Ekaterina Gamsriegler: Yeah,
two to three things that I would
really be looking into, like first,
trial to purchase rate is low.
Is it caused by the voluntary
or involuntary churn?
Because these two groups completely
different sets of solutions, right?
I see roughly.
Four cases when lowering the price.
Makes sense.
The first one is when you are
charging something that is way off,
the mental category into which you
are falling into the customer's head
Jacob: Hey, C Katrina,
thanks for joining us today.
Um, you, you recently gave a presentation
at, uh, growth Festival features a, a
growth festival conference, and one of
the core messages was growth is one.
I, I love that message because it
gives us all hope, but what do you
think the most important steps are
to, to figuring out your growth?
Where do, where do we start?
Ekaterina Gamsriegler: Hi, Jacob.
It's really a pleasure to be here,
and thank you for the invite.
Uh, yes, indeed.
Uh, I also think it's a pretty
nice, um, message and mantra,
um, to, to do growth by.
Uh, but, um.
I think, um, for me there are
maybe three, uh, core steps, which
I would say should get you there.
Um, two of them already should get
you there, like 80, 90% of the way.
Um, so first I would say
that you definitely need, um.
Um, reliable, somewhat reliable,
um, maybe not perfect map of your,
um, user behavior and, um, how
your users behave and, and convert.
So for me, growth does not
start with implementing an MMP.
It does start with implementing a
product analytics tool, which shows
you what your users are doing in
the app, um, after the install.
High level metrics like, um,
performance of your ads or how
users behave in the stores.
Uh, you can also, of course, um, like
find in other tools, uh, but overall
this picture of what happens from
a impression if you're running paid
ads or maybe from the store listing
visit and impression to conversion
does not have to be perf perfect.
Uh, but it has to be there.
And after this, I would say, um.
The, another critical step would
be, um, to break things down so we,
um, all have a set of KPIs in mind.
We might have north star metrics,
we might have, um, different ways of
setting the goals, uh, but usually
each of the metrics can be broken down
further into, um, different input.
And I think this is often the step that,
um, teams, um, and also, um, um, yeah,
companies, uh, might be missing out on.
So, um, if for example, you again
are running in a paid acquisition
and you see in a certain week
that your CPIs go up sometimes.
It's very easy to say, oh, CPIs go up,
uh, let's, um, um, fix the budgets,
let's, uh, decrease the spend.
Uh, instead of really looking deeper,
figuring out what's happening.
Because there are also like a lot of parts
that contribute, uh, or can contribute
to CPIs going down, starting with CPMs
and, uh, how heated the auction is, or
if there is any seasonality outside.
And ending with conversion from,
um, click to to download, which
has a lot to do with, um, how
your, um, store listings look like.
Are you using CPPs and
customer store listings or not?
And, um.
Yeah, overall, um, and click
through rates of course.
Um, very important part are your, um,
creative concepts, um, still performing.
Um, does, is the performance getting
worse over time and the ever sin
that actually contributes to that?
So, um, every metric can be broken
down and I think it's critical to
separate these inputs that you have
or don't have the control over.
Um.
And, uh, additionally, uh, you can also
add this extra level, like of complexity.
Uh, but it's actually not complexity.
It's very helpful.
Instead, it's that you also
break down by attributes.
So you see what's happening and then you
try to understand, um, if the strengths
are the same and like hold across
platforms, geos, channels, et cetera.
Right.
Um, so, um, yeah, this
would be my step number two.
Um, break things down.
Um, see, um, like across, um, different
attributes, what's, uh, where exactly
in which segment, um, the problem is.
And, um.
This is where I would say 80% of success
already lies because the last, um.
Step here in, I don't know, my mental,
um, like mental model is to, um, think of
the solutions and prioritize those after.
So once you see which of the inputs
of on for the KPIs is the problem and
which of the segments or channels, um,
or like attributes is the problem then?
Um.
You can find the solutions.
Uh, personally, I've never seen
a team or a company where there
would not be enough ideas for
solutions, to be completely honest.
So I think here, prioritization, like
really ruthless prioritization, keeping
the focus and saying no to things
becomes a very, very critical sin.
And, um.
Uh, I, regardless of the
company size, it doesn't matter.
I think like if you are a team of
20 or if you are a team of, um,
200, uh, or 2000, there is never
enough resources, um, to test this.
Um, I think everybody can try to
create, um, ticket for, for a data
analysis team of the product team and
see what happens, um, and how, how
soon it gets prioritized and done.
