Seth Marrs: Hello everyone and
welcome back to the Innovative
Revenue Leader Podcast.
Today we have the opportunity to
take a deep dive into marketing
to what what is called the
marketing data mirage.
So that's that occurs when
metrics suggest programs are
performing well, but the
underlying signals are
unreliable, they're inflated,
they're not tied to real buying
behavior.
So so basically what you think
is happening is kind of not
happening, which makes it very
hard to do the things that
you're trying to make decisions
on things.
So it makes weak programs look
strong, mass gaps in execution,
enforces sales team to chase
opportunities that were never
real to begin with.
So I think many people are
dealing with this, so I'm
looking forward to digging in.
Demand Science just released a
report around it, and they they
talked to 750 people, senior
marketing leaders, focusing on
this specifically.
So to talk through this, I had
the the privilege of having Bill
Habib on.
He's the CMO of Demand Science
with more than 25 years of
executive experience.
Bill's built and led high-impact
marketing teams at both
established technology leaders
and fast-growing innovators.
He's played a pivotal role in
category creation.
I worked with him at Barrisent
when he was working through that
category.
Go to market strategy for
multiple companies, Oracle,
Bullhorn, Data Robot, Barrisend,
and he's helped guide the
creation of multiple unicorn.
He's also been a marketing
leader at three companies that
were acquired at 10X valuation.
So very distinguished guests to
have on.
And you guys, you were at the
center of this.
You and I actually talked, hey
Bella, it's it's great to have
you.
SPEAKER_00: Great to see you
again.
Seth Marrs: Yeah, I mean, you
and I had this conversation
before we we did this.
Just we were talking about it,
and and I've always really liked
like you're very thoughtful in
the way that you think about
things.
And you could see it in the
report and how you put it
together, the way you sequence
this, the bytes around the top
five, like all this stuff.
So I'm super excited to jump in.
Let's let's just start with a
lot of times when people do
these surveys, they do a general
survey where they think the ant
where they're trying to get the
answers they want versus like a
very target, I have a
hypothesis.
Like which one of this was for
you?
Did you know the data mirage at
the beginning and you were
trying to dig in, or were you
looking in general at the
problems that are happening
around the business, things that
are working in marketing, and
you came to this conclusion?
SPEAKER_00: Yeah, it's a really
great question.
And so the answer is a mix of
both.
I knew from 25 years of
marketing experience that people
were dealing with problems with
data and signals, with content,
with activation, with um now
with AI.
How do I incorporate that in my
processes?
How do I continuously innovate?
Because a lot of the value comes
in the iteration loops and the
speed at which you could do
that.
So I've seen it and lived it
myself.
I've built big stacks, Martex
stacks, and found I didn't get
the value of them.
So a lot of these were problems
I've personally seen and
experienced as a marketing
leader.
And I'm in lots of CMO forums
hearing from my peers who all
talk about these.
So we had a theory that these
problems were there.
And so we went out to test some
of those, plus other areas to
see, hey, is there anything else
that we've missed?
All around the lines of like
everybody's got to drive higher
performance from marketing.
Where are there gaps that are
potentially sitting in sort of
um plain sight that we could
help marketing leaders close and
help them drive more revenues?
So we had a thesis, but we also
went kind of broad with our
questions to see that the thesis
get proven and were there other
areas or insights that we
missed.
Seth Marrs: Oh, cool.
So you kind of knew it was gonna
go there.
So you asked the right questions
on it, but then you expanded out
just in case you had other
things and it kind of pulled the
air awesome.
SPEAKER_00: Exactly.
Um, for example, we asked a lot
of questions about AI and
marketing.
Um, we didn't report out all of
the findings because a lot of
people have seen some of those
things already, but the things
that we did find that aren't out
there, we uh we reported back
because you thought those are
pretty innovative.
Yeah, that's like AI-driven
content isn't working very well.
And 71% of people said AI-driven
content is hurting their brand.
I'm like, this is not
surprising, but it was
interesting.
We didn't know what was going to
come back on that.
So let's dig into that a little
bit, right?
Seth Marrs: Like 72% say it's
hurting their brand.
Like, does that mean they
shouldn't use it?
Are they using it wrong?
Like, what on what that that was
kind of a shocking stat in there
because everybody now is just
pushing out AI-driven content.
I don't know.
I would say most content these
days is at the very least going
through AI for a second look.
