Marcus Burke: Meta is to this date
pretty bad at kind of diversifying their
and distributing spend more evenly.
Oftentimes it will pretty quickly
hone in on like a creative winner
saying, well, this is the very best
ad and I'm gonna spend 80% plus of my
budget in an ad set on that very ad.
But if meta always pushes all your
spend into one creative, then that
is pretty risky in the end because
if that creative stops working,
then performance is gonna die.
So it's always your job to kind of.
Push away from that a bit.
I've seen apps doing very, very
well, um, with not offering a
trial and optimizing for that.
I think it's a nice kind
of, challenge to think about
do we actually need the trial?
Do we need the trial on every product?
What kind of signal would it give us
if there was direct purchases involved?
Just because kind of a, someone buying
something on day one and actually giving
you money is a very, very good signal
for the algorithm and someone just
starting a trial isn't, and I think it
makes sense to at least challenge that
idea, that kind of everything always
needs to be kind of trial, um, on all
the products in seven days just because.
Jacob: Marcus, thanks for joining.
I'm super excited to
have you here in chat.
I, uh, I wanted to reach out
because I heard you gave an amazing
presentation at Growth Festival,
and I read the presentation.
I got a lot out of it, but
I wanted to dig into more.
I had so many follow up questions
after reading through everything, so
I, I'd love to dig in to each slide,
go over the questions with you.
Marcus Burke: Sure, yeah.
Great to be here.
And yeah, that event was amazing feature.
Did a great job here in Berlin.
And, uh, excited to go over the
slides with you to see what, uh,
additional context I can give there.
Jacob: Cool.
So yeah, one of the first things I was
seeing is that,, you, you were talking
about how blended CPA is irrelevant.
I, I was thought it was so interesting.
I think that we all have these
metrics that we rely off of, and,
and the, the changing ad, , world
sometimes means that, you know, these
metrics aren't relevant anymore.
And I'm not an AD acquisition,
uh, you know, growth acquisition
expert, and others have told me
that, you know, looking at blended.
RO roas return on ad spend is the
only true way to go to understand
what's actually going on.
So I was curious to understand the
difference in your mind, how is blended
CPA different from blended roas and,
and was curious to dig in more, uh,
to what you're talking about there?
Marcus Burke: Sure.
Um, so I mean, first of all, for
context, I, the, the presentation.
Was named something like Meta Ads AI
aligning your kind of full funnel.
So in the end, um, I looked into
kind of what are the levers.
I, as a full funnel growth marketer
can still be pulling while there's a
lot of stuff that is taking over by
ai, uh, sooner, sooner than later.
And so.
When I talk about blended CPAI talk about
like what's reported by meta in the end.
Um, I totally agree with everyone you talk
to, that you always want to look at your
overall blended business numbers because
if your ad platform tells you everything
looks fine, but you are losing money
on the back end, then nothing is fine.
Um, but when looking at your CPA
metrics or cost per trial, cost per
subscription, whatever you are feeding
that ad platform, I mostly see people
kind of running their creative tests and
optimizing, optimizing their campaigns
just based on the kind of cost per
result that is reported in Ads Manager.
But then meta is.
Such a vast platform, like you
can be running ads on something
like 20 plus placements.
Um, with each of these having totally
different user behavior, totally
different audiences behind them.
And therefore, if you see a 10 bucks cost
per trial, let's say, um, in your cost per
results, and that's from Instagram reels.
Then that's gonna be a very different
story compared to if it's 10 bucks
cost per trial coming from something
like a Facebook feed, just because
that's used by different audience.
So don't just look at what's kind of
coming from your campaign in total.
Look at the breakdowns.
Figure out kind of that traffic
composition to understand, am I driving
valuable traffic or just cheap traffic?
And I mean, oftentimes people ask me like,
what's a good cost per trial on meta?
And it's like, well, that.
Depends on like your down funnel, the
kind of your pricing, and in the end,
that traffic composition, like I've seen
apps scaling profitably with a hundred
dollars, uh, cost per trial, and I've seen
apps burning money at $10 cost per trial.
Um, so in the end, planet CPA
doesn't matter for that reason.
Jacob: Can you set, um, different
CPA goals per placement or are you
saying post the fact, do your analysis
and try to break down by placement?
Marcus Burke: So in the end, you
have a few tools available there
to kind of steer that composition.
One is creative.
In the end, creative equals targeting.
So depending on the ad you're using that
is gonna address a different audience
and a different kind of placement.
For example, if you run a static ad, it's
much more likely to run on Facebook feed.
If you run a 90 16 short form video, then.
More likely to run in Instagram
reels just by kind of where
that native media type belongs.
And, um, then the audience target
targeting comes, comes on top.
If you speak to like a pain point for
elderly people, then that's likely
to show up on Facebook just because
that's where that audience hangs out.
Um, while it's, it's if it's a
young, um, a younger issue, then.
That's usually gonna go on
Instagram and potentially reels.
Um, so based on the creative type,
you can already steer targeting.
And then what I do is basically group
my creatives by where I'm expecting
them to deliver and update that over
time as I actually see where it goes.
So if I find, well, I have this kind
of short form video, it always goes
on Instagram reels, then I have an.
Set that only contains these types of, um,
creatives while I have another ad set that
only contains the types of creatives that
go onto Facebook feed and like the more
mature high value, um, uh, placements.
And over time that grows quite complex.
Like at times like then I have like
not just one business as usual campaign
with one ad set doing all the work, but
I have like maybe five, 10 different
ad sets that all contain kind of
different messaging angles, media types
that I know in the end will result
in a different down funnel behavior.
Um, while each of them is just
targeted broadly, like they all
have the same setting, but the
creative does the targeting.
