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Dallas Burnett: Hey everybody.

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We're talking to Jason R today.

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What an amazing guy.

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He is a behavioral scientist and
entrepreneur who's cracked a code

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on habits, talents, and innovation.

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From Stanford's lab to leading Walmart's
first behavioral science team, he has

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some incredible stories about transforming
businesses with psychology and building

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top performing teams at Persona.

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he's a great new friend of mine.

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You don't want to miss this
incredible conversation.

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Welcome to the last 10%.

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Your host, Dallas Burnett, dives into
incredible conversations that will

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inspire you to finish well finish strong.

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Listen as guests share their journeys in
valuable advice on living in the last 10%.

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If you are a leader, a coach, a
business owner, or someone looking to

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level up, you are in the right place.

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Remember, you can give 90% effort and
make it a long way, but it's finding

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out how to unlock the last 10%.

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That makes all the difference in your
life, your relationships, and your work.

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Now here's Dallas.

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Welcome, welcome, welcome.

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I am Dallas Burnett, sitting
in my 1905 Koch brothers

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barber chair in Thrive Studios.

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But more importantly, we
have a great guest today.

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Jason Aya is a behavioral scientist.

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He's the author of a
book called Real Change.

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He's the CEO of persona, where
he uses some real cutting edge

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psychometric tools to connect
companies with exceptional talent.

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He pioneered Walmart's
behavioral science team.

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He's been advising tech startups.

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Man, he's all over the place.

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We're excited to have him on the last
10% today, So welcome to the show, Jason.

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Jason Hreha: Hey, thanks for having me.

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Dallas Burnett: Yeah, man.

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So just tell our listeners,
like what's your background?

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You've got into all this behavioral you've
done all this work with Walmart, written

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the book, but what was your background?

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How did you get started?

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Jason Hreha: Oh, it's a great
question and I, I don't even

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know where to begin actually.

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so I got interested in human psychology,
human behavior in high school.

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I was just living my life doing my thing.

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And I went to an interesting high school
where we actually could choose our English

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class, our junior year of high school.

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And I chose, to take a course or, yeah,
on, it's called Ancient Eastern Thought.

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And we read, like Buddhist texts, Hindu
texts, like all these just interesting,

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ancient, texts, from different
religious, religious philosophies.

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And at that time there was a guy at the
University of Wisconsin named Richard

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Davidson, who was doing these MRI studies
on monks where he would put experienced

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meditators, monks in MRI machines.

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And he would look at their brains and see
is there a functional difference here?

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Is there, is the structure different?

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And he noticed that there were significant
differences between experienced meditators

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and non-experienced meditators in
the prefrontal cortex of the brain.

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Dallas Burnett: Yes.

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Jason Hreha: at that time,
national Geographic had a,

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an issue on this research.

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And there's a picture of a monk on
the cover with electrodes all over,

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his head and my teacher brought
that into class and showed it to us.

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And I just was fascinated by it.

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And I knew at that moment I said,
okay, this is what I wanna study.

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I wanna study the brain.

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And so I read, I borrowed that issue, I
read it, and then I just bought every book

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I could on the brain and I said, Okay.

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this is what I wanna go to college for.

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This is what I wanna study.

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And then I went off to Stanford
and studied neuroscience.

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Dallas Burnett: Neuroscience at Stanford.

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That's incredible.

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And I, that is fascinating 'cause I
actually have seen those studies as well

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Jason Hreha: yeah,

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Dallas Burnett: fascinating
that you could, that meditation

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actually change your brain just

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Jason Hreha: totally.

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Dallas Burnett: this process.

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So it's just that, that also
was very impactful to me.

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I thought that was just fascinating.

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So you may get to Stanford, you're
in, you're studying neuroscience.

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next, what happens at Stanford?

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Jason Hreha: Yeah, that's a good question.

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like any college student, I didn't,
I knew what I wanted to study going

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in, but I had really had no idea
about what I wanted to do with it.

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So I was just, I just knew that I
wanted to learn about what makes

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us tick, why do, like how do we
think, why do we do what we do?

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I was just interested in these
sort of philosophical questions.

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but I always wanted to do something
applied or, impactful with this knowledge.

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Dallas Burnett: I.

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Jason Hreha: And so when
I was in college, I.

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started looking around to see, okay,
like what different paths could I take?

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So of course I, if you're taking
like a biological science as a major,

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most of the people in your class
are, want to go to med school, right?

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They, most of 'em wanna be doctors.

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And so I got interested.

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I was like, okay, should I
go down the medical PA route?

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And so I actually, shadowed a
doctor at the Stanford Hospital.

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And I also was able to see some
operations while I was there and was just

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exploring the medical side of things.

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But I just didn't really
like being in the hospital.

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I didn't really like that environment,
that setting, and I just wasn't

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all that interested in it.

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And so I decided, okay,
I'm not gonna be a doctor.

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that's not the path I'm gonna go down.

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maybe I'll become a researcher.

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So I worked in two different
labs when I was at Stanford.

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One lab was a genetics lab.

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Dallas Burnett: Oh,

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Jason Hreha: and we were looking
at actually, more or less like stem

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cell genetics, we're looking in fly.

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it's been a long time since I've
thought about this, to be honest.

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But we were looking at in flies, what
are the genes involved in keeping these

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adult stem cells versus allowing them to
differentiate in other types of cells.

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So we're looking at kind of
the genes involved in that,

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and like more, local signaling.

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And so I was in a lab doing that.

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And then I joined a lab that was more
or less a behavioral neuroscience lab.

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it was a neuro ethology lab, that
was the technical term for it.

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and so what we did is we studied, the
idea is you wanna study animals in

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their natural environments to really
understand like the natural behavior.

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And then we looked for things called,
in that field there's an important

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concept called a fixed action pattern.

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Basically the idea is that in
animals, they operate in a much more

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reflexive, in instinctual way, right?

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And so the whole idea there is let's
uncover kind of these instinctual

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reflexive behaviors that they do, and
figure out like what are the signals that

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unlock these like genetically encoded,
behaviors and try and understand like

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the, genetic side of I guess you could
say behavioral, yeah, genetic side of

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what, what causes behavior, et cetera.

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So we were very interested in that.

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So I studied that, for a period
of time I was in that lab.

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And to be honest, I didn't
really like research that much.

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Dallas Burnett: Yeah.

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Jason Hreha: it's just not a,
it's, I don't like being in a lab.

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I just realized, Okay.

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Dallas Burnett: percent.

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Jason Hreha: I don't
like being in a hospital.

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I don't really like being in a lab,
doing everything that's involved in that.

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So as I approached graduation, I
was a little bit lost and I was

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wondering, okay, what should I do here?

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should I  become a management
consultant or something like that, or

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should I go into finance or, should
I just figure out something else?

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and then I encountered right as I was
graduating, actually, I encountered

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the work of this guy, BJ Fog, who was
at Stanford, and he ran this lab that

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was, at the time it was called the
Stanford Persuasive Technology Lab.

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Now it's called the Stanford Behavior
Design Lab, but at the time it was

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called the Persuasive Technology
Lab because they're really focused

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at that time on, so BJ had invented
this field, he called it captology.

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And the idea was, okay, you have
technologies, you have like mobile

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phones, you have computers, et cetera.

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These things, we interact
with them every day.

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they're a huge important
part of our lives.

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And so, you know, they're
changing our behavior.

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They're shaping what we do, like

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Dallas Burnett: yes.

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Jason Hreha: regardless, like
it, they just will impact us.

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And so the question became, how
could you actually modify these

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systems or use these systems and
harness them to change behavior

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in a very deliberate, mindful way?

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So how could you actually make it so
that these systems could be designed

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to, let's say, shape, health behavior
or shape other types of behaviors

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to, to make people's lives better?

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And so the lab really studied how do
you change behavior using technology?

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In order to change behavior using
technology, first you have to understand

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just behavior change in general.

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So a lot of the research of
the lab was just around, okay,

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how can you change behavior?

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What tactics work?

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What tactics don't work?

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What are the determinants, et cetera.

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And so I emailed him actually and I
said, Hey, I'm graduating more or less,

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can I still be a member of your lab?

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And so I started doing research for him
after I graduated and, worked with him.

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I think, I don't remember
the exact amount of time.

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it, was probably a year and a half or
two years I was collaborating with him.

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And I just, that, that changed my life.

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'cause it was at that point I realized,
okay, you actually can use these

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tools in a very, behavioral science
tactics, behavioral science knowledge.

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You can use it in a very tactical way
in order to get real world results.

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Dallas Burnett: Mm-hmm.

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Jason Hreha: if you think about it, that's
what most companies are trying to do is,

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a company is only as good as its ability
to like change consumer behavior, right?

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If you can't get anybody to change their
habits and start shopping at your store,

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or if you can't get people to suddenly,
build a routine around your application,

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if you can't get people to do any of these
things, then you're, you're outta luck.

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Right?

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And so if you just look at the
most successful, for example,

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technology products, Facebook,
Instagram, TikTok, et cetera, right?

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What the, their defining feature
is that they're very engaging and

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that a lot of people use them,
and a lot of people love them.

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Dallas Burnett: Oh yeah.

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I think that's so fascinating too.

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there's a lot of things about your
story that, res that resonate with me.

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coming out, I came out of a very
technical degree as well and didn't

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kinda know what I wanted to do with that.

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Jason Hreha: Yeah.

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Dallas Burnett: of the things I went to
is similar to you, I went to a biomedical

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engineering lab and I was like, well,
maybe this is cutting edge stuff.

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I'll try this out.

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And I will, I'll be honest, walking
around and seeing all the different

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research projects I can imagine it
was, is mind blowing at Stanford.

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And more mind blowing in genetics lab is
seeing what all these brilliant people

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were working on and engaging with that.

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So for a semester I'm just, I'm in
it looking at all these things, but

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man, I got to the end of it and I
was like, I cannot be in, there's

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no way this is gonna work for me.

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And we were working on some really cool
stuff and I wanted to know all about it.

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I just didn't wanna be

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Jason Hreha: Totally, totally.

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I'm the same boat.

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.
Dallas Burnett: yeah,
so the other thing is.

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It's so interesting because I've just read
in the last 24 hours, read two articles

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that just really jumped out at me.

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That is exactly like center mass of
what you were just talking about.

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And one of them that just recently
just published I think was how

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don't know, I think it was MIT,
but someone was looking at how our

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brains are functioning with AI and.

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If you use AI to write it, they used
a word like cognitive deficit or

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something like that, where there's
just not, you don't even remember

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what you wrote like 10 minutes ago.

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Well, the reason is you're
not writing it, but it's

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Jason Hreha: Yeah.

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Dallas Burnett: They were talking about
how it's changing our brains and so

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they kinda, you kinda read all through
this research and it gets to the end.

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It says, no, you can still use ai, but
just you write it first and use it to edit

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it and you'll remember and own it more.

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Essentially your brain will own it more
and function at a higher level than if

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you just have it, which kind of seems
to me to be very fairly intuitive.

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Right.

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That will make sense.

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But it's exactly what you're saying

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Jason Hreha: Yeah.

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Dallas Burnett: how is this changing?

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And then also, was re-released recently in
the news where, I think it was, I think,

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yeah, it was chat GBT that said, Hey.

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actually been increasing
people's likelihood of believing

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in conspiracy theories.

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Jason Hreha: Sure.

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Dallas Burnett: And then when they
called out the AI on it, it was like,

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you need to call people at chat,
GBT and the media and let them know

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that's what we've been doing here.

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You know?

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And it's okay, wow.

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but to your point, we're interfacing
with technology so much, and it's

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only getting more and more and more.

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So before it was just, you're logging
on your computer and you're just using

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tools on your computer, like Word.

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Then all of a sudden social media
comes and everybody's got it in

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their pocket and their phone.

248
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And now with ai now we're
integrating it into actual

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Jason Hreha: Yeah.

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Dallas Burnett: and things like that.

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It's just, we're seeing this integration.

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So totally relevant.

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I know you're like it because working
with, BJ Fog and his, his research is

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just, I mean, it was center mass of
what's going on in, in the world today.

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So I know that you had some
serious actionable things

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that you could take out of

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Jason Hreha: Yeah, absolutely.

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Absolutely.

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working for him was incredible because,
when you study the behavioral sciences

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and neuroscience in an academic concept
context, you understand behavior very well

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for me in ab, in an abstract way, right?

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Like you study a lot of these
lab experiments that are done

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in a very, artificial context.

264
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You learn about these different
theories, but bridging the divide

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between, okay, I read about this
in a book to I'm, I can use it and

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actually experience it in real life.

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there's a huge divide there.

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There's a huge gap and I think it
requires a lot of creativity and a lot

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of, just a practical mindset that is.

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that you don't really learn
in an academic context.

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And with BJ I could see, I saw the way
he thought and the way he was able to

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break down problems and really hone
in using his different models on what

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the bottlenecks were for a behavior
not occurring or his ability to just

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see a situation and just break down in
a, just a very logical and, systematic

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way, all the behavioral dynamics and
then come up with potential solutions.

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It just blew my mind.

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And

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Dallas Burnett: Oh

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Jason Hreha: working with him, I,
he really was the one who gave me

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the confidence and really the tools,
I think, to start this career.

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And, I owe so much to I personally
think that he is the, the most,

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The most brilliant kind of applied
behavioral scientists, of our era.

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I, I really do.

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And I do think that his materials, like
I always, I recommend his materials

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to everybody I can because, 'cause
I think that if you're interested in

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just getting started in this field or
if you're interested in just learning

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how to think, in a practical way about
behavior and behavior change, I do think

288
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that the fog behavior model, his main
behavior model is the best place to start.

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Dallas Burnett: Wow.

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That's awesome.

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That's

292
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Jason Hreha: Yeah.

293
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Dallas Burnett: to look
up some of this stuff.

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I

295
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Jason Hreha: Yeah.

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Absolutely.

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Dallas Burnett: any of his in,
his material, but that now I

298
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want to, so I'm gonna have to

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Jason Hreha: You should.

300
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Yeah.

301
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Dallas Burnett: awesome.

302
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if you were to think back

303
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Was there one thing that stood
out to you that was like, my

304
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gosh, this is mind blowing.

305
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this is just something
I was not expecting.

306
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Some unexpected insight,

307
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or was it different?

308
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Was it more like unveiling the
reality you kind of already knew?

309
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Jason Hreha: No, I mean, I, I would say
that learning the fog behavior model,

310
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his main behavior model for the first
time was mind blowing for me because,

311
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it in an academic context, right?

312
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Like, well, first of all,
behavior is very complex.

313
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Like we do things for, there's so
many different variables that kind

314
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of go into, any given decision.

315
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we, humans are extremely complex.

316
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The brain is extremely complex.

