46:35.602
The knowledge gap is they don't know all the potential drug interactions by

00:46:35.602 --> 00:46:41.062
heart. So how do you design something that makes sure that when they get those

00:46:41.062 --> 00:46:46.062
meds out for that patient, that they're not giving two meds that would interact negatively?

00:46:46.502 --> 00:46:49.602
And so we came up with a system that did that.

00:46:51.096 --> 00:46:55.556
Absolutely. And, you know, when you're coming up with this methodically,

00:46:55.556 --> 00:46:59.376
there's a process, of course, to it.

00:46:59.616 --> 00:47:04.056
I like the iterative nature of the process that you emphasize.

00:47:04.816 --> 00:47:09.376
So, for example, with task analysis, there are explicit phases.

00:47:09.376 --> 00:47:13.856
It's not just, oh, we're iterating, but you've got the first stage that has

00:47:13.856 --> 00:47:18.016
a certain purpose and it has a beginning and end and you have an idea of when it's done.

00:47:18.136 --> 00:47:22.116
And then you have two or three stages in the end, But they're different.

00:47:22.836 --> 00:47:26.496
They have a slight qualitative difference to them.

00:47:27.676 --> 00:47:32.616
It's about the specificity or the details of the task. You don't have to get

00:47:32.616 --> 00:47:35.576
mired in the details with your initial analysis.

00:47:35.876 --> 00:47:40.136
You do the five main steps, and then you can break each one of those down into

00:47:40.136 --> 00:47:43.836
five to 20 steps, and then break those down if you need to.

00:47:44.036 --> 00:47:46.416
Usually by the second one, you have enough information.

00:47:47.256 --> 00:47:49.016
But yes, it's methodical. And

00:47:49.016 --> 00:47:52.996
it's done so that you don't get mired in the details and forget details.

00:47:54.456 --> 00:47:59.716
And that's where you start to identify where the knowledge is that people have to have.

00:47:59.856 --> 00:48:04.936
In this step, you have to be able to make sure that you don't have potential drug interactions.

00:48:05.236 --> 00:48:12.556
So that means that the system has to inject that knowledge into the flow for

00:48:12.556 --> 00:48:14.356
the user because they can't do it themselves.

00:48:15.196 --> 00:48:19.696
Yeah, one of the ways I look at that too is in kind of the knowledge company.

00:48:20.056 --> 00:48:21.436
We could talk about that as well.

00:48:21.876 --> 00:48:27.336
And I see it as, you know, again, it's back to humans are trying to achieve a task.

00:48:27.656 --> 00:48:33.436
Then you've got this company with lots of data and a massive amount of knowledge.

00:48:33.436 --> 00:48:36.796
How can we design things

00:48:36.796 --> 00:48:39.736
so that we're not depending you point

00:48:39.736 --> 00:48:42.616
this out um repeatedly throughout the book is you know

00:48:42.616 --> 00:48:45.876
not depending on the individual skills and

00:48:45.876 --> 00:48:49.656
individual knowledge of a person but leverage

00:48:49.656 --> 00:48:52.476
all of the information that

00:48:52.476 --> 00:48:55.236
we have in the task right and you

00:48:55.236 --> 00:49:00.136
introduced that the concept of what kind of company are you a service company

00:49:00.136 --> 00:49:06.256
a product company um are you a non-profit you're none of the above you're a

00:49:06.256 --> 00:49:09.676
knowledge company people come to you not because of your product or service

00:49:09.676 --> 00:49:12.576
but because of the knowledge about that product or service,

00:49:13.216 --> 00:49:17.576
so the more knowledge you can share with your customers in the design of your

00:49:17.576 --> 00:49:23.256
product the more successful your product will be so people don't like i said

00:49:23.256 --> 00:49:28.076
people don't want a quarter inch drill they want a quarter inch hole and so.

00:49:29.077 --> 00:49:35.457
Think about, you know, I tell the story about the toolbox in there and task analysis.

00:49:36.537 --> 00:49:41.057
Say you buy a used car and it needs spark plugs and you figure you can change

00:49:41.057 --> 00:49:44.617
the spark plugs yourself so you don't have to spend hundreds of dollars, right?

00:49:45.037 --> 00:49:48.777
And your developer friend says he has a garage full of tools.

00:49:48.777 --> 00:49:51.437
He's not going to be home this weekend, but you're welcome to use it.

00:49:51.597 --> 00:49:55.357
Don't worry, all the drawers are labeled, right? And sure enough,

00:49:55.377 --> 00:49:58.417
you go over there, open the garage door, wall-to-wall tool chest,

00:49:58.537 --> 00:50:01.897
and you see the labels on every drawer and think, this is going to be easy.

