CRO Spotlight

Warren Zenna: The CRO Spotlight
Podcast, Growth Forum Production.

Hi, I'm Warren Zenna, founder and
CEO of the CRO Collective, and

welcome to the CRO Spotlight Podcast.

This podcast is for Chief Revenue
Officers, aspiring CROs and

CEOs who are looking to hire
or support a CRO to succeed.

To join me and my expert guests as we
debate, discuss, and tackle today's

complex revenue growth challenges,
and provide practical insights

to help CRO succeed in the role.

We're really excited to have you with us.

Uh, let's get to it.

All right.

Hello.

Welcome to this episode of
the CRO Spotlight Podcast.

This is Warren Zenna.

I'm the founder and CRO
of the CRO Collective.

And, uh, we've been some really
great guests in the last month and

I'm really excited about today.

Gonna talk about ai, of course, right?

Who else isn't talking about AI?

Everyone's talking about ai, so
we might as well talk about ai.

I'm, I'm, see, I'm seeing how this
whole craze, I probably get seven

or eight AI based emails or inbound
messages a day from various people

all over the world, offering me
some newfangled way to use AI too.

Improve this or increase that,
or reduce this or do that.

And I've been playing around a little bit.

As a matter of fact, my guest today
gave me some tips on it, but we'll get

into it because, um, this platform,
you know, it may or may not have an

impact on Chief Revenue Officers.

Um, you know, I'm, I'm sort of
weary of these spades of excitement

that happened with these things.

I mean, I think you look at AR and
VR, everyone was freaking out about

it and nothing happened with it.

That doesn't mean it's not gonna be huge.

It doesn't.

I still think those things probably
will be, but these things take a

lot longer, you know, to seep into
the like regular flow of things.

And it's usually people that
are on the edges who are really

innovative, that are out in front,
that have the most to say about it.

And also there's a lot of misinformation.

I hate using that word, but
you know, there is, there's a

lot of stuff that's going on.

I hear fear is a big one.

I have a client of mine who has
an AI platform and they're telling

me that clients are worried about.

Adopting their system, they're
worried it's gonna take away their

jobs, like it's some evil creature.

And I think that what I wanna talk to
the guest today, who is Heidi Messer,

she's the founder of Collective I.

Well, we'll talk about that in a second,
is she's really smart about this, and

I think she'll be really interesting to
talk about the implications of this stuff

from perspective of revenue forecasting,
et cetera, et cetera, and using AI to

better provide intelligence, right?

That maybe, maybe we need
to do this differently.

So anyway, Heidi, welcome.

I'm so glad you're here.

It's great to, great to see you.

Heidi Messer: Thank you.

I'm so happy to be here, Warren.

Warren Zenna: Yeah.

Well, great.

So I'll tell you a little bit about Heidi.

So Heidi's been an active entrepreneur
and investor in the digital economy since

the commercialization of the internet.

She's like me, she's been around for.

You know, since there was a thing
called the internet, we both have

a lot of stories we can tell.

Um, she's the co-founder and
chairperson of Collective I,

which is short for Collective
Intelligence, which we'll get into.

Uh, the platform uses artificial
intelligence to help businesses

forecast, manage, and grow revenue.

So I'm really excited about this
and I'm gonna kind of end there in

terms of all her background, cause
I want her to talk about it more.

But Heidi, so, you know, Heidi
and I bumped into each other.

I, we turns out that, you know,
we kinda like have some similar

backgrounds and people that we know and.

Got talking about these things and what it
is that Heidi's doing is very fascinating.

So I'd like to talk to you a bit more
about yourself, your background, how you

got to where you are today, what evolu,
what the evolution was that got you to

the point where you found this sort of.

Early adoption of this thing
and built a company around it.

And then we can get into a little bit
more about this newfangled AI stuff and

how it could impact the industry that
you and I are both, uh, floating around.

So tell us a little bit about, more
about your, how, how you got here.

Heidi Messer: Oh my gosh.

Well, um, Warren, as you were so
gracious in saying we, we, we have

a lot of experience in this area.

It's a nicer way than saying we're,
we've been in it a long time.

Uh, and I think for my part, I've seen
the evolution of the digital economy.

Uh, starting in the nineties when
my co-founder, uh, my brother and

I actually started a company that
was one of the first ad networks.

And it's interesting just to bring
it back to sales at that time, uh,

everyone's objection was no one's
gonna give their credit card online.

No one's gonna buy goods and
services over the internet.

And so that.

Sort of subterfuge gave a lot of people
the excuse not to adapt to things that

were, were, in my opinion, inevitable.

Uh, you're sort of at a similar
juncture today where, uh, you

can see all the signs that buyers
have evolved faster than sellers.

And the real benefit that I see in the
technology that we're about to discuss

in artificial intelligence is that it's a
way for sellers to leapfrog and catch up.

