Hello, hello, hello. Hello, this is Samuel and
welcome to another episode of Behavioral Design Podcast.
This episode is part of our ongoing miniseries where we get
a chance to speak with the colleagues we have at Nuance
Behavior. And here there are unique
perspectives on what it means to design for baby change in
practice. So Nuance is home of some
fantastic people and today's guest is no exception.
I'm very thrilled to welcome Rouge von Dynehoffen to the
podcast. With a background in baby change
and neuropsychology, Rouge specializes in applying
behavioral science to design human centric solutions for some
of society's most pressing challenges.
Her experience spans diverse sectors from cyber resilience
and digital literacy to mental health and habit formation,
always with the focus on making a positive impact.
We had a really fun conversation and discussion exploring some
interesting topics including the Behaviour Change Core framework,
how it's been applied in digital health and what it reveals about
designing effective digital products like what makes for
strong onboarding and why retention remains such an
ongoing challenge. We also talk about Big E versus
Little E engagement, where we're really diving to why real world
baby change matters so much and why we should maybe favorite a
bit more than just focusing on E nap engagement, but also how
wearables and other data can really make that easier.
And of course, I can resist putting rules on the spot with a
controversial question, this one specifically personal for
someone from the Netherlands. So All in all, you can expect a
fun and wide-ranging conversation, particularly
focused on the digital side of the signing for Baby Change.
So without further ado, let's dive into this episode with
Roosh Happens. To.
Murgatroyd, I'm very excited to say welcome Roos to the
Behavioral Design Podcast. Thanks for every.
Yay, this has been a long time coming.
If you like. We've had a lot of conversations
but none of them on a podcast before.
This is fun. Yeah, it's going to be fun.
I think this is going to be the first episode that I'm not going
to listen to myself. Yeah, of course.
Yeah, I know I can relate. And I feel like I've just come
to the point where I just had to learn to suffer through sometime
having listened to myself. And yeah, but yeah, it's it's up
to you if whether you want to listen to it, but you have no
choice but to participate. So, so, yeah, I think given that
this conversation is always built a little bit of the last
podcast we had with Susan Murphy.
So yeah, I just maybe to start, I'm just curious, what's your
impression of that? Episode.
Yeah, I thought it was super interesting episode.
And especially that she, I think at some point made a comment
about that it's just really difficult to stay engaged over a
longer period of time. It's sometimes relatively easier
to get people to start something new, but then to actually
maintain that over time, it's just really difficult.
So I noticed a few things that she said that kind of overlapped
with the work that we're doing and the findings that we have
found. Yeah, I agree.
And I think it's fun in some ways above because part of me
was thinking about you a little bit because we did some work I
think maybe a year ago or so where we actually work with kind
of messaging specifically and we're working with this kind of
large insurance provided. We wanted to basically help with
getting people to take a test to see their cardiovascular risk.
And I wanted to like find ways to develop a messaging strategy
to to ensure that that happened. And that was fun because we
applied in some ways a lot of those principles in that
example. And we were able to actually
increase with like 40 or 50% the people who did this.
That was like one of those like interventions that went really
well. That was that was fun.
But yeah, it reminded me of what we did there.
But also like taking that maybe even to a further degree with
specifically using some of these kind of AI elements that we
didn't I think use as much at that time.
Yeah, yeah. I especially find it super cool
that you have these tailoring variables, right, that she was
talking about. And then you can use this to see
how responsive a person is going to be to the intervention, but
then also it adapts to the behavior over time.
So what we did was this framework of, OK, we have these
different, maybe we even made like a behavioral persona group
or I'm not actually sure if we did that for that one.
But you can either segment your audience and then send those
tailored messages. But then when they don't respond
to it, OK, then you kind of, yeah, need to adapt.
But now with Jedi, that kind of does that adapting for you,
which is super cool that that's that that's possible.
And hope, yeah, also way more effective than doing everything
yourself. Yeah, Yeah, I think that's true.
And I think you like, that was also a good example where, you
know, we probably would have liked to do more advanced
things, but maybe used as a reporting from the front lines
of paper science. Like sometimes he was also
working with a large insurance company that has certain
internal tools that they prefer to use that are maybe, yeah, not
as AI driven, let's say. So yeah, you have to make the
rest of the contacts you're in, but but yeah, that's, that's
cool. So I guess to to build on this,
is there anything else you wanted to take away from that
conversation or like highlight as an important kind of
insights? Well, that AI needs to stay
grounded in behavioral science, right?
We're pioneering. I think that was a really nice
thing that that got stuck in my head.
