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Hello, hello, hello. 
Hello, this is Samuel and 

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welcome to another episode of 
Behavioral Design Podcast. 

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This episode is part of our 
ongoing miniseries where we get 

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a chance to speak with the 
colleagues we have at Nuance 

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Behavior. 
And here there are unique 

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perspectives on what it means to
design for baby change in 

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practice. 
So Nuance is home of some 

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fantastic people and today's 
guest is no exception. 

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I'm very thrilled to welcome 
Rouge von Dynehoffen to the 

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podcast. 
With a background in baby change

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and neuropsychology, Rouge 
specializes in applying 

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behavioral science to design 
human centric solutions for some

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of society's most pressing 
challenges. 

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Her experience spans diverse 
sectors from cyber resilience 

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and digital literacy to mental 
health and habit formation, 

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always with the focus on making 
a positive impact. 

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We had a really fun conversation
and discussion exploring some 

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interesting topics including the
Behaviour Change Core framework,

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how it's been applied in digital
health and what it reveals about

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designing effective digital 
products like what makes for 

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strong onboarding and why 
retention remains such an 

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ongoing challenge. 
We also talk about Big E versus 

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Little E engagement, where we're
really diving to why real world 

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baby change matters so much and 
why we should maybe favorite a 

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bit more than just focusing on E
nap engagement, but also how 

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wearables and other data can 
really make that easier. 

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And of course, I can resist 
putting rules on the spot with a

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controversial question, this one
specifically personal for 

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someone from the Netherlands. 
So All in all, you can expect a 

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fun and wide-ranging 
conversation, particularly 

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focused on the digital side of 
the signing for Baby Change. 

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So without further ado, let's 
dive into this episode with 

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Roosh Happens. 
To. 

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Murgatroyd, I'm very excited to 
say welcome Roos to the 

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Behavioral Design Podcast. 
Thanks for every. 

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Yay, this has been a long time 
coming. 

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If you like. 
We've had a lot of conversations

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but none of them on a podcast 
before. 

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This is fun. 
Yeah, it's going to be fun. 

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I think this is going to be the 
first episode that I'm not going

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to listen to myself. 
Yeah, of course. 

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Yeah, I know I can relate. 
And I feel like I've just come 

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to the point where I just had to
learn to suffer through sometime

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having listened to myself. 
And yeah, but yeah, it's it's up

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to you if whether you want to 
listen to it, but you have no 

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choice but to participate. 
So, so, yeah, I think given that

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this conversation is always 
built a little bit of the last 

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podcast we had with Susan 
Murphy. 

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So yeah, I just maybe to start, 
I'm just curious, what's your 

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impression of that? 
Episode. 

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Yeah, I thought it was super 
interesting episode. 

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And especially that she, I think
at some point made a comment 

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about that it's just really 
difficult to stay engaged over a

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longer period of time. 
It's sometimes relatively easier

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to get people to start something
new, but then to actually 

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maintain that over time, it's 
just really difficult. 

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So I noticed a few things that 
she said that kind of overlapped

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with the work that we're doing 
and the findings that we have 

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found. 
Yeah, I agree. 

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And I think it's fun in some 
ways above because part of me 

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was thinking about you a little 
bit because we did some work I 

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think maybe a year ago or so 
where we actually work with kind

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of messaging specifically and 
we're working with this kind of 

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large insurance provided. 
We wanted to basically help with

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getting people to take a test to
see their cardiovascular risk. 

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And I wanted to like find ways 
to develop a messaging strategy 

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to to ensure that that happened.
And that was fun because we 

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applied in some ways a lot of 
those principles in that 

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example. 
And we were able to actually 

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increase with like 40 or 50% the
people who did this. 

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That was like one of those like 
interventions that went really 

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well. 
That was that was fun. 

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But yeah, it reminded me of what
we did there. 

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But also like taking that maybe 
even to a further degree with 

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specifically using some of these
kind of AI elements that we 

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didn't I think use as much at 
that time. 

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Yeah, yeah. 
I especially find it super cool 

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that you have these tailoring 
variables, right, that she was 

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talking about. 
And then you can use this to see

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how responsive a person is going
to be to the intervention, but 

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then also it adapts to the 
behavior over time. 

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So what we did was this 
framework of, OK, we have these 

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different, maybe we even made 
like a behavioral persona group 

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or I'm not actually sure if we 
did that for that one. 

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But you can either segment your 
audience and then send those 

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tailored messages. 
But then when they don't respond

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to it, OK, then you kind of, 
yeah, need to adapt. 

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But now with Jedi, that kind of 
does that adapting for you, 

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which is super cool that that's 
that that's possible. 

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And hope, yeah, also way more 
effective than doing everything 

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yourself. 
Yeah, Yeah, I think that's true.

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And I think you like, that was 
also a good example where, you 

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know, we probably would have 
liked to do more advanced 

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things, but maybe used as a 
reporting from the front lines 

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of paper science. 
Like sometimes he was also 

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working with a large insurance 
company that has certain 

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internal tools that they prefer 
to use that are maybe, yeah, not

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as AI driven, let's say. 
So yeah, you have to make the 

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rest of the contacts you're in, 
but but yeah, that's, that's 

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cool. 
So I guess to to build on this, 

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is there anything else you 
wanted to take away from that 

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conversation or like highlight 
as an important kind of 

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insights? 
Well, that AI needs to stay 

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grounded in behavioral science, 
right? 