The other, um, tool that was
recently released actually, or that
recently was, uh, released in Ads
Manager UI at least is value Rules.
Um, and that's a way to actually
encode different, um, value
on an audience placement base
even when you're running broad.
So you can say, well.
My, uh, money should be spent on all
placements, and you decide where,
uh, based on the creative, but I'm
already encoding in the algorithm
that, for example, Instagram reels
is 30% less valuable because I
know it never performs as well.
Or you're saying, well, my 45 plus
age group male, um, should always be
bit up by 40% because I know that's
kind of my, um, most interesting ICP.
Jacob: Got it.
Yeah.
And so a lot of it is trying
to get more control of your ad
sets, your ads, your creatives.
Then Facebook gives you, Facebook
used to have these toggles and
controls and they've, you know, e
everything is driven by the algorithm
now, but there's still kind of ways
to force this first starting with.
Just different creative formats,
ratios, types of creative are
gonna get different placements.
We know inherently these placements go
to different audiences, not by Facebook,
but by just who uses these platforms.
Certain people look at reels, certain
people scroll the Facebook feed.
I haven't scroll the Facebook
feed in years, but supposedly,
you know, maybe older people do.
Uh uh.
Marcus Burke: Interesting
to try and learn from that.
Jacob: Yeah, yeah, yeah.
Uh, uh, every once in a while, but,
but I, I try, I try not to, but,
but, but yeah, so, so, so inherently
through those and, and so then, um,
you know, you probably know now.
Okay.
Which, but, but it may change by product.
Which of these placements, uh,
goes to certain demographic groups?
But then even if, for newer people
launching new ad campaigns, they
don't have to know this off the bat.
They can launch, see where these
placements go, and then create your ad
sets based on the groupings you see.
And if you can create those groupings,
then you can place those CPA goals
on the certain ad sets, uh, or ads.
Um.
Then understand, okay, now we don't
have to rely on this abundant CPA
or targeting different placements,
different audiences, different
demographics by their actual value to us.
Um, that, that makes a lot of sense.
How many people, like what, what
percentage of, uh, like advertisers
do you think are doing that today?
Marcus Burke: I mean, most of
the accounts that I'm looking
into aren't really doing it.
Um, some are splitting out
statics and videos at least.
Um, but I mostly don't see
that happening too much.
Um, I think it's driven by meta in the end
and kind of everyone always telling you,
consolidate, consolidate, consolidate.
The algorithm is gonna do the job.
And that's.
True to some extent, but that's why like
I always try to kind of work in kind
of synergy with the algorithm, kind of
finding like where do I need to nudge
it a bit into a direction, as you said,
like usually it doesn't make sense to
force anything and to like put in hard
targeting, um, or like trying only to
get like delivery on one placement.
Because that just gets really
expensive and is inefficient.
I mean, they have a good algorithm, they
know what they're doing, but then it
has this quirks and like over time you
will kind of figure them out and see
that, well, kind of my audience delivery
always kind of skews in one direction.
So you need to find kind of these
little nudges just to like make sure it
actually goes after a broader audience.
Because mostly everyone is after broad
and they think they're targeting broad,
but they're not really, um, like if.
Uh, you're using the same creative types
and always let meta decide where they go.
Then usually they go, um, after the
same audience over and over and over.
And your frequency might actually be quite
high despite targeting the whole world.
Um, just because the algorithm
interprets your signal.
That way
Jacob: That makes sense.
We, we guide the algorithm,
Marcus Burke: we guide it.
Yeah, we don't force it.
Jacob: We, we, yeah, we, we
give it a nudge here and there.
Uh, uh, that makes sense.
And so.
know, in your presentation you have the
slide of kind of, uh, you know, breakdown
of what placements are, are, uh, showing.
Where does, how do you get this
placement data out of Facebook?
Is it, uh, readily available?
Marcus Burke: Um, I mean, you can just,
you can directly an ads manager in the
UI break down any campaign by placement
age, gender, um, you can pull it from
ads reporting, which is where I usually
go, um, for any, like if I do an audit
or anything, because then like it's,
'cause I'm gonna be like one off for any
clients I'm advising or that I'm working
with on like, um, um, continuous basis.
Usually we would.
Set up, um, like through the
marketing API and something like
a Super Metrics into Looker.
And then we can over time always see,
well, how is our composition changing?
And we will quickly spot while we launch a
new creative that seems to be pushing much
older, much younger, into a new placement.
Um, and that's usually what you're kind
of, um, want to be aware of because
that means, well, something is most
likely gonna happen down funnel as well.
And we want to kind of figure out quickly.
Is it getting better?
Is it getting worse?
And then apply that in terms
of the goals we're setting.
Um, and yeah, kind of, you can
either set the goals as actual bid
or cost caps, which is something that
don't many people use these days.
I've seen some good success with
it before, but it's hard to manage
on app promotion campaigns at least
just because, um, like their, the
campaigns aren't so reactive to it.
Meta has little signal, so kind
of calculating proper conversion
rates got harder over the years.
So giving them a goal often
really, uh, cripples delivery.
Um, and then the campaign just
dies by adding kind of a cap.
But the other way, just in the end,
you can still set a goal of course,
for a campaign and optimize for that.
Even if you don't put it in, um, you can
just look at, well, what's my performance?
And is it on goal or below?
And then start scaling up or down.
And that's usually the most.
Um, kind of basic case here where you kind
of, you split out two different ad sets.
One aesthetic, one is video.
You see them going after
different audiences.
You try to figure out how they
convert down funnel and then optimize
for a different cost per event.
Target.
Jacob: That makes sense.
That makes sense.