00:50:02.417 --> 00:50:06.557
And then you start opening the drawers and realizing, I don't even know which

00:50:06.557 --> 00:50:10.217
tools to use. So you open the socket drawer, and there's 200 sockets in it.

00:50:10.417 --> 00:50:14.877
I don't know which one to use, but do you remember your ex-friend said she had

00:50:14.877 --> 00:50:18.497
a garage full of tools also, and you're welcome to use that.

00:50:18.497 --> 00:50:22.837
You go over there, open your garage door, wall-to-wall tool chest,

00:50:22.997 --> 00:50:25.577
and all the drawers are labeled, but they're labeled differently.

00:50:26.697 --> 00:50:30.857
It says, change oil, change tire, change spark plugs.

00:50:31.037 --> 00:50:35.437
And you open the spark plug drawer, and there's a socket, just one,

00:50:35.577 --> 00:50:39.937
an extender, and a ratchet handle, and a rubber hose.

00:50:40.457 --> 00:50:42.637
And you wonder what the rubber hose is for.

00:50:43.357 --> 00:50:48.257
Well, after you take the spark plugs out, you realize you can't fit your fingers

00:50:48.257 --> 00:50:52.497
down in the cylinder head to put the spark plug in.

00:50:52.657 --> 00:50:56.537
So you put the hose on the end of the spark plug, stick it down and start the

00:50:56.537 --> 00:50:58.097
spark plug with the rubber hose.

00:50:58.577 --> 00:51:02.437
Who knew that? She knew that. And so she put it in the drawer.

00:51:02.957 --> 00:51:06.217
So all the right tools were in that drawer just for you.

00:51:07.137 --> 00:51:10.477
Wow, I love that story. yeah so

00:51:10.477 --> 00:51:13.177
um one of the things

00:51:13.177 --> 00:51:16.937
i wanted to touch on is the um

00:51:16.937 --> 00:51:23.137
the story i'm looking forward here it was a um it was about a search and rescue

00:51:23.137 --> 00:51:28.197
kind of use case where there's big maps spread out on a table and there's this

00:51:28.197 --> 00:51:33.177
process war on the floor i wanted to talk about that a little

00:51:33.377 --> 00:51:37.917
because I thought it was a striking example of a complex kind of work scenario

00:51:37.917 --> 00:51:40.057
where something needs to be designed for it.

00:51:40.537 --> 00:51:44.057
So the Coast Guard, U.S.

00:51:44.197 --> 00:51:48.957
Coast Guard, brought us in to help them figure out how to sell more.

00:51:51.217 --> 00:51:56.737
I guess it was satellite receiving equipment. When a boat or an airplane goes

00:51:56.737 --> 00:52:01.697
in the water, a signal automatically generates, and it's an SOS signal.

00:52:02.610 --> 00:52:05.950
And they thought, we need to sell more of this equipment. Well,

00:52:06.110 --> 00:52:09.630
what we observed was the equipment was fine.

00:52:09.790 --> 00:52:12.670
People could use that in a manner of seconds.

00:52:13.030 --> 00:52:17.130
But it was the next six hours where they were trying to figure out,

00:52:17.290 --> 00:52:23.110
based on the wind direction, the tides, what the sea swell was,

00:52:23.510 --> 00:52:27.970
what different ships and planes were in the area to help us look.

00:52:27.970 --> 00:52:34.870
They were trying to put together a search plan because say there's a sailboat

00:52:34.870 --> 00:52:39.870
that starts sinking and there's another sailboat, you know, 30 miles away from it.

00:52:40.830 --> 00:52:43.750
Coast Guard will call over to them and say, can you see them?

00:52:43.970 --> 00:52:48.330
Well, if you're on a sailboat, you can only see maximum a couple of miles in

00:52:48.330 --> 00:52:51.630
the water to be able to see anybody floating in the water.

00:52:52.230 --> 00:52:57.170
But if you're on a freighter, you know, you're higher up on the bridge, you can see farther.

00:52:57.170 --> 00:52:59.890
And so there might be a freighter going by and

00:52:59.890 --> 00:53:02.650
they'll have them look but they don't want them looking in the same

00:53:02.650 --> 00:53:06.250
piece of ocean because then it's a wasted resource so

00:53:06.250 --> 00:53:10.170
they try to map out figure out where are we going to have all of these people

00:53:10.170 --> 00:53:14.390
look what's their they're going to go in circles or they're going to go in squares

00:53:14.390 --> 00:53:18.710
and you might have an airliner flying over and you get the airliner to drop

00:53:18.710 --> 00:53:22.710
down to see if they can see anything and you know and that airliner is going

00:53:22.710 --> 00:53:26.090
to go in one line and you want to figure out what line that should be.