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We're pioneering. 
I think that was a really nice 

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thing that that got stuck in my 
head. 

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When you sometimes get 
frustrated when something is 

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just so complex and you're 
trying to figure it out, but 

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then you can't really find the 
information or just take so long

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to figure out and you have a lot
of ups and downs. 

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Of course, they have a science 
is super cool and I get super 

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excited about it, but it can 
also give you a headache if you 

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don't know how to sell 
something. 

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And she already made this nice 
point about, well, we're at a 

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frontier science. 
It's very logical that there are

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some questions that we don't 
have the answers to and it's 

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just really cool to work in an 
industry and contribute to 

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trying to figure out the answers
for those questions that are 

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giving you a headache. 
So I think that was a really 

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nice one. 
And yeah, that's that's stuck in

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my head. 
So that's that's a take away. 

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Yeah, yeah. 
I think that's definitely one of

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those things like I was tempted 
to put as my alarm clock or 

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something as like in the morning
being like you're a part of a 

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frontier science. 
We're doing frontier work, you 

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know. 
Yeah. 

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Well, to, to get to get that 
kind of motivational doing, 

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we're doing good stuff. 
We're doing important stuff 

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because. 
Yeah, definitely. 

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It's nice to hear that. 
And I agree as well. 

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And I guess to stay on track of 
engagement, we had this report 

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that we published together with 
everyone in Nuance, which was 

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called the Baby Change Score 
report, focusing on digital 

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health products and 
interventions, an app 

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specifically. 
And basically we introduced this

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baby change score. 
And so yeah, maybe you want to, 

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you want to explain what what 
the heck, What is the baby 

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change score and how does it 
work? 

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Sure. 
Yeah. 

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So the baby change score like 
you said. 

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In summary, we published the 
first report which was 

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specifically about fitness apps,
but the whole idea is that there

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is just so much information 
about behavioral science and 

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behavioral design and behavior 
change techniques and behavioral

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strategies. 
And they give all these terms. 

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And for someone who is maybe new
to the field but does know like 

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that behavioral science is maybe
valuable for their product, it's

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sometimes quite hard. 
Like where do you even start? 

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And it's one thing to have all 
the theory, but then how do you 

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apply that to an actual product 
that you're working on, right? 

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For example, you may know that 
giving reward is important to do

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as a strategy, but then people 
start putting like badges. 

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You can collect badges for all 
sorts of behavior that you have 

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in your app, like when we 
reviewed all these apps to 

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create a behavior change score 
in the end. 

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But one app that I reviewed then
gave me a badge for uploading my

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profile picture. 
Yeah, great, that's cool. 

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But what is that going to 
actually? 

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How is that going to contribute 
to me doing the actual behavior 

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that I want to change? 
Wait wait, so you're saying that

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you didn't share the badge with 
your partner or what? 

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Like you didn't share with your 
woohoo? 

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Got my first badge. 
No, sadly I didn't. 

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But yeah, no, that's just not 
really related to the actual 

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behaviors. 
Just sometimes when you're done 

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looking at so many apps that are
supposed to help you change your

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behavior. 
And in this case, it wasn't 

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about fitness, so about physical
activity. 

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You're supposed to or you have a
goal. 

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That's why you even open it up 
in the 1st place. 

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You want to be more fit or go to
the gym more often and then you 

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open these apps and then these 
apps are supposed to help you 

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get there, right? 
But it feels like nowadays there

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are sometimes behavioral quote 
UN quote strategies just 

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sprinkled on top. 
I heard someone say this once. 

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I found that a really nice 
quote, but sprinkled on top 

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without much strategic thought 
because like a bench for my 

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profile picture. 
Really. 

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How is that going to help me get
to the gym? 

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So yeah, the whole goal of the 
Behavior James Score report is 

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to, on one hand, score different
apps. 

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And we just now looked at the 
most popular fitness apps and 

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see how they're doing in terms 
of behavioral science and 

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behavior change techniques. 
So what we did is go through 

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lots of behavioral research and 
models and frameworks and have 

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it forming technology, all that 
kind of stuff. 

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And we then categorized the 
different behavioral elements 

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that are important when you want
to form a habit and engage 

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people, maintain that 
engagement. 

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And then we mapped that out 
across the whole user journey, 

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so from onboarding, activation 
to engagement and retention. 

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And we just started scoring 
like, how well are apps using 

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these behavioral techniques that
are needed to, for example, have

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a successful onboarding? 
Yeah. 

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And I think one big part of why 
we ended up creating this 

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Bayesian score is kind of 
alluding to what you were saying

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in many ways in terms of that 
product teams are in a tricky 

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situation. 
Like it's not easy to develop 

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products and it's it's kind of 
tricky space where you know, 

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it's easy to look at 
competitors. 

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It is to get excited about what 
features to build and what 

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things to do. 
But it's easy also then to get 

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carried away and building 
things. 

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Even though if you have this 
motivation to like wanting to 

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use more behavioral driven 
features or product psychology 

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in your product or app, that's 
great. 

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That's like a great intention. 
But at the same time, many 

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product teams struggle there 
because as you mentioned, they 

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kind of like they don't really 
know how to actually always put 

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that in place. 
And so in many ways, we wanted 

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to put together something that 
kind of meet product teams where

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they are, where they can kind of
get some understanding of how 

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this all of these hundreds of 
concepts we cover in behavioral 

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science, how they relate to 
specifically their app at hand 

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or their product at hand. 
And how we then based on that 

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can move from first 
understanding like what doesn't 

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work? 
What are the things that maybe 

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are areas for improvements? 
And then kind of identifying 

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those things instead of just 
kind of getting into the trap 

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where I think a lot of product 
team struggle is like they just 

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hear about all these exciting 
people saying strategies like 

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you said, and they just want to 
like add them all, but they 

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don't really know which problem 
they're trying to solve for in 

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there in the rest way. 
Yeah, Yeah. 