Like, what what do you do with
that?
Like, how how it is do you stop?
Like, what is it?
SPEAKER_00: Yeah, it's a great
question.
And and especially if you
consider people are using AI to
generate the content so that
ChatGPT and other LLMs consume
the same content.
And so you've got chat and LLMs
creating the same content
they're gonna consume.
And at some point, you've got to
get out of this sort of circular
loop.
Um, so what do you do with this?
I believe, first of all, the
human element to this thing is
super important in two respects.
Um, one, um, to what extent are
you using AI as sort of the idea
generator and the first draft
generator as opposed to the
final arbiter?
Um, and so are you generating
stuff that's not having enough
human involvement?
But secondly, how do you make
sure this is both um consumable
by the machines, but consumable
and relevant to the human?
Um so even if if if chat or
another LLM um consumes it or
you show up on AI overviews, is
it the right content for your
personas that's gonna resonate
with their pains and problems?
And because that's what's gonna
actually cause that content to
convert into initial meetings
and subsequent stages in the
pipeline.
So I think there's a piece of
this that's simply make sure
your humans are involved in this
process pretty actively.
And are you really are the
people driving this, do they
really know what your target
personas want and need and what
their visceral pains and
problems are, what's gonna
resonate with them on a tactical
basis for their jobs and an
emotional basis?
And machines just aren't so good
at that.
Seth Marrs: And so no, like what
you were saying around it, it
kind of makes me think.
Do you remember way back?
I mean, uh both of us would
remember this, like when the
when the internet started and
people people were doing like
really weird stuff.
Like they would just put a
string of keywords into their
website because they wanted it
to come up in the different
search site.
It kind of feels similar in that
you're trying to placate the
engine rather than the person.
Yes.
Inevitably, one backfires
because the person doesn't care
about that, and two, eventually,
all these tools are gonna just
wipe that out because it's it's
actually the raw, it's cheating.
SPEAKER_00: It's cheating.
It's it's like the old, like you
mentioned, it was the keyword
stuffing in the metadata tags or
at the bottom of website pages,
but in the meta the meta tags
that you were you were stuffing
in there.
And yes, at some point this is
gonna run its course.
I I I firmly believe, and we
adopt AI and use it all all the
time in our content and other
things.
I'm a massive believer in it.
It makes me more efficient, it
makes my team more efficient,
makes every marketing team more
efficient.
But you've got to build into
this process, making sure
someone knows who's my target
persona, what jobs do they have
that that have to get done, what
matters to them for success in
their job.
And how are you making sure your
content is provocative and
insightful for them to bring new
insights that help them do their
jobs better?
And like, I don't know that
machines can do that very well
today.
You still have to have humans
involved to make sure that all
fits.
Seth Marrs: Yeah, I mean,
because a lot of the biggest
campaigns that work that really
drive interest are not the same.
Like they the way they stand out
is by being different than any
different before.
So if you don't have somebody
trying to look for the
differences, or even if you're
using eye to prompt properly
with your expertise to find the
differences, yeah, it's the same
old crap and everything just
looks gray rather than, and then
the one or two people who are
really making it stand out stand
out almost even more.
SPEAKER_00: That's exactly
right.
And and what we learned from the
from the finding, which we
didn't go into as as a as a core
theory, but we learned from it
was when, you know, on the on
the revenue topic, when people
and marketing leaders are trying
to drive more pipeline, almost
any sales or marketing leader
can emphasize with with I think
this statement, oh crud, we're
halfway through the quarter and
we're short on our pipeline
target.
Like, okay, like everybody can
what do people do?
They generate more content, they
buy more data, they advertise
more.
Um, and and but what's happening
is they're buying, they're
dialing up more of the same
instead of saying, gosh, do I
have a connective thread between
my signals, my content, and my
activation?
It in ways are gonna make this
really efficient, or do I just
dial up more of what I was doing
and hope that's gonna yield more
pipeline?
Seth Marrs: Yeah, so this takes
us into another part of it.
And you and I had this
discussion around this.
The like you talked about 80
seven, 80, 80, and this kind of
goes into what you were talking
about with 87% of marketers
chase fandom signals and only
26% become opportunities.
And what I told you before on
that, I'm like, that actually
sounds not too bad because when
you're sitting at the top end,
it is very hard to understand
what a real signal is.
And even a signal that's real at
the top end, customers are
fickle, right?