When you sometimes get frustrated when something is
just so complex and you're trying to figure it out, but
then you can't really find the information or just take so long
to figure out and you have a lot of ups and downs.
Of course, they have a science is super cool and I get super
excited about it, but it can also give you a headache if you
don't know how to sell something.
And she already made this nice point about, well, we're at a
frontier science. It's very logical that there are
some questions that we don't have the answers to and it's
just really cool to work in an industry and contribute to
trying to figure out the answers for those questions that are
giving you a headache. So I think that was a really
nice one. And yeah, that's that's stuck in
my head. So that's that's a take away.
Yeah, yeah. I think that's definitely one of
those things like I was tempted to put as my alarm clock or
something as like in the morning being like you're a part of a
frontier science. We're doing frontier work, you
know. Yeah.
Well, to, to get to get that kind of motivational doing,
we're doing good stuff. We're doing important stuff
because. Yeah, definitely.
It's nice to hear that. And I agree as well.
And I guess to stay on track of engagement, we had this report
that we published together with everyone in Nuance, which was
called the Baby Change Score report, focusing on digital
health products and interventions, an app
specifically. And basically we introduced this
baby change score. And so yeah, maybe you want to,
you want to explain what what the heck, What is the baby
change score and how does it work?
Sure. Yeah.
So the baby change score like you said.
In summary, we published the first report which was
specifically about fitness apps, but the whole idea is that there
is just so much information about behavioral science and
behavioral design and behavior change techniques and behavioral
strategies. And they give all these terms.
And for someone who is maybe new to the field but does know like
that behavioral science is maybe valuable for their product, it's
sometimes quite hard. Like where do you even start?
And it's one thing to have all the theory, but then how do you
apply that to an actual product that you're working on, right?
For example, you may know that giving reward is important to do
as a strategy, but then people start putting like badges.
You can collect badges for all sorts of behavior that you have
in your app, like when we reviewed all these apps to
create a behavior change score in the end.
But one app that I reviewed then gave me a badge for uploading my
profile picture. Yeah, great, that's cool.
But what is that going to actually?
How is that going to contribute to me doing the actual behavior
that I want to change? Wait wait, so you're saying that
you didn't share the badge with your partner or what?
Like you didn't share with your woohoo?
Got my first badge. No, sadly I didn't.
But yeah, no, that's just not really related to the actual
behaviors. Just sometimes when you're done
looking at so many apps that are supposed to help you change your
behavior. And in this case, it wasn't
about fitness, so about physical activity.
You're supposed to or you have a goal.
That's why you even open it up in the 1st place.
You want to be more fit or go to the gym more often and then you
open these apps and then these apps are supposed to help you
get there, right? But it feels like nowadays there
are sometimes behavioral quote UN quote strategies just
sprinkled on top. I heard someone say this once.
I found that a really nice quote, but sprinkled on top
without much strategic thought because like a bench for my
profile picture. Really.
How is that going to help me get to the gym?
So yeah, the whole goal of the Behavior James Score report is
to, on one hand, score different apps.
And we just now looked at the most popular fitness apps and
see how they're doing in terms of behavioral science and
behavior change techniques. So what we did is go through
lots of behavioral research and models and frameworks and have
it forming technology, all that kind of stuff.
And we then categorized the different behavioral elements
that are important when you want to form a habit and engage
people, maintain that engagement.
And then we mapped that out across the whole user journey,
so from onboarding, activation to engagement and retention.
And we just started scoring like, how well are apps using
these behavioral techniques that are needed to, for example, have
a successful onboarding? Yeah.
And I think one big part of why we ended up creating this
Bayesian score is kind of alluding to what you were saying
in many ways in terms of that product teams are in a tricky
situation. Like it's not easy to develop
products and it's it's kind of tricky space where you know,
it's easy to look at competitors.
It is to get excited about what features to build and what
things to do. But it's easy also then to get
carried away and building things.
Even though if you have this motivation to like wanting to
use more behavioral driven features or product psychology
in your product or app, that's great.
That's like a great intention. But at the same time, many
product teams struggle there because as you mentioned, they
kind of like they don't really know how to actually always put
that in place. And so in many ways, we wanted
to put together something that kind of meet product teams where
they are, where they can kind of get some understanding of how
this all of these hundreds of concepts we cover in behavioral
science, how they relate to specifically their app at hand
or their product at hand. And how we then based on that
can move from first understanding like what doesn't
work? What are the things that maybe
are areas for improvements? And then kind of identifying
those things instead of just kind of getting into the trap
where I think a lot of product team struggle is like they just
hear about all these exciting people saying strategies like
you said, and they just want to like add them all, but they
don't really know which problem they're trying to solve for in
there in the rest way. Yeah, Yeah.