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So basically when you boil it 
down to like just a few main 

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questions is actually OK, what 
things are important when it 

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comes to behavioral factors? 
What is important to say form 

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habit if you want to build a 
habit, what is important from 

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behavioral science to form a 
habit and how do you translate 

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that to a product? 
So which element should be 

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present in your habit forming 
product? 

232
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And then also we looked at what 
are the specific journey phases 

233
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that people go through, So like 
from onboarding and activation 

234
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to like engagement and retention
and like what happens after a 

235
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failure state. 
So when people stop using the 

236
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app, like how do you get them 
back, all that sort of stuff. 

237
00:13:23,200 --> 00:13:26,320
And then for this report, we 
looked at fitness apps 

238
00:13:26,320 --> 00:13:29,760
specifically. 
So what we did is we per app, we

239
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had at least two people 
reviewing it. 

240
00:13:32,440 --> 00:13:36,320
So we actually used the apps. 
We were very fit for summer. 

241
00:13:37,680 --> 00:13:42,280
We actually used these FS and 
then we started scoring them on 

242
00:13:42,800 --> 00:13:46,400
all these, all these elements as
we were going through all phases

243
00:13:46,400 --> 00:13:52,000
of uses. 
And yeah, by by scoring them, we

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00:13:52,000 --> 00:13:56,680
actually looked at, OK, these 
variables are necessary to have 

245
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say, an effective onboarding. 
And does this app include these 

246
00:14:01,200 --> 00:14:03,680
elements, yes or no or like to 
what extent? 

247
00:14:03,880 --> 00:14:06,320
Because it's also possible that 
it's like somewhat there, but 

248
00:14:06,320 --> 00:14:08,360
maybe not fully in the right 
way. 

249
00:14:08,400 --> 00:14:12,560
So we were able to say things 
like across all the apps, 

250
00:14:12,560 --> 00:14:16,000
certain certain behavioral 
elements were more or less 

251
00:14:16,000 --> 00:14:19,120
present or some things were used
a lot other things were 

252
00:14:19,120 --> 00:14:22,120
completely overlooked. 
But also per app, we could say, 

253
00:14:22,360 --> 00:14:24,560
oh, you're really doing well on 
onboarding. 

254
00:14:24,720 --> 00:14:28,320
But we see that on retention, 
there do not really seem to be a

255
00:14:28,320 --> 00:14:32,440
lot of elements or features that
seem to be behaviorally informed

256
00:14:32,440 --> 00:14:35,960
or that are actually reaching 
out to us when we drop off and 

257
00:14:35,960 --> 00:14:40,000
do not use this app anymore. 
So we could see an overall trend

258
00:14:40,000 --> 00:14:43,520
in behavioral techniques in 
fitness apps in this case, and 

259
00:14:43,520 --> 00:14:47,240
we could zoom into one app 
specifically and see, hey, 

260
00:14:47,240 --> 00:14:49,960
you're doing well on this front,
but hey, on on this other thing,

261
00:14:49,960 --> 00:14:52,080
there is actually some room for 
improvement. 

262
00:14:52,280 --> 00:14:54,760
Yeah, it's funny. 
In terms of that we, we chose 

263
00:14:54,760 --> 00:14:56,880
fitness apps. 
I think it was in good timing 

264
00:14:56,880 --> 00:14:59,680
because we were looking at 
starting the first report with 

265
00:14:59,880 --> 00:15:01,880
some few different types of 
categories. 

266
00:15:02,320 --> 00:15:05,640
But I guess we we land on 
fitness apps there, the state of

267
00:15:05,640 --> 00:15:09,080
preference was because we wanted
to look at like hard to change 

268
00:15:09,080 --> 00:15:11,960
behavior that is like long 
lasting, like something related 

269
00:15:11,960 --> 00:15:15,600
to fitness in this case. 
But I guess maybe also there was

270
00:15:15,600 --> 00:15:19,160
some kind of behind the scenes 
preferences for people to get 

271
00:15:19,160 --> 00:15:21,960
fit in the team. 
So I think we've kind of done 

272
00:15:21,960 --> 00:15:23,400
our best to kind of support each
other. 

273
00:15:24,000 --> 00:15:27,160
It's kind of meta actually, 
because we were kind of our we, 

274
00:15:27,200 --> 00:15:31,280
we were like the whole team was 
each other's social commitment 

275
00:15:31,880 --> 00:15:35,280
buddy or something. 
We were really committed to 

276
00:15:35,280 --> 00:15:38,600
doing these workouts. 
It was, it was really funny. 

277
00:15:38,600 --> 00:15:43,560
I remember with with Jared, 
Jared and I were were working 

278
00:15:43,560 --> 00:15:47,240
out with the same map and it was
like we had quite a high degree 

279
00:15:47,240 --> 00:15:51,920
of competitiveness in the app, 
like it was made to compare with

280
00:15:51,920 --> 00:15:56,080
your friends and so on. 
So it was fun to see and compare

281
00:15:56,080 --> 00:16:00,400
ourselves. 
And yeah, I want to reveal who 

282
00:16:00,400 --> 00:16:01,880
won here. 
That would be unfair to Jerry. 

