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Connor Jeffers, CEO of Aptitude 8, interviews marketing, sales, and customer success leaders about how they are using artificial intelligence to innovate, optimize, and scale their go-to-market operations.
There's a huge opportunity for AI
to summarize and recommend at scale.
To a human operator, a human strategist
who then takes that as input.
What follows is a
conversation with Evan Dunn.
We talk about his early work in AI back
in 2017 and why it didn't work then.
AI versus human agents, the categories
of AI software he's most excited about,
actual GTM work he's done leveraging
AI, when to systematize versus when to
experiment, and the absolute best way to
test your demand gen strategy, AI or not.
Let's listen in.
All right, Evan, welcome.
What's up, man?
Hey, thanks for having me, Connor.
Great to podcast with you again.
Good to podcast with you again.
Our producer at the beginning of the show
asked Evan if he knew how to Riverside.
And Evan was like, "yo, man, we like,
Connor and I met on a Riverside.
Like I had him on my show.
So like, I kind of know what's up."
I have this theory, Connor, that podcasts
exist for networking that's kind of
It's not theory.
You're just a marketer.
Right.
Right.
Like, I meet great people through
podcasting and those are the
relationships I get more than the
podcast producing, like, a big
audience and tons of revenue, you know?
Totally.
Totally.
It's like, come on my
stage and I'll talk to you.
And like people are like,
"Oh, I like stages."
And then you're like, ah ha!
Now we will be partners
and friends forever.
Yeah.
All from the podcast.
So before, before we get into anything
maybe a good place to start is like.
What's your background?
Why are we, why are we talking to Mr.
Evan Dunn?
I met you when you were at Syncari
but you have been doing cool stuff
in the tech universe as a marketer
and a product guy for a while.
And so we can start wherever
you think it makes sense.
Yeah, you're the one who asked me, so I
should ask you why you're talking to me.
No, I, I have been in marketing
for over a decade now, which
makes me feel really old.
I, I didn't know, I never really knew
what I was doing, but I had the good
fortune of landing like Macy's and Verizon
Wireless as clients through Twitter.
By reaching out to them back when
marketing was growth hacking,
if anyone remembers that time
and now sales is growth hacking.
And that's what we're going to talk
about when we talk about GTM with AI.
But the, that consulting job
eventually pivoted into an analytics
firm that eventually pivoted into an
AI product for media, working with
HBO and Disney, Sinclair broadcast
groups and really cool things we did.
Which I know we're going
to talk about in a bit.
What year was the AI stuff?
Oh yeah, like, I dont want to date you.
No, it's all right.
It was like 2017 to 2020 basically.
Pre AI coolness.
Pre-AI coolness.
We, half the time we called it machine
learning because was like kind of
cooler in some ways and sounded more
robust, but now I feel like most of the
time AI is mentioned without machine
learning in the loop and there's
AI that's not machine learning now
so like, It's just a different
Wild West, still a Wild West.
And then joined Convoy and
it's a performance marketing.
I actually was going for a product
marketing role coming from a head
of product role at Resonance AI
and they asked if I had WordPress
expertise and I said, "yes."
So all of a sudden I was
in digital marketing.
You're you're you're the
guy that does the website.
Yeah.
As the digital marketing guy.
That's basically what it became.
It was a good time.
We had a good run of it.
In fact, I left before Condoy,
Convoy's Zenith time period was over.
So it was still mountaintop experience.
And if you, you know, Convoy,
if you're not familiar
listeners, is gone completely.
And if you still look for load board,
which is a 14, 000 searches per
month term, they're still number two.
This is why SEO is really important
because it lasts for a long time.
Trust Evan.
Yeah, right.
It's like spam.
You know, you put it in the basement, you
come back when the apocalypse hits, it's
still there ranking number two on Google.
That's a weird analogy.
Never used that before.
And then joined Airwallex as U.
S.
growth marketing lead, really struggled
with product market fit in the U.
S.
And And so that was a brief stint we can
talk about too, and then joined Syncari
for a year and a half, really great stint,
really great product, and then went on
to a head of marketing at ServiceBell,
which is where I'm at, at now.
And I think the themes that make this a
fun conversation for me to have, Connor,
I've always tried new tools, new tactics,
new tech, I'm kind of a junkie for it.
I think a lot of digital marketers are.
And I've just seen so many
interesting things now with
prospecting, the role of the SDR,
sales and AI and AI for everything.
There's it's kind of hard to wrap your
head around it and parse the themes out.
So that's what I love to
talk about later in the show.
Yeah,
Well, let's maybe start with, with
resonate something that you had
said, I don't remember if it was
in the, in the pre or the now, but
you were sort of talking around
like, Hey, we did this thing and it
didn't maybe work or where it was.
And.
If that was 2017, 2020 what, what was it?
And if you're, what do
you think is either?
I don't know, would it work now
or, or is it just completely off
base and was it ahead of its time?
Or, I don't know, what's
your perspective now?
It was very expensive for
us to do what we were doing.
So let me talk about what it was.
And, so early on, we did some work with
Disney where we had social listening data.
Mentions of shows, over many periods, I
got, I became kind of an expert in social
listening technology, which is also a
category that's largely disappeared.
It was very made possible by Zerp
you know, economics, where you're
paying 40 to a hundred thousand
dollars for a tool that just scans
the internet for keywords, basically.
And we combined that with
like viewership data, right?