So basically when you boil it down to like just a few main
questions is actually OK, what things are important when it
comes to behavioral factors? What is important to say form
habit if you want to build a habit, what is important from
behavioral science to form a habit and how do you translate
that to a product? So which element should be
present in your habit forming product?
And then also we looked at what are the specific journey phases
that people go through, So like from onboarding and activation
to like engagement and retention and like what happens after a
failure state. So when people stop using the
app, like how do you get them back, all that sort of stuff.
And then for this report, we looked at fitness apps
specifically. So what we did is we per app, we
had at least two people reviewing it.
So we actually used the apps. We were very fit for summer.
We actually used these FS and then we started scoring them on
all these, all these elements as we were going through all phases
of uses. And yeah, by by scoring them, we
actually looked at, OK, these variables are necessary to have
say, an effective onboarding. And does this app include these
elements, yes or no or like to what extent?
Because it's also possible that it's like somewhat there, but
maybe not fully in the right way.
So we were able to say things like across all the apps,
certain certain behavioral elements were more or less
present or some things were used a lot other things were
completely overlooked. But also per app, we could say,
oh, you're really doing well on onboarding.
But we see that on retention, there do not really seem to be a
lot of elements or features that seem to be behaviorally informed
or that are actually reaching out to us when we drop off and
do not use this app anymore. So we could see an overall trend
in behavioral techniques in fitness apps in this case, and
we could zoom into one app specifically and see, hey,
you're doing well on this front, but hey, on on this other thing,
there is actually some room for improvement.
Yeah, it's funny. In terms of that we, we chose
fitness apps. I think it was in good timing
because we were looking at starting the first report with
some few different types of categories.
But I guess we we land on fitness apps there, the state of
preference was because we wanted to look at like hard to change
behavior that is like long lasting, like something related
to fitness in this case. But I guess maybe also there was
some kind of behind the scenes preferences for people to get
fit in the team. So I think we've kind of done
our best to kind of support each other.
It's kind of meta actually, because we were kind of our we,
we were like the whole team was each other's social commitment
buddy or something. We were really committed to
doing these workouts. It was, it was really funny.
I remember with with Jared, Jared and I were were working
out with the same map and it was like we had quite a high degree
of competitiveness in the app, like it was made to compare with
your friends and so on. So it was fun to see and compare
ourselves. And yeah, I want to reveal who
won here. That would be unfair to Jerry.
I'll, I'll save that for when, when Jerry comes on the podcast.
But yeah, obviously the report is free, unavailable on our
website and you can find it easily.
But he has to be interesting to maybe to talk about.
One interesting thing from the report would probably be that we
looked at these 4 stages of the usage journey and we mapped the
scoring of these apps against how well they performed across
from kind of on boarding all the way to activation, engagement
and retention. And we can see both individually
how these apps were performing at these stages, but also we can
see some trends how they were performing and so on.
Maybe you can share some some of those trends that we noticed
from those kind of yeah journey stages.
Yeah, sure. So overall, we saw that on
boarding perform the best of across all the journey stages.
And while retention towards the end of the journey got way less
attention and we were thinking about like why this would be the
case. And we thought, well, during
onboarding, there's a lot of data that you can actually quite
easily collect, right? You can see how long people
spend on doing a sign up or like going through an onboarding flow
or where they may drop off. Like you have quite a lot of
data points to collect there, but at the same time, first
impressions matter, right? So during the onboarding, I want
to impress people a little bit, but also you, you kind of need
to collect information from the user to be able to personalize
the journey later on. And because there is just way
more, yeah, data to actually measure.
It's just that's maybe the reason why the onboarding
performed best, whereas retention is so much can happen
in someone's life that makes them drop off or not come back
to an app. And it's just really hard to
figure out why that is the case. And what actually was that
barrier? Why did people drop off?
Was it, was it me, was it about the app or was it something in
their lives? I don't know, maybe they got a
kid, they don't have time anymore to go to the gym or
maybe they're just really busy with work.
Like there's so many unknowns there and therefore it's also
quite hard to then design for that.
So I think that might be the reason why on Boarding perform
so much better than retention. Yeah.
No, I think it's so true. There's so many kind of
incentives to do it as well in terms of just like everyone
wants to get people to sign up for that and pay for that or
whatever it is. And then like you say, I think
we obviously try to beat the drum often times about
experimentation as payable scientists.