317
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When you study it in this very
rigorous academic way, it's

318
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a little bit overwhelming.

319
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it's a little bit overwhelming because

320
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Dallas Burnett: Yeah.

321
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Jason Hreha: this
infinitely complex problem.

322
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But what BJ did that I think is so
brilliant is he said, okay, listen.

323
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In an applied context, when we're thinking
about behavior, really there are three

324
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elements that you need to think about.

325
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There are really only
three major elements.

326
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One is called motivation, one is
ability, and the other is trigger.

327
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He calls tri, he changed trigger
to prompt now, so he says

328
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motivation, ab ability and prompt.

329
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And so the whole idea here is that
as we walk around the world, we

330
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have, varying levels of motivation
for different behaviors or different

331
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outcomes in our life, right,

332
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And there are some days when I'm
extremely motivated to work out.

333
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And in those situations when I'm
extremely motivated to work out,

334
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I can be, far away from a gym.

335
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Dallas Burnett: right.

336
00:14:36,163 --> 00:14:40,033
Jason Hreha: I could have a low ability
to go to the gym that day, but because

337
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I'm so motivated and amped up, I'll
actually end up, I'll going and doing it.

338
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Dallas Burnett: right.

339
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Jason Hreha: so it's the combination
of motivation and ability kind of

340
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determines my probability or propensity
to do a given behavior when prompted,

341
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Dallas Burnett: I

342
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Jason Hreha: if you can understand that.

343
00:14:56,425 --> 00:14:56,725
Yeah,

344
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Dallas Burnett: totally.

345
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That is so interesting because
you hear the other models, and I

346
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know Duhigg has his habit model,

347
00:15:02,672 --> 00:15:03,212
Jason Hreha: Yeah.

348
00:15:03,295 --> 00:15:04,855
Dallas Burnett: James
Clear has Atomic Habits.

349
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One

350
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Jason Hreha: Yeah.

351
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Dallas Burnett: and one has four.

352
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I think one is you have the trigger
that I think they still use trigger

353
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they then the actual behavior and
then the reward, which kind of

354
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Jason Hreha: yeah,

355
00:15:14,805 --> 00:15:16,995
Dallas Burnett: The other one has,
I think the other one has motivation

356
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tied or emotion tied into it.

357
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And I think that's somehow or something,
I may be wrong on that, but it's been

358
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a while since I've read those books.

359
00:15:23,220 --> 00:15:27,560
But, I love how you're positioning
it that way because in a sense.

360
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you're there is the behavior.

361
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But it's saying before you even get to
that, your motivation has got to outweigh,

362
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any obstacles that are impeding it.

363
00:15:37,293 --> 00:15:40,083
if you're not highly motivated,
you're not going to actually do

364
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the behavior that you could do.

365
00:15:42,574 --> 00:15:42,864
Jason Hreha: sure.

366
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Dallas Burnett: if there's some, if the
motivation is extremely high, you'll

367
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still find a way because you're just like

368
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Jason Hreha: Totally.

369
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.
Dallas Burnett: to do it.

370
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And so I love how it starts
with that motivation piece.

371
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I just, I like that framing.

372
00:15:53,894 --> 00:15:55,834
That's a very interesting
way to, Way to frame it up.

373
00:15:55,834 --> 00:15:59,014
I Now, speaking of Atomic
Habits, now this is amazing.

374
00:15:59,224 --> 00:16:02,314
So you have written a book
called Real Change, and

375
00:16:02,344 --> 00:16:02,884
Jason Hreha: Yeah,

376
00:16:03,694 --> 00:16:07,564
Dallas Burnett: by James Clear in
Atomic Habits, which is awesome.

377
00:16:07,754 --> 00:16:09,674
that's a super huge call out.

378
00:16:09,674 --> 00:16:16,934
And so, um, tell, tell us how that
book is related to habit formation and

379
00:16:16,934 --> 00:16:20,324
kind of your, the background of that
book and your time at Stanford, how

380
00:16:20,354 --> 00:16:20,744
Jason Hreha: sure.

381
00:16:20,744 --> 00:16:21,464
Dallas Burnett: of goes together.

382
00:16:22,094 --> 00:16:22,274
Jason Hreha: Yeah.

383
00:16:22,274 --> 00:16:23,084
I do want to clarify.

384
00:16:23,084 --> 00:16:27,014
James Clear, he, he quoted
my, my definition of habit

385
00:16:27,014 --> 00:16:28,844
in Atomic Habits years ago.

386
00:16:29,024 --> 00:16:31,944
My book is significantly newer,
so he didn't, he didn't cite

387
00:16:31,944 --> 00:16:33,474
my book or call out my book.

388
00:16:33,724 --> 00:16:36,504
he just, called out, I believe,
actually it was, years ago.

389
00:16:36,504 --> 00:16:38,544
I think I put out a,
I believe he found it.

390
00:16:38,664 --> 00:16:42,324
It was a tweet where I laid out
my definition of habit because.

391
00:16:42,374 --> 00:16:46,634
I guess to kind of fast forward a little
bit, after I graduated college and

392
00:16:46,634 --> 00:16:50,864
worked with bj after that I decided,
okay, I just want to just start

393
00:16:50,864 --> 00:16:52,394
doing this applied behavioral work.

394
00:16:52,454 --> 00:16:53,774
And I just want that to be my career.

395
00:16:53,774 --> 00:16:57,524
I just wanna work on applied behavioral
science projects in industry.

396
00:16:57,914 --> 00:17:02,264
And since I graduated from Stanford, a
lot of my friends were in the technology

397
00:17:02,264 --> 00:17:05,744
world building technology startups or
working for large technology companies.

398
00:17:05,954 --> 00:17:08,944
And when I was looking around seeing,
okay, where could I apply this stuff?

399
00:17:09,344 --> 00:17:10,904
tech was the obvious answer.

400
00:17:11,084 --> 00:17:18,004
Just because number one, it's a lot easier
to make, product changes in a technology

401
00:17:18,004 --> 00:17:21,634
product versus a physical product or
versus a real world situation, right?

402
00:17:21,634 --> 00:17:24,994
Like in order to make a change
to a technology product, you can

403
00:17:24,994 --> 00:17:26,404
just sit down with a developer.

404
00:17:26,404 --> 00:17:31,129
I. In a period of a few hours or a
few days, you can make a new feature

405
00:17:31,129 --> 00:17:32,449
or you could tweak things, right?

406
00:17:32,449 --> 00:17:35,929
And so you can shape the environment of
a technology product a lot more easily

407
00:17:35,929 --> 00:17:37,639
than a real world situation, right?

408
00:17:37,729 --> 00:17:41,059
And so number one, technology
allows you to iterate very quickly.

409
00:17:41,059 --> 00:17:44,209
It allows you to make these big changes
and run experiments very accurately.

410
00:17:44,479 --> 00:17:48,667
Number two, with a technology product,
the sample sizes are often very large.

411
00:17:49,102 --> 00:17:49,672
Dallas Burnett: Right.

412
00:17:49,695 --> 00:17:51,705
Jason Hreha: with Facebook, you have
like billions of people using it.

413
00:17:51,705 --> 00:17:54,645
And if you're an academic and you're
trying to study human behavior in a lab,

414
00:17:54,855 --> 00:17:58,345
you can maybe recruit a couple hundred,
maybe, students or something like that.

415
00:17:58,495 --> 00:18:01,225
But if you're Facebook or if you're
one of these large companies, you

416
00:18:01,225 --> 00:18:04,155
can get, hundreds of thousands or
millions of people in your experiment

417
00:18:04,155 --> 00:18:07,635
very quickly, and you can segment
them very easily and then you can

418
00:18:07,635 --> 00:18:09,165
actually track exactly what they do.

419
00:18:09,165 --> 00:18:11,175
So it's very easy to measure the behavior.

420
00:18:11,355 --> 00:18:15,605
So technology products are really like the
perfect, laboratory for human behavior.

421
00:18:16,100 --> 00:18:16,760
Dallas Burnett: Sure.

422
00:18:17,375 --> 00:18:18,605
Jason Hreha: great, this
is what I want to do.

423
00:18:18,875 --> 00:18:19,085
So I.

424
00:18:19,085 --> 00:18:20,375
created my own consulting firm.

425
00:18:20,375 --> 00:18:21,545
At the time it was called Dopamine.

426
00:18:22,025 --> 00:18:24,515
It was just me and I would
hire contractors to help me

427
00:18:24,515 --> 00:18:26,115
out, with different projects.

428
00:18:26,355 --> 00:18:29,415
And so I started doing this
applied behavioral work and doing

429
00:18:29,415 --> 00:18:30,375
this applied behavioral work.

430
00:18:30,375 --> 00:18:34,545
What I realized very quickly was, okay,
what all the clients that I work with

431
00:18:34,545 --> 00:18:36,645
want is they want habit formation.

432
00:18:36,795 --> 00:18:38,625
They want to build habit forming products.

433
00:18:38,625 --> 00:18:41,805
They want people to come into their
products, love their product, and

434
00:18:41,805 --> 00:18:43,395
just start using it continuously.

435
00:18:43,515 --> 00:18:46,785
And so I was spending a lot of
time for many years thinking about,

436
00:18:46,905 --> 00:18:50,125
okay, the academic literature
says certain things about habits.

437
00:18:50,255 --> 00:18:51,935
I'm experiencing some of
that in the real world.

438
00:18:51,935 --> 00:18:54,875
I'm seeing my, I'm coming up
with my own insights as well.

439
00:18:54,905 --> 00:18:57,720
And so I wanted to come up with
a very crisp, clear definition

440
00:18:58,000 --> 00:18:59,290
of habit for me to use.

441
00:18:59,290 --> 00:19:00,400
It was really for my own benefit.

442
00:19:00,400 --> 00:19:01,780
And I believe I tweeted that out.

443
00:19:02,080 --> 00:19:06,130
James, I think doing the research for
his stuff and his book, saw that and

444
00:19:06,130 --> 00:19:07,600
then decided to use that in the book.

445
00:19:07,600 --> 00:19:07,960
Yeah.

446
00:19:08,275 --> 00:19:09,685
Dallas Burnett: Oh my goodness.

447
00:19:09,715 --> 00:19:10,585
That's awesome.

448
00:19:10,685 --> 00:19:11,345
that's awesome.

449
00:19:11,345 --> 00:19:15,440
Takes your definition for the book
on habits, like That's awesome.

450
00:19:16,100 --> 00:19:16,760
Jason Hreha: No, it was cool.

451
00:19:16,790 --> 00:19:19,860
I mean, it was very, I'm, you know,
I'm, I'm very honored and, so it's

452
00:19:19,860 --> 00:19:21,600
just, it was and very, very nice of him.

453
00:19:21,600 --> 00:19:21,960
Yeah,

454
00:19:22,275 --> 00:19:22,515
Dallas Burnett: Cool.

455
00:19:22,515 --> 00:19:23,415
Shout out tool.

456
00:19:23,415 --> 00:19:23,835
Shout out.

457
00:19:23,835 --> 00:19:24,495
That's really great.

458
00:19:24,545 --> 00:19:26,945
so let's talk a little bit
about that, because you

459
00:19:27,020 --> 00:19:27,320
Jason Hreha: yeah,

460
00:19:27,635 --> 00:19:30,855
Dallas Burnett: together these models
and seeing it you were able to iterate

461
00:19:30,855 --> 00:19:32,415
so much, I think during that time.

462
00:19:32,415 --> 00:19:35,535
'cause it sounds like what you're able
to do is you've got this massive sample

463
00:19:35,535 --> 00:19:40,635
size and then you can just experiment
on real, in real time, on real people

464
00:19:40,915 --> 00:19:41,205
Jason Hreha: yeah.

465
00:19:41,295 --> 00:19:41,925
Dallas Burnett: technology.

466
00:19:41,925 --> 00:19:43,065
So that's amazing.

467
00:19:43,065 --> 00:19:43,845
That's amazing.

468
00:19:43,845 --> 00:19:45,955
information and data
that you could process.

469
00:19:46,195 --> 00:19:46,375
So

470
00:19:46,570 --> 00:19:46,900
Jason Hreha: Totally.

471
00:19:46,915 --> 00:19:49,895
Dallas Burnett: bit about what
you found, specifically let's talk

472
00:19:49,895 --> 00:19:54,305
about as it relates to and habits,
'cause you've got Atomic Habits,

473
00:19:54,305 --> 00:19:55,415
but in your book Real Change.

474
00:19:55,655 --> 00:19:58,545
Let's talk a little bit about
all of that and what you found

475
00:19:58,545 --> 00:20:00,015
in that time as a consultant.

476
00:20:00,015 --> 00:20:01,774
In, in, in the Valley.

477
00:20:01,774 --> 00:20:01,944
Jason Hreha: yeah.

478
00:20:01,944 --> 00:20:05,814
so my time as a consultant working with
a lot of different technology companies,

479
00:20:05,864 --> 00:20:07,154
it was eyeopening, it was enlightening.

480
00:20:07,184 --> 00:20:10,664
and the big thing that I realized
very quickly actually was, very early

481
00:20:10,664 --> 00:20:14,954
on I realized that a lot of what you
learn about in the academic world just

482
00:20:14,954 --> 00:20:17,244
doesn't really apply, in the real world.

483
00:20:17,574 --> 00:20:20,244
and what I noticed is, so my original
approach, when I first started doing

484
00:20:20,244 --> 00:20:23,904
this work, my entire strategy was okay.

485
00:20:24,174 --> 00:20:27,814
I saw myself as the, the
translator for translating

486
00:20:27,814 --> 00:20:30,794
academic work into, the real world.

487
00:20:30,794 --> 00:20:34,303
And so what I would do is, so whenever
I would work with a company, my first.

488
00:20:34,309 --> 00:20:38,719
Task was to go to the literature, to go to
the academic literature, do a literature

489
00:20:38,719 --> 00:20:43,069
review, just l see, look up as many
different papers as I could on these types

490
00:20:43,069 --> 00:20:46,889
of behavior change problems, interventions
that have been tested in a laboratory

491
00:20:46,889 --> 00:20:48,689
context or in a real world context.

492
00:20:48,819 --> 00:20:52,509
by academics,  gather all the
interventions or the po the possible

493
00:20:52,509 --> 00:20:56,029
interventions that I could, and
then evaluate each of them, figure

494
00:20:56,029 --> 00:20:59,239
out which ones make the most sense
for the given product or the given

495
00:20:59,239 --> 00:21:01,759
behavior change problem I'm working
on with the technology company.

496
00:21:02,089 --> 00:21:06,079
And then I, we would work to run
experiments around, okay, let's take this

497
00:21:06,079 --> 00:21:09,919
tactic and add it to the product or add
it to a marketing campaign or whatever,

498
00:21:10,189 --> 00:21:11,569
and then just see what the results are.