Like how many people, households are
watching a show across its lifetime.
And data about the content itself.
So Disney piloted this, and then we
landed a contract for the same kind of
thing with more machine learning driven
tagging mechanisms on the content for HBO.
I can't go too much detail...
sure.
Sure.
on engagement, but we were telling
them why season 2 of a really
popular show failed where season 1
was super successful and it was it
was whenever all things held equal,
and I'll talk about that in a sec.
It was when this one character, one
of the protagonists, was on screen.
People left and we're not retained.
If you think about watching a show,
it's not so much like, adding viewers,
people don't hop into a show, there's
no like, you grow a show through the
content, it's you keep them engaged,
right, there's drop off, there will
be disintegration, if anyone's done
video analytics, right, you know that
it's how much drop off how many
viewers were still present at 25
percent and versus 50 percent and 75
percent and completion is never 100%.
It's 90 percent in, and all that stuff.
And so we were tying that to
30 plus proprietary algorithms.
Shout out to Will Henderson Drager, who
helped make this vision become life.
We called it a resonance score.
So we would look at the color,
the darkness, the transitions,
characters on screen.
Will and his team built the most
powerful character recognition
that I've still ever seen.
Where if someone's back was
turned, you know, it'd still
track them across the screen.
If
But when you, you're like, like
literally the character in the
show is what you're referring to.
The character in video.
Yeah, like the raw video.
So we call true color footage, right?
Like, the final output, right?
And really complex stuff.
And then you, you essentially render
the content analytics, you know, many,
many rows across the, the duration of
the show and pull in two sets of second
by second household viewership data
and then check for possible influencing
factors, massive algorithm, right?
To essentially find
where the drop off was.
So we were outputting this and this is one
of the first things I learned about AI.
No one cares about the AI.
They care that you can do a lot of
things at scale very quickly, less
expensive than hiring a bunch of people.
We were innovating on top of like what
dozens of little you know, apps
with people tagging stuff in them
would do where it would like load
manually tag stuff and have all
these quality checks on time.
These tools still exist for
tagging training data sets
for for algorithms, right?
But we were trying to basically
automate that entire process.
And the first problem is like, you can
come up with these amazing, like, you
know, our statistical analysis charts
of, of influence and confidence, and
no one's going to care about those be
like, well, what should I do about it?
Right?
Like, should we kill the show?
Should get rid of that character?
Kill him off the next
episode, this kind of stuff.
And this, this in TV world is
called content strategy, right?
Now it's pretty much all driven by gut.
Even at Netflix, a lot
of the content strategy.
It is still just, do we think
this is a show worth funding?
They get lauded for House of Cards,
which is the first time say that AI,
like, picked up a show and promoted it.
But really, if you look at the Netflix
engineering blog, AI is really mostly
used for, like, recommendations
of similar types of content.
Surfacing clips, even though
that produces some really lame
trailer type videos in their app.
Anyways, just a couple things.
Can you even still enjoy media now?
Or are you just like, I worked, I worked
at the sausage factory and I think it's
Yeah, man.
I mean, a great show is a great show.
We loved we're watching Julia on
HBO right now about Julia Child.
But Mad Men, man, some, you
know, some shows are just truly
there's a lot of junk out there.
And you do wonder, like,
Netflix makes a lot of ripoffs.
Like, are they using AI to
analyze, like, oh, that's popular,
let's What are the themes in it?
Let's steal it.
Anyways, so then the final product that,
that we ideated, Will and I made was this
resonance score negative 100 to positive
100 for influence, either driving people
away or keeping people watching for every
attribute from the algorithms, every
character, every setting, every theme.
And then we would take that to HBO to
the customers and say you know, "this
is what's happening in your content".
Ultimately, They didn't really
take the recommendations.
Didn't care.
Well, and like, what are we saying?
Like a machine told you this
is what you should do, right?
Like it's really hard to base 5 million,
10 million, a hundred million dollar bets.
Do you think that that's like, is that
a byproduct of the trust in the technology
at the time, or is that a like a human
condition aspect that you think is...
I think it's both.
And that's such a good question, Connor.
I think it's both First, it's
a by product that's still there.
People are only making tiny, tiny,
tiny risk averse bets on AI right now,
make my email language and I'm still
going to quality check it, right?
It's low risk stuff.
Like, that's, that's what we see in any
research we've done or any customers we
work with is everyone's like, look, I
really want to try this, but like, let's
maybe like generate landing page copy.
And you're like, what about, I don't know.
A sales bot.
And you're like, that
really freaks me out.
Yeah, absolutely.
Like AI chatbots get deployment on, on
tool teams that are more risk friendly
because they have nothing to lose.
They don't have a ton of traffic.
They're not a big brand.
They can't sacrifice brand equity.
They have no brand equity to sacrifice.
Yeah, I think that's a really good point.
I also think it is totally a human
condition, like really well put.
Like we still need to feel
like we're in control.
So what AI needs to do is surface
options and say, here's what we think
is going to happen to your pipeline.
If you take this audience on
and drop this audience, right?
Macro level, things like that.
There's a huge opportunity for AI
to summarize and recommend at scale.
To a human operator, a human strategist
who then takes that as input.
But to, to prove that out, you're right.
Like we've got to really nail the
confidence scoring mechanism to surface
the confidence scores to the end user.
Right?
Like there's too much smoke and mirrors.
ChatGPT has a lot of things.