283
00:16:01,920 --> 00:16:05,320
I'll, I'll save that for when, 
when Jerry comes on the podcast.

284
00:16:05,680 --> 00:16:09,320
But yeah, obviously the report 
is free, unavailable on our 

285
00:16:09,320 --> 00:16:10,640
website and you can find it 
easily. 

286
00:16:11,400 --> 00:16:14,320
But he has to be interesting to 
maybe to talk about. 

287
00:16:14,320 --> 00:16:18,360
One interesting thing from the 
report would probably be that we

288
00:16:18,360 --> 00:16:22,960
looked at these 4 stages of the 
usage journey and we mapped the 

289
00:16:22,960 --> 00:16:29,040
scoring of these apps against 
how well they performed across 

290
00:16:29,400 --> 00:16:34,240
from kind of on boarding all the
way to activation, engagement 

291
00:16:34,280 --> 00:16:38,840
and retention. 
And we can see both individually

292
00:16:38,840 --> 00:16:41,440
how these apps were performing 
at these stages, but also we can

293
00:16:41,440 --> 00:16:44,280
see some trends how they were 
performing and so on. 

294
00:16:44,920 --> 00:16:48,040
Maybe you can share some some of
those trends that we noticed 

295
00:16:48,320 --> 00:16:50,640
from those kind of yeah journey 
stages. 

296
00:16:51,880 --> 00:16:55,160
Yeah, sure. 
So overall, we saw that on 

297
00:16:55,160 --> 00:16:58,920
boarding perform the best of 
across all the journey stages. 

298
00:16:59,320 --> 00:17:03,200
And while retention towards the 
end of the journey got way less 

299
00:17:03,200 --> 00:17:06,520
attention and we were thinking 
about like why this would be the

300
00:17:06,520 --> 00:17:07,920
case. 
And we thought, well, during 

301
00:17:07,920 --> 00:17:11,359
onboarding, there's a lot of 
data that you can actually quite

302
00:17:11,359 --> 00:17:14,920
easily collect, right? 
You can see how long people 

303
00:17:14,920 --> 00:17:18,720
spend on doing a sign up or like
going through an onboarding flow

304
00:17:19,240 --> 00:17:21,520
or where they may drop off. 
Like you have quite a lot of 

305
00:17:21,520 --> 00:17:25,200
data points to collect there, 
but at the same time, first 

306
00:17:25,200 --> 00:17:27,800
impressions matter, right? 
So during the onboarding, I want

307
00:17:27,800 --> 00:17:30,640
to impress people a little bit, 
but also you, you kind of need 

308
00:17:30,640 --> 00:17:34,000
to collect information from the 
user to be able to personalize 

309
00:17:34,200 --> 00:17:37,080
the journey later on. 
And because there is just way 

310
00:17:37,080 --> 00:17:40,240
more, yeah, data to actually 
measure. 

311
00:17:41,680 --> 00:17:44,720
It's just that's maybe the 
reason why the onboarding 

312
00:17:44,720 --> 00:17:49,600
performed best, whereas 
retention is so much can happen 

313
00:17:49,600 --> 00:17:53,040
in someone's life that makes 
them drop off or not come back 

314
00:17:53,040 --> 00:17:55,120
to an app. 
And it's just really hard to 

315
00:17:55,120 --> 00:17:58,480
figure out why that is the case.
And what actually was that 

316
00:17:58,480 --> 00:18:00,120
barrier? 
Why did people drop off? 

317
00:18:00,120 --> 00:18:05,640
Was it, was it me, was it about 
the app or was it something in 

318
00:18:05,640 --> 00:18:07,800
their lives? 
I don't know, maybe they got a 

319
00:18:07,800 --> 00:18:11,120
kid, they don't have time 
anymore to go to the gym or 

320
00:18:11,280 --> 00:18:12,880
maybe they're just really busy 
with work. 

321
00:18:12,880 --> 00:18:15,920
Like there's so many unknowns 
there and therefore it's also 

322
00:18:15,960 --> 00:18:17,600
quite hard to then design for 
that. 

323
00:18:17,880 --> 00:18:22,440
So I think that might be the 
reason why on Boarding perform 

324
00:18:22,440 --> 00:18:25,320
so much better than retention. 
Yeah. 

325
00:18:25,560 --> 00:18:27,160
No, I think it's so true. 
There's so many kind of 

326
00:18:27,160 --> 00:18:29,480
incentives to do it as well in 
terms of just like everyone 

327
00:18:29,480 --> 00:18:32,360
wants to get people to sign up 
for that and pay for that or 

328
00:18:32,360 --> 00:18:34,760
whatever it is. 
And then like you say, I think 

329
00:18:34,760 --> 00:18:38,280
we obviously try to beat the 
drum often times about 

330
00:18:38,280 --> 00:18:40,080
experimentation as payable 
scientists. 

331
00:18:40,720 --> 00:18:43,360
And I think a lot of teams to 
their credit these days, like 

332
00:18:43,360 --> 00:18:46,200
there's a lot of experimentation
and AB testing and various types

333
00:18:46,200 --> 00:18:49,480
of testing that's being done. 
But obviously depending on your 

334
00:18:49,760 --> 00:18:53,760
amount of users you have and so 
on, it can be like kind of 

335
00:18:53,760 --> 00:18:56,400
tricky if you wanted to test 
some form of feature related to 

336
00:18:56,400 --> 00:18:59,600
your retention, because let's 
say you wanted to test your 60 

337
00:18:59,600 --> 00:19:02,480
day retention. 
Well, it will take at least 60 

338
00:19:02,480 --> 00:19:05,280
days to find the answer of 
whether it worked or not. 