If I can understand it, someone
else can understand it, that
actually, you know, knows how to
manage a, a Facebook campaign.
So I think that'll be
super helpful to people.
Um, and, and then you also have.
Uh, your age and gender data, um, do you
get that directly from the platform or do
you have to supplement that by, you know,
asking, you know, early in the onboarding
flow for an app, you know, age and gender,
and then tie that to conversion data.
Marcus Burke: Yeah, so usually I kind of
look at these two data sets separately,
where kind of I have my metadata.
I have my breakdowns and then I enrich
my product data with the very same
breakdowns where in onboarding we ask
for the same kind of age brackets, we
ask for gender, and that means then I
can look at trial to paid conversion.
For each of these segments, I can
look at how much are they inviting
other people, how of do they renew?
Kind of over time, you can get quite
sophisticated about understanding
each segment's, um, value to your
business and then infer that back
into meta by then saying, well.
I wanna, I know for example,
45 to 54 is very expensive
because they're older people.
Um, they're valuable to everyone
because they have high purchase power.
But I also know they're driving
a lot of value for my business.
So I want to find ways to make
meta spend more money on them.
Jacob: Got it.
Got it.
And so it's kind of this feedback loop
of understanding the conversion rate
from your product data, the onboarding
how old are you, where'd you come from?
Gender if you want.
And, and then going, Hey.
Actually, um, we can maybe, maybe we can
create a separate ad group or targeting
for these older audiences and try to bid
more now that we understand that these
audiences are actually more valuable,
uh, uh, to us and convert higher.
And so it's, it sounds like generally.
Starting a little simpler first with
these different placements, understanding
where these placements, these ad types
are going, understanding how those
convert, add some complexity there.
Layer on another level of complexity,
uh, with targeting, with, with ad sets,
ad groups, uh, um, with the kind of
demographic and age data, if you see that
there's trends there, or you see maybe
on the flip side that 18 to 24 never is
gonna pay you, so let's stop bidding on
them, which is I think, a common trend.
Marcus Burke: That's pretty common.
Yeah.
And yeah, in the end, like there's no,
like, I would say like none of these
segments, audiences is necessarily kind
of bad for your business or something
you shouldn't target, uh, from the start.
But then some are just gonna be
a lot closer to goal than others.
And then kind of focus on
what looks most interesting.
And I'm sure that for 18 to 24, for
example, your cost per event target
would have to be a whole lot lower just
because they don't have the money to
purchase a 50 bucks a year subscription.
Um, and yeah, kind of inform that back
into meta and like start going after the
most, um, interesting audiences first.
And then over time, really it, as
you said, it goes more complex.
Probably your asset structure grows a bit
as you're trying to figure out how to go
after all of these different subsegments.
And it's not just demographic,
I mean also onboarding.
You can ask for user goal.
Um, and then again based that,
uh, on your creative messaging.
And then you might have some ads
asking like, people to use the app
for like a career related reason,
which is usually very good conversion
driver because they're seeing
kind of the monetary value in it.
While some apps are more after, well, I'm
using this for like leisure or for like
free time, and then that usually means
conversion is gonna be lower just because.
It's just for fun and depending on
your app, of course, that can grow
just as complex with people using
it for all the different reasons.
Jacob: Yeah.
Yeah.
And so, yeah, two of the messages there.
First, marry the third
party and first party data.
Pull your Facebook data, pull your, your,
your first party data, put people actually
answering, giving the information.
Combine those together to understand
who you're actually targeting,
who's converting, who's not.
And then I think that like, uh,
uh, you know, idea about the
creative is the targeting, right?
I think that's another message you have
and, and some multiple levels, right?
The creative is the targeting
through, what ratio is it,
what type of creative is it?
But then also what's the actual
message on the creative 'cause That'll
inherently get shown to different people,
different people it'll resonate with.
And then you can understand how those,
um, creative messages convert based
on user goals or other questions
that you asked during onboarding.
And then, uh, figuring out how to layer
it all together is the, the tricky part.
Marcus Burke: That's
where it gets interesting.
Yeah.
And yeah, kind of that media type
format piece is something people tend
to forget, like they think of well.
When I call out women or men in my hook,
then it's gonna target women versus men.
But then, yeah, really if, if
you never make aesthetic, then
your likelihood of showing up on
Facebook feed is a whole lot lower.
So kind of the media type has a
big, big role in this just because
different content types are kind of
native to these different placements.
So really.
As you're kicking off a new count,
like always test very broadly, like
look into carousels, video carousels,
statics, uh, nine to 16, one to one.
Um, I'm just testing.
I'm, uh, preparing a test with playable
for, for a client of mine, which are
actually only work on Facebook feed.
From what I know, I never tried
them before with Facebook.
Um, so yeah, there again that if
that, um, ad is only gonna show up in
Facebook feed, then that could be super
interesting because that's where kind
of a high quality older audience lives.
And if you never made a playable before,
then you would never see kind of a
hundred percent Facebook feed distribution
or any ad, because even a static also
goes on Instagram feed and others.
F there's always gonna be kind of some
wastage somewhere where kind of due
to the broad targeting, your ad ends
up in places where you shouldn't be.
And then you can always kind of try
to optimize and figure out like, how,
how do I push it in the kind of best,
uh, the most optimal distribution.
Jacob: And so, you know, what I'm hearing
is to really kind of scale campaigns, you
need to figure out these efficiencies.
You need to understand where the
opportunities are to kind of get hit
your, hit your goals, and to be able
to scale there where you're, if you're
just hitting broad on everything.
You know, you're good at things are gonna
get expensive, uh, uh, faster, um, and
you're not gonna really understand why,
Marcus Burke: Yeah, pretty much like
in the end you are, you are, you're
gonna hit a ceiling quite quickly just
because meta, if you throw everything
into one campaign, one ad set, then meta
just optimizes for the kind of lowest.