It's not transparent.
So, yeah.
Okay.
So, so previous AI experience didn't,
didn't really go anywhere because of
product and is it just like, Hey, this is
super cool, but like we don't really care.
Yeah, everyone fascinated.
We were all, even as we were
building it and our clients were
all on the edge of our seats for
like, what's this going to be?
How does, how does this bias you
against what, like, whether it's real
or not, like the very material hype
train that has left the station on AI.
such a naysayer.
I mean, there, there some things,
there are some things that
will absolutely you use it for.
Like one of our competitors
at Service Bell is Drift.
And I went to, and they have a
lot of negative reviews on G2.
So I summarize them with AI and
I use that in messaging, right?
It's great.
That's a that's a great strategy, dude.
We should, we should expand on that.
How did you do They that?
There's an ask CSV tool that lets you
import any CSV file and it'll summarize
whatever column you choose, with AI.
So, which I, yeah, it's, it was
very simple, very lightweight.
Yeah, but the, the,
there's such a problem.
And I observed this early on last
year when ChatGPT was getting big
and quickly I found like perplexity
at AI, which provided links to
the sources it was referencing.
When I would ask it a question or
prompt, it would, it would return with
a summary and where it got it from.
So I could double check.
And immediately was like, well, I'm
going to use this and never go back
to chat GPT because paper trails,
man, like I need to know who's, who's,
what the ingredients in the are.
We need like CYA dot AI.
I'm going to go see if I can
buy that domain right now.
All right.
I'm that's but that's really good.
Yeah.
CYAI cover your AI.
Yeah, I think there's a lot, and there's
probably stuff I'm not thinking about,
but I'm sure product leaders and AI
founders need to be the forefront of,
of AI transparency and trust, not I, I'm
not so concerned about privacy stuff.
Don't quote me on that, obviously, but,
but like with AI, like what, yeah, with
main concerns I have are like, are we
going to start giving C suites these
dashboards with AI recommendations
and they go shift the company and then
whoops, the training data was bad.
CRM training, CRM data as training data.
Is that different?
How is that at all different than, I
think it was like, I think I'm stealing
this, this idea from Mark Andreessen,
who did a Lex Friedman interview, which
I highly recommend, but and they were
talking about like rockets and who
decides if the rocket, like, you know,
does it explode or not, basically,
which is much more high risk thing.
But, but I think the
concept here is the same.
I would argue to your point that that's
not at all different than, like, the
analysts who set up this data and
who put it together didn't normalize.
It didn't do these data
sets, didn't have it.
And now there are people making decisions
off aggregated data and that data wrong.
And...
Now we're talking about the
problem of data teams generally.
Sure.
Yeah.
But like, is the AI better
at being a data team?
If you train it to check for
inaccuracies and discrepancies and
give it some contextual clues, this is
where the human resource needs to go.
It needs go towards quality checking
at the end, but also training set.
Optimization, right?
So how do I give it ample context clues?
A little bit of, of tagging on
a training set goes a long way.
For instance, we, when we did our
character tracking, we would do human
in the loop on about a hundred times on
each major character, meaning yes, no,
this is that person, this is this person.
I don't know who this person is.
And it would feed into we do it on one
episode for a 10 episode series, and it
would then be perfect across the series.
There are, there are
analogies for that today.
We're like.
CRM data, like just go clean up
that, that, deal notes field or,
or give your own like prompt for how
to interpret, here's the jargon we
use, here's what these terms mean.
Give it some context.
So it doesn't summarize based on, because
right now when you deploy an LLM or like
a chat GPT type thing for your specific
scenario, it's trained on something else.
You see this with images, right?
We're like, how does it make
a new image of a person?
It has billions of images of other people.
That's what it's using.
So those images are not what you want,
you know, and take that to text, take that
to data and recommendations analytics.
If that training data isn't, doesn't
have enough of the signal that you want,
it's not going to give you an AI version
that's better.
It's actually going to be worse
because it's going to be steps
removed from the already bad data.
So you're just rabbit
holing down the wrong path.
Yeah.
No, I, I totally agree that something
I say often is, is I think I, when I
was at a marketing ops conference thing
last year and we were talking about this
and, and it was always this question
of like, oh, well, this means that.
Do the ops teams and the people that
are managing and building all these
stuff and like, they get replaced.
I'm like, no, like, if they
screw up, it's really bad now.
Like, it's, it's not like, oh, yeah,
sometimes that feels kind of weird.
And like, we changed it then.
And but like, just if you do
this one thing, it's fine.
Don't use that report, use this report.
And instead now that's
like, AI has no clue.
It doesn't context.
It doesn't know that like, you know,
Greg built a crazy formula and he
hasn't worked here in three years.
And every created under Greg's
tenure is like off by whatever.
And we normalized it.
It just knows that the data
set is the data set and here's
what it thinks you should do
That's right.
And it has no way of knowing.
And that's, you know, I think
you're actually articulating the
biggest gap with AI and opportunity.
And, and this applies to sales AI stuff.
We'll talk about in a bit, right?
But like crawlers plus AI is huge.
Crawlers plus an LLM
summarization capability is huge.
And I say that because you have a CRM.
It has thousands of fields on
multiple properties, right?
An inability to scan through those.
Obviously, you're beholden to
HubSpot Salesforce limitations
and what they let you do.
But if you can pull all those out,
scan them, look at the names and
what's in those columns, those those
fields for similarities and say, "Oh,
it looks like there's a couple of
redundant fields on your deal object.