And I think a lot of teams to their credit these days, like
there's a lot of experimentation and AB testing and various types
of testing that's being done. But obviously depending on your
amount of users you have and so on, it can be like kind of
tricky if you wanted to test some form of feature related to
your retention, because let's say you wanted to test your 60
day retention. Well, it will take at least 60
days to find the answer of whether it worked or not.
And that's in the case of a lot of users.
In case if you don't have enough users, it might take several
months to collect enough data on what worked.
Whereas if you do some form of thing to improve the onboarding
and 1st day of of usage, you get that data straight away.
And it's it's so much more data and immediate data.
So just easier as well to I think experiment.
So maybe one thing is like, I think it's also because of the
difference between in app behavior and outside of the app
behavior. Like and onboarding is really
like in the app, right? You need to fill in questions
that are in the app. You need to go through like, I
don't know, a tutorial of how the app works.
Maybe you need to fill in or experiment or try out a few of
the features. But then where when you're
actually using the app, and in this case it's still use fitness
apps as a as an example, like you need to actually either do a
homework at work, out at home, or you need to go to gym and
perform the workout, do the work.
And that is something that's maybe using outside of the app,
which is then harder to measure. And yeah, therefore you also
maybe see less features about it because it is so hard to
measure. Yeah, I think that leads us
perfectly into something I wanted to discussed related to
this as well, which is talking about Biggie and literally
engagement. So we had actually the author of
the paper that introduced this framework on the podcast before
I had the code Lewis. So if you want to listen to a
whole discussion about this, check out that podcast.
But basically it's this idea of understanding digital health
behaviors with these lens of defining them as either being
kind of biggie engagement or small engagement like you're
that's what you're talking about.
And I think it's really great to to get a chance to talk about
that. So I guess what we see often
times, as you mentioned, with kind of like this small E in
that behavior often times is that, as you say, it's easy to
measure. But then the big elephant in the
room, outside of the room, I don't know what to call it, but
like, Oh my God, it's. A big elephant.
It's an E. Yeah, that's true.
The Big E elephant, that's true, I tell you.
Well, mind blow, I didn't. That was not on purpose.
That was a subliminal or something.
But the Big E, the big elephant is that, as you say, there's so
much stuff happening in the real world.
And I think you probably have this experience as well where
you kind of work on product teams and everything kind of, it
seems to go great until you're like, well, what about what
people actually do in the real world?
And they're like. Don't know, don't know and.
Especially when we talk about digital health or digital
sustainability or like the stuff that we work with is basically
all of these kind of often times context and industries and
sector where people kind of have to do something in the real
world. It's not enough that they just
log into the app, for example. So, so yeah, that is the
challenge, how to better understand and think about this
kind of Big E behavior wraps in the real world.
So yeah, how do you think about? That yeah, Yeah, good question.
So the little ES is about in app behaviors, right?
And it's about the not only using the app, but also
interacting with the behavior change features or the
intervention, because only through the intervention, you
will actually help people to do the big, big E the the target
behavior that you actually care about.
South for fitness, you can in the app have a feature, maybe
have a commitment with a partner that you can can go for runs
together, but then actually going for the run together is
then the big E. So yeah, it is sometimes to be
hard to measure this Big E in the real world because of the
fact that it's outside of the app.
So it's hard to track that within the app.
Or you can ask people to log their run, but then the logging
the run becomes a behavior in itself.
So then people need to and go for the run and log the run
because otherwise it doesn't count.
So yeah, it is really difficult. But what you can do sometimes is
to look for metrics or proxy metrics, and a combination of
different and carefully thought out proxies can then approximate
the target behavior that you actually care about.
And I was thinking about this the other day when my partner's
dad had to go to a sleep lab to measure things for his sleep
right. Like when you kind of you're,
you're basically you're plugged into these machines and you
sleep overnight to measure and stuff.
Exactly. So you had to go there and all
this like stuff stuck on his head and his face and they don't
you sleep there and then they observe you and they do all
these measures and that's how you measure sleep and sleep
quality, right. But if you, if you build a
product that's actually helping people improve their sleep, it's
just not really feasible to invite everybody over to one of
those labs and just sleep there. It's not a hotel.
It's like it's just too expensive to to do that, right?
So instead what you can do is break it down into little things
that are related to sleep quality.
So you can look at things like how long did it take someone to
fall asleep? Or like how long did they sleep?
How often did they wake up? If you have a wearable or so you
can also say like how much movement was there or something.
And then together, even though they may not directly measure
the outcome goal or measure the quality of sleep, but like still
a combination of these things and ideally also a combination
of different types of data. So like behavioral data, but
also attitudinal data. Ask someone when they wake up,
how do you feel? Do you feel rested or not?