Cost per event, and especially for
a subscription app that's usually
not related to business value.
You're driving cheap trials.
A cheap trial isn't worth
anything until it converts.
So the algorithm just shouldn't taking,
shouldn't be taking all the decisions.
You need to kind of infer that
additional knowledge around what's
happening after someone starts a.
Jacob: Yes.
And so passing the right signal, right
data, the right trials so meta can go
find more of those trials that you say
are important, not what it infers are, are
Marcus Burke: I mean that's, that's
then already kind of signal engineering.
You can of course, try to pass
it as an actual signal into the
algorithm so it gets smarter.
Even if you don't do that, then
you have that kind of knowledge.
So use that knowledge to build your
campaigns in a way that it spends on
the audiences that are most relevant.
That's kind of the two sides there.
Um, yeah, signal engineering also
very hot, hot term these days.
Um, did a webinar with Thomas
with Revenue CAD the other day.
Um, like the closer you can move
your, um, conversion signal to an
actual business outcome, the better.
Of course, meta gets at buying
the right audiences and not just
the cheapest ones in the end.
Um, but yeah, that's
definitely advanced territory.
Like smaller advertisers tend to
be starting on like a star trial
or maybe a subscription purchase if
they kind of have also like direct
purchases, um, on their paywall.
Um, and then over time maybe you move into
something like a qualified trial where
you figure out, well, let's kind of not
send signal for some of these people.
Or like only kind of infer
some additional data points.
Like, I don't know, OS is usually an
interesting one where like older, uh, like
newer devices and os is tend to convert
better, um, age, as we already said.
So you can get really smart over
time and there's like companies like.
Day 30 journey, um, those signal
engineering providers that help
you basically look across all of
your data points and like find out,
well, if we cut these 40%, how much
higher would ultra conversion be?
Um, and that definitely gets very
interesting as you scale, because.
As that AI is taking over more and more.
So really creative and signal are the
two main things you can be working on.
There is some work to be done with
account architecture as we just said,
but then other than that, it's not like
you're gonna set up 50 ad sets, some
targeting male and female in different age
groups as you used to back in the days.
Um, so definitely Signal is one of the
levers that you can still be pulling.
Jacob: Yep.
And so signal engineering, more
advanced strategy, you can manually.
Guide Facebook algorithm to, to
the valuable users through kind of
everything we just talked about.
Uh, love it.
Cool.
Let's, um, let's talk
about account architecture.
how do I architect an ad account?
Uh, so, so I think, um, there, there's.
I mean, some of, we kind of just
talked about, right, how to structure
your, your creatives, your ad set.
So we, we kind of got
into that a little bit.
Um, but I, I, I, I thought one of your
messages was, was really interesting.
I didn't fully understand
it, so I'd love to ask more.
You, you talk about stop over
consolidating, um, and, and
you show kind of that level of
consolidation versus conversion type.
targeting doesn't actually equal
broad reach and tailor your
architecture to your conversion event.
Um, can you tell me more about
what, what you mean there?
Marcus Burke: Yep.
Um, that's kind of the summary
bit of what we just talked about.
I would say, so in the end, like the.
Uh, if you are optimizing for a very
invaluable conversion event, something
that is very shallow, like, I don't
know, for example, you have to optimize
for just a complete registration.
Just because, I don't know, your
purchases are so expensive, um,
because you're like a premium product.
They happen infrequently.
Meaning, um, you would never collect
enough signal for the algorithm
to optimize for that event.
So you're moving up the funnel.
Um, optimizing for something
that is less valuable.
Um, but that means a lot
can happen after that event.
Like it doesn't have
any business value yet.
Meaning you need to guide the algorithm
based on what you know happens after.
So like, who are the audiences
who then are likely to convert to
their premium purchase on which
placements does that happen?
So due to that, you will need a lot
more ad sets to basically be able to
push into one direction or another.
Um, because your, the, the algorithm
knows very little about what makes your
business profitable in the end, because
you're optimizing for that cello event.
Um, while if you're moving closer and
closer to, to business value, then you
have a smarter algorithm, meaning you
can have more call it consolidation just
because it can actually see that well.
A cheap cost per registration from
Instagram reels isn't worth anything.
Um, because people don't
convert to that, um, uh, to that
subscription or to that purchase.
Meaning it will theoretically over
time start, um, deprioritizing that
placement because it sees it's not
driving an efficient kind of cost
per purchase for your business.
Um, but of course there is.
Like in the end, I, why I say like,
don't over consolidate is that whole
part that we discussed before, that
in the end different creatives go
onto different placements and meta is.
To this date pretty bad at
kind of diversifying their and
distributing spend more evenly.
Oftentimes it will pretty quickly
hone in on like a creative winner
saying, well, this is the very best
ad and I'm gonna spend 80% plus of my
budget in an ad set on that very ad.
And of course.
Everyone talks about kind of
creative diversity and like
creating an account that of course
is sustainable and free of risk.
But if meta always pushes all your
spend into one creative, then that
is pretty risky in the end because
if that creative stops working,
then performance is gonna die.
So it's always your job to kind of.
Push away from that a bit.
And that's why I kind of tend
to tell my clients, well, don't
put all your eggs in one basket.
Make sure we have a few ad sets with
that are different, um, delivering
to different kind of placement mix,
its audiences and if one dies, then
we're still good with the others.
And of course that architecture in the
end should then be driven by how much
does meta actually know about my business?
This is very, are they very smart?
Am I optimizing for value because
I'm kind of sending back my purchases
with values attached to them?