499
00:21:11,839 --> 00:21:14,839
And what I realized very quickly, and
this was actually quite disheartening

500
00:21:14,839 --> 00:21:18,558
at the time, was  so many of the tactics
that I'd read about in books or in

501
00:21:18,558 --> 00:21:22,483
papers that I tried to apply in the real
world, they would just have no effect.

502
00:21:22,495 --> 00:21:25,224
This happened over and over again.

503
00:21:25,224 --> 00:21:26,784
It happened quite a number of times.

504
00:21:26,784 --> 00:21:30,294
My first, let's say, two or three
years doing this applied work.

505
00:21:30,624 --> 00:21:33,684
And I remember at the time, my,
my point of view was, either the

506
00:21:33,684 --> 00:21:37,423
research is wrong, I'm implementing it
incorrectly, or I'm just like a total

507
00:21:37,423 --> 00:21:39,583
idiot and just misreading these things

508
00:21:39,823 --> 00:21:41,683
Dallas Burnett: you had to have
some, you had to have some patience.

509
00:21:41,683 --> 00:21:43,303
That had to be a little bit discouraging

510
00:21:43,513 --> 00:21:44,143
Jason Hreha: oh, it was ex

511
00:21:44,563 --> 00:21:46,363
Dallas Burnett: time and
going through all this stuff.

512
00:21:46,363 --> 00:21:46,843
Yeah.

513
00:21:47,083 --> 00:21:48,193
Jason Hreha: it was
extremely discouraging.

514
00:21:48,433 --> 00:21:50,948
But, I started then reading the
papers with a bit more of a critical

515
00:21:50,948 --> 00:21:53,908
eye in looking, looking at the
methodology, looking at how the

516
00:21:53,908 --> 00:21:55,408
researchers set up these experiments.

517
00:21:55,408 --> 00:22:00,220
And  I started getting quite suspicious
and thinking, okay, a lot of these studies

518
00:22:00,220 --> 00:22:02,230
are done with very small sample sizes.

519
00:22:02,230 --> 00:22:05,275
a lot of the times the, the, these
papers just didn't really pass the

520
00:22:05,275 --> 00:22:08,425
sniff test, the closer I looked at the
methodology and so I started thinking,

521
00:22:08,425 --> 00:22:13,990
I. I think that there might be either
some, just  massaging or just, potentially

522
00:22:13,990 --> 00:22:15,520
outright fraud in some of these papers.

523
00:22:15,520 --> 00:22:18,610
it's hard to say because a lot of
these, a lot of academics do not,

524
00:22:18,850 --> 00:22:21,340
now it's becoming much more common,
but in the past they wouldn't like

525
00:22:21,430 --> 00:22:23,080
necessarily release the raw data.

526
00:22:23,320 --> 00:22:26,370
and or today what, what a lot of
academics do is called pre-registering

527
00:22:26,370 --> 00:22:29,650
where you, before you, run the
experiment, you say, here's the

528
00:22:29,650 --> 00:22:30,790
experiment where we're gonna run.

529
00:22:31,030 --> 00:22:32,140
Here's how we're gonna do it.

530
00:22:32,200 --> 00:22:34,000
Here are the analyses that we're gonna do.

531
00:22:34,270 --> 00:22:36,220
And you lay it all, you put
your prediction out and your

532
00:22:36,220 --> 00:22:37,390
whole plan out ahead of time.

533
00:22:37,720 --> 00:22:39,910
And so you can't deviate from that plan.

534
00:22:39,940 --> 00:22:40,480
'cause what I'll.

535
00:22:40,975 --> 00:22:43,525
Dallas Burnett: hypothesis, you
have to say, this is my hypothesis

536
00:22:43,525 --> 00:22:44,845
and this is how I'm gonna build it.

537
00:22:44,845 --> 00:22:46,135
To test this hypothesis

538
00:22:46,180 --> 00:22:49,600
Jason Hreha: you pre-register it, you
pre-register it so that you can't later.

539
00:22:49,810 --> 00:22:53,720
So let's say you pre-register like
this and then, or let's say you don't

540
00:22:53,720 --> 00:22:57,230
pre-register this and you run the
experiment, then you can say, okay, well

541
00:22:57,650 --> 00:23:01,340
the original findings that we were going
out to look for, we didn't find, but we

542
00:23:01,520 --> 00:23:05,600
reanalyzed the data and these other ways
and we found this significant effect.

543
00:23:05,750 --> 00:23:07,580
You can then publish
that significant effect.

544
00:23:07,730 --> 00:23:10,970
But for a variety of reasons, it gets
pretty complicated, but for a variety of

545
00:23:10,970 --> 00:23:15,200
reasons, that just like methodologically
is just not, it's not proper.

546
00:23:15,200 --> 00:23:16,100
You really don't wanna do that.

547
00:23:16,100 --> 00:23:17,960
You can get a lot of
false positives that way.

548
00:23:17,960 --> 00:23:18,180
Dallas Burnett: Yes.

549
00:23:18,260 --> 00:23:20,690
Jason Hreha: And what I started
noticing doing this applied work is

550
00:23:20,690 --> 00:23:26,810
okay, something is not right in the
academic applied behavioral science

551
00:23:26,840 --> 00:23:28,430
slash behavioral science world.

552
00:23:28,635 --> 00:23:29,495
And it was interesting.

553
00:23:29,495 --> 00:23:32,435
So I started having these realizations
and I started writing about it.

554
00:23:32,435 --> 00:23:35,615
And then what happened was I.
There was a big project called

555
00:23:35,615 --> 00:23:37,565
the, reproducibility project.

556
00:23:37,745 --> 00:23:42,665
It was run by this guy Brian Sik,
and the idea was they'd ran this, big

557
00:23:42,665 --> 00:23:47,015
study where they took, they selected
a hundred behavioral science papers

558
00:23:47,015 --> 00:23:51,695
from top journals, and then they had
labs, do, reproduce, try to reproduce

559
00:23:51,785 --> 00:23:53,705
the studies and reproduce the findings.

560
00:23:54,245 --> 00:23:57,365
And, I don't have the study in front of me
right now, but if I remember correctly, I

561
00:23:57,365 --> 00:24:02,225
believe it was 36%, so 36 out of the 100
papers where they were able to reproduce.

562
00:24:02,225 --> 00:24:02,675
So

563
00:24:02,855 --> 00:24:03,095
Dallas Burnett: my

564
00:24:03,095 --> 00:24:06,245
Jason Hreha: a majority of the research,
they could not reproduce the findings.

565
00:24:07,095 --> 00:24:07,785
Dallas Burnett: amazing.

566
00:24:07,785 --> 00:24:09,075
That's the vast majority.

567
00:24:09,075 --> 00:24:11,025
That's a huge number that they

568
00:24:11,155 --> 00:24:12,645
Jason Hreha: yeah, yeah, it's,

569
00:24:12,885 --> 00:24:13,665
Dallas Burnett: gosh.

570
00:24:13,815 --> 00:24:15,285
Jason Hreha: meant that
it's more likely than not.

571
00:24:15,285 --> 00:24:19,125
If you encountered a new study back then,
it was more likely than not that it would

572
00:24:19,125 --> 00:24:20,835
not, the findings would not hold up.

573
00:24:20,895 --> 00:24:24,375
And so the, it was, that was a
interesting experience for me

574
00:24:24,375 --> 00:24:26,325
because when this research came out.

575
00:24:26,760 --> 00:24:30,630
It was, validating because I
realized, okay, I'm not wrong.

576
00:24:31,560 --> 00:24:35,210
Like the, what I was experienced, what
I experienced was due to, research

577
00:24:35,210 --> 00:24:37,320
quality rather than, anything else.

578
00:24:37,630 --> 00:24:37,840
it,

579
00:24:37,908 --> 00:24:38,388
Dallas Burnett: crazy.

580
00:24:38,388 --> 00:24:38,538
I'm

581
00:24:38,883 --> 00:24:39,483
Jason Hreha: I'm not crazy.

582
00:24:39,488 --> 00:24:39,698
Yeah,

583
00:24:39,738 --> 00:24:40,038
Dallas Burnett: Yeah.

584
00:24:40,038 --> 00:24:42,048
You're actually getting the real results.

585
00:24:42,048 --> 00:24:45,048
You're seeing what's really
going on in your tech, in the

586
00:24:45,048 --> 00:24:46,308
world of tech at that time.

587
00:24:46,518 --> 00:24:49,068
Whereas what you were basing some
of your assumptions on was not

588
00:24:49,833 --> 00:24:50,043
Jason Hreha: Yeah.

589
00:24:50,043 --> 00:24:51,723
So that was, I mean, it was disheartening.

590
00:24:51,853 --> 00:24:54,783
but it was validating and disheartening
at the same time because this is

591
00:24:54,783 --> 00:24:57,543
what I had spent the last many
years thinking about, right?

592
00:24:57,603 --> 00:25:02,193
So at that time, I probably would've
been, I don't know, 24, 25, 26 years old.

593
00:25:02,193 --> 00:25:05,773
I'd been studying this stuff
intensely since 16 or 17.

594
00:25:05,773 --> 00:25:08,773
So I'd spent almost a decade, just like
full-time thinking about this stuff.

595
00:25:08,833 --> 00:25:10,543
And so it was a very interesting.

596
00:25:10,543 --> 00:25:11,263
period for me.

597
00:25:12,073 --> 00:25:15,703
And so what I started really
getting obsessed with was, okay.

598
00:25:15,883 --> 00:25:17,863
What research is good and
what research is not good.

599
00:25:17,863 --> 00:25:20,443
And so I really started spending
a lot of my time trying to figure

600
00:25:20,443 --> 00:25:24,233
out, okay, I don't think the entire
field of the psychological sciences,

601
00:25:24,233 --> 00:25:26,663
the behavioral sciences, I don't
think the whole thing is rotten.

602
00:25:26,933 --> 00:25:28,133
I think a lot of it's rotten.

603
00:25:28,433 --> 00:25:30,073
But, what is the truth here?

604
00:25:30,073 --> 00:25:33,413
And so I started really trying to
figure out on my own, based on my own

605
00:25:33,443 --> 00:25:37,313
applied work, based on my reading of
the research, based upon just my own

606
00:25:37,313 --> 00:25:39,263
thoughts, okay, what's the truth here?

607
00:25:39,503 --> 00:25:42,023
What actually causes behavior change?

608
00:25:42,023 --> 00:25:43,313
What's actually effective here?

609
00:25:43,313 --> 00:25:45,503
And what's like the accurate
model of human behavior?

610
00:25:45,503 --> 00:25:48,073
And so that has led me
down this entire path.

611
00:25:48,343 --> 00:25:50,953
And that eventually led
to this book Real Change.

612
00:25:51,053 --> 00:25:52,718
and it led to my current company.

613
00:25:53,018 --> 00:25:55,568
and it led to, other
work I've done, but Yeah.

614
00:25:55,883 --> 00:25:56,873
Dallas Burnett: Wow.

615
00:25:56,933 --> 00:26:01,403
I think that is very interesting
and it's definitely coincides.

616
00:26:01,403 --> 00:26:07,083
I've read over the past, I don't know,
five to 10 years, different reports out

617
00:26:07,083 --> 00:26:10,863
that's very similar to the one that you
did where people are going and looking at

618
00:26:11,298 --> 00:26:13,608
and it's outside of just
the behavioral science.

619
00:26:13,608 --> 00:26:16,298
it's really, it's really across the board.

620
00:26:16,388 --> 00:26:19,558
And, what I've seen is they've had a,
they've had a lot of problems and I

621
00:26:19,558 --> 00:26:24,148
think that in, in some ways, I,  look
at it and go, gosh, you always are

622
00:26:24,148 --> 00:26:26,068
asking the question what is true, right?

623
00:26:26,153 --> 00:26:26,443
Jason Hreha: Yeah.

624
00:26:26,488 --> 00:26:26,848
Dallas Burnett: is true?

625
00:26:26,848 --> 00:26:27,598
Is that right?

626
00:26:27,598 --> 00:26:29,488
Is that, are those facts or.

627
00:26:30,313 --> 00:26:30,823
it been massaged?

628
00:26:30,823 --> 00:26:33,763
Because at the end of the day,
what is true is we're all human.

629
00:26:34,185 --> 00:26:34,845
Jason Hreha: Yeah.

630
00:26:35,130 --> 00:26:38,550
Dallas Burnett: like we all have
cognitive biases that can creep in.

631
00:26:38,550 --> 00:26:38,880
Even

632
00:26:38,930 --> 00:26:39,150
Jason Hreha: Yep.

633
00:26:39,150 --> 00:26:42,600
Dallas Burnett: professors, we can have
bandwagon effect or you know, we can have

634
00:26:42,600 --> 00:26:43,890
a Dunning Kruger, we can do all that.

635
00:26:43,890 --> 00:26:45,450
We can, we are susceptible as that.

636
00:26:45,450 --> 00:26:49,520
So it's like, wow, some of these
reports, it's not all, but some, or a

637
00:26:49,520 --> 00:26:54,740
lot, in many cases are not as accurate
as what we, as would subscribe to them.

638
00:26:54,740 --> 00:26:59,050
So I think it's really awesome that
you we're in a position not only to

639
00:26:59,050 --> 00:27:04,450
wonder that, but to literally have the
tools at your disposal to test it, to

640
00:27:04,495 --> 00:27:04,915
Jason Hreha: Mm-hmm.

641
00:27:05,230 --> 00:27:08,050
Dallas Burnett: in the real
world and go, okay, this is.

642
00:27:08,380 --> 00:27:12,190
This has been tested and this kind
of been proving to, to be wanting

643
00:27:12,190 --> 00:27:13,510
because it's not lining up with

644
00:27:13,660 --> 00:27:13,900
Jason Hreha: Totally.

645
00:27:13,960 --> 00:27:14,530
Dallas Burnett: at all.

646
00:27:14,740 --> 00:27:17,770
So I think that's really cool because
I think definitely gives you a unique

647
00:27:17,770 --> 00:27:22,390
perspective because you have seen the
academic side, you spent years in that

648
00:27:22,390 --> 00:27:26,620
research and really understanding it, but
then you were able to apply it and see

649
00:27:26,620 --> 00:27:28,620
what actually was working in real life.

650
00:27:28,620 --> 00:27:29,910
So that's really cool.

651
00:27:29,910 --> 00:27:33,410
I, so let's talk a little bit about that
because I'm interested in habit change.

652
00:27:33,590 --> 00:27:39,820
So if a lot of the research, I mean two
thirds at least was not even reproducible.

653
00:27:40,420 --> 00:27:46,330
That means a lot of the books and advice
and things that everybody wrote for years

654
00:27:46,330 --> 00:27:48,130
and years based on all that research,

655
00:27:48,385 --> 00:27:48,675
Jason Hreha: Yeah.