339
00:19:05,680 --> 00:19:08,400
And that's in the case of a lot 
of users. 

340
00:19:08,400 --> 00:19:12,000
In case if you don't have enough
users, it might take several 

341
00:19:12,000 --> 00:19:14,280
months to collect enough data on
what worked. 

342
00:19:14,600 --> 00:19:18,000
Whereas if you do some form of 
thing to improve the onboarding 

343
00:19:18,000 --> 00:19:21,560
and 1st day of of usage, you get
that data straight away. 

344
00:19:21,560 --> 00:19:24,360
And it's it's so much more data 
and immediate data. 

345
00:19:24,360 --> 00:19:26,680
So just easier as well to I 
think experiment. 

346
00:19:26,680 --> 00:19:29,680
So maybe one thing is like, I 
think it's also because of the 

347
00:19:29,680 --> 00:19:32,840
difference between in app 
behavior and outside of the app 

348
00:19:32,840 --> 00:19:35,720
behavior. 
Like and onboarding is really 

349
00:19:35,720 --> 00:19:39,160
like in the app, right? 
You need to fill in questions 

350
00:19:39,160 --> 00:19:42,360
that are in the app. 
You need to go through like, I 

351
00:19:42,360 --> 00:19:44,920
don't know, a tutorial of how 
the app works. 

352
00:19:44,920 --> 00:19:48,320
Maybe you need to fill in or 
experiment or try out a few of 

353
00:19:48,320 --> 00:19:51,320
the features. 
But then where when you're 

354
00:19:51,320 --> 00:19:54,640
actually using the app, and in 
this case it's still use fitness

355
00:19:54,640 --> 00:19:58,440
apps as a as an example, like 
you need to actually either do a

356
00:19:58,440 --> 00:20:01,800
homework at work, out at home, 
or you need to go to gym and 

357
00:20:01,800 --> 00:20:04,040
perform the workout, do the 
work. 

358
00:20:04,320 --> 00:20:07,440
And that is something that's 
maybe using outside of the app, 

359
00:20:07,480 --> 00:20:11,080
which is then harder to measure.
And yeah, therefore you also 

360
00:20:11,160 --> 00:20:14,440
maybe see less features about it
because it is so hard to 

361
00:20:14,440 --> 00:20:16,920
measure. 
Yeah, I think that leads us 

362
00:20:16,920 --> 00:20:20,360
perfectly into something I 
wanted to discussed related to 

363
00:20:20,360 --> 00:20:24,200
this as well, which is talking 
about Biggie and literally 

364
00:20:24,200 --> 00:20:28,080
engagement. 
So we had actually the author of

365
00:20:28,080 --> 00:20:31,600
the paper that introduced this 
framework on the podcast before 

366
00:20:32,080 --> 00:20:35,400
I had the code Lewis. 
So if you want to listen to a 

367
00:20:35,480 --> 00:20:38,520
whole discussion about this, 
check out that podcast. 

368
00:20:39,120 --> 00:20:44,040
But basically it's this idea of 
understanding digital health 

369
00:20:44,040 --> 00:20:49,040
behaviors with these lens of 
defining them as either being 

370
00:20:49,160 --> 00:20:52,640
kind of biggie engagement or 
small engagement like you're 

371
00:20:52,720 --> 00:20:53,560
that's what you're talking 
about. 

372
00:20:53,560 --> 00:20:55,840
And I think it's really great to
to get a chance to talk about 

373
00:20:55,840 --> 00:21:00,400
that. 
So I guess what we see often 

374
00:21:00,400 --> 00:21:03,360
times, as you mentioned, with 
kind of like this small E in 

375
00:21:03,360 --> 00:21:07,000
that behavior often times is 
that, as you say, it's easy to 

376
00:21:07,000 --> 00:21:09,880
measure. 
But then the big elephant in the

377
00:21:09,880 --> 00:21:11,920
room, outside of the room, I 
don't know what to call it, but 

378
00:21:11,920 --> 00:21:13,880
like, Oh my God, it's. 
A big elephant. 

379
00:21:13,880 --> 00:21:16,040
It's an E. 
Yeah, that's true. 

380
00:21:16,360 --> 00:21:18,200
The Big E elephant, that's true,
I tell you. 

381
00:21:18,320 --> 00:21:20,680
Well, mind blow, I didn't. 
That was not on purpose. 

382
00:21:20,720 --> 00:21:22,880
That was a subliminal or 
something. 

383
00:21:23,480 --> 00:21:29,200
But the Big E, the big elephant 
is that, as you say, there's so 

384
00:21:29,200 --> 00:21:30,560
much stuff happening in the real
world. 

385
00:21:30,880 --> 00:21:34,080
And I think you probably have 
this experience as well where 

386
00:21:34,080 --> 00:21:38,560
you kind of work on product 
teams and everything kind of, it

387
00:21:38,560 --> 00:21:42,360
seems to go great until you're 
like, well, what about what 

388
00:21:42,360 --> 00:21:43,680
people actually do in the real 
world? 

389
00:21:43,680 --> 00:21:46,400
And they're like. 
Don't know, don't know and. 