I maybe even set a raw as target,
um, something that is not super
common for subscription apps,
but for example, in gaming.
Um, there, of course you have to
worry much less about them buying
cheap traffic that's not converting.
While if your event is very upper
funnel, then you need to do the job
of structuring campaigns in a way
that they go after the audiences
that you know have highest value.
I hope that makes sense.
Jacob: really interesting.
Yeah.
Yeah, it does.
It does.
Um, and so for, for pretty much any
app with a seven day trial, like.
Until you get a lot of data and
a lot of understanding of kind
of what users are valuable, it's
kind of impossible to get to that
revenue, or real value metric, right?
Sending that back.
So you're always stuck with this
trial that's kind of incomplete.
And so that goes back to what we were
talking about before of trying to
guide the algorithm to the right users.
Do you.
Think it makes sense for more subscription
apps to test not having a trial or
test certain plans with no trial to get
some of some more revenue data earlier.
Marcus Burke: Um, I've seen apps doing
very, very well, um, with not offering
a trial and optimizing for that.
Um, but that was in the AI vertical
and I think kind of AI apps, like there
is a very high willingness to pay.
Like we had purchase rates beyond 10% even
without offering a trial, which is crazy.
Um, so.
In the end, I think it's a nice
kind of, um, challenge to think
about, like, do we actually.
Do we actually need the trial?
Do we need the trial on every product?
What kind of signal would it give us
if there was direct purchases involved?
Just because kind of a, someone buying
something on day one and actually giving
you money is a very, very good signal
for the algorithm and someone just
starting a trial isn't, and I think it
makes sense to at least challenge that
idea, that kind of everything always
needs to be kind of trial, um, on all
the products in seven days just because.
Yeah, you can't really optimize
for, for like, um, a, a purchase.
Then it's also outside the
kind of, uh, the attribution
window of seven days post click.
And also with a EM, which is
their, their fingerprinting.
In the end, the accuracy deteriorates
quite badly, um, over time just
because signal isn't available anymore.
So you need to optimize for
something that happens on day one.
Preferably in the first session.
And with a trial, as we said before, we
can enrich it a bit and try to figure out,
well, which are my most, uh, relevant and
high quality trials, and only send those.
Um, but of course it's never
gonna be a true purchase.
And having that in the mix somehow
can be very helpful, I think.
Um, yeah, and like it.
In the end, like a lot of headaches were
gone when you, when we just optimized
for, um, optimized for pure purchases,
it was clear that meta only receives kind
of who is, uh, in the end valuable to
us and, um, that made sure we're never
wasting any money on, uh, low quality
audiences, which was great to see.
Jacob: Yeah, it's interesting how
your business model as an app.
Uh, sometimes is if you optimize your,
your business, your subscription, your
product types, to giving the best signal
to ad networks so you can scale your app.
Like that's more important
than uh, uh, kind of like.
LTV because, uh, you know, it doesn't
matter if you can convert higher, someone
with a slightly higher LTV if you can't
get more people and grow your app.
It, it's kind of interesting that, you
know, your, your, your product design
is almost guided by, can you feed the
right signal to the ad networks to grow?
Okay.
Marcus Burke: Yeah.
Yeah.
And sometimes goals can
be quite inverse there.
Like I had this interesting case
recently where like, um, an app that
was optimizing for like direct purchases
happening, um, on the paywall because
they had a mix of like the free trial
product, um, which was on the yearly,
and then a few direct purchases on
like, I think weekly or monthly or so.
And we only optimized for the people that
were, um, converting directly without a
trial, which gave the algorithm a good
signal going after the right audiences.
What we actually did in product was
optimizing more and more for pushing
people into the yearly because they
had the highest LTV, but that meant
fewer and fewer signal for meta.
And so we ended up in a place where
it was like, well, if we just move
this a little bit more, then we're
not gonna have any signal anymore.
And then what do we optimize for?
So in the end, yeah, you
always have to consider like
one, what is kind of the best.
LTV or like best for like conversion in
the product, but then, then also what
kind of signal does it create or leave
for the app platform to optimize for?
And there are times where that's not
kind of aligned very well and you have
to kind of figure out that challenge.
Jacob: Yeah.
Yeah.
Where I think that's, There's different
levels, uh, of kind of complexity
in how you think about things.
Uh, I think we, we've kind of done
a good job of kind of progressively
going through, okay, well first
you've got some different ad sets
with some different placements,
they're gonna reach different people.
We layer in our kind of demographic
and age data, and then we can start
getting, uh, uh, much more nuanced in
terms of how do we actually design in our
product the right signals to send back
through the actual revenue, the value.
Sometimes that may be actually
through understanding.
Um, uh, different user goals or,
or more behavioral things and
passing back kind of the signal
engineering, we're talking about
passing back the right signals there.
Uh, uh, but it's kind of this
ever shifting game right.
Of, of figuring out, uh, and it's not
like you aren't gonna figure it out.
You're not gonna find the answer.
It's not gonna be done.
Uh, yeah.
Yes, yes.
Um, I, we, we touched upon it briefly.
Can you, um, tell me how
you're using value rules today?
Because I think that was a huge.
Uh, update by, by meta that
I think people, a lot of
people were excited about.
Marcus Burke: Yeah, I mean they were
available for evers through the API,
but you had to get whitelist as well.
So especially for smaller accounts,
usually they weren't available
because to get whitelisted you
needed an account manager and
only meta only gives that to you.
If you spend like, I dunno, like
300 K for like half a year in a row
and then they start talking to you.
Um, so in the end, they now
have that in the UI directly.
Um, they're even working on it and
adding more, which I think is cool to
see because in the end it goes a bit
against everything else they're doing.