656
00:27:48,793 --> 00:27:49,513
Dallas Burnett: in the same boat.

657
00:27:49,513 --> 00:27:51,533
Because they're going, look
at this research from this and

658
00:27:51,533 --> 00:27:52,493
look at this research from that.

659
00:27:52,493 --> 00:27:54,713
And it's ah, yeah, that was part of
the two thirds that wasn't right.

660
00:27:54,713 --> 00:27:57,893
So what can you tell
us about habit change?

661
00:27:58,718 --> 00:28:00,828
Efforts and maybe why they fail.

662
00:28:00,918 --> 00:28:06,048
And maybe what are we, what do we
think we know that just isn't so that,

663
00:28:06,058 --> 00:28:07,348
as it relates to change behavior.

664
00:28:07,708 --> 00:28:07,948
Jason Hreha: Yeah.

665
00:28:07,948 --> 00:28:09,718
my perspective has shifted
a lot over the years.

666
00:28:09,718 --> 00:28:13,348
I'd say that right around you had right
around the time when I was running all

667
00:28:13,348 --> 00:28:15,778
these tests and having bad results.

668
00:28:16,048 --> 00:28:18,808
And right around the time when the
reproducibility project, came out

669
00:28:18,808 --> 00:28:23,848
with their results and we saw this, I
started looking, I started looking at

670
00:28:23,848 --> 00:28:27,058
kinda okay, what areas of research seem
are like the longest lived, that have

671
00:28:27,058 --> 00:28:28,968
the highest, re reproducibility rates?

672
00:28:28,968 --> 00:28:30,318
The highest replication rates?

673
00:28:30,678 --> 00:28:34,278
And I just, it became very interested
also just in looking at, with the

674
00:28:34,278 --> 00:28:37,578
companies that I've worked with,
where we've had success, like

675
00:28:37,578 --> 00:28:40,918
what's true versus, like what things
succeeded versus which things failed.

676
00:28:41,403 --> 00:28:41,693
Dallas Burnett: Yeah.

677
00:28:42,403 --> 00:28:45,733
Jason Hreha: my perspective really
did shift around from one of, okay,

678
00:28:45,763 --> 00:28:47,420
so  let me zoom out a little bit.

679
00:28:47,450 --> 00:28:53,030
I would say that the, the typical
approach in the habit formation

680
00:28:53,030 --> 00:28:54,200
and behavior change world,

681
00:28:54,435 --> 00:28:54,855
Dallas Burnett: Mm-hmm.

682
00:28:55,100 --> 00:28:58,370
Jason Hreha: approach goes something
like this, okay, you have a group,

683
00:28:58,400 --> 00:29:02,240
you have a person or a group of
people, and you have a behavior.

684
00:29:02,390 --> 00:29:08,270
And the whole job is, okay, we want
these people, or sorry, we want

685
00:29:08,270 --> 00:29:09,980
these people to do this behavior.

686
00:29:10,160 --> 00:29:12,140
They're not doing this
behavior for whatever reason.

687
00:29:12,440 --> 00:29:13,790
How can we nudge them?

688
00:29:13,790 --> 00:29:20,230
Or how can we apply some sort of stimulus
or signal to them in order to get them to

689
00:29:20,260 --> 00:29:23,170
suddenly start doing this thing That for
whatever reason, they're not doing more.

690
00:29:23,170 --> 00:29:24,490
So that's the general model.

691
00:29:24,910 --> 00:29:25,600
Dallas Burnett: Right.

692
00:29:25,750 --> 00:29:27,600
Jason Hreha: model is it's one-to-one.

693
00:29:27,660 --> 00:29:31,350
It's like we have a behavior and this is
inflexible, and then you have a group of

694
00:29:31,350 --> 00:29:33,690
people and how do we get them to do this?

695
00:29:34,005 --> 00:29:34,335
Dallas Burnett: Yeah.

696
00:29:34,440 --> 00:29:34,620
Jason Hreha: My.

697
00:29:34,860 --> 00:29:35,565
Dallas Burnett: like a reward.

698
00:29:35,565 --> 00:29:37,335
Can I give them a reward or,

699
00:29:37,378 --> 00:29:37,878
Jason Hreha: Sure.

700
00:29:38,493 --> 00:29:40,543
Dallas Burnett: or, show 'em
some, something to make them

701
00:29:40,543 --> 00:29:41,713
click or do or whatever.

702
00:29:41,713 --> 00:29:41,953
Yeah.

703
00:29:41,983 --> 00:29:42,343
I got

704
00:29:42,358 --> 00:29:42,538
Jason Hreha: Yeah.

705
00:29:42,538 --> 00:29:46,558
So the typical advice, then turns into
something along the lines of, okay, if

706
00:29:46,558 --> 00:29:50,348
they're not doing this behavior, maybe
it's because, the reward isn't big enough.

707
00:29:50,348 --> 00:29:52,628
So maybe can we add a
reward to the situation?

708
00:29:52,628 --> 00:29:56,448
Or, okay, if they're not doing this
behavior, maybe it's because, it's

709
00:29:56,448 --> 00:29:59,988
just too hard or it's too, the,
their circumstances just make it.

710
00:29:59,988 --> 00:30:00,858
too inconvenient.

711
00:30:00,858 --> 00:30:03,398
And so how can we make it,
easier or more convenient?

712
00:30:03,668 --> 00:30:06,158
Or, Hey, maybe there's
forgetting about this thing.

713
00:30:06,348 --> 00:30:08,448
they're forgetting to do this
behavior and then it's too late in

714
00:30:08,448 --> 00:30:09,708
the day by the time they remember.

715
00:30:09,708 --> 00:30:13,218
And so maybe if we remind them or
prompt them early in the day, we

716
00:30:13,218 --> 00:30:14,298
could get them to do this thing.

717
00:30:14,718 --> 00:30:17,032
And, over the years, I started
to notice there, there are a lot

718
00:30:17,032 --> 00:30:19,732
of problems with this general
mindset or this general frame.

719
00:30:20,122 --> 00:30:23,637
And the problems are, number one,
a lot of behaviors, you just,

720
00:30:23,997 --> 00:30:25,227
let's just start with easiness.

721
00:30:25,317 --> 00:30:26,907
A lot of behaviors you
just cannot make easier.

722
00:30:28,452 --> 00:30:28,752
Dallas Burnett: Yeah.

723
00:30:28,842 --> 00:30:29,982
Jason Hreha: by their very nature.

724
00:30:30,072 --> 00:30:30,402
Right.

725
00:30:30,402 --> 00:30:30,822
Dallas Burnett: Yeah.

726
00:30:30,852 --> 00:30:32,812
Jason Hreha: so learning,
like learning a new skill.

727
00:30:33,842 --> 00:30:36,092
of course you can make the
instructional materials better, maybe

728
00:30:36,092 --> 00:30:38,802
you can get somebody a tutor, maybe
you can do these sorts of things.

729
00:30:38,802 --> 00:30:43,152
But a lot of skills, a lot of fields are
just really hard by their very nature.

730
00:30:43,152 --> 00:30:46,482
And so good luck trying to
make it significantly easier or

731
00:30:46,482 --> 00:30:47,682
significantly more convenient.

732
00:30:47,862 --> 00:30:51,702
Kind of the whole point is, the whole
point of learning a new skill is that

733
00:30:51,732 --> 00:30:53,562
it's something that is hard for you.

734
00:30:53,562 --> 00:30:56,472
It's something outside of your
capabilities, and so therefore, like

735
00:30:57,102 --> 00:31:00,102
can, I don't even know if you could,
in a lot of situations make it easier

736
00:31:00,102 --> 00:31:02,892
without destroying the whole purpose of
doing the behavior in the first place.

737
00:31:03,042 --> 00:31:03,792
Dallas Burnett: Exactly.

738
00:31:04,002 --> 00:31:06,672
Jason Hreha: So a lot of behaviors
just, you cannot make easier.

739
00:31:06,972 --> 00:31:10,702
And then a lot of behaviors, on the
motivation side is, there's a similar

740
00:31:10,702 --> 00:31:13,802
story here, which is, okay, if you're
trying to get somebody, let's say

741
00:31:13,802 --> 00:31:18,432
to learn a new, a new, area of math,
for example, There's not really a,

742
00:31:18,432 --> 00:31:20,112
and they're not motivated to do it.

743
00:31:20,142 --> 00:31:22,362
There's not really a whole
lot you can do there, Right.

744
00:31:22,542 --> 00:31:25,602
Maybe you could turn it into, try
to gamify it, turn it into a game.

745
00:31:25,722 --> 00:31:28,572
But gamification in general has
a pretty mixed track record.

746
00:31:28,572 --> 00:31:32,412
It's generally pretty good at getting
people to change their behavior in the

747
00:31:32,412 --> 00:31:34,482
short run, for a small period of time.

748
00:31:34,542 --> 00:31:37,032
But most games end, Right.

749
00:31:37,032 --> 00:31:40,842
Like most video games end, most board
games end, most games are not indefinite,

750
00:31:40,842 --> 00:31:44,892
and so it can provide a little boost
for a period of time, but it's not

751
00:31:44,892 --> 00:31:47,342
really a sustainable long-term, tactic.

752
00:31:47,392 --> 00:31:49,732
just from, in my experience,
from everything that I've seen.

753
00:31:49,982 --> 00:31:51,237
yeah, maybe you could try to gamify it.

754
00:31:52,112 --> 00:31:56,922
Maybe you could add some sort of tangible
reward, pay people to do it, et cetera.

755
00:31:56,922 --> 00:31:57,162
But.

756
00:31:57,762 --> 00:31:58,992
Number one, that's expensive too.

757
00:31:58,992 --> 00:32:00,222
It's not that sustainable.

758
00:32:00,352 --> 00:32:03,272
and then you can get this, there is
some research showing that you do get

759
00:32:03,272 --> 00:32:07,292
this, reward, this sub substitution
effect where if people start doing

760
00:32:07,292 --> 00:32:11,132
something for extrinsic reasons, for
reasons other than the activity itself

761
00:32:11,132 --> 00:32:13,792
and the rewards it provides, let's say
if they're just, you're just paying

762
00:32:13,792 --> 00:32:18,412
them to do it, you can get this effect
where, they start to rely on the,

763
00:32:18,462 --> 00:32:20,472
extrinsic, the reward that you added.

764
00:32:20,472 --> 00:32:23,232
And then if you take that away later, then
they lose motivation for the activity.

765
00:32:23,232 --> 00:32:23,832
So that's a,

766
00:32:24,012 --> 00:32:24,312
Dallas Burnett: Makes

767
00:32:24,312 --> 00:32:26,792
Jason Hreha: a bargain with the
devil, in, in a certain sense.

768
00:32:27,062 --> 00:32:30,512
And so you could do that to
try and increase motivation.

769
00:32:30,662 --> 00:32:32,492
Maybe you can make the activity social.

770
00:32:32,492 --> 00:32:36,062
You could add a fun social element to
it or add a fun competitive element

771
00:32:36,062 --> 00:32:37,912
to it, to make it fun and social.

772
00:32:37,912 --> 00:32:40,552
But number one, not
everybody's competitive, right?

773
00:32:40,552 --> 00:32:43,212
So if you add in points on a
leaderboard and stuff like that,

774
00:32:43,212 --> 00:32:44,292
some people will like that.

775
00:32:44,292 --> 00:32:45,432
A lot of other people won't.

776
00:32:45,627 --> 00:32:46,077
Dallas Burnett: Yes.

777
00:32:46,092 --> 00:32:49,212
Jason Hreha: and then some activities,
you don't really want to be social

778
00:32:49,212 --> 00:32:52,152
or it's just super inconvenient for
it to have to be social all the time.

779
00:32:52,422 --> 00:32:57,398
So for a lot of behaviors,
increasing the motivation is

780
00:32:57,398 --> 00:32:58,898
extremely hard, if not impossible.

781
00:32:58,965 --> 00:32:59,295
Dallas Burnett: Right.

782
00:32:59,460 --> 00:33:02,430
Jason Hreha: so what I started
noticing over time was, okay, there

783
00:33:02,430 --> 00:33:04,590
are a lot of behaviors that you
just really can't make a lot easier.

784
00:33:04,590 --> 00:33:07,410
There are a lot of behaviors that
you just really can't motivate people

785
00:33:07,410 --> 00:33:10,990
to do that much, above and beyond
their intrinsic motivation for it.

786
00:33:11,260 --> 00:33:14,980
And so if this is the case, then
how do you actually get people

787
00:33:14,980 --> 00:33:15,970
to do these things, right?

788
00:33:15,970 --> 00:33:16,990
It's a good question.

789
00:33:17,275 --> 00:33:17,635
Dallas Burnett: That's a.

790
00:33:18,760 --> 00:33:23,440
Jason Hreha: And so my perspective
shifted from, okay, let's just figure

791
00:33:23,440 --> 00:33:26,500
out how to get people to do this one
behavior to one behavior matching.

792
00:33:26,590 --> 00:33:31,450
So my perspective became, okay, let's
say we want to get people running, right?

793
00:33:31,500 --> 00:33:33,900
let's say That's the habit we're
trying to get people to form.

794
00:33:34,440 --> 00:33:36,090
And for whatever reason we have a.

795
00:33:36,090 --> 00:33:37,590
group of let's say 10 people.

796
00:33:37,604 --> 00:33:38,324
They're not running.

797
00:33:38,624 --> 00:33:40,334
We really wanna get them
all running regularly.

798
00:33:40,334 --> 00:33:41,891
We want them to form a running habit.

799
00:33:41,891 --> 00:33:43,480
Once again, running's really hard.

800
00:33:43,480 --> 00:33:44,770
You can't really make
it a whole lot easier.

801
00:33:44,890 --> 00:33:46,780
Running is not that pleasant.

802
00:33:46,810 --> 00:33:49,800
Maybe if you have a running group, it's
more fun than it would be if you ran

803
00:33:49,800 --> 00:33:51,240
on your own or if you ran at the gym.

804
00:33:51,420 --> 00:33:53,810
But, at the end of the day,
it's like a lot of people

805
00:33:53,810 --> 00:33:55,910
aren't that socially motivated.

806
00:33:55,910 --> 00:33:58,550
there is a lot of individ, there are
a lot of individual differences there.

807
00:33:58,640 --> 00:34:02,540
And so in a situation like that,
my perspective turned into,

808
00:34:02,600 --> 00:34:05,240
okay, why are we trying to get
people to run in the first place?

809
00:34:05,930 --> 00:34:06,890
What's the goal here?

810
00:34:07,310 --> 00:34:13,190
Behaviors are done for a reason
like organisms, animals, did not

811
00:34:13,190 --> 00:34:16,790
evolve the ability to do behaviors
just for no reason at all.