All those things combined and wrapped up in a nice little
package together, like they could still give you an
indication if your product is working or not.
So there's one way to go about the big elephant.
Is the. Big elephant, Yeah.
No, I think that's, that's such a good way of talking about it.
It really, you know, something that you highlight that these
days it is actually more possible than a lot of product
teams think when it comes to understanding and measuring Big
E as well and understanding kind of this, this Big E behaviors.
And I hate to do this, but we do have actually like a a episode
that all about this coming up quite soon as well, later
probably this year, early next year.
So as a teacher, if you're really interested in measuring
Big E and stuff, we have an expert on specifically this
topic, which kind of blew my mind in terms of how you can
kind of find ways to measure people.
So so yeah, that is beautiful. I think we covered so much
interesting stuff around this, and I think there's so much more
to probably say about Biggie and Small E, But again, I'll
recommend the episode with Heather Co Lewis on that.
And then again, if you want to learn about the Asian Square
port, I'm sure we'll link to it in the show notes, but also you
can find it on the new one's website to download.
And yeah, thanks for sharing your wisdom on those topics.
Cool. This has been so much fun and I
could talk many more hours to you about many of these
subjects, but I want to ask you a controversial question or a
conversial opinion basically because that's the theme of this
season is controversial opinions about AI.
But I kind of want to do a little bit tweaked version of
this because I know of course that you are Dutch and you're
very proud of that. And therefore, I want to kind of
like tweak this question to be a little more kind of close to
home for you, quite literally so.
Super curious. With bicycles, there's a lot of
bicycles in in Netherlands, but there's increasingly much, much
more electric bicycles. The conversion question here is
like, what do you think about electric bicycles?
Is it a good thing or like overrated or underrated
basically? Oh, that's a difficult one.
It depends. You can't say the offense that's
the that's a Babel scientist in you.
That's. OK, then Overrated.
OK, overrated. Yeah, because let me explain.
So I never had an electric bike myself and my partner did.
And I really love that because in Amsterdam, sometimes when it
gets windy, it gets super windy. And if you need to go to the
other side of the city, OK, for a Dutch person, just you need to
bike like 20 to 30 minutes. And other people who say a
tremble time of 20 to 30 minutes is nothing.
But for Dutch standards, that's long, especially when it's windy
and you need to go. The Netherlands is flat, but in
Amsterdam you have little bridges and everything you need
to cross. So you need to go up and down a
tiny bit with a lot of wind and then it's cold.
So then it's kind of nice to have an electric bike to not
arrive at your destination completely sweaty.
But actually we sold the electric bike not too long ago
because yeah, so my partner got actually tired of it because it
was like, I'm just not really moving so much anymore.
Because on this bike, honestly, it's not that you really need to
put in an effort. You just go quite fast without.
You just need to keep the pedals going.
Like it's not that you really put in an effort and you
couldn't really turn it off. So he decided to sell it and
just get a normal bike and actually move.
So I think they're overrated because they're also just
dangerous, like you go way too fast on a bike lane where also
normal people are biking. So a lot of accidents are
happening with those things as well.
Still, I enjoyed the time study headed so that I could sometimes
borrow it. Yeah, Full disclosure, I have an
actually literally bicycle. I know.
I I am still on the underrated camp in some ways, but I feel
like I would say the same reasons that you mentioned, but
like more extreme. Like in Sweden it's even colder,
the distance is even longer in Stockholm it's like more
sprawling city than I. Get that?
There you go. OK, well, good to hear your
expert opinion on both people science and on cycling and, and,
and having a proper bicycle. So this is so much fun.
It was. I really appreciate you coming
on. And yeah, it was really a blast
to talk about all these interesting topics together.
So thanks for all of the great work you're doing and sharing
with us. And yeah, thanks for stopping
by. Yeah.
Thanks for having me. Quick word on Nuance Behavior
where we help organizations build impactful digital products
using behavioral design. We only take on a few clients at
a time to ensure the highest level of quality for our
tailored evidence based solutions.
If you'd like to become one of our special projects, e-mail us
at hello@nuancebehavior.com or we could call directly on our
website, nuancebehavior.com. Happens to Murgatroid.
The. I know that obviously you're
from the Netherlands. You're a proud Dutch person,
actually. How do you say Dutch?
Dutch. How do you say Swede?
How do you say? Swedish person, Dutch, British.
Oh, I don't know. Dutchess.
Dutch Dutchess. Dutch Dutch men, but then I'm a
Dutch woman. That's also weird.
No, just say Dutch person.