So my point here is, um, there
are a lot of things to fix.
There are a lot of ideas and solutions,
but uh, it's really a skill to
learn and train of, like focusing on
the two, three core problems, each
of which has two to three tactics
with maybe the highest confidence
that can move the needle right now.
So, um, yeah, it's about knowing what
to fix first, having a high certainty,
um, somewhat high certainty in the, uh,
solutions and keeping the focus on those.
Jacob: Yeah.
Um, so let's unpack some of that.
'cause I think we
Ekaterina Gamsriegler: Yeah.
Jacob: uh, we can kind of build
a whole career, a whole growth
team off everything in there.
So that, that, that was awesome.
So starting off, to understand
what's going on, um, in terms
of product analytics and I
think that's a great take of.
Uh, product analytics tool is probably
more important than your MMP to start,
uh, especially in this modern mobile
marketing world where MMPs are, are,
uh, quickly decreasing in value,
uh, and, and, and the value offered.
And so like understanding, okay,
what are people actually doing?
Uh, and then how do those
feed into your, your metrics?
And I love the idea of inputs.
To those metrics.
I, I mean, I'm curious your take
here, I think a lot of times.
We, um, less sophisticated teams
focus on these metrics and say,
okay, how do we move these metrics?
Or really, you need to be focusing
on the inputs to these metrics.
How do we move these, uh, um,
know, uh, these inputs that
actually influence the metrics?
And, and first you have to understand
what are the inputs, what influence this
metric to actually, to move this metric?
We're focusing on that metric at the end
is usually always, um, the wrong path.
What, um, maybe, maybe like we, we
think about, we, we give some specific
examples here of, of some like a core
metric like, you know, CPI, uh, uh,
cost per install or cost per trial
scarf, you know, subscription apps.
What do you think are, are common inputs
to that metric that are maybe missed?
Or, or just like, what, what do,
how do you break that down for you
or your team to actually focus on?
Ekaterina Gamsriegler: I would say
the most common, uh, framework or like
approach that I use, um, for that is to
build, um, a hierarchy of metrics, or
it's often called three, um, of metrics.
I think there are, like, of course, the
best practices there and mistakes to
avoid, but, um, most of the time you
can easily get it right if you just
put a bit of effort and thinking into,
um, figuring out what, uh, contributes,
um, to a certain, uh, KPI that you
are working on, um, on a daily basis.
But let's say, um, if I think about, uh,
the trial optin rate, uh, maybe, um, my
first steps, uh, towards like figuring
out what the problems can be, would be,
um, trying to answer the two questions.
The first one is are use enough
users seeing my paywall at all?
And the second one would be, um, like.
Users converting after they see it.
So I think these are the two,
um, important parts here.
Like first, uh, understanding your, uh,
measuring your payable impression rate.
And second, uh, yeah, measuring
the conversion rate from,
uh, from this pay, right?
Um, and, uh, here the solutions.
Um, once you, um, deciphered this
a little bit and got some insight
into it, the solutions will
differ a lot because if you are.
Payable impression rate from like the
first app open, uh, to payable, um,
impression is that 50%, uh, let's say
then you can very easily increase, um,
already probably the absolute number
of your, uh, trial optins or purchases
just by optimizing your onboarding.
So here.
Uh, the solutions would be around, um,
measuring the drop offs, uh, for every
screen in the onboarding that you have.
Like starting with, uh, first open,
open, open and thinking, okay, uh,
where do users drop off the most?
Um, is there a solution for that?
Maybe they don't wanna sign up.
Maybe you sign up.
Options are not convenient
in, uh, convenient enough.
Uh, maybe they're buggy.
Uh, or let's say if, um, we are thinking
about the, um, second, uh, common problem
with enough users are aware of your paid
offer, but nobody's still converting.
So this is a little bit
harder to figure out.
Uh, but definitely also possible.
Like in my head, there are the
usual suspects would be to check
if the value proposition is clear
and if it's compelling at all.
So like if you offer, uh, makes sense, uh,
to the user, then I would check that the
design and the pay will overall looks, um.
Trustworthy signals quality.
Um, I think quality plays a big role,
um, in here in terms of if user systems
something that is very poorly executed
in terms of design, I think this
already turns a lot of people off.
And, uh, most importantly, um, how much
does the price, uh, align with the value
that your users expect or perceive so far?
Um, and here again, you can
think of so many different
solutions to address each of this.
Uh, so for me, um, it was,
um, at some point I figured I
need a structure for this too.