812
00:34:16,790 --> 00:34:20,958
We do behaviors to gain things or to
solve problems in our lives, Right.

813
00:34:20,958 --> 00:34:24,408
there's a function behind each behavior
that we do, even if we're not aware of it.

814
00:34:24,828 --> 00:34:28,428
So if you say, okay, I want
this group of 10 people to go

815
00:34:28,428 --> 00:34:30,838
running, the question becomes why
do you want them to go running?

816
00:34:31,573 --> 00:34:32,023
Dallas Burnett: Right.

817
00:34:32,023 --> 00:34:32,863
Jason Hreha: they want to go running?

818
00:34:33,193 --> 00:34:35,683
Is it to gain better
cardiovascular health?

819
00:34:35,683 --> 00:34:36,853
Is it to lose weight?

820
00:34:37,243 --> 00:34:39,673
Is it to just relieve stress?

821
00:34:40,423 --> 00:34:44,613
Whatever the reason is that the only
way to achieve that end goal is the

822
00:34:44,613 --> 00:34:46,905
only way to lose weight by running?

823
00:34:46,905 --> 00:34:47,835
No, of course not.

824
00:34:47,975 --> 00:34:49,715
you can lose weight a
thousand different ways.

825
00:34:49,715 --> 00:34:53,255
You can play tennis, you can go
swimming, you can have a diet, you

826
00:34:53,255 --> 00:34:55,085
can take a GLP one drug today, right?

827
00:34:55,205 --> 00:34:55,775
Dallas Burnett: Yeah.

828
00:34:55,865 --> 00:34:56,345
Jason Hreha: do there.

829
00:34:56,345 --> 00:34:59,045
There's so many different
ways to get that end goal.

830
00:34:59,465 --> 00:35:05,015
And so my entire perspective shifted from,
instead of trying to force people or use

831
00:35:05,015 --> 00:35:09,785
these hacks and tricks to get people to do
behaviors that they obviously do not want

832
00:35:09,785 --> 00:35:15,185
to do or cannot do, let's instead, based
on the goal that we have in mind for them,

833
00:35:15,515 --> 00:35:20,225
let's actually match them with A behavior
that's better suited to their individual

834
00:35:20,285 --> 00:35:23,225
unique characteristics and to their goals.

835
00:35:23,700 --> 00:35:27,720
Dallas Burnett: man that's yeah, that
just, it goes right back to what we

836
00:35:27,720 --> 00:35:30,150
were talking about at the beginning of
the conversation when you're talking

837
00:35:30,150 --> 00:35:36,160
about is your motivation, great enough
to overcome these obstacles or your

838
00:35:36,160 --> 00:35:38,020
ability, your ability to do something.

839
00:35:38,385 --> 00:35:38,675
Jason Hreha: Yeah.

840
00:35:39,040 --> 00:35:41,150
Dallas Burnett: that's, it's so
fascinating 'cause what you're talking

841
00:35:41,150 --> 00:35:44,120
about is let's say like you're just
saying essentially this is the,

842
00:35:44,120 --> 00:35:45,440
you're starting with the result.

843
00:35:45,945 --> 00:35:46,235
Jason Hreha: Yeah.

844
00:35:46,415 --> 00:35:49,570
Dallas Burnett: okay, now let's, it's
the different ways to skin a cat.

845
00:35:49,570 --> 00:35:54,190
So if we gonna have this result, how
many different things can we come up with

846
00:35:54,400 --> 00:35:56,320
that could be used to get to that result?

847
00:35:56,320 --> 00:35:57,700
And hey, pick your poison.

848
00:35:57,700 --> 00:36:01,600
You can go and choose any one of
these things as long as you're

849
00:36:01,840 --> 00:36:03,490
motivated to do that thing.

850
00:36:03,490 --> 00:36:06,010
So it's almost like you're just
tapping into that intrinsic

851
00:36:06,010 --> 00:36:07,840
motivation that's already there.

852
00:36:08,005 --> 00:36:08,725
Jason Hreha: Yep.

853
00:36:08,740 --> 00:36:12,700
Dallas Burnett: or aligning it to
one that has, like for example, if

854
00:36:12,700 --> 00:36:16,180
they're just really not motivated
to exercise and they're just like, I

855
00:36:16,180 --> 00:36:17,920
am, that's just not my thing at all.

856
00:36:17,920 --> 00:36:19,420
I'm not playing tennis, I'm not running.

857
00:36:19,660 --> 00:36:23,060
Then, taking one of those,
weight loss, that might be the

858
00:36:23,270 --> 00:36:23,900
Jason Hreha: Sure.

859
00:36:23,900 --> 00:36:25,400
.
Dallas Burnett: person's
I just don't like running.

860
00:36:25,400 --> 00:36:25,970
It's mindless.

861
00:36:25,970 --> 00:36:26,990
I just can't get into it.

862
00:36:27,170 --> 00:36:28,130
I like competing.

863
00:36:28,160 --> 00:36:29,750
Okay, maybe the tennis is a good option.

864
00:36:29,750 --> 00:36:32,010
Or maybe, pickleball or
whatever, racketball.

865
00:36:32,340 --> 00:36:34,080
I think that's really fascinating.

866
00:36:34,350 --> 00:36:37,771
It definitely puts the onus though, on the

867
00:36:40,276 --> 00:36:45,442
To really get to know the individual,
because at that point you can't, I

868
00:36:45,442 --> 00:36:47,032
don't know, do it just seems like

869
00:36:47,137 --> 00:36:48,097
broad brush.

870
00:36:48,332 --> 00:36:50,902
Jason Hreha: I think in the
past this sort of approach was

871
00:36:50,902 --> 00:36:53,452
impractical and often impossible.

872
00:36:53,672 --> 00:36:57,962
I think that the reason that these
approaches of, okay, we want everybody

873
00:36:57,962 --> 00:37:02,927
to do a. How do we just force,
force or like prod everybody to do

874
00:37:02,927 --> 00:37:07,427
a, I think that approach was more
justifiable or more reasonable in

875
00:37:07,427 --> 00:37:11,247
the past when we didn't have, when we
didn't have the technologies and the

876
00:37:11,247 --> 00:37:13,647
means to personalize things at scale.

877
00:37:14,127 --> 00:37:14,697
Dallas Burnett: Agreed.

878
00:37:14,817 --> 00:37:17,197
Jason Hreha: a government, and
you're just looking at like a hundred

879
00:37:17,197 --> 00:37:21,487
million or however many people and
you're, it's a nearly impossible

880
00:37:21,487 --> 00:37:25,357
task to say, okay, let's help each
person pick the right thing for them.

881
00:37:25,387 --> 00:37:28,087
I think governments and companies,
et cetera, I think everybody can do

882
00:37:28,087 --> 00:37:29,887
more on the personalization front.

883
00:37:30,337 --> 00:37:33,217
or could have done more on the
personalization front in the past.

884
00:37:33,607 --> 00:37:36,967
I think they could have, but I think
today, there's no excuse, right?

885
00:37:36,967 --> 00:37:38,017
We have new technologies.

886
00:37:38,017 --> 00:37:40,207
We have ai, we have LLMs, right?

887
00:37:40,207 --> 00:37:44,827
These sorts of things where actually
today we're entering the era where, okay,

888
00:37:44,917 --> 00:37:48,997
instead of just trying to push people
and prod people into doing like a thing

889
00:37:49,237 --> 00:37:52,807
or two things, I think we can really
actually, now we're getting to the point

890
00:37:52,807 --> 00:37:57,277
we're at scale where for individuals,
for groups of people, for huge groups of

891
00:37:57,277 --> 00:37:59,987
people, help matching them to the right.

892
00:38:00,017 --> 00:38:02,777
right activities and the right
things for them to accomplish the

893
00:38:02,777 --> 00:38:04,427
aim that we or they have in mind.

894
00:38:04,982 --> 00:38:07,142
Dallas Burnett: Ah, I love that.

895
00:38:07,547 --> 00:38:07,847
Jason Hreha: Yeah.

896
00:38:07,892 --> 00:38:10,112
Dallas Burnett: that's so true and
I agree with you a hundred percent.

897
00:38:10,512 --> 00:38:15,207
It is that it takes the focus less,
it puts the focus less on the thing,

898
00:38:15,207 --> 00:38:19,647
whatever that motivating thing is,
and puts it more on the result and

899
00:38:19,647 --> 00:38:22,797
saying, okay, what are all the things
that we can do to get the result?

900
00:38:22,797 --> 00:38:26,127
And then allowing that personalization
to take place in the workplace.

901
00:38:26,127 --> 00:38:27,707
I think that's think that's so good.

902
00:38:27,707 --> 00:38:29,117
I think that is really good advice.

903
00:38:29,117 --> 00:38:32,747
So if you're listening today
at the last 10%, put that down.

904
00:38:32,747 --> 00:38:33,677
Mark that down.

905
00:38:33,677 --> 00:38:35,717
That is a great idea.

906
00:38:35,717 --> 00:38:38,617
That's a great, great truth that
we've got today on the show.

907
00:38:38,887 --> 00:38:40,837
I wanna talk a little bit about,
'cause we've got a lot of leaders

908
00:38:40,837 --> 00:38:42,007
that listen to the last 10%.

909
00:38:42,007 --> 00:38:43,177
They're leading teams are growing

910
00:38:43,392 --> 00:38:43,812
Jason Hreha: Mm-hmm.

911
00:38:44,017 --> 00:38:50,317
-
Dallas Burnett: teams and you went
to Walmart and literally built like

912
00:38:50,347 --> 00:38:52,897
your own behavioral science team

913
00:38:52,934 --> 00:38:53,224
Jason Hreha: Yeah.

914
00:38:53,367 --> 00:38:55,047
Dallas Burnett: at this massive company.

915
00:38:55,347 --> 00:38:55,587
All right.

916
00:38:55,587 --> 00:38:57,537
You gotta give us a little
play by play on that.

917
00:38:57,537 --> 00:39:00,597
Like a, how in the world did that happen?

918
00:39:00,957 --> 00:39:04,727
And B what was the biggest
challenge, like getting into

919
00:39:04,727 --> 00:39:06,587
Walmart and just building this team.

920
00:39:06,587 --> 00:39:07,487
And we will talk about

921
00:39:07,622 --> 00:39:07,912
Jason Hreha: Yeah.

922
00:39:08,027 --> 00:39:08,957
Dallas Burnett: how
you led it in a minute.

923
00:39:09,497 --> 00:39:11,197
Jason Hreha: I can't say enough
good things about Walmart.

924
00:39:11,277 --> 00:39:12,867
Walmart's an incredible company.

925
00:39:12,917 --> 00:39:14,712
they're, I think they're the
best retailer in the world.

926
00:39:14,762 --> 00:39:17,012
the people that are incredible,
the leaders are incredible.

927
00:39:17,172 --> 00:39:19,268
the stores are  insane
in a great way, right?

928
00:39:19,268 --> 00:39:22,328
I think that they're just like,
they're, you have over a hundred

929
00:39:22,328 --> 00:39:25,418
thousand different unique products
in the Supercenters, right?

930
00:39:25,698 --> 00:39:26,928
they're, I think they're marvels.

931
00:39:26,928 --> 00:39:27,948
they're absolute marvels.

932
00:39:28,008 --> 00:39:32,633
And the effort and the work and the care
that goes into building the stores and

933
00:39:32,633 --> 00:39:35,863
running the stores and making sure that
they're a great experience, it's it's

934
00:39:35,863 --> 00:39:37,333
insane how much work goes into that.

935
00:39:37,333 --> 00:39:41,353
And I was just so blown away by
just the level of talent and just

936
00:39:41,353 --> 00:39:42,883
the level of care at the company.

937
00:39:43,153 --> 00:39:44,903
And first of all, I just wanna
say I just, I love Walmart.

938
00:39:44,903 --> 00:39:46,493
I'm the biggest Walmart fanboy ever.

939
00:39:46,793 --> 00:39:47,183
and

940
00:39:47,573 --> 00:39:47,663
Dallas Burnett: I

941
00:39:47,723 --> 00:39:49,913
Jason Hreha: the way that happened
was pretty interesting actually.

942
00:39:49,913 --> 00:39:51,773
I actually got recruited.

943
00:39:51,833 --> 00:39:53,273
There was a guy at Walmart.

944
00:39:53,373 --> 00:39:55,023
and so I actually co-led the group.

945
00:39:55,348 --> 00:39:58,498
in the early days with a guy named
Omar, who is just incredible.

946
00:39:58,808 --> 00:40:01,718
so he had been at Walmart for many
years and he had gotten permission,

947
00:40:02,058 --> 00:40:06,648
from the leadership at Walmart to build
this applied behavioral science group.

948
00:40:06,648 --> 00:40:08,718
And so the two of us co-led the group.

949
00:40:08,718 --> 00:40:11,148
So he brought me in to
co-lead the group there.

950
00:40:11,568 --> 00:40:15,438
and the whole idea was, okay, if you
think, as I mentioned earlier in this

951
00:40:15,438 --> 00:40:18,828
conversation, every business is in
the business of changing behavior.

952
00:40:19,308 --> 00:40:23,058
Every business is in the business
of understanding the person that

953
00:40:23,058 --> 00:40:26,858
they're, that who is the customer,
the consumer, and getting them

954
00:40:26,978 --> 00:40:31,148
to engage with that business, in
positive ways as much as possible.

955
00:40:31,298 --> 00:40:34,088
And so Walmart's very, a very
forward thinking company.

956
00:40:34,388 --> 00:40:37,388
And so they, the executives there
thought that was a great idea.

957
00:40:37,388 --> 00:40:39,338
And so the two of us built
out this group together.

958
00:40:39,998 --> 00:40:44,408
And, I. it's probably my favorite
job I've ever had by far.

959
00:40:44,458 --> 00:40:49,018
because we were actually given, we
were given a lot of freedom because

960
00:40:49,408 --> 00:40:52,798
I'd say the executives there understood
that, they're not, they weren't

961
00:40:52,798 --> 00:40:54,688
experts in applied behavioral science.

962
00:40:54,718 --> 00:40:58,198
Like we were the experts and we
understand the toolkit and the

963
00:40:58,228 --> 00:41:00,748
capabilities and what's possible there.

964
00:41:00,748 --> 00:41:04,828
And so the leaders of the company did give
us some strategic objectives and strategic

965
00:41:04,828 --> 00:41:09,498
projects to work on, but actually a lot
of our time there was spent they gave

966
00:41:09,498 --> 00:41:13,158
us freedom and leeway to figure out,
okay, given everything that you know,

967
00:41:13,158 --> 00:41:16,428
and given everything that you see at the
company, what do you think we can do?