00:53:26.470 --> 00:53:31.270
So there's all of these different calculations that are constantly going on

00:53:31.270 --> 00:53:32.650
and they're all done by hand.

00:53:32.930 --> 00:53:36.310
And they're all done, you know, calculating in the head with calculators and

00:53:36.310 --> 00:53:39.870
keeping track of things. A lot of cognitive effort going on there.

00:53:40.609 --> 00:53:44.689
And so that's one of the things that I like to point out is when you're doing

00:53:44.689 --> 00:53:48.249
your user observations, you're looking for four key things.

00:53:49.029 --> 00:53:52.429
One is something manually intensive.

00:53:52.469 --> 00:53:57.769
And with all these maps and these rulers and all of that, it was this process

00:53:57.769 --> 00:54:01.749
of figuring out what the search pattern should be was very manual.

00:54:02.469 --> 00:54:05.429
And then you look for things that are cognitively demanding.

00:54:05.469 --> 00:54:08.509
And these people were trying to remember things in their heads,

00:54:08.629 --> 00:54:12.489
do mental calculations and all that. A lot of cognitive demand there.

00:54:12.909 --> 00:54:17.109
And then there was a potential for errors if they wrote something down wrong,

00:54:17.229 --> 00:54:18.689
measured something wrong, whatever.

00:54:19.329 --> 00:54:24.709
Or in the case of the Coast Guard thing, they had to use a radio to call out

00:54:24.709 --> 00:54:28.989
to all of these other ships and planes and everything and tell them what the

00:54:28.989 --> 00:54:30.369
coordinates were and everything.

00:54:30.909 --> 00:54:36.109
And for the sailboat, for instance, they might get 100 coordinates that they

00:54:36.109 --> 00:54:38.309
would have the map into their GPS system.

00:54:38.449 --> 00:54:41.509
They always got something wrong. always.

00:54:42.629 --> 00:54:46.369
So that's 100% failure, right? And the other is a knowledge dependency.

00:54:46.569 --> 00:54:51.569
Is there a specific knowledge, like the nurses had to remember all the drug interactions?

00:54:52.189 --> 00:54:55.769
So you look for all of those things, the manually intensive,

00:54:56.029 --> 00:54:58.909
cognitively demanding, error prone, and knowledge dependent.

00:54:59.269 --> 00:55:04.749
And that'll tell you where an opportunity is that you can design for and solve.

00:55:04.749 --> 00:55:07.749
And so this so this

00:55:07.749 --> 00:55:10.549
this coast guard thing turns out that what they

00:55:10.549 --> 00:55:13.729
needed wasn't so much new satellite receiving equipment

00:55:13.729 --> 00:55:16.609
but equipment to not only

00:55:16.609 --> 00:55:19.489
figure out what the search map was but to

00:55:19.489 --> 00:55:23.329
be able to send it out in a more efficient manner because it could take an hour

00:55:23.329 --> 00:55:29.309
to tell somebody what their map would be you know over a voice so a digital

00:55:29.309 --> 00:55:33.989
format that you can send the coordinates out to and then be able to track on

00:55:33.989 --> 00:55:37.989
their GPS how they're doing on that and respond back to you,

00:55:38.189 --> 00:55:44.749
the Coast Guard, so you could adjust any other assets and their search map as well.

00:55:45.902 --> 00:55:51.742
Yeah, that's excellent. And so, and one of the last concepts to get to here

00:55:51.742 --> 00:55:54.422
that I want to make sure I get to is the UX strategy stuff.

00:55:55.742 --> 00:56:02.502
And you talk about it as a, like it's an explicit deliverable to the business

00:56:02.502 --> 00:56:05.642
because a lot of UX researchers kind of think of like, okay,

00:56:05.762 --> 00:56:12.122
well, I'm producing this knowledge and hopefully we can influence the way the business does things.

00:56:12.122 --> 00:56:18.962
But this is producing a strategy. And so I want you to talk about that.

00:56:19.202 --> 00:56:23.782
So that's what the output of the priority matrix is.

00:56:24.002 --> 00:56:28.762
You have to agree that since these are all your priorities, you're going to

00:56:28.762 --> 00:56:34.482
settle on as a business, as a technology component, as the user advert.

00:56:34.682 --> 00:56:40.802
You're going to settle on what this one driving paradigm is.