It looks like there's a couple of
redundant fields in your company
and your contact object, do you
want these to be combined human?"
And like basically human steps for
providing documentation, then it spits
out like a documented CRM that you
then deploy right towards training.
So like, can someone do this duly...
I think, I think that's going to happen.
I think the CRM vendors have to do it.
Like, I think that that's I don't know.
I get questions all the time by basically
CRM market analysts who are we'll
pay you and ask you what you think.
And I, I tell them and I'm like, I've,
you just, I'd have a beer with you.
And I'm like, I love telling you,
come on my podcast, but sure.
But I think to your point like,
I, I I think there's this sort of
thing of like, Oh, well, what can
HubSpot like can HubSpot add this AI
functionality and what
could they charge for it?
And could Salesforce add this
AI functionality and what
could they charge for it?
And I think that that question misses
the actual core element of what's
happening, which is, I think that
this just moves into like, this is
a standard expectation of what the
software is and what the software does.
And if you don't do that, and you
are at your core, a software that
is driving decision making or making
recommendations, like you're useless,
you're dead and you just lose.
And I don't think this is, it's
kind of like, I don't know, maybe
there was a time that people were
like adding, no, this was happening.
Remember when people would charge
for API access, that was like a thing
that you would have like a SKU on and
you built like no one does that now
ever because it's an expectation of
like, and I think that this is going
to be the same thing where that just
moves to being baseline expectation.
If you don't have this,
I won't work with it.
Yeah.
There are analogs here too with
Google Analytics, Google AdWords.
been doing AI recommendations
for a long time.
Never thought they were good, and I
never took them, but I'm actually not
the majority of their users, right?
It's always good to remember
the majority of your users are
not actually the domain experts,
right?
Like they're, they're just people who
got handed a tool they're not familiar
with and are Googling their way into.
So if you can, yeah, if you
can circumvent their need to go
externally to find guidance and give
it inside your tool, absolutely.
Yeah.
HubSpot could absolutely.
And you know, they would be the one to
do it knowing their leadership, but,
if there was, and what we're talking
about here really is AI co pilots, right?
Like essentially increasing adoption
by, if you have a platform that has
many things you do with it, many possible
things you can do with it, increasing
adoption through a chat and recommendation
interface for figuring stuff out.
It's no different than what AI
things have been doing on the help
center, but it's kind of like also
looking at that person's installation
and, you know, where is it at?
What have they done?
Have you noticed mismatches or missing
fields that seem important, required
fields that no one tried, wants to fill
in, you know, these are all things you
could help people navigate that would
be huge to your business and theirs.
Where, what, so what
are you working on now?
Or where, where are you deploying AI?
So I think the thing that's
really interesting, right.
As you talked to already
of like, Hey, I'm a tinker.
Here are the things that I've, and maybe
you don't need to bash anything, but
like, here's, here's what I've done.
And what I thought was sort of like,
here's where I feel like it's BS
versus what are you actually using?
What are you excited about?
What, what have you used that has
been disappointing sort of being out
in the field and, and experimenting.
Yeah, there's there's a lot that
I'm really excited about and
I have to give a shout out to
clay.com because of two things.
They have claygent which is like
your AI co pilot for Audience
research, messaging development.
You tell it a command to go scrape,
you know, websites that are like
XYZ and it comes back with results.
This thing is a little trippy
and how powerful it is.
And we're very early days still,
but even before Claygent, you
could incorporate ChatGPT for
some really interesting commands.
And so one of the reasons, that we're
talking is, is I actually built a phone
validation waterfall myself in clay.com so
that it checks two data sources for phone
numbers, sees if the phone numbers match.
And so I asked ChatGPT in the
loop inside of clay, inside of the
table, do these phone numbers match?
If so, don't say anything else, just
output the phone number that matches.
You kind of have to coach
it to shut up, right?
Or else like, " w we found
matching phone numbers like this".
And like, I don't want to
have to remove all this.
No, no more information.
Just, just, are they the same?
Yes.
Honestly, this is one proof that AI is...
I love the idea of like the AIs
are just gotten really mouthy
and you're like, "stop talking".
It's, it's very much the case.
How hard it is to get
them to stop talking.
Like I like five different versions
of the command in there and it still
says like output colon phone number.
I'm like, "no."
You're like, no, no
output, just phone number.
You're making a whole
nother workflow for me.
Export, Google sheet, find, replace.
That's really funny.
Anyways, so then I, and then I bring in
additional phone data vendors, right?
And it keeps checking in a waterfall
and it's, it's difficult to build.
I actually found fullenrich.com.
Shout out to these guys, they built it.
That you basically go and buy, subscribe
to a waterfall credit paste and for email
or phone seven different sources just on
a simple credit system, very easy to use.
Already testing a list with
one of my SDRs, as we speak.
And you're, you're using these for lead
gen for sales team with accurate data
segments, like all this kind of stuff.
Exactly, yeah, exactly.
Phone validation, if you're not
familiar in the wild listener, is
basically don't call every phone number
because that can lead to a lot of
bad things and a lot of wasted time.
Find the phone numbers that are most
likely that person's phone number, or
even for sure that person's phone number.
And typically what I've seen doing
this validation is it's really good
to look at multiple data vendors.
And if they all report the
same phone number, then they've
all got high confidence.