I mean, they're trying, they've
been taking kind of control away
more and more and everything.
It's automated.
They leave the controls in for
now, but kind of the default is
always, everything is automatic.
Now they're adding value rules, which
gives back some control where I can
actually say, well, I want to kind of
scale certain placements over others or
assign different values to them, which
I think is cool to see that they're
not going kind of full Google ads.
UAC, you can't do anything anymore.
Um, in the end by now, you can
set value rules on um, os um, so
iOS, Android, um, desktop mobile.
You can set them on placement level.
You can set them on age,
on gender, on location.
So what that really allows you to
do is to consolidate while still
having control over distribution
within that consolidated set.
So, for example.
One way that I'm using them is,
um, in my country targeting, I
always build country clusters.
Usually it's us standalone, and then I
have like tier one, tier two, and I can
consolidate those much further or like
much better now on a gran more granular
level because in the end, even if I
put like 20 countries in a set, they're
likely not all gonna have kind of the
same down funnel conversion value to me.
But in the end, value rules.
Then allows me to say, well, I'm
gonna treat them all the same.
They're gonna get the same ads
and the same ad set just for kind
of the sake of being manageable.
And then in the value rules, I
tell the algorithm, well, a trial
from New Zealand is actually 20%
less valuable than from Australia.
And that's actually, I dunno, 30% more
valuable than a trial from the uk.
Um, so that I can basically
make sure I minimize the wastage
while kind of consolidating,
um, uh, different countries.
And same goes with age,
gender placement usually.
I tend to not set rules on placements
often because I feel like placement for
me is just placement drives audience,
like the conversion is lower or higher
because someone is from a certain
audience and sees that my app is a
relevant solution to their pain point
and has the money to spend on it.
Uh, it's not that they're an
Instagram reels user that inherently
makes them better or worse.
So therefore, I trend tend to set
the rules rather on demographics
than on certain placements.
While sometimes there is accounts
where you just see that, well,
somehow the algorithm just loves to
spend a whole lot of money on one
placement, and I don't want to be
that aggressive there, then maybe you
wanna set kind of a rule of saying,
well, let's bid this down by 10, 15%.
Other than that audience for me is
the most, um, audience demographics
are the most relevant ones.
And there what I start with is usually
just looking in my product data,
what are my trial to paid conversions
on age and gender, and then feed
that back into the algorithm so it
has a better understanding of who's
gonna convert, um, down the funnel.
And that's.
Usually kind of a quick unlock you
can have with just a little bit of
product data that most apps have
already gathered when, for example,
I come in to work with them.
Um, and that's to me kind of the
most basic, um, way to leverage
value rules for, for your account.
Jacob: Yeah, that makes sense.
That makes sense.
And so as with other tactics, see what's
happening on your own product first.
There aren't just broad rules that
are gonna work for every product.
Understand how your audiences differ, and
then go and slightly adjust those rules,
see what happens, adjust up and down based
on kind of performance, and continue to
kind of refine those as you, as you scale.
Um,
Marcus Burke: Pretty much, and you
can have a few by now, like when
they launched, you could have just
one set, which was like, well, I
want to have different value world
sets for different campaign types.
For example, if I'm running a web
quiz compared to an net promotion
campaign, then of course I need
different kind of multipliers there.
By now, you can create multiple sets.
Um, so kind of, you can make them,
you can become quite granular
here based on what you learn.
And in the end, meta gives you all
that demographic breakdown data.
You should have it in your,
um, app as well, uh, in your
app, uh, analytics as well.
And yeah, make use of that.
Oftentimes the answers
are kind of already there.
Jacob: Yeah.
Yeah.
Makes sense.
Makes sense.
That's great.
Um, let's, let's
transition into onboarding.
Uh, for
Marcus Burke: Let's.
Jacob: for your app.
Uh, you, you've got a bunch of tactics
to improve your onboarding flow.
You've got social proof describing the
problem before, after commitment prompts.
I think a few more.
Uh, um, these are all interesting
tactics to try and optimize.
Uh, do you see, I, I see them as more
optimization as kind of later stage.
You have something that, that exists.
How do we improve the
conversion from there?
Do you see a through line between
onboarding flows that convert
well and, and those that don't?
Um, is it, is it all trial?
Trial and error and
testing and, and iteration?
Is it simply explaining the app well?
Is it instilling emotion?
Uh, yeah.
Curious if you, if you see kind
of through lines that, that hold
true across, uh, multiple apps.
Marcus Burke: Yeah.
Um, I mean, it definitely doesn't come
down to like having one of these tactics
or that there's like the secret thing
you do and it works for every app.
That's never how it works in the end.
And kind of your onboarding will
look massively different based
on kind of the vertical you're in
and the um, kind of intent level
that users have as they come in.
I would say.
In the end, the point I was trying to
make in that section was about like the
context switch from coming from paid
social to going into an app like this
is, this is in search where people like
were looking for a certain solution
to a problem that they have in that
moment and they're ready to solve it.
They were just.
Doom scrolling on Instagram board.
It might be that they're on
the train, they don't even
have time for something else.
So in the end, what you need to
do is you need to sell a lot more
and you need to actually make
them aware of that problem again.
And you need to get them back into
that thinking of Ah, yeah, true.
Actually, that's something I
do struggle with and I would
be happy to pay for a solution.
And that's where I think.
For example, apps, utilities, something
like that, that grow through a SO, um,
just kind of, um, organic store traffic.
Um, are, their onboarding is gonna
look so much different for something
you wanna do unpaid social because
that's where kind of that cold traffic
is gonna hit your app and in the end.
You wanna kind of smoothen that
transition of someone being entertained
on a social feed to coming to your app.