Like, I may be interested at
this point, but my interest dims
as I go.
So you're going to have this
natural flow through.
But I think it, I think the
point that you're making with it
goes back to what you've just
been talking about.
Like, what why like is is that
like when you think about that
challenge, like is there a way
to really keep that all the way
through or more than like I
thought 26%.
I'm like, oh, that's not so bad.
But you looked at it, you're
like, no, like there's there,
there's a there's a real
opportunity here.
SPEAKER_00: Yes, yes.
And here's the um here's the
extra layer of of thought here.
And I'm a marketer who was
involved for the last 15 years
in pushing intent companies and
others to get those signals.
Who's clicking on my stuff,
who's clicking on my
competitor's stuff?
Like, what are the indicators of
somebody potentially being in
market so I can point my
machinery toward going against
them?
Here are the two problems and
the challenges, and why that
that that that delta stum kind
of jumped out at us.
First of all, what's a signal?
Um, and all of the companies in
the intent space today are also
kind of making, you know,
raising this sort of question.
Is a signal um somebody from um
NVIDIA clicked on one piece of
content or a few people clicked
on pieces of content about
supply chain?
Does that mean NVIDIA is in the
market for supply chain
software?
No.
If what you do without having
the context and provenance for
that is simply tell your selling
team, hey, NVIDIA, you're seeing
signals for NVIDIA around supply
chain, and then your sales force
goes against those.
What's gonna happen when they
when they um these flame out and
they don't they don't turn
anything?
At some point, people are gonna
say, This is the marketing
organization that cried wolf
telling me to go chase these
signals that aren't really
signals, they're just clicks,
they're anonymous clicks from a
company on certain topics, and
that's what you know is part of
what's leading to that 89%.
People have gotten really
committed to chasing these
signals that we thought were
intent, which are nothing more
than clicks on topics.
So that's problem one, and it's
a phenomenal um a misuse and
waste of resources if you're
because then you direct your ABM
campaigns against the companies
that have the signals.
Seth Marrs: Yeah.
SPEAKER_00: Um are they signals?
They have to be validated, they
have to have provenance, you
need to know the context of
those and how important they
are.
So that's problem A, and that's
solvable with provenance and
greater context and sort of
opening up the black box of what
these are so you can better
evaluate them.
But I would volunteer, there are
multiple types of signals.
There are the clicks, there are
click signals on on content, and
those are sort of well
established, but there are other
signals.
Um MA activity, um, investment,
whether it's VC investment or PE
investment, um, new hires,
especially at an executive
level.
Um, you joined Sandler not all
that all that long ago last last
summer.
I joined Demand Science last
August.
We were about a month or two.
A new executive hire joins for
senior hire, even um a revenue
potential CRO, CMO, or people at
the VP level, they often are
brought in because there are new
ideas that they're going to
bring in.
And and so um executive hires
can be a signal.
Market growth, um, um MA
complexity.
What's the complexity in their
sales stack or their Martech
stack or their supply chain
stack?
So people can use tools to know
the complexity, risk,
compliance, there are um um a
variety of signals, firmographic
uh is is yeah, people do that
for 30 years.
Yeah, even technographics,
people have been doing for a
while.
But these other signals could be
greater indicators of a
likelihood or propensity for
somebody to buy or be in market.
And are you looking at those
signals as well?
And how do you incorporate all
those into your engine for ABM,
for account scoring, for
building your book of business
and sales?
Those are signals too.
Seth Marrs: So I if I
understand, I mean, that makes
total sense to me because what
you're saying, and and I agree
completely, is don't take the
onesie twosies aggregate as a
marketing team, aggregate them
all together and turn them into
a unified signal and pass that
signal because signal A, B, C,
and D together about that
account says yes.
So pass that one, not the five
signals that you got that
individually mean nothing.
They go to sales and they have
five actions versus I've
actually thought we we've pulled
together an algorithm that looks
at these things, and when we see
these five, six, seven things
happening, it's important.
Now you're past it.
So instead of 89%, it may it I
totally, yeah, that makes total
sense.
SPEAKER_00: Bingo.
And here's how people, here's
how you can monetize this
actually.
So this isn't just sort of
theoretical.
So I'll give you two, I'll give
you two examples.
Um uh I talked to um uh a head
of demand gen just two days ago
who is using these kinds of
signals, and he's driving um an
ROI of um of CAC to LTV of one
to seven one to seven.