390
00:21:47,520 --> 00:21:49,880
Especially when we talk about 
digital health or digital 

391
00:21:49,880 --> 00:21:52,760
sustainability or like the stuff
that we work with is basically 

392
00:21:53,000 --> 00:21:56,520
all of these kind of often times
context and industries and 

393
00:21:56,520 --> 00:21:59,400
sector where people kind of have
to do something in the real 

394
00:21:59,400 --> 00:22:00,520
world. 
It's not enough that they just 

395
00:22:00,520 --> 00:22:05,920
log into the app, for example. 
So, so yeah, that is the 

396
00:22:05,920 --> 00:22:11,160
challenge, how to better 
understand and think about this 

397
00:22:11,160 --> 00:22:14,120
kind of Big E behavior wraps in 
the real world. 

398
00:22:14,480 --> 00:22:17,440
So yeah, how do you think about?
That yeah, Yeah, good question. 

399
00:22:19,000 --> 00:22:22,480
So the little ES is about in app
behaviors, right? 

400
00:22:22,480 --> 00:22:25,520
And it's about the not only 
using the app, but also 

401
00:22:25,520 --> 00:22:28,280
interacting with the behavior 
change features or the 

402
00:22:28,280 --> 00:22:31,520
intervention, because only 
through the intervention, you 

403
00:22:31,520 --> 00:22:36,600
will actually help people to do 
the big, big E the the target 

404
00:22:36,600 --> 00:22:38,000
behavior that you actually care 
about. 

405
00:22:39,440 --> 00:22:43,240
South for fitness, you can in 
the app have a feature, maybe 

406
00:22:43,760 --> 00:22:47,400
have a commitment with a partner
that you can can go for runs 

407
00:22:47,400 --> 00:22:49,600
together, but then actually 
going for the run together is 

408
00:22:49,600 --> 00:22:53,280
then the big E. 
So yeah, it is sometimes to be 

409
00:22:53,280 --> 00:22:56,840
hard to measure this Big E in 
the real world because of the 

410
00:22:56,840 --> 00:22:58,680
fact that it's outside of the 
app. 

411
00:22:58,680 --> 00:23:01,280
So it's hard to track that 
within the app. 

412
00:23:01,560 --> 00:23:04,600
Or you can ask people to log 
their run, but then the logging 

413
00:23:04,600 --> 00:23:06,480
the run becomes a behavior in 
itself. 

414
00:23:06,480 --> 00:23:09,840
So then people need to and go 
for the run and log the run 

415
00:23:10,000 --> 00:23:11,160
because otherwise it doesn't 
count. 

416
00:23:12,080 --> 00:23:15,720
So yeah, it is really difficult.
But what you can do sometimes is

417
00:23:15,720 --> 00:23:23,640
to look for metrics or proxy 
metrics, and a combination of 

418
00:23:23,640 --> 00:23:28,000
different and carefully thought 
out proxies can then approximate

419
00:23:28,000 --> 00:23:30,800
the target behavior that you 
actually care about. 

420
00:23:30,840 --> 00:23:33,720
And I was thinking about this 
the other day when my partner's 

421
00:23:33,720 --> 00:23:38,160
dad had to go to a sleep lab to 
measure things for his sleep 

422
00:23:38,200 --> 00:23:40,360
right. 
Like when you kind of you're, 

423
00:23:40,400 --> 00:23:43,320
you're basically you're plugged 
into these machines and you 

424
00:23:43,320 --> 00:23:45,000
sleep overnight to measure and 
stuff. 

425
00:23:45,560 --> 00:23:47,280
Exactly. 
So you had to go there and all 

426
00:23:47,280 --> 00:23:50,960
this like stuff stuck on his 
head and his face and they don't

427
00:23:50,960 --> 00:23:54,640
you sleep there and then they 
observe you and they do all 

428
00:23:54,640 --> 00:23:59,360
these measures and that's how 
you measure sleep and sleep 

429
00:23:59,360 --> 00:24:02,080
quality, right. 
But if you, if you build a 

430
00:24:02,080 --> 00:24:05,360
product that's actually helping 
people improve their sleep, it's

431
00:24:05,360 --> 00:24:08,160
just not really feasible to 
invite everybody over to one of 

432
00:24:08,160 --> 00:24:10,360
those labs and just sleep there.
It's not a hotel. 

433
00:24:10,880 --> 00:24:13,840
It's like it's just too 
expensive to to do that, right? 

434
00:24:13,840 --> 00:24:19,080
So instead what you can do is 
break it down into little things

435
00:24:19,080 --> 00:24:21,840
that are related to sleep 
quality. 

436
00:24:21,840 --> 00:24:24,720
So you can look at things like 
how long did it take someone to 

437
00:24:24,720 --> 00:24:26,720
fall asleep? 
Or like how long did they sleep?

438
00:24:26,720 --> 00:24:30,120
How often did they wake up? 
If you have a wearable or so you

439
00:24:30,120 --> 00:24:33,960
can also say like how much 
movement was there or something.

440
00:24:34,880 --> 00:24:39,320
And then together, even though 
they may not directly measure 

441
00:24:39,320 --> 00:24:42,960
the outcome goal or measure the 
quality of sleep, but like still

442
00:24:42,960 --> 00:24:46,600
a combination of these things 
and ideally also a combination 

443
00:24:46,600 --> 00:24:49,720
of different types of data. 
So like behavioral data, but 

444
00:24:49,720 --> 00:24:53,360
also attitudinal data. 
Ask someone when they wake up, 

445
00:24:53,360 --> 00:24:55,200
how do you feel? 
Do you feel rested or not? 