And to me that means kind of having an
onboarding that has a bit of entertainment
value and kind of packages this kind of
education and nurturing in an enjoyable
way so that people aren't like, well.
I could at any point just click X and
go back to my social feed where I'm
gonna get, see engaging content and I'm
gonna kill my boredom in a much nicer
way than having to go through your quiz.
That is not visualized, that is
asking for a ton of like permissions.
Um, so that's the way
that I view it in the end.
Like keep find a way to keep
entertaining people so that
they don't drop off immediately.
And I think that's where paid social
is just a bit different from, um,
some of these other traffic sources.
Um, and then that's where all
of these tactics come in handy.
Why they will differ a lot
based on what your app is about.
One thing that is really cool to have is
some sort of aha moment value delivery.
Um, because that often means kind of you.
Like in the end have proven that
like what you can do for the
user within that first session.
And that's a great thing to do
before asking for payment because
that means they understand what
they're gonna use the product for.
And most products aim for just.
Explaining that during the trial,
some when, so that they convert.
Um, but of course that's very late.
Like you're losing 75% of traffic until
kind of day one retention already.
Um, like if you've proven value
beforehand, um, you can probably
bring that up quite a bit.
Um, but it's hard.
Like I haven't, I, I had a few examples
in the, in the, uh, presentation of good
aha moments, but oftentimes that's for
products where it's a bit more obvious.
Um, I had a picture of this in there,
and they have kind of, it's like
one of these, uh, plan scanners.
In the end, they have kind of a default.
Template scan in there where they just
have a picture of a plan saying, well,
this is, we brought this plan for you.
You can now scan it, and you just click
a button and it scans over it, and then
presents what the plan is about, which
is, of course, nothing like a proper
plan scan, but it gets the point across.
I had resume in there, which had
like, um, basically a import for,
from a recipe, uh, from like a block.
Into the app, and it kind of shows
how a messy long, uh, recipe article
is made into like a small, um,
saveable piece that you can revisit.
Um, one interesting one there I
had, and there was from a head,
uh, uh, an emotions coach, um,
they call themselves in the end.
It's a bit like Duolingo for emotions,
uh, as they say in their ads, as
everyone does in their ads these days.
Um, where I would say that's a
bit more complex because it's
kind of an education app and like
creating this easy aha moment.
But in the end, what they did is
they, they have that whole quiz in the
beginning and ask people about their
emotional stage and like what they're
struggling with, what they're good at,
and then they just give them an analysis
of like, Hey, here's your emotional
strengths and weaknesses, which.
In the end isn't any of
their product features after.
But again, it just creates
this value of something.
Well, I answered a bunch of questions,
now I get something told about
myself that I probably haven't
thought about in that way before.
And that creates that moment of, well,
I learned something and I directly see,
well, here's my weakness, which is a nice
kind of segue to then saying, well, pay us
so that we can make your weakness go away.
Jacob: Yeah.
And then I think after that they
have like a, they recommend a few
personalized plans based on what you see.
They tie it all together where, here,
here's, here's where you could improve.
And oh, actually we can
help directly there.
And so it kind of ties that whole
value and I think, so, so one
insight I wanna, I want to go
back to there is that, uh, you're.
New user experience should change based
on acquisition source where, uh, if
you're a simple utility app, someone
is coming in probably from search
or saying, I need to fax a document.
You don't need to convince them.
They, they have intent.
They just wanna get the job done.
Don't delay it.
Don't have a long onboarding
flow of, yeah, yeah.
Please put a check mark here.
Hold to commit to facts.
Uh uh, no, where there's more
emotional products and, and I think.
The insight that someone is coming
from scrolling from their feed.
They're not looking to get the
problem solved, but your ad
has caught their attention.
They're curious, they're not ready yet.
Um, they have some level of intent.
They spent, they downloaded your app.
So that's something, uh, they, they
waited for it to install, they opened it.
So that's, it's something, but.
You're not there yet.
Uh, to get convince to actually pay.
You need to build that
excitement, build that emotion.
And I, I, I agree on,
on the, uh, aha moment.
I think was, that, that was what I
was gonna say as well, that, that
the, the through line is, it's,
it's not necessarily always that
you've, you're teaching someone the
exact product experience and the
onboarding flow before the paywall,
but you uncover value somehow.
Connected to the core value of your app.
There's some unlock, there's
some positive experience.
You understand.
It's called aha moment for a reason.
You want the user to
go, oh, aha, I get it.
Uh, uh, and, and, and if you could
build that kind of connection before
you ask for payment, you probably
have a better chance of success.
So a hundred percent agree with you there.
Um, and it's, not easy, right?
It's, it's not like.
Uh, you're gonna figure it out, but
it, but it's important to kind of try
to drive towards and test and, and,
and figure out how to create a quick,
compelling, emotional, fun or, or
somehow interactive experience that,
that actually gets people bought in.
I,
Marcus Burke: Yeah.
Yeah.
I focus quite a bit on onboarding.
I think there is so much value to
be gained from this, and that's
where still you have kind of the
majority of your traffic, so.
Um, yeah, I would say definitely
look at your onboarding,
uh, quite, quite frequently.
Put some resources there.
Usually there is still wins to be
made from where you're at today.
And like the landscape is changing
like quite often, like more apps are
figuring out new stuff all the time.
Um, so make sure you kind of stay on top
of what's, what's going on out there.
And yeah, as you mentioned
with kind of the traffic source
in the end, like you're not.
Or like in most cases, you're not gonna
be able to link a specific traffic
source to a specific onboarding.
But in the end, as your traffic
composition changes, like if you're
heavily focused on meta right
now, then this is your reality.