968
00:41:16,428 --> 00:41:17,568
Or what should we do?

969
00:41:17,748 --> 00:41:23,028
And so I spent a lot of my time going
around the company, going to stores,

970
00:41:23,238 --> 00:41:26,838
going to different teams, learning about
what they're working on, what are their

971
00:41:26,838 --> 00:41:30,828
strategic objectives, and then based
upon that, I could, I. Propose projects

972
00:41:30,828 --> 00:41:32,148
or I could work on different projects.

973
00:41:32,148 --> 00:41:36,888
And so it's hard for me to go into a
ton of detail around exactly what we

974
00:41:36,888 --> 00:41:39,858
did, just because it is confidential
and I don't wanna, the last thing

975
00:41:39,858 --> 00:41:43,738
I'd wanna do is, spill any secrets,
but some of our projects are public.

976
00:41:43,768 --> 00:41:47,438
so for example, one of the big projects
that, that we did work on, was, Sam's

977
00:41:47,438 --> 00:41:49,328
Club, Sam Walmart owned Sam's Club.

978
00:41:49,328 --> 00:41:51,998
I don't know how many people know
that, but so Walmart, Sam's Club

979
00:41:51,998 --> 00:41:56,018
is like the, the big, the warehouse
store similar to Costco of Walmart.

980
00:41:56,468 --> 00:41:59,468
And, they actually created
a new store format.

981
00:41:59,518 --> 00:42:01,918
the first store is in Dallas,
it's called Sam's Club now.

982
00:42:02,128 --> 00:42:05,248
And so my group is very, very much
involved in kind of building out

983
00:42:05,248 --> 00:42:09,688
that new store format and figuring
out the behavioral dynamics there,

984
00:42:09,688 --> 00:42:11,008
and then help launching that.

985
00:42:11,248 --> 00:42:15,268
we were also involved heavily with
Sam's Club in kind of, they have

986
00:42:15,268 --> 00:42:18,698
an application called Scan and Go
where you can check yourself out.

987
00:42:19,088 --> 00:42:20,738
At the store, so you don't
have to go through a line.

988
00:42:20,738 --> 00:42:22,568
You can use an app in order
to check yourself out.

989
00:42:22,758 --> 00:42:26,688
and so we were heavily involved in
increasing the, usage and engagement

990
00:42:26,868 --> 00:42:30,028
with that application and getting
members onboard and into that.

991
00:42:30,538 --> 00:42:33,778
We were heavily involved in the new
member experience at Sam's Club as well.

992
00:42:33,778 --> 00:42:37,518
So somebody signs up for, a
membership at Sam's Club, how

993
00:42:37,518 --> 00:42:39,908
do we ensure that they, love it.

994
00:42:40,098 --> 00:42:43,818
understand exactly how to use
it, and then build a habit around

995
00:42:43,868 --> 00:42:45,758
coming to the warehouse stores.

996
00:42:45,968 --> 00:42:48,278
And so we worked on a lot
of different projects there.

997
00:42:48,548 --> 00:42:51,118
And it was fascinating for me
because this was really my first

998
00:42:51,178 --> 00:42:55,498
intense experience working on what
I'd call real world behavior change.

999
00:42:55,618 --> 00:42:59,788
And by real world, I mean in the physical,
real world versus in the digital world.

1000
00:42:59,878 --> 00:43:00,268
Yeah.

1001
00:43:01,183 --> 00:43:04,453
It becomes a lot more, everything becomes
significantly more complicated when you're

1002
00:43:04,453 --> 00:43:06,613
dealing with people in physical spaces.

1003
00:43:06,923 --> 00:43:11,043
or when you're dealing with people,
moving and changing their activity

1004
00:43:11,043 --> 00:43:14,853
in the real, in, in a physical
world versus a digital world.

1005
00:43:14,853 --> 00:43:17,133
Because in a digital world,
it's pretty constrained, Right.

1006
00:43:17,133 --> 00:43:19,023
It's like you just have a
little screen in front of you.

1007
00:43:20,013 --> 00:43:22,113
People can only do so many
things on the screen, right?

1008
00:43:22,113 --> 00:43:24,843
if you add a button, it's very
likely they're gonna tap the button.

1009
00:43:24,843 --> 00:43:27,333
But in a physical space, there's
so many different variables.

1010
00:43:27,333 --> 00:43:28,533
There's so much happening, right?

1011
00:43:29,283 --> 00:43:32,493
Dallas Burnett: Yeah, you've got all
the senses, the sight and the sounds and

1012
00:43:32,553 --> 00:43:33,153
Jason Hreha: Yeah.

1013
00:43:33,663 --> 00:43:34,983
.
Dallas Burnett: and then
the directional things.

1014
00:43:34,983 --> 00:43:37,803
you've got, all kind of other
things that can impede, what

1015
00:43:37,803 --> 00:43:39,273
you're wanting to get 'em to do.

1016
00:43:39,463 --> 00:43:41,923
there's so many things that's interesting
about that because the team, when you're

1017
00:43:41,923 --> 00:43:44,533
talking about building this team, I
can totally see what would be a team.

1018
00:43:44,533 --> 00:43:48,643
'cause when you, and obviously I'm
sure listeners are just enjoying this,

1019
00:43:48,643 --> 00:43:53,023
because the amount of planning and
detail that goes into just checking

1020
00:43:53,023 --> 00:43:55,573
out, just the checking out of products.

1021
00:43:56,353 --> 00:43:59,263
a team of people that's working
on making that experience

1022
00:43:59,473 --> 00:44:00,133
Jason Hreha: Yeah.

1023
00:44:00,133 --> 00:44:03,613
Dallas Burnett: seamless and as
essentially have it forming and

1024
00:44:03,643 --> 00:44:07,613
as easy as possible so that you,
don't mind, but enjoy doing it.

1025
00:44:07,643 --> 00:44:10,873
And when you were talking about that
though, it makes me feel like it, it

1026
00:44:10,873 --> 00:44:13,633
just reminded me of a ux ui designer,

1027
00:44:13,793 --> 00:44:14,213
Jason Hreha: Mm-hmm.

1028
00:44:14,453 --> 00:44:17,633
Dallas Burnett: that are, because you're
talking about behavior, but the behavior

1029
00:44:17,633 --> 00:44:19,723
is Closely linked to design as well.

1030
00:44:19,723 --> 00:44:20,563
So it's almost like

1031
00:44:20,698 --> 00:44:20,878
Jason Hreha: Totally.

1032
00:44:20,983 --> 00:44:25,113
Dallas Burnett: kind of hybrid between
knowing how people think and then

1033
00:44:25,113 --> 00:44:27,903
designing a, instead of a computer page.

1034
00:44:27,903 --> 00:44:32,393
Now you're designing an environment
to fit that way that people

1035
00:44:32,393 --> 00:44:34,183
operate very fluidly, for where it

1036
00:44:34,408 --> 00:44:34,858
Jason Hreha: Absolutely.

1037
00:44:35,503 --> 00:44:38,593
Dallas Burnett: That's just a fascinating,
so tell us, one of the things like

1038
00:44:38,593 --> 00:44:40,878
when, and you don't have to go into
details about projects, but, those

1039
00:44:40,878 --> 00:44:44,478
were great, but as it was relates to
building that team, did you have any,

1040
00:44:44,778 --> 00:44:48,618
did you have any challenges or, did
you have any, things that you had to

1041
00:44:48,618 --> 00:44:51,318
work through with your team in terms
of leading and growing that team?

1042
00:44:51,348 --> 00:44:54,468
'cause that's a huge company, and this
was a big, you had a lot of projects

1043
00:44:54,468 --> 00:44:57,588
and stuff going on, but anything
that you had to, as you got into it,

1044
00:44:57,588 --> 00:45:00,888
you said, man, this was something I
learned as a leader, running this or

1045
00:45:00,888 --> 00:45:02,748
growing this behavioral science team.

1046
00:45:03,268 --> 00:45:06,023
Jason Hreha: I would say that the biggest
thing I learned is,  and actually this

1047
00:45:06,023 --> 00:45:09,383
is something that kind of influenced
my perspective going into persona, my

1048
00:45:09,383 --> 00:45:13,166
current company, the, I'd say the biggest
thing I learned is that  recruiting's not

1049
00:45:13,166 --> 00:45:14,726
everything, but it's almost everything.

1050
00:45:16,406 --> 00:45:21,736
and what I mean by that is, I really
think that, a company culture or

1051
00:45:21,736 --> 00:45:24,046
number one, if you just zoom out
a company, what is a company?

1052
00:45:24,046 --> 00:45:26,416
A company is just a group
of people working together

1053
00:45:26,416 --> 00:45:27,916
towards a common goal, right?

1054
00:45:28,086 --> 00:45:28,087
Dallas Burnett: Yes,

1055
00:45:28,366 --> 00:45:31,036
Jason Hreha: So you have two,
two el almost two elements there.

1056
00:45:31,036 --> 00:45:33,766
You have the goal or the mission,
and you have the people, right?

1057
00:45:34,126 --> 00:45:39,326
and then, so I guess in that
situation it's okay, like you have

1058
00:45:39,326 --> 00:45:40,616
basically two things you can tweak.

1059
00:45:40,616 --> 00:45:44,396
We can, you can tweak the mission and
the product, let's say that kind of

1060
00:45:44,426 --> 00:45:46,536
is built to instantiate that mission.

1061
00:45:46,806 --> 00:45:49,766
And then you can also tweak who
you have in your organization.

1062
00:45:49,796 --> 00:45:55,841
And so I really think that, for me, when I
was at Walmart, I just, I learned so much

1063
00:45:55,841 --> 00:46:00,681
just looking around the company around
okay, Walmart has done such a great job

1064
00:46:00,711 --> 00:46:05,261
at the corporate level, just and at the
store level bring, just finding the right.

1065
00:46:05,261 --> 00:46:08,381
people, bringing them on board,
nurturing them, making sure

1066
00:46:08,381 --> 00:46:09,913
that they uplevel and upskill.

1067
00:46:10,271 --> 00:46:14,621
And I really just saw how, like
important, it was just like being

1068
00:46:14,621 --> 00:46:18,491
very mindful about who you select
to be a part of your organization.

1069
00:46:18,491 --> 00:46:23,731
And when I was at, when I was recruiting
for that team, I realized how hard it is,

1070
00:46:23,761 --> 00:46:25,081
especially in the behavioral sciences.

1071
00:46:25,111 --> 00:46:27,811
'cause the Applied Behavioral
sciences are, it's a pretty new field.

1072
00:46:28,111 --> 00:46:31,781
And, when I started doing this work
back in, in Silicon Valley, like right.

1073
00:46:31,781 --> 00:46:36,101
after working in the persuasive technology
lab, I was one of like maybe two or three

1074
00:46:36,101 --> 00:46:39,581
or maybe four people total doing this type
of work in Silicon Valley at the time.

1075
00:46:39,641 --> 00:46:45,431
Now there are a lot more people, but even
when I was at Walmart, the pool of applied

1076
00:46:45,431 --> 00:46:49,301
behavioral scientists with like real
world practical experiences was very low.

1077
00:46:49,421 --> 00:46:53,472
And so in order to recruit people,
number one, we started reaching out

1078
00:46:53,472 --> 00:46:57,432
to a ton of different people with a
behavioral science background in, in

1079
00:46:57,522 --> 00:46:59,172
an academic or an applied context.

1080
00:46:59,172 --> 00:47:01,722
And I just, would bring them in to
interview or I'd interview them on the

1081
00:47:01,722 --> 00:47:06,342
phone and I realized very quickly that
finding the right person was number

1082
00:47:06,342 --> 00:47:08,142
one, very hard finding the Right,

1083
00:47:08,142 --> 00:47:13,302
person that had the kind of the realistic
and, I'd say calibrated academic.

1084
00:47:13,767 --> 00:47:16,947
Point of view that not the point
of view that I was talking about

1085
00:47:17,007 --> 00:47:19,977
much earlier in the conversation of
the not reproducing point of view.

1086
00:47:20,007 --> 00:47:21,807
'cause a lot of people have
the point of view that's built

1087
00:47:21,807 --> 00:47:23,337
on non reproducing research,

1088
00:47:23,592 --> 00:47:23,942
Dallas Burnett: right.

1089
00:47:24,207 --> 00:47:26,437
Jason Hreha: but finding people that
have the accurate point of view, built

1090
00:47:26,437 --> 00:47:31,997
on the good stuff that also have, real
wor like that also have like practical

1091
00:47:31,997 --> 00:47:37,337
skills so that they can instantiate those
ideas into something real in a company.

1092
00:47:37,340 --> 00:47:37,627
Dallas Burnett: Yeah.

1093
00:47:37,772 --> 00:47:38,552
Jason Hreha: very small.

1094
00:47:38,552 --> 00:47:43,292
So what I, my perspective at Walmart
when recruiting for this team

1095
00:47:43,292 --> 00:47:47,702
basically became, wow, finding the
right person I. Especially in a

1096
00:47:47,702 --> 00:47:50,952
specialized context like this, you're
looking for a needle in a haystack.

1097
00:47:50,982 --> 00:47:51,612
A haystack.

1098
00:47:51,612 --> 00:47:55,742
And so I realized very quickly when
I was there, okay, a lot of number

1099
00:47:55,742 --> 00:47:59,312
one, A, a company is just a group
of people working towards a mission.

1100
00:47:59,552 --> 00:48:02,012
And a company is made up of
smaller teams that works towards

1101
00:48:02,072 --> 00:48:03,752
sub missions or smaller missions.

1102
00:48:03,812 --> 00:48:08,432
And so if I'm going to build the best
organization and possible, I have

1103
00:48:08,432 --> 00:48:11,072
to build the best teams possible and
to build the best teams possible,

1104
00:48:11,102 --> 00:48:12,422
I have to find the right people.

1105
00:48:12,512 --> 00:48:14,132
And finding the right
people is really hard.

1106
00:48:14,132 --> 00:48:17,912
And you, have to sift through a lot of
different talent to find the right fit for

1107
00:48:17,912 --> 00:48:19,982
your specific organization and its needs.

1108
00:48:20,282 --> 00:48:25,862
And so my mindset at Walmart really
shifted into this whole idea of recruiting

1109
00:48:25,862 --> 00:48:28,142
is a number, is largely a numbers game.

1110
00:48:28,156 --> 00:48:31,366
And if it's a numbers
game, you have two options.

1111
00:48:31,366 --> 00:48:33,796
Number one is you just, if you're
a company leader, you have to

1112
00:48:33,796 --> 00:48:35,236
spend all your time doing this.

1113
00:48:35,266 --> 00:48:36,136
that's option one.

1114
00:48:36,316 --> 00:48:39,286
Option two is you build
and use tools to do this.