Like I'm personally a big
fan of structuring things.
So if you have seen my slides,
you probably noticed there was
one was the paywall anatomy.
Because once you start thinking about
it, like of all the best practices
you come across, uh, of all the things
you, you tried, you've tried, uh, then
you can say how much you can actually.
How many things you can play around
with that all play into timing,
call to action messaging, uh, layout
structure, uh, like offers, um,
tru trust queues and all of that.
So there is really like a
lot to figure out there.
Jacob: And I think, you know, thinking
about that, um, that paywall impression
rate, know, sometimes I, I'm curious, your
take sometimes kind of a mixed bag where.
Getting, uh, a higher paywall impression
rate by maybe, um, removing onboarding
screen doesn't always work in terms
of like increasing your trial start
rate, where sometimes qualifying
those users more and sometimes, um.
There's an acceptable level of drop off of
users going through your onboarding funnel
and only the most qualified reach your,
your, uh, uh, paywall to actually see it.
So how do you kind of think about
that kind of, that, that continuum
of, um, maybe longer onboardings,
users, further building intent, but
actually a smaller percentage are
actually a paywall impression versus
having everybody see the paywall, but
maybe it's lower conversion rates.
Is there a right answer?
How do you tackle that?
Ekaterina Gamsriegler: I don't
know if there is a right answer.
Like, um, I, uh, definitely
know what you're talking about.
I think, uh, it's, um, always about
the balance, maybe even in this sense,
uh, mixing your, but I mean, um.
I usually rely on two things.
So this is the data that I see, and
these are, would be like the insights
that I'm, uh, getting from users, right?
And only by combining, uh, both I
believe you can come up with, uh,
the good solutions that are more
likely to work out than not, right?
So like in this example, um, I think
it's definitely not about the number
of, um, screens in your onboard
and like it can be, uh, but um.
Not necessarily.
And that's why I think here again, for
me, the first step would be to look at
the dropoffs on each of the screens.
Because if there are, if you there are
definitely causing it, uh, then, uh,
like high dropoffs, um, in between,
then you definitely, like, I would say
it's your job to figure out why and
figure out if this, uh, can be fixed.
I think what's completely
normal is to see.
Like somewhat high drop offs, um, on
the first, um, screen, uh, especially
if you are, uh, running like web, uh,
web onboardings and using web funnels.
Um, I think another typical, uh,
drop off point is the signup screen.
And here the key is to make sure
that you're offering like the.
Um, options for the, um, sign
in, uh, for your users and
that they work, um, smoothly.
And the third one is of
course, like the paywall.
Like everything is between, I would say
rarely causes, um, that big of a drop off.
But, um, this screens that I mentioned,
I think here it's really critical to
figure, uh, to, to do this more complex
step that I mentioned before and to,
um, break it down by attributes to see,
um, is the drop off on the signup screen
coming from Android devices or io.
Devices, for example, if it's, um,
coming from end end devices, then,
uh, what can be the reason behind it?
Maybe it's because we are not
offering, uh, the, um, the Google,
um, like signups through Google,
um, like random example, right?
But, um, this can differ.
They can be boxed on different platforms.
Uh, they can be different user intent
coming from different channels.
So some might have higher intent
and will be willing to go through
a hundred screens and sign up.
In the end, some will not.
So I think here it's really
about, um, figuring out like
what's exactly dragging it down.
And, um, another um, thing here is.
Uh, the insights from users, right?
Uh, because we can see the metrics, we
can break it down, see the drop offs, but
only users can tell us why, uh, the, um,
like not converting, for example, like if
we're talking about the low tropin rate.
And, um, yeah, I think here the
best solutions you can figure
out through conversations with
them to understand like, um.
This is, uh, I think this is
way more nuanced than just drop
offs in onboarding screens.
And you really need to figure out
like what exactly is the problem?
Um, what is off Putin, uh, what are the
barriers, um, why it's not happening.
I.
Jacob: Yeah.
I think the, your point on segmenting
by attributes, looking at drop off
rates, not as a whole, but by kind of
different attributes is so important.
I think that.
It's such a basic thing that I think
people don't do, uh, a a lot and, and
you, you learn so much there where
it's usually when you're looking
at averages for everything, like
averages are always wrong, right?
Uh, uh, they're, they're just
inherently, that's how they are.
And so when you actually break out.
Different, uh, uh, kinda attributes,
user types, um, what's, you
know, what's someone's goal?
What are the answer for
that during onboarding?