446
00:24:55,960 --> 00:24:59,280
All those things combined and 
wrapped up in a nice little 

447
00:24:59,280 --> 00:25:01,640
package together, like they 
could still give you an 

448
00:25:01,640 --> 00:25:05,760
indication if your product is 
working or not. 

449
00:25:05,960 --> 00:25:09,880
So there's one way to go about 
the big elephant. 

450
00:25:10,560 --> 00:25:13,680
Is the. 
Big elephant, Yeah. 

451
00:25:13,680 --> 00:25:15,920
No, I think that's, that's such 
a good way of talking about it. 

452
00:25:16,000 --> 00:25:20,560
It really, you know, something 
that you highlight that these 

453
00:25:20,560 --> 00:25:23,680
days it is actually more 
possible than a lot of product 

454
00:25:23,680 --> 00:25:26,160
teams think when it comes to 
understanding and measuring Big 

455
00:25:26,160 --> 00:25:29,920
E as well and understanding kind
of this, this Big E behaviors. 

456
00:25:30,280 --> 00:25:35,640
And I hate to do this, but we do
have actually like a a episode 

457
00:25:35,640 --> 00:25:38,360
that all about this coming up 
quite soon as well, later 

458
00:25:38,360 --> 00:25:39,680
probably this year, early next 
year. 

459
00:25:39,920 --> 00:25:43,040
So as a teacher, if you're 
really interested in measuring 

460
00:25:43,040 --> 00:25:46,520
Big E and stuff, we have an 
expert on specifically this 

461
00:25:46,520 --> 00:25:49,720
topic, which kind of blew my 
mind in terms of how you can 

462
00:25:49,720 --> 00:25:54,000
kind of find ways to measure 
people. 

463
00:25:54,000 --> 00:25:57,280
So so yeah, that is beautiful. 
I think we covered so much 

464
00:25:57,280 --> 00:25:59,920
interesting stuff around this, 
and I think there's so much more

465
00:26:00,400 --> 00:26:05,480
to probably say about Biggie and
Small E, But again, I'll 

466
00:26:05,480 --> 00:26:10,440
recommend the episode with 
Heather Co Lewis on that. 

467
00:26:10,640 --> 00:26:14,400
And then again, if you want to 
learn about the Asian Square 

468
00:26:14,400 --> 00:26:17,320
port, I'm sure we'll link to it 
in the show notes, but also you 

469
00:26:17,320 --> 00:26:20,920
can find it on the new one's 
website to download. 

470
00:26:21,520 --> 00:26:27,680
And yeah, thanks for sharing 
your wisdom on those topics. 

471
00:26:28,480 --> 00:26:32,520
Cool. 
This has been so much fun and I 

472
00:26:32,640 --> 00:26:35,680
could talk many more hours to 
you about many of these 

473
00:26:35,680 --> 00:26:40,080
subjects, but I want to ask you 
a controversial question or a 

474
00:26:40,080 --> 00:26:43,080
conversial opinion basically 
because that's the theme of this

475
00:26:44,040 --> 00:26:46,520
season is controversial opinions
about AI. 

476
00:26:46,880 --> 00:26:49,040
But I kind of want to do a 
little bit tweaked version of 

477
00:26:49,040 --> 00:26:52,240
this because I know of course 
that you are Dutch and you're 

478
00:26:52,240 --> 00:26:55,840
very proud of that. 
And therefore, I want to kind of

479
00:26:55,840 --> 00:26:59,600
like tweak this question to be a
little more kind of close to 

480
00:26:59,600 --> 00:27:01,440
home for you, quite literally 
so. 

481
00:27:01,680 --> 00:27:05,080
Super curious. 
With bicycles, there's a lot of 

482
00:27:05,080 --> 00:27:10,160
bicycles in in Netherlands, but 
there's increasingly much, much 

483
00:27:10,160 --> 00:27:13,600
more electric bicycles. 
The conversion question here is 

484
00:27:13,600 --> 00:27:15,680
like, what do you think about 
electric bicycles? 

485
00:27:15,880 --> 00:27:20,200
Is it a good thing or like 
overrated or underrated 

486
00:27:20,200 --> 00:27:24,720
basically? 
Oh, that's a difficult one. 

487
00:27:25,400 --> 00:27:28,600
It depends. 
You can't say the offense that's

488
00:27:28,640 --> 00:27:30,240
the that's a Babel scientist in 
you. 

489
00:27:30,240 --> 00:27:31,880
That's. 
OK, then Overrated. 

490
00:27:31,920 --> 00:27:35,760
OK, overrated. 
Yeah, because let me explain. 

491
00:27:35,800 --> 00:27:39,760
So I never had an electric bike 
myself and my partner did. 

492
00:27:39,800 --> 00:27:42,920
And I really love that because 
in Amsterdam, sometimes when it 

493
00:27:42,920 --> 00:27:45,160
gets windy, it gets super windy.
And if you need to go to the 

494
00:27:45,160 --> 00:27:48,560
other side of the city, OK, for 
a Dutch person, just you need to

495
00:27:48,560 --> 00:27:51,640
bike like 20 to 30 minutes. 
And other people who say a 

496
00:27:51,640 --> 00:27:53,840
tremble time of 20 to 30 minutes
is nothing. 