1115
00:48:39,766 --> 00:48:40,276
Dallas Burnett: Right.

1116
00:48:40,861 --> 00:48:43,616
Jason Hreha: it was at Walmart where
I think the, when I was doing all

1117
00:48:43,616 --> 00:48:47,516
this, I had the first kind of inkling
of what I wanted to end up building,

1118
00:48:47,546 --> 00:48:52,826
which is I wanted to build a, an
automated or a semi-automated set of

1119
00:48:52,856 --> 00:48:58,506
recruiting tools that would allow me
to, At scale, find people that kind

1120
00:48:58,506 --> 00:49:00,946
of fit the archetype that I want

1121
00:49:01,646 --> 00:49:01,726
Dallas Burnett: Hmm.

1122
00:49:02,146 --> 00:49:03,256
Jason Hreha: and then bring them on board.

1123
00:49:03,353 --> 00:49:05,423
Dallas Burnett: a great
way to, to take it though.

1124
00:49:05,453 --> 00:49:07,343
That's because when you think
about it, and you talked about,

1125
00:49:07,343 --> 00:49:11,183
you kinda hinted at the beginning,
is that culture is so important

1126
00:49:11,366 --> 00:49:11,726
Jason Hreha: Yeah.

1127
00:49:11,863 --> 00:49:15,733
Dallas Burnett: and your culture
is your people, like your people.

1128
00:49:15,733 --> 00:49:21,743
And that what they do and how they do it
ultimately is the culture, their mindset,

1129
00:49:21,743 --> 00:49:23,813
their shared values and all these things.

1130
00:49:24,683 --> 00:49:24,743
you.

1131
00:49:25,628 --> 00:49:30,218
you can start by bringing in people
that you already know are in alignment

1132
00:49:30,218 --> 00:49:34,028
with that, not only with just
their values, but their skillset.

1133
00:49:34,028 --> 00:49:37,578
Because if you bring in, if you
have a high performers on the team,

1134
00:49:37,578 --> 00:49:41,058
you bring in somebody that's not
a high performer, a mid to low

1135
00:49:41,058 --> 00:49:42,978
performer, it's just gonna, it's.

1136
00:49:43,198 --> 00:49:44,278
That's not going well.

1137
00:49:44,318 --> 00:49:45,188
for either party.

1138
00:49:45,248 --> 00:49:48,488
And so by just being able to align
some of those metrics, it gives

1139
00:49:48,488 --> 00:49:51,488
you more of an ability, I think, to
create a stronger culture faster.

1140
00:49:51,488 --> 00:49:54,338
Because you're bringing in people
who are already getting it.

1141
00:49:54,368 --> 00:49:57,218
They're like, oh, they're gonna
see it and go, oh yeah, totally.

1142
00:49:57,248 --> 00:49:57,998
Yeah, that's, I get it.

1143
00:49:57,998 --> 00:49:58,118
Yeah.

1144
00:49:58,423 --> 00:50:02,813
I'm there, versus somebody who you're
having to either bring up to speed or

1145
00:50:02,813 --> 00:50:05,693
spend more time training on, or maybe
they're just not equipped to begin.

1146
00:50:05,693 --> 00:50:06,413
They're just not.

1147
00:50:06,413 --> 00:50:08,973
It's a, round peg in a square hole
kind of thing, and you're just

1148
00:50:09,048 --> 00:50:09,378
Jason Hreha: Totally

1149
00:50:09,483 --> 00:50:09,693
Dallas Burnett: there.

1150
00:50:10,398 --> 00:50:11,028
Jason Hreha: couldn't agree more.

1151
00:50:11,163 --> 00:50:11,793
.
Dallas Burnett: good could.

1152
00:50:11,853 --> 00:50:12,813
That's a really good now.

1153
00:50:13,173 --> 00:50:15,063
All right, we got one more
topic I wanna hit before

1154
00:50:15,138 --> 00:50:15,228
Jason Hreha: Okay.

1155
00:50:15,243 --> 00:50:15,543
Dallas Burnett: the show.

1156
00:50:15,543 --> 00:50:16,293
And that is this.

1157
00:50:16,293 --> 00:50:17,403
And we could talk for three hours.

1158
00:50:17,433 --> 00:50:18,338
'cause I'm in this is awesome.

1159
00:50:18,338 --> 00:50:19,358
It's a really good conversation.

1160
00:50:19,718 --> 00:50:24,288
But I, I'm very interested in, because
you're a behavioral scientist and

1161
00:50:24,318 --> 00:50:29,028
we have a lot of leaders who are
managing teams that are in the office.

1162
00:50:29,208 --> 00:50:31,428
We have some that are managing teams.

1163
00:50:31,428 --> 00:50:32,658
That's a hybrid situation.

1164
00:50:32,658 --> 00:50:35,178
And we have some, this management
teams is completely remote.

1165
00:50:35,658 --> 00:50:38,028
I'm just interested and you're
not, I'm not asking you to

1166
00:50:38,028 --> 00:50:38,958
throw shade on any of it.

1167
00:50:38,958 --> 00:50:40,908
They're all existing
post COVID that I know.

1168
00:50:40,908 --> 00:50:42,228
A lot of people have moved remote.

1169
00:50:42,588 --> 00:50:46,098
You've been in the tech, I know Tech
is, a lot of remote as well, but then

1170
00:50:46,098 --> 00:50:49,058
some that are, wanting people to be
in, creating these spaces in the go.

1171
00:50:49,063 --> 00:50:50,553
you hear the stories about the Google,

1172
00:50:50,688 --> 00:50:50,978
Jason Hreha: Yeah.

1173
00:50:51,318 --> 00:50:53,178
Dallas Burnett: and free
lunches and the futons and ping

1174
00:50:53,178 --> 00:50:54,108
pong tables and that stuff.

1175
00:50:54,588 --> 00:50:58,858
So in your opinion, as a behavioral
health science, expert, do you

1176
00:50:58,858 --> 00:51:03,088
see an advantage for people being
together in an office environment

1177
00:51:03,358 --> 00:51:05,821
versus remote or virtually the same?

1178
00:51:05,821 --> 00:51:09,873
And then how would you say we
would manage remote differently

1179
00:51:09,873 --> 00:51:11,438
than say a, an environment?

1180
00:51:11,438 --> 00:51:12,548
I'm just very curious to hear your

1181
00:51:12,613 --> 00:51:15,523
Jason Hreha: Yeah, so I, I think
it's undoubtedly true that the

1182
00:51:15,523 --> 00:51:17,623
office is better than remote, right?

1183
00:51:17,623 --> 00:51:19,003
All, all things equal.

1184
00:51:19,233 --> 00:51:19,523
Dallas Burnett: Yeah,

1185
00:51:19,573 --> 00:51:21,623
Jason Hreha: I think the issue
is that, let me just say this.

1186
00:51:21,673 --> 00:51:26,053
I think that offices better than remote,
but remote allows a lot of companies

1187
00:51:26,053 --> 00:51:29,893
to hire better people than they would
otherwise be able to hire in person.

1188
00:51:30,703 --> 00:51:31,243
Dallas Burnett: that's true.

1189
00:51:31,433 --> 00:51:31,723
Okay.

1190
00:51:32,383 --> 00:51:37,155
Jason Hreha: So if you had the ability
to, get every single person in the office,

1191
00:51:37,425 --> 00:51:40,985
like talent, quality, talent level, Equal.

1192
00:51:41,015 --> 00:51:43,845
Yes, of course that's better
than being like, remote.

1193
00:51:44,205 --> 00:51:48,437
But I think that a lot of companies
unfortunately, just  can't do that.

1194
00:51:48,467 --> 00:51:52,597
It's like you may be in, you may be
in a, a second or third tier talent

1195
00:51:52,597 --> 00:51:57,678
city,  and the people that you know
are in your local market just aren't

1196
00:51:57,678 --> 00:51:58,938
quite at the level that you want.

1197
00:51:59,148 --> 00:52:02,268
But you can hire somebody from,
anywhere in the world who's

1198
00:52:02,268 --> 00:52:03,438
at the level yet you want.

1199
00:52:03,648 --> 00:52:08,908
And since remote is a perk, a lot of
people that you, number one that aren't

1200
00:52:08,908 --> 00:52:12,208
in your area or that you wouldn't
otherwise be able to convince to work

1201
00:52:12,208 --> 00:52:14,938
for you, now you can get to work for you.

1202
00:52:15,238 --> 00:52:18,534
And So I think that, I think that
on balance, I think remote just

1203
00:52:18,534 --> 00:52:21,654
makes a lot of sense for probably
most companies to have at least

1204
00:52:21,654 --> 00:52:23,094
part of their workforce remote.

1205
00:52:23,274 --> 00:52:24,564
Just because once again, it's.

1206
00:52:24,614 --> 00:52:28,574
are you gonna be able to otherwise
locally find people at the same

1207
00:52:28,574 --> 00:52:29,804
level to do exactly what you want?

1208
00:52:29,804 --> 00:52:30,524
Probably not.

1209
00:52:30,874 --> 00:52:34,009
but if you're somebody like
Google or Meta or Apple.

1210
00:52:34,749 --> 00:52:37,209
where number one, you can
just throw money at problems.

1211
00:52:37,209 --> 00:52:42,229
And number two, you have a, you're so
desirable and high status as an employer.

1212
00:52:42,324 --> 00:52:42,954
Dallas Burnett: that's right.

1213
00:52:43,099 --> 00:52:45,994
Jason Hreha: I think in those
situations, those companies I think

1214
00:52:46,174 --> 00:52:48,154
should probably all just be in person.

1215
00:52:48,184 --> 00:52:52,154
And they have the ability to
convince, basically, not everybody,

1216
00:52:52,154 --> 00:52:55,274
but most people to just, Hey,
listen, you wanna work for us?

1217
00:52:55,364 --> 00:52:57,044
You're gonna come into the office, right?

1218
00:52:57,044 --> 00:53:00,644
Or, Hey, cool, I know cost of living's
higher in the Bay Area, we'll pay you

1219
00:53:00,644 --> 00:53:02,384
extra, but we want you to be here.

1220
00:53:02,504 --> 00:53:03,404
That makes sense for them.

1221
00:53:03,584 --> 00:53:06,384
But for most companies, I think
that, yeah, most companies aren't

1222
00:53:06,384 --> 00:53:08,094
Apple, most companies aren't Google.

1223
00:53:08,124 --> 00:53:10,674
Most companies don't have that
level of power to do that.

1224
00:53:10,774 --> 00:53:12,634
and don't have the pocketbooks
to be able to do that.

1225
00:53:12,724 --> 00:53:14,284
And so for most companies, I think.

1226
00:53:14,574 --> 00:53:15,114
Okay.

1227
00:53:15,594 --> 00:53:18,474
We can get incredible people
from all over the world.

1228
00:53:18,504 --> 00:53:20,364
They don't just have to
be in our specific area.

1229
00:53:20,424 --> 00:53:23,884
We can hire people from, different
states, different countries, and

1230
00:53:24,094 --> 00:53:28,574
we can just, with the whole global
market as our talent market, I think

1231
00:53:28,574 --> 00:53:29,894
they can get much better people.

1232
00:53:29,924 --> 00:53:33,284
And so I'd say for most companies,
remote makes a lot of sense.

1233
00:53:33,654 --> 00:53:37,134
the whole point of it is you can then,
now you have access to so much more talent

1234
00:53:37,134 --> 00:53:40,524
and you can find the exact right fit and
the, and significantly better people.

1235
00:53:40,542 --> 00:53:41,292
Dallas Burnett: I think you're right.

1236
00:53:41,292 --> 00:53:45,072
if I found the best person, but they're
working remote, let's say we're only

1237
00:53:45,072 --> 00:53:47,532
gonna get 90% of their best at remote,

1238
00:53:47,577 --> 00:53:48,177
Jason Hreha: Sure.

1239
00:53:48,282 --> 00:53:49,362
Dallas Burnett: 10% if they're in person.

1240
00:53:50,017 --> 00:53:53,377
But if in my market I can only hire
the average person for that job,

1241
00:53:53,377 --> 00:53:55,327
it's like you're saying, if you
don't have top tier talent in your

1242
00:53:55,327 --> 00:53:57,247
market, then maybe they're 50%.

1243
00:53:57,277 --> 00:53:58,327
obviously if I get,

1244
00:53:58,857 --> 00:53:59,307
Jason Hreha: Yeah.

1245
00:53:59,377 --> 00:54:02,247
Dallas Burnett: if I get the 90%
of the best, that's still 40% more

1246
00:54:02,247 --> 00:54:03,327
than what I'm getting in my market.

1247
00:54:03,327 --> 00:54:06,957
So I can see how that would
be, an advantageous, move.

1248
00:54:06,957 --> 00:54:10,327
I just, I'm a, maybe I'm old school,
but I love that connection piece.

1249
00:54:10,327 --> 00:54:13,117
When you talk about culture,
it's, to me, it's so much

1250
00:54:13,117 --> 00:54:14,677
easier to build that connection.

1251
00:54:14,887 --> 00:54:18,987
I think about, from an employment
standpoint or an employer standpoint,

1252
00:54:18,987 --> 00:54:22,187
we know each other, so I want to,
you're going through something at

1253
00:54:22,187 --> 00:54:24,287
home with the kids or whatever.

1254
00:54:24,287 --> 00:54:27,087
I'm like, Hey, I'm, I know you, I
see you at the office every day.

1255
00:54:27,297 --> 00:54:29,667
Whereas if you're just on the other
screen, the other side of the screen

1256
00:54:29,667 --> 00:54:31,107
somewhere else, we have no connection.

1257
00:54:31,347 --> 00:54:31,917
There's just.

1258
00:54:32,252 --> 00:54:34,412
There's something that's different
there and I just feel like

1259
00:54:34,412 --> 00:54:37,482
we'll relate just a little bit
differently, if that's the case.

1260
00:54:37,482 --> 00:54:40,392
But, I would love to hear, is there
things that you've seen, you've been in

1261
00:54:40,392 --> 00:54:43,272
the tech space, you've been at Walmart,
you've been in this behavioral space.

1262
00:54:43,302 --> 00:54:46,122
Is there things that
you've seen as a remote?

1263
00:54:46,212 --> 00:54:48,972
If I'm leading a remote team,
is there things that you've

1264
00:54:48,972 --> 00:54:50,412
seen that's worked really well?

1265
00:54:50,412 --> 00:54:54,522
Or as a behavioral scientist,
leaders could do if they're in this

1266
00:54:54,522 --> 00:54:56,052
environment of leading remote team?

1267
00:54:56,292 --> 00:54:58,092
What's some things that
you've seen work really well?

1268
00:54:58,482 --> 00:55:00,157
Jason Hreha: number one, I think
that I don't think everybody's

1269
00:55:00,157 --> 00:55:02,347
equally well suited for remote work.