Some of them are referencing the same
data sets since you want like four or
five, six data vendors in the loop.
Yeah, so that use case is interesting.
I'm really big fan of
account research tools.
What, what's that?
Wait, I, I, okay.
So I don't.
ahead, I don't do any outbound at all.
Don't need to
I have.
I have.
I'm not, I'm not, I don't know if
I would be like, I'm like a heart.
I think, I think for, for
Aptitude 8 specifically, right?
Like we're, we're B2B services.
We're predominantly higher ticket.
Like I compared a lot to heart surgery.
Like it's really hard to prospect
for heart surgery and kind of weird.
And so instead we focus on like,
who are the physicians and we
sort of think about partners and
ancillary stuff and everything else.
But for hapily stuff I don't know
that I would say that we're like
anti doing that ever, but it's been
a long time since I've done that.
What, when you're thinking about sort
of adding AI into that prospecting
data creation loop, what is the primary
value output that you're getting?
Is it the time it takes
you to do the workflow?
Is it the quality of the data?
Like what is, what is the thing that
you're like, here's what I'm unlocking by
using AI in this, in this flow.
It's no different.
You know, one of the things we should
talk about, it's no different than
automation improvements ultimately, right?
It's, it's cause it's the whole
problem like, should I spend an hour
building this special AI workflow,
automation workflow, because it's always
a mix of AI and automation, right?
When I have a step tool, like Clay saying,
do this, then this, then this, right.
IFTTT basically piloted this
stuff for all of us back in 2014.
And versus like 10 minutes of checking
a hundred numbers across two data
vendors to see if they're the same.
Sure, I can do that one time, but
then if I have to build lists multiple
times, I really want that system.
I do think there's a risk of over
systematizing things here, right?
Where, and so that's why briefly,
like, let's talk about how
AI is not a category, right?
You have to just, and what's
frustrating about this is
people treat it like one now.
I get questions like, what's
ServiceBell's AI roadmap?
Like, well, what do you mean?
AI in chat, AI dynamic videos, AI
What kinds of software are
you guys going to build?
That's exactly the question, right?
Totally,
founders who are in the AI space really
fall victim to this, thinking they
are producing an AI product, saying
that to VCs, saying that to customers.
No one cares.
People care as far as they look at your
website and talk about it on social media.
That's it.
That's why hype cycles happen, right?
But buying, like literally, if you
pictured like AI.com like what does it do?
I don't know.
It could do anything.
But, but it can't do everything
and it takes training and wisdom.
And we were talking about
the data input problem.
So if the, what systems should I
build as systems versus keep them
in manual spreadsheet format, right?
That's the question I think for marketers
like myself, ops people like you.
To really grapple with today.
One of the ones that I think is really
exciting, the phone validation one,
I think is big, but I think not, not
everyone is a cold caller and all that
stuff, but one of the things I think is
really exciting for ops pros everywhere,
rev ops, GTM, ABM is account research.
Everyone I see who's succeeding in
the like, sequencing, automation and
AI in the loop for for prospecting
is automating, is building system for
account research per segment often.
So let's say you want a
company that was founded.
Before 2010, because that means there's
a certain cultural milieu you want
that they've raised around recently,
or that they've done layoffs recently,
like AI can get this for you with a
little bit of help on where to go look.
Or you combine like virtual assistants,
looking at the LinkedIn insights tab
on the LinkedIn company page with AI
summarizers and all that stuff, and you're
a thousand dollars into, a you know, a
gold mine of, of insight that you then
hand for your 2024 target account list and
boom, you've got something really strong.
So think about like, what are the trigger
events, the really specific things that
are happening in your potential customers
or in your customers and working with,
Jordan Crawford is everyone should be
following his material on AI and content.
And we're talking about a new
product he's working on where
you summarize, job description
datasets for existing customers.
Did they open up a job for a title
that should be a user, or should
be an owner of our platform?
What's in that, what initiatives are
they going to be responsible for?
Did they recently close a job?
For some, like, think what you can do.
You could basically trigger like
automated workflows for upsell, right?
And companies across the
spectrum are leaning into this.
I'm talking with the chief of staff at
a 1200 person open source software type
type company who is trying to bring some
of this innovation in and she's launching
experiment pods of SDRs, like two SDRs and
Dimension Marketer who are just working
these new ideas so that she doesn't
have to turn the whole ship right away.
And I think that's brilliant.
So, so
what are the systems?
I want to I want to expand on what you
just said, because I think that that
is actually, I think 1 of the number 1
things that we've seen in some of these
conversations and research we've done
around this is the biggest obstacle
as people being like, how do I start?
Like, where can I go?
And like, moving the whole
ship is really, really hard.
I think what you just highlighted is
if you're in a larger organization,
the ability to pilot this thing with a
smaller group of users is so valuable.
And without plugging too much on
CRM and RevOps and the rest of
it, but being able to have the
nimbleness in your system structure.
To go and carve that out and be like,
we're going to have these three users,
we're going to have them work
in a, and you don't necessarily
have to air gap it, right?
But like, we're going to have these users
do a different process that's segmented
from what everybody else is doing.
Your ability to experiment with
that and iterate against that.
I think, especially as things move
really, really quickly becomes a
massive competitive differentiator.
So kudos to pulling that off at
1200 person organization is a
lot of things and nimble is not
one of them is my assumption.
So that's huge.
Absolutely.