I, I think, you know, devices
is the obvious starting point,
but there's so much more, right?
Um, when I, I, um, I usually advise not
to do, but I'm curious, your take is,
For, for more complex apps,
I think it makes sense.
But do you, how do you think
about like splitting and branching
different onboarding flows based on
questions where you see, okay, one,
um, one user type is dropping off.
Uh, do we, um, try to fix the,
uh, um, whole kind of onboarding
flow for that user type?
Do we create a separate branch,
you know, for that user type to
try to improve conversion rates?
How do you, how do you
kind of prioritize that?
Ekaterina Gamsriegler: Yeah,
that's also a good question.
I, um, can't say that I'm a big fan
of aian complexity overall, so I.
Probably would agree that it makes sense
in Indeed for, um, bigger apps that, um,
already feel like they might be hearing
diminishing returns in other areas.
And then like only, um, this, uh, super
solid personalization is the solution.
Um, personally, um, I think.
I have not, um, I have not
really seen huge successes
with personalization myself.
Um, I've done a lot of experiments around
personalizing the onboarding a little
bit and personalizing the paywalls.
Um.
But when it comes to paywalls, I did not,
uh, usually see it having a solid impact.
Maybe it was a two to 3% uplift in
conversion rates, but I also think
like this is one of the things
where the idea might be right,
but the execution might be wrong.
So maybe my whole anecdotal evidence
is pointing to the fact that, uh,
the solution was just executed wrong.
Um, but when it comes to
personalizing, they onboarding.
I think what definitely works is this
idea of, um, giving the users reassurance
along the way that they're in the right
place, and most importantly, that you
also understand your users really well.
So, um, I can see the point
of, um, having this, um.
Empathy based, uh, reassurance based
on boardings, where after every few
questions you show them, we got you.
Uh, we got you.
We're the best app for this problem.
Uh, we have your back.
So, uh, I definitely see this working,
but to be honest with you, this is
as far as my experiments was, uh,
personalized, onboardings have gone.
So, um.
Apart from this, uh, like when it comes
to really adding complexity and then,
uh, personalizing way bigger journey
and especially everything like, um,
activation and engagement wise for,
uh, different segments in the products.
Like this is something I have not
really invested effort in yet.
Jacob: Yeah, I, I think that makes sense.
I, I've, I've seen similar, where
I've seen simple personalization work.
I think when you try to get too complex.
You, you, you're kind of guessing
you're not, uh, it's not kind of
informed hypotheses and it is too
easy to get wrong about what the
user actually wants or is feeling.
And I've seen like you're saying,
um, simple personalization based on
kind of the question they answered
showing a screen that references
that question, reassures them.
And it's either simple, you know,
copy personalization or adding an
image that references, you know, what
they answered for what they're, look
what goals they're looking to solve.
Um, but yeah, I, I, I've seen
the same, and I think a hundred
percent agree where simplicity is
usually always the right answer.
Keep it simple.
And, and I think the, what a lot
of people miss is that once you
start having different branches and
different flows, all of a sudden
decreasing your overall impact, right?
Where when you make an optimization
that only touches 20% of your new users.
impact is always lower.
Where if you can keep everyone going
through the same flow, have an experiment,
win that, that reaches everybody,
your your impact will be higher.
going back
Ekaterina Gamsriegler: Yeah, for sure.
This exposure.
Yeah, the exposure makes a lot of sense.
And yeah, I believe, um, for really
like huge apps, I can imagine, uh, if
especially the jobs to be done in the app
can be um, can vary significantly and you
can see that, okay, like by asking this
and these questions, we can say that,
okay, this users definitely fall into
the segment and they need this job done.
I believe it does make sense.
Maybe in most apps I've seen, uh,
this like jobs, um, are not so
clear cut and sometimes use users
also transition between them.
So I would really, um, yeah, I think
you need to have like a really high
degree of certainty that, um, because
it might be very easy to do wrong.
Even this, um, simple screens that
we put in between, uh, to confirm
that you are in the right place.
Even this is super easy to do wrong
because, um, I think there are a
lot of apps these days that, um,
use this tactic but mostly like have
them as some kind of check boxes,
uh, to say, ah, you wanna lose, um,
x um, kilos, uh, by a certain time.
Great.
Uh, we are right here for you, which
probably does not add that much value.
So I think like even getting this
personalization right and uh, delivering
value on the screens, um, is also
super, super hard to nail to do, right?
I.
Jacob: Yeah.
Yeah.
So you have to test it, have to
actually measure it for yourself.