1270
00:55:02,557 --> 00:55:06,297
I think that, it's even more
important to pick the right

1271
00:55:06,297 --> 00:55:08,367
people and to vet for the right.

1272
00:55:08,367 --> 00:55:08,817
people.

1273
00:55:08,847 --> 00:55:12,961
If you have a remote team, you want people
that are very self-motivated, right?

1274
00:55:13,382 --> 00:55:17,602
That without kind of a, without
a boss kind of hovering over them

1275
00:55:17,602 --> 00:55:18,772
and watching their every move.

1276
00:55:18,772 --> 00:55:20,752
People that are just self-motivated,
they're gonna follow through.

1277
00:55:20,752 --> 00:55:25,472
So I think, screening for that and being
very intense about looking for that.

1278
00:55:25,772 --> 00:55:28,652
is so much more important if
you're dealing with a remote team.

1279
00:55:29,312 --> 00:55:29,852
That's number one.

1280
00:55:29,852 --> 00:55:36,462
Number two is people who are like hyper
social, and just really hyper extroverted.

1281
00:55:36,942 --> 00:55:37,872
Not always the great.

1282
00:55:37,902 --> 00:55:39,792
best fit, right for remote team, right?

1283
00:55:39,792 --> 00:55:40,692
It's just not good for them.

1284
00:55:40,692 --> 00:55:42,312
they'll become demotivated over time.

1285
00:55:42,312 --> 00:55:45,402
And so I think that, I think you really
have to look for that personality match

1286
00:55:45,642 --> 00:55:47,862
much more intensely with a remote team.

1287
00:55:48,192 --> 00:55:51,482
And, so I think it just makes the
recruiting process even more important and

1288
00:55:51,482 --> 00:55:53,042
harder when you're dealing with remote.

1289
00:55:53,042 --> 00:55:53,672
So I'd say that

1290
00:55:53,767 --> 00:55:54,057
Dallas Burnett: Yeah.

1291
00:55:54,152 --> 00:55:57,442
Jason Hreha: the most part, I'd say
that's how it will, it should impact

1292
00:55:57,502 --> 00:55:59,692
how you think about remote work.

1293
00:55:59,752 --> 00:56:01,282
That's, those are the first two things.

1294
00:56:01,300 --> 00:56:04,150
The next thing is in an office,
I think you can be a little bit

1295
00:56:04,150 --> 00:56:08,480
more laissez-faire, a little bit
more chill about, about standups

1296
00:56:08,540 --> 00:56:10,040
meetings, these sorts of things.

1297
00:56:10,070 --> 00:56:12,830
Just because they're built
into the environment where

1298
00:56:13,010 --> 00:56:14,390
you're just around each other.

1299
00:56:14,540 --> 00:56:16,790
You're having these sa small
side conversations all the time.

1300
00:56:16,790 --> 00:56:19,130
You're catching up with your colleagues
and so you have this knowledge

1301
00:56:19,130 --> 00:56:23,370
transfer, this like kind of a natural
knowledge transfer and natural kind of.

1302
00:56:23,685 --> 00:56:24,075
Dallas Burnett: Yeah.

1303
00:56:24,360 --> 00:56:24,750
Jason Hreha: Water cooler.

1304
00:56:24,750 --> 00:56:24,930
stuff,

1305
00:56:25,050 --> 00:56:25,290
Dallas Burnett: cooler.

1306
00:56:25,320 --> 00:56:28,460
Jason Hreha: you also have accountability
built into it because if you're all in

1307
00:56:28,460 --> 00:56:32,260
the office and somebody's showing up
at, an hour late every day, or they're

1308
00:56:32,260 --> 00:56:36,520
just like slacking off, it becomes very
obvious very quickly, and they probably

1309
00:56:36,520 --> 00:56:40,000
will realize it very quickly and then
their behavior will shift based upon that.

1310
00:56:40,310 --> 00:56:44,810
but with remote, you don't have that
level of, the accountability layer is

1311
00:56:44,810 --> 00:56:48,230
often by default, a little bit lower and
there's just a little bit less oversight,

1312
00:56:48,230 --> 00:56:49,460
just of course 'cause they're at home.

1313
00:56:49,770 --> 00:56:53,430
and so I think that with remote, you
have to be much more mindful about

1314
00:56:53,790 --> 00:56:57,750
having daily standups and recurring
meetings and recurring check-ins

1315
00:56:57,750 --> 00:56:59,490
and just like these mechanisms, like

1316
00:56:59,720 --> 00:56:59,940
Dallas Burnett: Mm.

1317
00:56:59,940 --> 00:57:02,880
Jason Hreha: just have to build a
lot more of them into your company

1318
00:57:02,910 --> 00:57:05,950
and its culture, or else you're just
not gonna get as good performance.

1319
00:57:06,370 --> 00:57:09,800
And you also have to have more, I
think, explicit, knowledge capture

1320
00:57:10,280 --> 00:57:13,430
and like knowledge management,
let's call it systems in place,

1321
00:57:13,505 --> 00:57:14,195
Dallas Burnett: Yes.

1322
00:57:14,330 --> 00:57:14,550
Yes.

1323
00:57:14,660 --> 00:57:16,490
Jason Hreha: as I just
mentioned, like in, in person.

1324
00:57:16,503 --> 00:57:17,635
That's happening naturally.

1325
00:57:17,695 --> 00:57:18,775
Everybody's getting on the same page.

1326
00:57:18,775 --> 00:57:21,865
Everybody's picking up in these
little things, but remote all of

1327
00:57:21,865 --> 00:57:24,925
that side chatter, all those small
conversations are not happening.

1328
00:57:24,925 --> 00:57:27,360
And so you have to build that
then into your processes.

1329
00:57:27,390 --> 00:57:27,680
Yeah.

1330
00:57:28,090 --> 00:57:29,620
Dallas Burnett: I think that's great too.

1331
00:57:29,675 --> 00:57:31,535
I agree with that 100%.

1332
00:57:31,540 --> 00:57:33,750
you need to know and build those in.

1333
00:57:33,750 --> 00:57:35,010
I think that's really good advice.

1334
00:57:35,010 --> 00:57:36,120
Very well, very good.

1335
00:57:36,170 --> 00:57:40,980
screening for very motivated people and
maybe if you're hyper extroverted than

1336
00:57:40,980 --> 00:57:42,640
this, those are all very good things.

1337
00:57:43,090 --> 00:57:46,080
And so I, this has just been a
pleasure talking to you today, Jason.

1338
00:57:46,080 --> 00:57:46,140
I

1339
00:57:46,420 --> 00:57:46,710
Jason Hreha: Amen.

1340
00:57:47,070 --> 00:57:47,790
Dallas Burnett: your time today.

1341
00:57:47,790 --> 00:57:49,320
And so we got a couple things.

1342
00:57:49,320 --> 00:57:52,920
Number one, tell us about your
company and how people can

1343
00:57:52,920 --> 00:57:54,780
connect with you if they want to.

1344
00:57:54,840 --> 00:57:57,750
A, get your book, find out more
about your company or connect

1345
00:57:57,750 --> 00:57:58,830
with you, how will people do that?

1346
00:57:59,280 --> 00:58:00,840
Jason Hreha: Yeah, so my
company's called Persona.

1347
00:58:01,020 --> 00:58:03,210
Our website is persona talent.com.

1348
00:58:03,550 --> 00:58:07,420
so we do remote recruiting
and staffing for companies.

1349
00:58:07,610 --> 00:58:10,280
we focus on non-technical roles
just because we find that?

1350
00:58:10,280 --> 00:58:13,760
those are actually the hardest to
recruit for because with a technical

1351
00:58:13,760 --> 00:58:16,880
role, if you're trying to hire
a developer, it's pretty easy to

1352
00:58:16,880 --> 00:58:18,950
actually vet technical ability.

1353
00:58:18,950 --> 00:58:22,190
You can just look at their pass code,
you can look at their past projects.

1354
00:58:22,190 --> 00:58:26,630
It's quite simple to determine
whether or not they have the

1355
00:58:26,630 --> 00:58:27,920
skills that they claim they have.

1356
00:58:28,520 --> 00:58:30,770
But when you're dealing with these
non-technical roles where it's like

1357
00:58:30,770 --> 00:58:33,890
a little bit fuzzier, then you're
dealing with things like personality,

1358
00:58:33,890 --> 00:58:35,810
problem solving, ability, et cetera.

1359
00:58:35,870 --> 00:58:39,860
Those are very hard to measure, and that's
really our expertise at the company is

1360
00:58:40,100 --> 00:58:45,210
putting numbers, hard numbers on these,
like more fuzzy intangible skills.

1361
00:58:45,510 --> 00:58:47,340
And so that's persona talent.

1362
00:58:47,340 --> 00:58:50,125
So for any remote, non-technical
roles, I do think we're the

1363
00:58:50,125 --> 00:58:51,145
best in the world at that.

1364
00:58:51,152 --> 00:58:53,242
I I think, if we're not the
best in the world, we're at

1365
00:58:53,242 --> 00:58:54,442
least the most rigorous at that.

1366
00:58:54,502 --> 00:58:55,612
So I'll say that.

1367
00:58:56,912 --> 00:58:58,198
so there's that company.

1368
00:58:58,198 --> 00:59:00,353
And then, for my book's on
Amazon, It's called Real Change.

1369
00:59:00,838 --> 00:59:02,158
I have a very weird last name.

1370
00:59:02,158 --> 00:59:04,378
It's H-R-E-H-A.

1371
00:59:04,378 --> 00:59:05,578
So my name's Jason Rhea.

1372
00:59:05,608 --> 00:59:08,868
If you just type my name in or type in
Real Change, you'll see the book there.

1373
00:59:09,088 --> 00:59:11,028
the, the cover has a butterfly on it.

1374
00:59:11,028 --> 00:59:11,898
It's a cool cover.

1375
00:59:11,898 --> 00:59:12,438
I actually,

1376
00:59:12,573 --> 00:59:12,663
Dallas Burnett: a

1377
00:59:12,736 --> 00:59:14,946
Jason Hreha: I actually had the guy
that did, that did, some of Adam

1378
00:59:14,976 --> 00:59:18,216
Grant's covers and James Clear's
cover Do that cover actually.

1379
00:59:18,303 --> 00:59:19,423
Dallas Burnett: totally can see that.

1380
00:59:19,423 --> 00:59:19,543
I

1381
00:59:19,588 --> 00:59:20,548
Jason Hreha: yeah, yeah,

1382
00:59:20,593 --> 00:59:21,223
Dallas Burnett: that feel?

1383
00:59:21,223 --> 00:59:22,573
That's awesome.

1384
00:59:22,678 --> 00:59:23,218
Jason Hreha: He's the man.

1385
00:59:23,248 --> 00:59:23,428
Yeah.

1386
00:59:23,428 --> 00:59:25,648
He's the best, book, cover
designer in the world.

1387
00:59:25,653 --> 00:59:26,623
at least from my perspective.

1388
00:59:26,623 --> 00:59:27,733
I love him, love his work.

1389
00:59:27,763 --> 00:59:30,053
I was honored that he was, he
was willing to work with me.

1390
00:59:30,393 --> 00:59:32,088
and then I have a website.

1391
00:59:32,088 --> 00:59:34,638
It's the behavioral scientist.com.

1392
00:59:34,643 --> 00:59:35,813
I put articles up on there.

1393
00:59:35,813 --> 00:59:37,253
I have a newsletter,
you can sign up for it.

1394
00:59:37,253 --> 00:59:38,093
I share thoughts.

1395
00:59:38,173 --> 00:59:39,433
you can also connect with me on LinkedIn.

1396
00:59:39,433 --> 00:59:43,483
I put a lot of thoughts on there too,
and those are the best places to find me.

1397
00:59:43,663 --> 00:59:47,013
And, feel free to shoot me a line,
shoot me an email, from my website

1398
00:59:47,013 --> 00:59:48,303
and be happy to talk with you.

1399
00:59:49,098 --> 00:59:49,728
Dallas Burnett: That's awesome.

1400
00:59:49,728 --> 00:59:53,538
All right, so now we always ask
the guest on the last 10% to close

1401
00:59:53,538 --> 00:59:59,238
out the show, who would you like
to hear as a guest on the last 10%?

1402
01:00:00,243 --> 01:00:01,353
Jason Hreha: Can you
guess who I'm gonna say?

1403
01:00:01,621 --> 01:00:03,761
Dallas Burnett: I'm guessing
it might be James Clear.

1404
01:00:03,761 --> 01:00:04,211
Is that,

1405
01:00:04,211 --> 01:00:05,561
Jason Hreha: James Clear would be amazing.

1406
01:00:05,643 --> 01:00:06,843
Dallas Burnett: maybe BJ Fog.

1407
01:00:06,843 --> 01:00:07,443
What about that?

1408
01:00:07,443 --> 01:00:07,683
Am I

1409
01:00:07,908 --> 01:00:08,388
Jason Hreha: Exactly.

1410
01:00:08,388 --> 01:00:09,198
That's what I was gonna say.

1411
01:00:09,198 --> 01:00:10,218
I was gonna say BJ Fog,

1412
01:00:10,383 --> 01:00:10,743
Dallas Burnett: BJ

1413
01:00:10,863 --> 01:00:11,553
Okay, great.

1414
01:00:11,711 --> 01:00:15,131
Jason Hreha: I think he's like kind of
the father of modern applied behavioral

1415
01:00:15,131 --> 01:00:16,991
science, at least in the technology world.

1416
01:00:17,021 --> 01:00:19,031
So I think he's the most
important person in the field.

1417
01:00:19,301 --> 01:00:20,951
he's absolutely incredible.

1418
01:00:21,001 --> 01:00:21,871
he'd be a great guest

1419
01:00:22,096 --> 01:00:22,546
Dallas Burnett: man.

1420
01:00:22,546 --> 01:00:24,346
All right, we'll have to, we'll
have to get together off the show.

1421
01:00:24,346 --> 01:00:25,516
Maybe you can connect me.

1422
01:00:25,566 --> 01:00:29,286
connect us, on, and we'll see if he's got
some time for the last 10%, that would be

1423
01:00:29,316 --> 01:00:30,036
Jason Hreha: Yeah, would love to.

1424
01:00:30,516 --> 01:00:31,146
Dallas Burnett: So that's great.

1425
01:00:31,396 --> 01:00:35,196
Jason, again, thank you for your time
today and, look forward to, look forward

1426
01:00:35,196 --> 01:00:39,906
to checking out your book and you guys
check out persona and all that he's into.

1427
01:00:39,906 --> 01:00:40,836
So thanks again.

1428
01:00:41,526 --> 01:00:42,126
Jason Hreha: Thanks for having me.