So they spend a dollar in
customer acquisition costs, they
are getting seven dollars in LTV
and with a very small BDR team
and and an agile marketing team,
in large part by using these
kinds of signals.
Seth Marrs: Yeah, the aggregate
being smart at the top end, it
allows because I mean the most
expensive resource in that whole
thing is your salesperson.
So if you pummel them, it
doesn't even include the fact
that they what you talked about
earlier, they lose interest.
That's exactly right.
Yeah, that makes total sense.
Interesting.
SPEAKER_00: Yeah.
You know, different example.
Um, a very big cloud company, I
I have to leave them nameless,
was spending like 600 some odd
dollars per account to qualify
from from marketing.
When they better leverage both
signals and um a much more
tightly integrated kind of
multi-channel activation
approach, they lowered that cost
for marketing to acquire an
account from like 600 a change
to 100 a change.
It's like a 400% improvement by
better signals and better
picking those signals against
their ICP and then activating
those accounts in a in a very
tight closed loop sort of way.
This is what we what we saw in
the report is sort of
repeatedly, if people weren't
able to connect these threads,
then their waste was higher.
Um and they're they realized it
and they said, My revenue
opportunity potential if I did
this well is enormous.
They actually recognize there's
a there's a weak spot here.
It's costing me for not doing
this well.
Like 25% of their marketing
budgets are spent on stuff that
looks okay in a dashboard.
They look at the dashboard, they
say, Oh, my impressions are
good, my stage one pipeline
first meetings look good.
If you if you measure M2Ls, my
M2Ls are equivalent, look good.
Um, but then it's not
converting.
And then so the CMO has to go.
Seth Marrs: Well, this takes us
into that that what you have
talked about in the report
around the around the confidence
paradox.
Yes.
Which so like you you you talked
about it in 89% of marketers
trust their data, but only 43%
uh say the metrics look good but
don't convert.
Yeah, so 43%.
So you you've got this, like you
what you you're basically
talking about that gap.
Like I when I looked at that,
I'm like, okay, well, if I've
got good data, shouldn't that
flow in?
So it felt to me like we're
measuring the wrong things.
And I think what what you're
saying from the previous
conversation is you shouldn't be
measuring the individual signals
that come in.
You need to build aggregated
signal signals that that say I
mean to a certain extent, isn't
this like what what you're
hearing with people like Terry
Fleurerty, Amy Hawthorne, Kerry
Cunningham talking about buying
groups and how do I get a how do
I get signal aggregation into a
place where I can understand not
just the account stuff that's
going on, but I attribute it
into individual opportunities
and deals that you could push
through to a salesperson.
SPEAKER_00: Exactly.
So Kerry's talking about, you
know, their their most recent
market research talked about how
um early the buying journey
actually starts.
And if you're just chasing the
5% of the red market, what's
happened is you left the other
95% of your ideal customer
profile to poke around and learn
this on their own.
And then by the time they get to
the project stage, the RFP
stage, whatever, they've already
shortlisted the one, two, or
three vendors that they most
want to go after.
So you got the buying group
dynamic.
You also got the dynamic of are
people forming opinions really
early before the signal, you
know, um, you know, the strong
signal shows up.
So so what do I think is going
on with that um that particular
um finding for the report?
People said they have a
reasonably a rather high
confidence level in the state of
their data and analytics.
I feel that I have the right
people in place, the right
systems and processing in place.
In general, I feel pretty good
about the state of my data and
analytics.
But when pressed further to say,
how often do you have stuff
showing up on your dashboards
that looks good that doesn't
turn into pipeline?
It was like 43% or something
like that said these early
metrics might look good.
And that's not surprising.
These are sort of um
top-of-funnel sort of metrics.
It could be website visits,
which are fine.
But are they high intent leads?
Um are they MQLs if people are
still measuring those, or um
early stage pipeline like stage
one?
You and I talked about this when
we first met a year and a half
ago.
How often are companies, um,
organizations still measuring
sort of early stage pipeline
that means uh you know a meeting
has been accepted or fulfilled?
Um, and but it's not really
qualified yet.
And you can't build a forecast
off that.
Um, you could build a pipeline
model that says here are early
stage indicators, but you
shouldn't build a forecast off
it or pat yourself off the back
with vanity metrics about
impressions or website traffic
or things of that nature.