And, and that's so yeah, I
would say the two things I want
people to think about with AI.
One is like, what should I systematize?
And two is how do I manage it?
And that's where experimentation
frameworks come in.
And that this conversation I was
having with that chief of staff
basically is what was spawned
that experiment pod kind of concept.
Basically, what I've started doing is
I don't really use marketing plans.
I use experimentation frameworks
as far as pipeline growth and
acquisition is, is concerned.
And so I work with sales and SDR and,
and, run the SDR team now at ServiceBell.
But we basically set up like, here
are the top hypotheses we have, right?
And AI could be in there.
It could be in there for summarizing.
And finding the right people, summarizing
the company info and all that stuff,
but at first it doesn't really need
to be, we're going to go prove the
concept and then we're going to
figure out the system to scale, right?
We'll see what has teeth, what has
legs, whatever analogy of the body parts
you want to use for longevity, right?
What has strong hearts, robust,
no heart surgery needed and
sticks around long enough to say,
Hey, we should scale this up.
Look, we got two customers, five open ops
and 20 conversations that are positive
out of this test in the last month.
Well, let's put some more dollars there.
Let's run ABM ads alongside
the SDR outreach, right?
Like think in terms of like,
what are your, like, maybe one,
two, three scale of emotions.
And the third one is like full on AI.
Let's do this forever.
Super scale, right?
I want to, I want to expand on that
because what you just described and I
think about in the beginning of A8 and
then I was running all of our RebOps
services work that the, it was just
super interesting because we got so
much exposure to so many different
GTM teams and GTM leaders and the
ways in which they would do things.
And what you just described is.
In my opinion, the correct
borderline only way to figure
out new demand gen strategies.
And I, I think so many
people don't get it.
And I know that, that hapily when we've
gone through a couple of different, I
don't know that we've churned a whole
bunch of GTM leaders yet, but we've had
a bunch of people come in and do stuff.
And I think that if you are anyone
in any go to market function, and you
are experimenting with new demand gen
methodologies or frameworks trying to
figure out what works the absolute best
way to do that is what you just said.
And it's like, you can get fancy
and you can build CRM attribution.
You can do all sorts of stuff.
But at the end of the day, you can have
a sheet that's like, here is each thing
we're doing in column a here is how much a
very top of funnel generated and B like C
D E as many and far out as you want to go.
And if you do that, and you update
that, and you present that to whoever
your boss is on an ongoing basis, and
you're like, we are learning stuff.
I think the whole conversation around
like, well, should we be spending on this?
And what should we be doing here?
And like, does that make sense?
Like that is the answer.
And I I would just say for anyone
who is a if you manage a marketing
function, whether you're a CEO,
founder, whatever, like ask for that.
That's the best thing that you can get.
And if you are managing a demand
gen function, do that thing.
Like, and don't get distracted
by all of the pieces that I
think feed into and build that.
Yeah,
It can be totally manual.
That's fine.
Connor, you're the first person I've
spoken with who has described the sheet
exactly as I have always described it
literally I just recorded a podcast.
Every time I talk to you,
I want to work with you.
It's the reality.
Well this that's the thing is is what
you're doing, imagine this right you
have five salespeople and you had
to lay off 20 percent headcount last
year, really common scenario, right?
And you're like, well, where
are our best customers?
We kind of have info about them,
let's pull out records and notes
and CRM and like of our hundred, 200
customers, you know, even 500, what
are the 50, 10 top ones look like?
What do they do?
Okay.
Well, we found a few different threads
of attributes, let's go test those.
And we will test in order to fail
fast, meaning salespeople, this
isn't like you're on the hook
for making each of these work.
The CEO and Founder can't come down on
you and say, "well, you didn't, you just
don't know enough about that segment."
It's just like, no, we all agreed
with this was an experiment.
The point was to figure
out what not to pivot into.
The first time I did this
with this was a convoy.
LinkedIn ads for summer running and
built a whole business case used rice
scoring to argue for prioritization
of different test initiatives, and we
ran a spreadsheet of the spend and the
opportunities created and conversion to
customers for each of the experiments
in LinkedIn ads, then it air wallets,
no product market fit in the U.
S.
They hadn't done any research
before launching in the U.
S.
Back in, 2021 Midway.
And, the, the founder CEO would
not let me win the argument that we
didn't have private market pit cause
he wanted to have private market pit.
Well, so I said, okay,
eight industry hypotheses.
Bought validated phone data for one
SDR, two months of this work, he's
shaving into three industries that
are really working and three that are
really not, and two that are ambiguous.
He's booking a meeting a day.
And then he's booking two months
later, five meetings a day.
This is what experimentation can do for
you, is give you the, the, finally the
lens to focus on the growth avenues, the
critical paths that you know are there,
you just don't know how to find them.
And I
think to your point,
it's gotta be a sheet.
Yeah, I mean, I think to your point,
and I, I, I don't know, I think at
one point I probably had a title of
growth hacker at some company sometime,
but I think to your point, right?
I think, I don't know if it's that
origination of, of approaching a lot of
this, because my, my way into ops was,
was entirely through sales and then
marketing and then into the ops piece.
And I think that the thing that like,
people miss is that ultimately, I think
this is especially true in marketing.
I'm thinking about whether or not
it's true and everything else,
but at minimum it's, it's true
in demand gen and marketing is
like, it goes into two functions.