And, and I'll just give you
a couple of statistics that

are particularly upsetting.

One is, um, 83% of buyers who go through a
successful sales process are dissatisfied.

So 83% of people who actually
go through the motion that we've

put them through as sellers.

Come out on the other end and
say, I bought in spite of the

process, not because of it.

Warren Zenna: That sounds low.

I'm surprised not 90%.

You think it's a hundred percent.

That's so, yeah, I would say so.

I mean, I don't know anyone that's happy
with the sales process, but anyway.

Okay.

That, that's actually kind of in a
way like positive, but it's still bad,

but it's, I thought it'd be worse.

Uh, anyway.

Yeah.

Heidi Messer: Maybe, maybe the
other uh, 17% aren't admitting

it.

Warren Zenna: Um, yeah.

They're just not thrilled.

Heidi Messer: Right.

They're just not thrilled.

Right.

Exactly.

There's, there's nobody standing
up and saying, I love this

Warren Zenna: process.

I, I, I mean, I hate,
I hate sales processes.

We, we'll get into this cuz
there's a whole topic on that.

Yeah.

Heidi Messer: Cause you and bonded
over this, you know, about, about the

need for really quality over quantity.

Um, and then I think the second thing
is that, you know, you've looked

at productivity rates of sellers
actually plummet over time and so,

You have a situation where sales
management and you know, CROs all the

way down to the people that work for
them are spending more time looking

inward than they are looking outward.

Uh, I think the last statistic I saw was
something like 30% of time is spent with

buyers, um, which is an unbelievably low
statistic and could also explain, you

know, your 83 to a hundred percent of
dissatisfaction rates, um, that are there.

So you have people working harder.

Um, not generating results.

You know, you're, you say every other
statistic is dropping, by the way.

When rates are dropping, average
contract values are dropping, um,

and, and people are doing more
and more to get less and less.

And so that's really when, when, you
know, my co-founders and I looked at this

space and said, is there a place for.

Uh, a technology that could radically
improve the life for people who

are operating in the profession,
but also the life of people who

are influenced by the profession.

That's what led us to
create collective eye.

Warren Zenna: It's fascinating cuz
you're talking about something that

I talk about a lot, which is we're
again, we're looking at a chief revenue

officer who has this really complex job.

And they're managing this revenue
operation, which not only has a sales

operation, but also has a marketing
function and a customer success function.

And there are technologies that come
with all these different functions that

all have to interoperate, you know, and,
and the stack gets bigger and bigger.

There's more data.

The more data is the more garbage.

There's less intelligence.

Even though there's more data, there's
less intelligence, um, companies are

struggling to figure out ways to make
sense of all the data that they have.

They don't want to get rid
of data, they're afraid.

That's the good data.

So I just keep it, you know, so
they leave everything lying around

and try to make sense of garbage.

And as a result, you've got, like
you said, more and more constraints

are put on people within these
different departments to look at

dashboards or use tools or attribution
platforms or whatever they're doing.

To try and provide more and more
and more intelligence to everybody

in the organization so everybody
know, knows what's going on,

and it feeds the wrong purpose.

It's always a very inward thing.

You've got the CEO who needs to report
to a board that needs to give the board

numbers, and the board needs numbers.

The numbers come from the technology
and the numbers aren't good.

They go back and look at the technology
and I, I've been in this and you've

been in this situation thousand times.

And so what's happening is the, the,
the thing that's never mentioned ever

is the customer, like the customer
suffers from this cuz they have a lousy

experience when they're trying to.

Deal with a, with a company and
just trying to buy something and

then extract the value from it.

And then the other part of it is, what
I'm hearing a lot, and this is something

I wrote about recently, is the never
ending top-down projections that come

from the board or the c o that they're
pushed upon the organization, the c r

o, sort of in this devil's, you know,
deal where they have to say there no.

I'm not accepting these numbers.

They don't match up with
what's gonna happen.

But if they say no,
they're not a team player.

So they accept the numbers and
then of course it doesn't work.

And they wonder why was
the forecasting off?

You know, what's going on?

So the big, uh, sort of genie
in the bottle is how can

we get forecasting working?

How can we predict better, how can we
use data to make better predictions?

And um, that's where when you and
I talked about this, you explained

how artificial intelligence can act
as a way to maybe alleviate some of

that ability to look into the future.

So that it can help understand
better how to do things and

what the right things are.

But before, I wanna get into that, before
I do it, maybe just for some people who

don't get this stuff as well, I know the
differences with these things, but if you

could explain like the difference between
like, let's say machine learning and ai,

like how do you break up all these words
that are being thrown around right now, so

we understand what you're talking about?

Yeah.

Heidi Messer: Well, so, so
machine learning is a form of ai.

Um, it's just an older version of it.