You know, you can get ideas from
other people and copy other people,
but don't just, uh, uh, assume
that it's, it'll, it'll work right
out of the box for you as well.
Um, yeah, it makes a kind of sense.
So we talked a lot about onboarding.
We touched a little on paywalls earlier,
and I, I think we talked about, um,
you gave some tactics for, if you
have a low trial start rate and so.
Really, there's usually, for
your paywall, there's two areas.
You either have a low trial start
rate or low trial conversion rate.
Um, which is good I guess because
it, we, we know quickly which
one of those areas we're in.
Hopefully we're not in both.
Uh, uh, but it also, there's a lot of
different possible ways to fix this.
So maybe we go back to the low
trial start rate for, for a second.
And, um, do you have any examples
you can share there of, of kind of
how you've approached this or what
you've seen and then what you've done?
Ekaterina Gamsriegler: Um, yeah, I
would say, uh, low trial optin rate.
I'm just, uh, really trying to,
uh, figure out usually, um, like
the data is clear, but then by
understanding, uh, like users on the.
Um, why they're not starts in the trial?
Um, think again, only the users can
give you the answer about the why and
maybe, um, one of the examples would
be, back in my days at mimo, we were
regularly doing user research and
sometimes we would do user research
covering specifically monetization.
And, um, it was quite interesting
that among many, many other reasons.
Like, like answers to the question
of why don't you start the trial?
Uh, one was, um, becoming quite
a pattern and it was that, uh.
Uh, by going through our onboarding,
after downloading the app and
everything, um, users would still
believe that it's some kind of a
game that it's maybe a game for kids.
Uh, so, um, our value proposition
of, um, that we were very prominent,
uh, with on the paywall about
becoming a professional developer.
Was, uh, not really mentioned ever since
they've seen so far, like, uh, star
with, uh, the first app screen, uh,
with our mascot and ending like with
the, um, with the, uh, signup screen,
uh, or like, um, the, the onboarding.
So, um, clearly what was working
for us really well for activation
and retention, um, and user
engagement was not working that well.
Um, when it comes to.
Show and, and signaling quality depths,
um, because everything looks so easy.
Um, really cool, uh, really
awesome and friendly.
Um, but this, uh, user friendliness yeah,
was not really signaling the depths.
Um.
Again here, the solutions can be multiple.
Um, I'm not gonna say that doing a
rebranding is the solution to increasing
the revenue and your trial optin rate.
Uh, but sometimes even this can be
right, like, um, changing the colors, uh,
changing how you talk, um, making things
look a little bit more serious deep.
Um, if you, if your product is
about that, even this can already,
um, signal, um, a higher, um.
Uh, yeah.
Can help, um, increase the quality that
and the value that the users perceive.
And this can increase your
trial optin rates as well.
Jacob: And so that's, it's kind
of that, uh, expectation mismatch.
You're, you're
Ekaterina Gamsriegler: Yeah.
Jacob: thing during the onboarding flow.
You're, you're a victim
of your own success.
You made it too fun and too easy
and onboarding, and then when you
get to the paywall, people didn't
understand what they were getting to.
And so you, did you learn that through
user research or through testing?
Ekaterina Gamsriegler: Yeah, a lot
of users were, uh, simply not sure
that, uh, the, um, that this is, uh,
the right, uh, tool for their job
and the job we've been advertising.
Um, so a lot of people were coming
through our ads, uh, was become a
professional developer, and then, um.
If we would be probably advertising
very heavily for the audience, um,
of hobbies that would just love to
try out what programming is, I think
this would have been a perfect match.
Uh, but in this case,
there is another caveat.
We probably would not be able to charge
what we've been charging for the daily
subscription from this audience because
there intent and their job is, um,
like, might not be worth that much.
Um, so I would say, yeah, like,
uh, we, um, in the user research,
it was regularly popping up that.
It looks like a cool, um, a cool app for
my kids, uh, or, um, this is very fun
and very easy and they really love it.
Uh, but is there really enough content
in it, uh, to, for, for upgrading?
Is there enough content for
a year or maybe I'll run out
of it after a few months?
Uh, so clearly like some,
um, value, uh, depth, sorry,
some, uh.
Jacob: I like the mismatch or a value.
Ekaterina Gamsriegler: Like depths
to showcase value was missing.
Yeah.
Jacob: yeah, yeah.
That, yeah, that makes sense.
I think it's.
You can sometimes get at it by just
putting a bunch of screenshots of
your ads, of your onboarding flow,
of your paywalls all beside each
other and, and analyzing themselves.