You are either experimenting
and trying to find
new channels, new methods, new strategies,
new ways of, of doing things and, or
like does anyone want this at all?
It's like maybe even an
earlier point, right?
Or you're, you're optimizing and
building a lot of those channels.
And I think that when I look
at the, and marketers have
historically low tenures, right?
And I see a lot of, and I think
there's a lot of founders and CEOs
that don't have a marketing background.
And I think what ends up happening is,
you are looking for somebody who is an
experimenter and then you, you want to
judge them by their ability to manage and
optimize and or you're hiring somebody
who is a manager and optimizer and they've
never done the experimentation framework.
And I think the most seasoned and
long tenure and successful marketing
leaders that you're going to hire are
people have a lot of experience in
managing and optimizing existing things.
And they don't know the first thing
about building and testing and
managing all of those components.
And I think that's why you see a lot
of executives, but it's like the VP
marketing loop, right?
Yeah.
Well, and I think that's why demand
gen has stuck around is because of
that, that agility and experimentation
is pretty native to demand gen type.
Do
you think that's a different function?
Do you think that there's like something
like a a framework I talk about a lot
when I think about leaders on our team and
where it's been and in 2023 A8 was sort of
used to like doubling every year and 2023
it was like a little bit better than flat,
but like, definitely not at the same pace
It's a tough year.
Tough year.
And I think I think a lot of our leaders
are builder leaders like they love to
build and adapt and create something new.
And like, that's where they
thrive the most in that, like,
structured chaos environment.
And this, like, manage status quo was
just so much an antithesis to what they
love to do and want to do and can do.
And I think I've started to think
a lot about people that I hire and
work with and leaders that we build
as, are you somebody that's going to
go and be the builder of the thing?
Or are you the person that is going
to be the manager of the thing?
And there's a different time
and place for each one of those
profiles on any individual function.
Do you think that that applies
to like marketing and demand gen?
And like, these are different things
and it doesn't mean one person can't.
Do or be good at both, but are, are
they fundamentally different practices?
Yeah, they totally are.
I think because experimenting is like.
Lab coat, black, dark room, you
know, chemistry by yourself.
Might explode in your face.
Who knows?
Totally.
But you gotta be given permission
to go hide in a wormhole and
come out with meaningful stuff.
And you gotta be given budget to do that.
Now, now, at bigger companies it should
still be 80 percent of your budget is like
the stuff you know works and 20 percent
is always experimenting or the stuff you
know works stops working in two years.
That's everyone's paid budget in B2B SaaS.
We don't talk about that enough.
Like what happened to paid budgets?
Like they vaporized for smaller B2B SaaS.
First thing to go.
Cause then they didn't have
to cut headcount as much.
But, the, the manager leader, the,
the, the person who can see, we've
got some working things and I need
to be able to coordinate across
working things and non working things.
And a lot of different people is,
is a, is a soft skill, people,
person, project management mindset.
And that's just different and you need it.
You need stabilization.
The chaos to those people, right?
Is the antithesis of like, oh, man, if
we're trying to do a lot of unstructured
things in our system, like, the
system's not going to work anymore.
And that's like, very bad.
Yeah.
Like a good, a good analogy for
the question you're asking, I
think is like, at what point do
you need a CRM when you are right?
Like when you're starting out a company.
Obviously once you've got some
amount of like customers to, to
manage and relationships, to manage,
but it's not right away, right?
Right away is I need to go have a lot
of conversations and I, I could keep
a spreadsheet of them or I could use a
free CRM or something like that, but at
what point do I need like HubSpot level?
Salesforce?
I know HubSpot is a free...
but no, I so here's what's
what's interesting that you just
highlighted for me as I think about.
In the very, very, very early
days of HubSpot CRM product before
I think it was not even called.
It was the original sidekick product.
And then they started building a
little bit of that initial CRM stuff.
I was doing tons and tons of
early stage startup consulting.
And what we would basically, I would
always tell people like, "Hey, you
need something, go set all this up".
And I would talk to these CEOs
that are like, "Our, our investor
told us we need Salesforce".
And I was like, "no, no, no, no, no,
you don't, you don't know anything,
you don't know anything, you
don't know your business process.
Like you don't know anything."
This is not a good plan.
It's like, go put everything in HubSpot.
It'll track your emails.
You'll kind of know what's going on.
They have this little deal board.
Like, that's all you need.
You don't need anything else.
And then at some point
they'd be like, "Oh, wow.
We have like a manager and we have
a business process and we could like
explain it to you" and I was like, great.
Now we'll go move
everything to Salesforce.
And that was like the main thing that
we did in our original exposure and why
we were so prolific at the beginning of
HubSpot sales product is like, we were, it
was basically just like Salesforce starter
for everybody is what we were using it as.
And then I think that HubSpot
and I, to give credit, credit and
credence to their product team,
I think that people think about.
HubSpot starter professional
and enterprise as being like for
different size and scale of, of
companies or sort of a function.
I think that's actually the
wrong way to think about it.
And it's much more like, where are you on
your life cycle of like your GTM maturity?
And I think the best part of like,
HubSpot starter and some of the basic
things is like, look, you don't need
a lot of automation functionality.
You don't need a lot of customization.
Like you just need a place to
dump all your data and manage it.
And we have a product for that
and it's, it's free or it's
basically free.
Here's what people miss, right?
Is if you deploy something like
Salesforce, when you don't have a
known and delivering GTM with some
established processes and people
to admin and, and, and operate it.