497
00:27:53,840 --> 00:27:57,560
But for Dutch standards, that's 
long, especially when it's windy

498
00:27:57,560 --> 00:28:00,120
and you need to go. 
The Netherlands is flat, but in 

499
00:28:00,120 --> 00:28:02,160
Amsterdam you have little 
bridges and everything you need 

500
00:28:02,160 --> 00:28:03,480
to cross. 
So you need to go up and down a 

501
00:28:03,480 --> 00:28:06,720
tiny bit with a lot of wind and 
then it's cold. 

502
00:28:06,720 --> 00:28:10,800
So then it's kind of nice to 
have an electric bike to not 

503
00:28:10,800 --> 00:28:12,880
arrive at your destination 
completely sweaty. 

504
00:28:13,520 --> 00:28:17,600
But actually we sold the 
electric bike not too long ago 

505
00:28:17,640 --> 00:28:21,880
because yeah, so my partner got 
actually tired of it because it 

506
00:28:21,880 --> 00:28:24,200
was like, I'm just not really 
moving so much anymore. 

507
00:28:24,400 --> 00:28:27,400
Because on this bike, honestly, 
it's not that you really need to

508
00:28:27,400 --> 00:28:29,800
put in an effort. 
You just go quite fast without. 

509
00:28:30,000 --> 00:28:31,720
You just need to keep the pedals
going. 

510
00:28:31,720 --> 00:28:33,960
Like it's not that you really 
put in an effort and you 

511
00:28:33,960 --> 00:28:37,560
couldn't really turn it off. 
So he decided to sell it and 

512
00:28:37,560 --> 00:28:39,480
just get a normal bike and 
actually move. 

513
00:28:39,880 --> 00:28:44,560
So I think they're overrated 
because they're also just 

514
00:28:44,560 --> 00:28:48,960
dangerous, like you go way too 
fast on a bike lane where also 

515
00:28:48,960 --> 00:28:51,400
normal people are biking. 
So a lot of accidents are 

516
00:28:51,400 --> 00:28:52,640
happening with those things as 
well. 

517
00:28:54,040 --> 00:28:57,360
Still, I enjoyed the time study 
headed so that I could sometimes

518
00:28:57,360 --> 00:29:01,280
borrow it. 
Yeah, Full disclosure, I have an

519
00:29:01,280 --> 00:29:03,200
actually literally bicycle. 
I know. 

520
00:29:04,080 --> 00:29:09,440
I I am still on the underrated 
camp in some ways, but I feel 

521
00:29:09,440 --> 00:29:12,120
like I would say the same 
reasons that you mentioned, but 

522
00:29:12,240 --> 00:29:15,080
like more extreme. 
Like in Sweden it's even colder,

523
00:29:15,360 --> 00:29:17,800
the distance is even longer in 
Stockholm it's like more 

524
00:29:17,800 --> 00:29:19,360
sprawling city than I. 
Get that? 

525
00:29:20,920 --> 00:29:24,200
There you go. 
OK, well, good to hear your 

526
00:29:24,200 --> 00:29:30,160
expert opinion on both people 
science and on cycling and, and,

527
00:29:30,240 --> 00:29:33,840
and having a proper bicycle. 
So this is so much fun. 

528
00:29:33,840 --> 00:29:36,160
It was. 
I really appreciate you coming 

529
00:29:36,160 --> 00:29:38,600
on. 
And yeah, it was really a blast 

530
00:29:38,600 --> 00:29:41,600
to talk about all these 
interesting topics together. 

531
00:29:41,800 --> 00:29:45,280
So thanks for all of the great 
work you're doing and sharing 

532
00:29:45,280 --> 00:29:48,520
with us. 
And yeah, thanks for stopping 

533
00:29:48,520 --> 00:29:49,000
by. 
Yeah. 

534
00:29:49,360 --> 00:29:53,600
Thanks for having me. 
Quick word on Nuance Behavior 

535
00:29:53,600 --> 00:29:57,000
where we help organizations 
build impactful digital products

536
00:29:57,000 --> 00:30:00,000
using behavioral design. 
We only take on a few clients at

537
00:30:00,000 --> 00:30:02,520
a time to ensure the highest 
level of quality for our 

538
00:30:02,520 --> 00:30:04,760
tailored evidence based 
solutions. 

539
00:30:05,640 --> 00:30:08,440
If you'd like to become one of 
our special projects, e-mail us 

540
00:30:08,480 --> 00:30:12,080
at hello@nuancebehavior.com or 
we could call directly on our 

541
00:30:12,080 --> 00:30:16,040
website, nuancebehavior.com. 
Happens to Murgatroid. 

542
00:30:21,880 --> 00:31:15,640
The. 
I know that obviously you're 

543
00:31:15,640 --> 00:31:19,560
from the Netherlands. 
You're a proud Dutch person, 

544
00:31:20,320 --> 00:31:21,280
actually. 
How do you say Dutch? 

545
00:31:21,520 --> 00:31:23,280
Dutch. 
How do you say Swede? 

546
00:31:24,200 --> 00:31:26,560
How do you say? 
Swedish person, Dutch, British. 

547
00:31:26,920 --> 00:31:28,720
Oh, I don't know. 
Dutchess. 

548
00:31:28,760 --> 00:31:33,280
Dutch Dutchess. 
Dutch Dutch men, but then I'm a 

549
00:31:33,280 --> 00:31:34,720
Dutch woman. 
That's also weird. 

550
00:31:34,720 --> 00:31:35,760
No, just say Dutch person.