But usually you need some feedback, some
review, something to kind of trigger
that thought to just to kick that off.
Um, yeah.
Okay.
So let's go to, um, low
trial conversion rate.
What are the, um, what are the
common levers you see here?
What are the common causes and then
how do you, go about tackling those?
Ekaterina Gamsriegler: Um,
probably no big surprise here.
I also break it down.
So I think there are
two ways of, uh, like.
Yeah, two to three things that I would
really be looking into, like first,
um, trial to purchase rate is low.
Is it caused by the voluntary
or involuntary churn?
Because again, um, these two
groups, uh, different, completely
different sets of solutions, right?
Um, in my experience.
Involuntary churn, I can contribute,
um, to churn overall, but, um,
it's very rarely, um, a big lever.
Um, I personally like, um, I think
I have a, a lot of solutions for
fixing that, but I, but I never
really saw it have a huge impact.
Um, I think the, um, what's important
here to understand would be your target
audience and if it's maybe skewing
towards demographics or countries
where, um, the purchase power is lower.
So, um, then maybe involuntary
return can be a much bigger lever,
um, and, um, like, uh, to pool.
Uh, but, um, typically let's say, uh,
most people cancel because they want to.
And, uh, in this case, I would really also
look into when exactly this is happening.
And then do, do the user research to
figure out why, just to double check
your, um, assumptions and to validate
them because, um, what I typically see
is that if users cancel immediately,
like within, uh, from day zero to, I
don't know, day seven or 14, depending
on like, um, how long your trial is or
three, um, then it's usually because they
don't want, uh, to enable auto-renew.
They wanna feel in control.
They don't like, uh, being charged
and, uh, they wanna decide themselves
when exactly to, uh, auto renew or not.
So I think this is like the main
reason behind, um, cancellations
that happened within the first
day, after opting in for the trial.
Um, another thing is, um,
that they can cancel right?
When they receive the trial expiration
reminders, and here I believe the problem
is very deep and usually hard to fix,
so it means that they did not get the
value they expected during the trial.
A very easy, um, fix here is if your
trial is way too short and, uh, users
did not manage to get to the aha moment
yet, or, um, and that's why they cancel.
And so here you just can ex
offer longer trials, right?
Um, again, a harder problem is when
your trial period is long enough and yet
users don't get the value they expect
and that you promised on the paywall.
So this is bad.
This is a lot about, um, activation
and, um, especially, um, yeah, like
activation and short term retention.
And, um, another, uh, type of churn,
which is interesting I think and
really worth digging into, is when
users actually upgrade from the
trial, but then cancel immediately.
So this is, um, the category which,
um, might be skeptical about whether
there is enough, um, content.
In the app if, uh, there is enough value
for the whole year or the whole months.
Um, again, might be those who don't
wanna like be charged through currently.
And here there is another
maybe like breakdown I would
also think about, which is.
Are those, uh, other users that believe
that they're like really optimistic
and they will get their job done
within a year, and they simply will
not need your product after that.
So this will be a good, happy churn nurse,
which is okay, because this is, uh, for
whom the product is the perfect match
or maybe, um, you are just simply, um,
not communicating, um, the value enough.
Um, uh, and uh, or there is a
mismatch and they believe okay.
Simply, um, I'll be done was through,
um, this whole send in a few months
and I don't need this anymore.
Um, maybe they don't believe in
your approach, um, like in how you
structure workouts or how you teach.
And, um, yeah.
So this is also much
harder to fix in this case.
Jacob: And there's, you have a bad
product that people don't like, that's,
that's when it's harder to fix, right?
Where, where that's oftentimes if, if,
uh, uh, the product changes are much
harder than paywall optimizations tweaks.
And so, you know, the, there's
the common tactic of the trial
timeline or trial reminder.
Um, and that helps.
help prevent some of those
immediate cancellations, right?
Where that's where people are worried
about getting charged and forgetting.
So I think that's a good tactic.
Uh, I think, I think I remember you
suggested that, um, then activation
is a little more complicated, right?
I think I've seen that.
Um,
a good, a good goal I think for apps
and, and it's maybe a bit simplistic,
but it's just keeping people.
In that app for that first session longer.
Can you extend it?
Can you keep people engaged?
Can you keep people doing new things?
Can you educate them, continue
them with more guidance?
Yeah.
What, what else have you seen work
there, uh, in terms of the activation
and kind of decreasing or increasing
those trial conversion rates?