What really matters for revenue
leaders, if I I'm a marketing
guy, but what is from every
revenue leader is how many
qualified opportunities are we
generating that I can predict
will close at a certain
predictable win rate?
Every quarter that has to go up
by 2%, 3%, 5%, however, whatever
your growth rate is, right?
Whatever your growth rate is.
And so I think the learning for
us in this was people felt they
had a reasonably high comp, but
rather high level of confidence
in the general state of the data
analytics.
But when it comes down to it,
when you're running the demand,
you've got a lot of stuff that's
showing up green on your
dashboard.
Yeah.
But this still turns into red um
when it when you get to the
point of did I generate the
required pipeline to hit the
revenue targets.
Seth Marrs: Gosh, and I think
this leads in, I want to ask one
question, one more question
around the report.
A really kind of scary metric.
And I think you're talking to it
here a little bit.
It's 85% of marketers spend more
time fixing problems and
creating programs.
That was kind of disheartening.
But when w w what you've talked
about so far kind of leads to
it, right?
Like you're creating all these
vanity metrics and putting them
in place, but you really don't
believe in them, and then you
end up having to go fix them on
the back end.
Like, how do companies like one
recomm like what recommendations
would you give?
Because it also is a capacity
opportunity.
If you're spending 80 if 85% are
spending that much time fixing
stuff, if you could redirect
that into something like signal
aggregation.
SPEAKER_00: Eh bingo.
Um yes.
So um, and let me just
acknowledge as a CMO, I've been
one who sat there saying, Oh, we
got this problem.
Let's get more data.
Maybe we should get more tools.
You and I talked about this.
The rate at which AI-powered um
sales tech tools, Martech tools
are coming out.
You can't even keep up with
them.
It's it's it's it's every week
there's another tool, and which
one should you buy?
And and so what people do is I
buy more tools, I buy more data,
I get more signals, I generate
more content, I run more
campaigns, each in their
respective style without a
connective thread that unifies
all those.
So, what do you do about this,
especially this problem of
people spending lots more like
more time fixing than actually
creating?
So, what are things what are
things that the survey revealed
um the validated things that
I've seen or experienced?
So a few things.
One, um, buying more tools does
not result in more ROI.
Um, people who had bigger tool
stacks, as it's as the as the
Martex tool tool stack got
bigger, their their ROI went
lower.
They more of them questioned
their IRI after they bought more
tools.
Slim down your stack and think
about it in layers.
What do I have in my signal
layer, my content layer, my
activation layer?
Um, focus on quality of signals
over volume.
And so take that holistic view,
not just first party and
third-party data, but and not
just clicks, but what are the
real drivers of opportunities in
my space from a business
perspective?
Do I have those signals captured
well?
And am I acting on those not
just once to create an account
list, but continuously in a
narration loop that's going to
keep um adjusting these based on
new things happening?
So one of them is data, you
know, one of them is leaner,
leaner stacks, verified signals.
Um, better attribution came out
in in spades.
So you have to be transparent in
your end-to-end attribution so
you know what works.
That's its own kind of can of
worms, as anyone in sales and
marketing know.
Um drive your content off data,
not what somebody's thinking,
and and not just what the LLM
tool tells you, you know, should
be found, but um create content
that's informed by verified
buyer um um insights and
assumptions.
Um you can use AI to help you
know what some of those are and
and use AI generated templates,
but like make sure this stuff is
sort of personalized and
relevant.
And then like, how can you make
your systems operate in a much
more efficient way?
So you replace the time years,
like some of that's connectivity
via API, some of it is leaner
stocks.
Do I people said they have tons
of redundant capabilities across
multiple tools in their Martex
stack?
Seth Marrs: Well, which creates
complexity that creates wasted
time, but together.
SPEAKER_00: Yeah, and so um, and
lastly, I'd add my own dose to
it, which is um do less and
obsess.
Like what's really gonna move
the needle and and do that
really well across the signals,
the content, and the activation,
um, and and have the right
metrics in place that are
really, really oriented around
pipeline, what's really um
contributing to the building of
qualified pipeline.
So I double down on those
things, and um um, but it's it's
connecting all those things that
also were often siloed.
Um, so that's that's what I the
survey said that, and and I hear
the same thing from from CMOs
that I talked to.
Seth Marrs: Fantastic Bill, it's
so great to have you on.
I love the insects.
I always learn something when I
talk to you.
So yeah, it really is.