It's gonna be people that aren't
necessarily kind of problem,
solution aware in that moment.
They wanna be entertained.
So that's what your
onboarding needs to look like.
If you're then shifting that
composition and bringing in additional
channels, influenza, Google ads,
then maybe it needs to change.
So also kind of your onboarding will
evolve over time to make up for kind of
whatever traffic you're driving there.
Jacob: Yes.
Yes.
Understand where your
audiences are coming from.
A hundred percent.
So, uh, last piece I wanna talk about.
I, I really love your message
of enabling broad targeting by
using multiple price points.
Part of the reason I love it is
because this is what botsy enables
you to do with our dynamic pricing.
But aside from that, uh, uh,
tell me how, how you typically,
uh, uh, see this being done.
Well, I, I mean, typically I just see it
as like kind of second follow up offers.
Um,
Marcus Burke: Yeah.
Jacob: nuanced than that?
Marcus Burke: I don't think it's
necessarily done well to date.
Like yeah, most apps that I see doing
it at all just have multiple paywalls
with multiple price points, and I
think that's a good starting point
in the end, like based on what we
talked about, like if you wanna target
a broad audience, that means you.
Bringing in younger and older
traffic from different placements
with different levels of intent.
And not all of them are gonna be
willing to pay a lot of money,
some will and others won't.
And if you kind of want to maximize
your revenue, that means having
the right offer for each of them.
And so what I mostly see people do
is having two, sometimes even three
paywalls, where they start on like a high
intent paywall that is like high price.
Maybe even not have a trial on that.
Just kind of the kind of best
people should convert here.
Then afterwards have kind of a paywall
that has the normal kind of three SKUs.
Um, you use pricing psychology
to push them in the plan.
You want them in, they have a choice.
Um, they can start a trial just to
kind of lower the commitment a bit.
And that's where the majority of
your conversions is gonna come from.
And then many apps by now have
kind of a follow up offer, which
is like a discount often like.
50, 60, 70% that hits them at the end
of onboarding and then maybe sits on the
home screen, um, for like the first week
or so, converting that lower intent.
And therefore it allows you to capture
like more of the demand curve from,
um, from these different like, audience
segments that you're bringing in.
And sometimes I see a mismatch,
like you're, I dunno, you're,
you're doing a lot of like.
UGC short form video bringing in
young users, but you're pricing at 70
bucks, that's just not gonna work out.
Um, and the other way around, if
you're trying to go after Facebook
feed at high CPMs, old people that
are expensive to buy, but you're
only charging them 20 bucks a year.
It's also not gonna work out.
So in the end, your pricing should
adjust to kind of your creative strategy
in the end, because that determines
the audience you're bringing in.
And yeah, that's where I really see value
in something like Botsy, because that
can do that, of course, a little bit
smarter than just saying, well, we're
gonna show all offers, um, starting
with the highest one first, and then
people can convert where they want.
Um, because of course there is, you
can smoothen that curve, uh, even
further if everyone gets kind of the
best price point for where they're at.
Jacob: Yes.
Yeah.
The one, um, one interesting
approach I saw was, um.
I think it might have, you have,
I believe you have just fit
in, in your, your presentation.
I think it was them where I didn't
even, I lingered on the paywall.
I didn't do anything, but I just, I
didn't tap anything and after like 10
Marcus Burke: Yeah.
Yeah.
Jacob: discount automatically
changed and transition.
And so it's like, you know, there's
a, my my point being, there's a
lot of different things to try.
Uh, what you said before, keep on
researching, keep on exploring what
other people are doing out there, uh,
and, and learn from apps out there.
So, um, yeah, this was,
this was awesome, Marcus.
Uh, I really appreciate you
joining and talking through
Marcus Burke: Yeah, fun to be.
Jacob: we'll link to the presentation
in, um, in the show notes so people
can maybe have the listen to the
podcast and have a visual beside
it or watch it on YouTube and
Marcus Burke: That will
make it easier I think.
Jacob: Uh, and still go look
at the presentation because we
didn't cover everything in there.
It's packed full of more insights,
uh uh, and so go check that out.
Um, also, like Marcus, you're
probably one of the best people to
follow on LinkedIn if you, if you
work in a subscription app space.
Uh, it's just so much actual
tactical and real info from your
experience about really what to do.
And I, and I think I've really
been impressed with how much you've
been able to really bring those
real insights, uh, uh, to people.
Marcus Burke: Thanks.
Jacob: Yeah.
Anything else you wanna promote there?
Go follow Marcus on LinkedIn.
Anything else you wanna
Marcus Burke: Yeah, go,
go follow me on LinkedIn.
Um, I, yeah, kind of feel free to check
in if you have questions, um, through the
dms on, on that, uh, on that presentation.
Other than that, yeah, I'm mainly
advising, um, kind of mid stage, um.
Subscription apps on, uh, on meta ads.
Um, so I'm usually quite booked, but
if there's an interesting project,
I'm always happy to have to discuss.
So you're free to reach out if there's
anything that you're struggling with right
now or if you're growing very quickly
and that creates chaos and you need it
to kind of be structured by someone.
Jacob: Yeah, put a compelling pitch
together if you wanna work with Marcus.
Make it, make sure it's interesting.
Uh, cool.
Well, well really thank you again.
This was, this was really awesome.
Uh, I'm really excited, uh, uh, for, to
be able to share this with everybody.
Marcus Burke: Thanks for having me, Jacob.
Jacob: Alright, thanks.
Bye.
Thanks for listening.
Hope you enjoyed.
Please go to price power podcast.com
to see all the episodes.
Go to Spotify and YouTube and
give us a subscribe and follow
so you don't miss any episodes.
Alright, talk to you next time.
All.