Ekaterina Gamsriegler: That's, um,
yeah, that's a very good question.
Um, I definitely agree that
it's, um, very difficult.
Like a lot of the solutions will
revolve around, um, early, um, yeah,
activation and early retention.
Um, I mean, you are the
author of the Retention Block.
I think you know better.
Um, uh, I, I think you, you
know, the best, like how nuance,
nuance, um, the topic is and, um.
What I strongly believe in,
um, would be one thing is, um,
getting, uh, users to get, uh, to
experience the aha moment sooner.
So like decreasing the time to value.
Um, it can take various, uh, shapes and
forms and again, like to not go back
to it, but it can start with really.
Removing the, sometimes
removing the onboarding screens.
Nobody needs to, um, yeah, like you
said, maybe, um, making sure that like,
uh, making things even like a little
bit more complex, but making sure that
the users like get, um, the value, um,
out of the, uh, out of the product.
And I don't know if longer sessions.
Would be the metric I would
necessarily optimize for.
I think this is very hard to, to
again, nail down and do right.
Why?
Because sometimes by saying the
session should be X minutes.
Um.
We can set, um, like wrong goals
for ourselves and in reality make
everything just way more complex.
And the only reason users have
longer sessions is because,
um, something is hard to find.
Something is hard to understand, like
this is an extreme example, but I think.
Uh, here it would be about, um, let's,
like, how I would approach it is to look,
um, at data first, um, to see, um, what,
um, separates um, users who get to the
aha moment from those who do not, and
based on this, um, like make decisions,
do they follow a different path?
Uh, do, uh, do they have
a different journey?
Um, maybe they're coming from
a different, um, channel maybe,
um, their sessions are longer.
So this, for, for me, would be the,
maybe the first ways to validate it.
And then again, like move on to, um,
the, uh, qualitative insights from,
um, interviews to better understand
why, like, what is the ha moment?
How is it perceived?
Like I personally see that not always.
Um.
Signaling value is, um, very helpful.
Like you can, um, gamify things,
you can make things also like as
easy, um, as you possibly can.
It does not usually mean that, um,
people still like really experience
it, even though everybody loves like a
nice, smooth, um, delightful experience.
Jacob: Yeah.
Yeah.
You're a hundred percent right.
Like, we hope that someone's spending more
time in our app because they enjoy it.
But
Ekaterina Gamsriegler: Yeah.
Jacob: uh, it, it comes back, back to
what you said about the aha moment,
like we need to understand that what is
someone actually getting from our app?
What is the value?
Uh, and, and I think uh.
Said it many times, uh, it's user research
is so valuable here actually talking
to your users, like you're saying.
And I think a, a lot of times
in consumer marketing, we get
stuck in this quantitative
mindset of numbers and analysis.
We forget, actually
people using our product.
We can go talk to them.
We can go ask them what, what they think.
And that's so valuable.
So, yeah, I, I, I agree.
And, and it, it's not a simple thing.
Uh, but, but I think you're,
you're right, probably if you
don't understand your aha moment.
Um, that's probably the right
place, right place to start there.
Um.
Okay, cool.
This, this is super helpful and
we'll, um, I know you have a lot
of great examples, uh, and break
everything down in your presentation.
We'll link these in the show notes too,
uh, so people can go, go find that, uh,
and kind of see all those visuals there.
the last thing, um, we've,
we've got a few more minutes.
Last thing I wanna talk
about, uh, is pricing.
Um, how, like, how important is
it to think about your pricing?
Uh, how big a lever is pricing.
Yeah, we'll start there.
I have a few.
Ekaterina Gamsriegler: Mm-hmm.
Definitely a big lever.
Very powerful, massive.
So, um, however, um, I.
Most of the apps I've personally worked
with, and I would say been exposed
to, like throughout my career, usually
start monetizing very cautiously.
Uh, like so, um, they're more
likely to start with prices that
are, that are lower, um, than what
they can, uh, potentially charge.
And I don't see it as a bad sin.
I, I see it as a nice.
Way to start experimenting was like
how many users you can convert without,
um, pissing them off basically.
Because very early you usually have, um,
very small, very loyal, um, set of users,
uh, to whom you're providing value, right?
Uh, so, um, that's why I think overall,
um, in the industry it's much more common
to see the prices go up rather than, um.
Some subscription app lowering the prices.
Uh, we see it and we, uh, maybe to
some extent also get conditioned by
examples from, um, Netflix or Spotify,
which are, uh, content apps which
deliver very similar amount of value.