You actually do the inverse of helping
you hurt everyone go ask any demand
gen marketer Who's had to work with
Salesforce in a company in a unicorn?
I've worked at two unicorns both had
infuriating Salesforce instances and
in both we were slower than we should
have been because of Salesforce, right?
And sometimes that happens with
HubSpot, but I can actually go
change stuff in HubSpot myself
I can't do that in Salesforce.
Like I
No, I think to your point, the thing
that makes people, when they talk about
usability or they talk about something,
I think what you just described is, is
the actual component, which is, do you
need an engineer or a technician to make
an adaptation and make something change.
And if you don't, then you and I, we're
going to loop it all the way back to AI.
I think that this is what a lot of the
AI functionality does is it actually
decreases the level of skill required
in order to administer and manage and
do things in different business systems.
And then as a result, as you lower that
barrier to entry, you increase the speed
at which things can happen because the
person who feels strangleholded by the
system wants it to change, wants it
to adapt, doesn't need to go and like
find somebody with expertise who knows
what they're doing, who can manage
the thing and like actually edit it.
Instead
they're just like, yo, I
want this to be different.
And they do not need a high degree
of technical acumen to actually
make that happen and shift it.
Yes.
As long as we're talking about the
AI tools that spoon feed you the
like inputs and outputs, right?
I totally agree.
I do think it's interesting.
There is an analogy
here with AI, like clay.
com is actually pretty complicated
once you get it going and
doing high powered stuff.
So there are people, Jacob Tuiner,
who's part of a service bell,
but also his own consultancy.
Shout out to him.
What's it called?
Sculpted.
Because clay.
Where it's managed Clay implementations.
And so if you need really powerful
end to end, like account research
with AI and then deploying
emails out with custom messaging,
Wait, this is a guy who's
doing like a clay agency thing.
Okay.
I would introduce me to him.
I would love to talk to him.
Yeah.
Yeah.
He's, he's great.
He'll definitely help.
There's Eric Novoselovsky.
He's doing stuff like this.
Kellen K Spear, who's big on
the experimentation frameworks.
Brigitte Ruha, Scott Martinez.
There's a lot of these people who are
doing it, but, but they will be the
first to tell you, like, It's probably
less expensive to go buy their retainer
than it is for you to like spend the
time on your best people and learn
I think, so I think to your point.
And this is what I've told the
people when I get asked of like,
how are you worried about AI?
Like you run the services
business, like it's expertise,
like that's what you're selling.
And I think what you just said is right.
I don't think that there's, and I
think that this goes back to AI AI
labor and what it does to economies
and everything else is like, you do
not vaporize the value of expertise.
You just move the
threshold for expertise up.
You move the requirements of domain
expertise to achieve anything
down and what you end up actually
unlocking on the other side is just
net higher productivity for both.
I'm going to use the word
class, which feels wrong, but
maybe also really prevalent.
Both classes of people that are,
that are doing sort of the high level
expertise, high level of customization
work and the folks that aren't.
And I think one of the things and, and
that A8 is really looking at is, is how
do we go and build a lot of those pieces?
And, and that I think is becoming
more and more core to the
work that we're doing because.
Almost everyone is trying to figure out
how do I pull AI into the overall strategy
that I'm doing and how do we do that?
Well, which is not the same thing
as AI, making it easy for people to
do basic functions, which I think.
The value sort of impacts both.
Absolutely.
HubSpot, I've got hooked up to Clay.
I run my exclusion lists through it.
So I can check when I create new
account lists, like, are there
already customers that they're out
at hops, exclude those domains and
then move on with my clay prospect.
Yeah, these worlds are
colliding absolutely.
And I think the biggest theme that I'm
seeing from our conversation here is
like, you have to think about yourself
as a system builder, and in the early
days of systems, you want the rawest,
simplest form of experimentation that
just gets you insights quickly to
to validate or invalidate hypotheses
you have about growth this year.
And everyone should be doing it.
I talked to a lot of founders in
my last job hunt about a dozen or
so, and would ask them, like, is
your marketing plan gonna work?
They'd all say "no" I was
like, "why do you have it?"
And we'd talk about experimentation.
What are you doing with it then?
That seems really silly.
It's very politely obviously,
and then, you know, we talk
about experimentation frameworks.
They all love this idea.
Everyone loves this idea, right?
Like simple sheet projects to
manage growth experiments, right?
But, but then when you get to the point
of like, okay we've got some traction.
Now I've got some customers.
I need to make this CRM native data.
I need to make this a system where
AI can solve some of the gaps.
A crawler can solve some of the gaps
and just automate some of the pieces
so I don't have my demand gen marketer
spending 60 hours a month on, you
know, building lists basically, or,
or finding data and contact info.
Yeah, that's, that's, I think the name
of the game for any like, seed series
a and B2B SaaS this year is you're
probably struggling a little bit with
where are your clean growth lanes.
You know, what we're talking about, I
think is the way to, to figure that out.
Evan, I, just as the last time and every
time that I speak to you, could spend
hours and hours and hours talking to you,
but I only reserved so much of your time.
And so, I will do more of this, and
thank you so much for coming and
sharing super practical insights.
Check out Clay.
Check out, ask CSV.
We'll put a whole list of of tools that
Evan sort of plugged in the notes here.
But Evan, thank you so
much for joining us.
And I'll catch up with you more soon.
Thanks Connor.