The newer versions that you see.

Uh, are, are things called, um,
neural networks of deep learning,

which actually go one step beyond
and replicate human thinking, right?

Human reasoning.

So when you see the difference between,
say for example, like a traditional

search versus a chat G B T search, you
can actually have a conversation with

chat T B T because the way that it's.

Using data and ingesting data is
actually designed to replicate the way

you and I would have a conversation
versus just generate a, a search result.

So, um, the reason why people like me
are so excited by this kind of artificial

intelligence is because once you have
that ability to replicate analytical

thinking that human beings do, you can
remove a lot of very time consuming work.

From people and allow them to get
straight to execution, straight

to de demonstrating their skills.

Mm-hmm.

It's a much different, um,
it's a much different output.

So like, say a traditional
algorithm that used rule-based

training for, for the algorithm.

So it says if a deal is at stage
discovery, the odds are 25 to 30%.

So when that machine generates a
forecast, it just looks at that stage

and says, those are roughly the odds
that that opportunity falls into.

Mm-hmm.

A more advanced form of AI
would actually be looking at a

much more massive set of data.

So it would be looking at all of the
activities that a seller had done, all

of the activities that a buyer had done,
buyer patterns, seller patterns, economic

factors that might influence things.

And say, gosh, if I were an economist
and I were a PhD, and I were an analyst,

and I were to look at all this data
and be able to process it in real time,

what would I predict the odds would be?

And it's a much more precise way and
a much less biased way of predicting

outcomes because a machine is actually
ingesting huge amounts of data

in order to make that prediction.

Now that's the rub.

Why hasn't everybody
adopted this form of ai?

The amount of training sets of data
that you need in order to be able to

utilize and access this AI are massive
and no company on its own will be able

to use their own data to replicate that.

And very few companies have
gone the way that Collective

Eye has to aggregate that data.

So there's a training set of
data that's large enough that

can enable those kinds of

Warren Zenna: predictions to be made.

So, okay, that's an interesting, so,
cause when you were telling me this,

I was thinking in my mind, Well,
where is all that data coming from?

I agree.

Like it would make sense to
look at everything, right?

All these disparate factors
that go into a sales cycle.

It's not just whether the person said
yes or not, or it's the third phone call.

You know, it's a lot of things.

Um, and it gets really interesting, right?

It could be the quality
of the conversations.

I know that there's platforms that can
even detect the tone of the conversation

that you've had, whether they seem
positive or negative, and those things

become a factor, or certain keywords
are being used frequently by the

client that indicate patterns of, you
know, receptivity or interest, right?

And you're right, economic factors,
et cetera, and all that stuff.

But where does that data come from and
how does your system analyze it and how

does a company set themselves up so that
they're able to take that data, collect

that data in a way that is useful?

Heidi Messer: So, um, so the beauty
of it is with our system, you don't

actually have to set up your system to
collect that data in a way to be useful.

Because one of the things that we do is we
actually automate data capture into crm.

So maybe it would be helpful just to go
through sort of the status quo of the way

most organizations operate today, and then
sort of the change that comes afterwards.

So today we, we've done
a lot of things manually.

We've asked sellers to do things.

In fact, they used to have names.

They would put really, you
know, the world of CRM is great

at putting labels on things.

Um, you know, rigor.

You may recall that that was where
you said, you know, a great seller

had great rigor, meaning they were
really good at data entry into crm.

Um, anyone who's been a C R O knows that
there's almost an inversely proportionate

relationship between the people who
are great at entering data and crm and

the people who are great at selling.

Yep.

Um, your best sellers just
say, I'm not gonna do it.

I'm not

Warren Zenna: gonna do this.

I wanna have conversations with people.

I was that guy.

I was the one that the CFO used
to chase around the office all the

time saying, Warren, we don't have
any information in your system.

Like, I know because I'm talking
to people, leave me alone.

You know, and you know,
it's just, I, I get it.

I understand the need and the hunger.

That an organization has to collect
information so they can make analyses, but

it, it puts such constraints on someone
like myself who just likes to be out in

the marketplace having conversations with
people, and I, I find perhaps maybe where

you're heading here, Is how this could
supplicate that so that people like me and

you, I assume, don't have to sit around in
front of a dashboard all day long because

it's being done in a different way.

I mean, maybe Is that it?

Exactly.

Heidi Messer: It, it's been, it's been
a ridiculous choice that organizations

have had to make, which is, do I
sacrifice the productivity of my most

valuable asset, my sellers, right, in
order to get information that helps

me understand what they're doing.

Yep.

Or do I.

Forego having that information and
have no transparency into anything

that people are working on and
where they're spending their time.

And for the longest time, people were
willing to sacrifice that productivity,

but the problem was they weren't
even trusting the data that was being

input through through rigor, right?