Throughout the user experience.
And, uh, this is a completely, um,
separate category from, um, for
example, self-improvement, um, apps,
so like personal improvement apps.
Um, for this, I would say, yeah, it's not
very common to lower the prices over time.
Uh, and, uh, however personally
I see it, um, have seen it a
few times in the past as a very,
uh, valuable and working tactic.
Um, it definitely hits your lifetime
value, like that's for sure.
You need to be prepared for this.
You need to, um, be ready for this trade
off and you need to find the balance
between the two, between your conversion
to purchase rate and getting enough paying
customers and, um, the lifetime value.
Uh, so, um, in my head I see roughly.
Uh, four um, cases when
lowering the price.
Makes sense.
Like the first one is when you are
charging something that is way off,
um, the mental category into which
you are falling into the customer's
head, because each of us has that.
Um, there are products
that are a nice to have.
There are products that are, would,
would, let's say painkillers, we
are willing to pay different prices
for, um, different, uh, for them.
Uh, a second reason would be that if
you never localized prices before, and
in some countries, they also simply
don't match, uh, the mentality and the
acceptable price ranges, um, or the
payment methods that are used there.
Um, also lower prices, uh, might sometimes
simply aligned with your company mission.
So if you are about making
something accessible or, um.
Um, maybe monetize and through
donations, like I think this is,
uh, also fair, um, to go this route.
Um.
And another, uh, another reason can be
is when again, there is an established
category in the consumer's mind, and
all your competitors have a certain
feature parity, um, and, uh, deliver
the experience that the users expect.
And you don't have that.
You simply know that if free
critical things are missing for that.
So, um, all of these
that you, um, maybe can.
Um, get signals for, um, during user
interviews or like, um, exploring the
market during the competition research,
um, together with very low conversion
rate, um, to trial and purchase would
signal to me that, um, decrease in
the price might be a good test to run.
So, um.
If your users are well aware of your
offer, um, and yet, uh, nobody's buying,
um, of course, like sometimes the way you
communicate value can be the same to fix.
So let's say, um, you tell.
A certain story about your product
in the onboarding and on the paywall.
So sometimes you can try to fix the story,
uh, before you actually fix the number.
Uh, but sometimes you just have to
fix the number and, um, then end up
with, um, having a solid stream of
paying customers from whom you can
get more and more data and insights
to figure out where to go next.
Jacob: Yeah, I, I think that's
great advice because everybody
just says, raise your prices.
You're not charging enough.
Raise your prices.
You'll make more money.
But, but in reality, it's,
it's not that simple.
And going back to your original
point, it's all always about breaking
down and segmenting and looking
things with different attributes.
Um.
Oftentimes there's different
countries that aren't converting
as well that may, might, not be
getting the same value as other
countries or if you're not localized.
And so, yeah, I think understanding
when actually lowering prices can be
a good tactic is, is super valuable.
Um.
Well, um, any, anything, anything
else you, you, you want to add?
I think, uh, uh, so informative, all
this was, was amazing, um, and really
appreciate, uh, uh, you joining.
Um, is, uh, uh, is there anything
you want to, um, promote or,
or have people go check out?
We can, we can link anything
into, uh, the show notes you want.
Ekaterina Gamsriegler: To promote.
But thank you so much for having me.
And, um, if, um, anyone wants to connect
on LinkedIn and, um, talk about it
or debate, um, I'll be very happy to.
Jacob: Cool.
Yeah.
We'll, we'll, um.
We'll share your, your LinkedIn,
uh, profile in the show notes.
You also run a, um, a
great course on Maven.
Uh, so we can link that and people can,
uh, put a notification for when your
next, doing your next cohort there,
Ekaterina Gamsriegler: Yeah.
Jacob: vouch for the quality of that.
and, uh,
Ekaterina Gamsriegler: Yeah.
Probably sometime early next year.
Yeah.
Jacob: Perfect.
Perfect.
Yeah.
People, people can
Ekaterina Gamsriegler: I.
Jacob: for that.
Um, alright, well this was amazing.
I really appreciate you joining
Katerina so much, so many insights
and wisdom and, and yeah, looking
forward to sharing with everybody.
Ekaterina Gamsriegler: Thank
you, Jacob for having me.
It was really a pleasure
to chat with you as always.
Jacob: All right, thanks.
Talk to you later.
Ekaterina Gamsriegler: Thanks.
Bye.
Thanks for listening.
Hope you enjoyed.
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