00:56:40.802 --> 00:56:43.762
Like for pro flowers and it was pro flowers doesn't sell

00:56:43.762 --> 00:56:47.162
flowers it's about the occasion and so

00:56:47.162 --> 00:56:50.282
that drives every other step that drove marketing

00:56:50.282 --> 00:56:53.922
that drove design that drove development you

00:56:53.922 --> 00:56:57.142
know it was a codifying single

00:56:57.142 --> 00:57:00.502
purpose for that product and it

00:57:00.502 --> 00:57:03.622
provides focus for everyone and if it's

00:57:03.622 --> 00:57:07.022
well researched then it's the right thing to focus on right so

00:57:07.022 --> 00:57:09.922
like you know kennedy made his famous we're

00:57:09.922 --> 00:57:14.002
going to space speech back in uh 61 62

00:57:14.002 --> 00:57:17.222
can't remember what and so during

00:57:17.222 --> 00:57:20.522
the 60s you could go ask anybody an astronaut an

00:57:20.522 --> 00:57:24.022
engineer a flight surgeon a janitor

00:57:24.022 --> 00:57:26.762
pushing a broom down the hall at three o'clock in the morning what are

00:57:26.762 --> 00:57:30.082
you doing here we're going to the moon everybody said

00:57:30.082 --> 00:57:33.722
that we're going to the moon that single focus got

00:57:33.722 --> 00:57:37.142
them there excellent going to

00:57:37.142 --> 00:57:40.402
the moon achieving high goals and this book

00:57:40.402 --> 00:57:44.382
to help us out doing it disruptive research larry

00:57:44.382 --> 00:57:49.422
where's the best place for people to uh link up with you is it linkedin and

00:57:49.422 --> 00:57:54.582
find what you're working on and yeah i'm on linkedin uh almost exclusively i

00:57:54.582 --> 00:57:59.662
don't have any other social media channels i find linkedin to be the best as

00:57:59.662 --> 00:58:03.742
far as sharing knowledge and feel free to connect with me And, you know.

00:58:03.842 --> 00:58:06.702
I'll text with anybody.

00:58:07.522 --> 00:58:11.582
I work at LSA Digital. We're a digital transformation company.

00:58:11.782 --> 00:58:12.962
And so I'm reachable there.

00:58:13.742 --> 00:58:17.342
Larry.Marine at lsadigital.com.

00:58:18.862 --> 00:58:23.582
And I'd love to hear any of your questions or comments or anything like that.

00:58:23.902 --> 00:58:27.182
And the book is available on Amazon and Instagram.

00:58:28.322 --> 00:58:31.202
What's that called? I was going to say Instagram.

00:58:34.874 --> 00:58:39.214
Is it another bookstore? I'm not aware of any of that. Ingram Spark.

00:58:39.954 --> 00:58:46.774
Yeah, it's an international bookstore because some of my international people can't get it off Amazon.

00:58:47.894 --> 00:58:52.074
Gotcha, gotcha. So it's where everybody can get it. Disruptive Research.

00:58:52.534 --> 00:58:54.934
Larry Marine, thank you so much for being on the show today.

00:58:55.454 --> 00:58:59.514
Thanks. You did a great job preparing for this, by the way. I'm impressed. Well done.

00:59:00.554 --> 00:59:06.094
So to wrap things up, Today's conversation with Larry Marine challenged some

00:59:06.094 --> 00:59:10.114
of the thinking that has become commonplace in user research today.

00:59:10.574 --> 00:59:16.214
We explored the idea that most teams are skipping critical steps that could

00:59:16.214 --> 00:59:19.434
have changed the conclusions they drew from their research.

00:59:19.674 --> 00:59:24.834
Activities like task analysis, especially when combined with a deeper understanding

00:59:24.834 --> 00:59:27.994
of user knowledge, are incredibly powerful.

00:59:27.994 --> 00:59:32.894
We discussed a methodology that has been shaped by years of practice,

00:59:33.254 --> 00:59:38.294
adapted from classical forms of task analysis, and turned into something new.

00:59:38.494 --> 00:59:43.374
It's a method that's been shaped and refined across hundreds of real-world projects.

00:59:43.394 --> 00:59:48.594
It's been built to cut through the noise and illuminate what truly matters.

00:59:48.874 --> 00:59:54.494
And in doing so, it creates fertile ground where new disruptive ideas can take root.

00:59:54.674 --> 00:59:59.474
And why does this work? Well, it's because by reframing the problem methodically,

00:59:59.694 --> 01:00:05.714
we can move the picture frame of our imagination into a new solution space.

01:00:05.934 --> 01:00:10.534
This is a space that encompasses fresh ideas. And these are the kinds of ideas

01:00:10.534 --> 01:00:13.294
you would need to have a disruptive impact.

01:00:13.454 --> 01:00:18.154
If you want your product to stand out or your service to really get it right,

01:00:18.394 --> 01:00:22.674
you need to investigate this space that most teams are overlooking.

01:00:22.674 --> 01:00:28.314
And as Larry shows us, that space begins with rethinking how we research and

01:00:28.314 --> 01:00:33.414
doing so in a way that we see old problems with a fresh pair of eyes.