So that led to all day long
pipeline reviews, right?

So imagine this if you're
a seller, somebody said to

you, I don't want you talk.

I'd rather you sacrifice time in front of
buyers to input all the things you already

know, like what you've been doing all day.

And by the way, I don't
trust you when you do it.

So now we're gonna have an all day review
of everything that you typed in manually

into CRM system so that I can critique it.

And then on top of that, we're gonna add
in an all day long forecasting process.

Mm-hmm.

So what you have is a situation
where there's really, when you nail,

when you go down to it, there's one
person who has a bunch of information

that everybody wants to know.

And so we've now created three days worth
of work in order to get that information.

So here's, mm-hmm.

Here's the first step of
where AI can be very useful.

So, One of the things I said,
as I mentioned, we automate

data capture into crm.

So as you're working, literally
the machine is recognizing from the

metadata, meaning the to and from in
your emails who you're talking to.

That's a buyer.

It's taking a snapshot of that
and it's throwing in into crm so

you don't have to easy, right?

Once you eliminate that, suddenly
you also clean up CRM data,

which nobody trusted and not.

Wrongfully.

So, because, you know, sellers are busy,
sometimes sellers forget to put things in.

Sometimes sellers
intentionally omit things like

Warren Zenna: when a deal goes.

So is this sort of like, um, just to
understand it better, more from like

a really on the ground is, is it just
like a listening platform that's just

hearing and watching what's going on
and making assessments automatically

so I don't have to do anything?

Is that sort of like a.

Surveillance in a way.

I don't use that word.

I know it's a negative word,
but is it sort of like that?

It's like a, like a, like a
like an Alexa type situation?

It's,

Heidi Messer: it's less like that
and more of a screening mechanism.

So if there's nothing in the metadata,
you know, that to and from section

that has a buyer or a seller in it,
we don't even wanna see that email.

We don't even wanna entering our system.

We don't, we don't, you know, so
like an Alexa can listen to you

all the time, this is like, if
it's not related to a sales motion.

We, we do not want

Warren Zenna: it.

We don't want it.

So in your, in your case, just
so I'm clear, you're talking

about right now, are you looking

Heidi Messer: only at email?

So any source of of seller activity that
a, an organization wants to connect us to?

So they might want us to connect
to Zoom, for example, right?

Like

Warren Zenna: Slack conferencing,
text and all that stuff, right?

So,

Heidi Messer: well, yes, as long as
they're company devices and Sure.

Um, and Slack is, is less.

Typical, because that's usually
internal communication, but any,

any system like, you know, it could
be DocuSign, Adobe Sign, it could

be box, Dropbox, anything that are
sort of reflecting sales activities.

By the way, they're, they're not
limited to sales activities by sellers.

Anyone who's in sales knows
legal has a tremendous impact.

Sure.

Warren Zenna: Sure's, a lot of
people get involved in a lot

of different communications,

Heidi Messer: marketing.

And so, so this notion that a
seller is solely responsible for an

outcome, I think is getting debunked,
I'm hoping, is getting debunked

when you start to have the ability
with AI to understand everybody's

participation, the role everyone plays.

Cuz if you ask me my opinion, entire
organization should be accountable

for revenue, not a single salesperson.

Warren Zenna: A hundred percent correct.

So let's talk about that for a second.

So, cuz again, the cro.

Needs to align multiple functions in
relation to revenue growth, right?

What's marketing's
contribution to the sale?

What's the customer success
contribution to selling?

What's sales contribution to selling?

What's product, you know,
contribution to sales, right?

So would an AI system be able
to monitor all those elements?

And look at the whole picture from
the entire system and see not only

what types of communications were you
having with the client, but also how

are they impacted by the marketing
messaging and what do they respond to?

I mean, do these factors get played
into this or is it all mostly

relegated to sales conversations

Heidi Messer: or?

No, any, any relevant
factor plays into Okay.

Into, uh, uh, an AI assessment.

So that's what I mentioned about,
it's like having, um, It, it, it's

like having a thousand Jarvis analyst.

Warren Zenna: Jarvis,
almost like Jarvis, right?

Jarvis is, you know, Tony Stark's
robot, you know, that was pretty much

ubiquitously there all the time and
collecting all the data all the time,

and was able to, now what he did,
Jarvis, was there temperature, your

heart rate, what you just ate, who
you're talking to, what time it is,

how long that bomb's gonna go off.

Background information on
the person you're talking to?

Yeah.

Heidi Messer: I mean, it sounds
more nefarious when you put it that

way, but it's, it's more designed
to, like, if you could, if you

could access a sales assistant.

So if every, if you took every seller
and said, I don't like to input

data into crm, can you do it for me?

Mm-hmm.

It's your sales assistant for a sales
professional and a sales manager.

It's an analyst, right?

Yep.

So, um, It's an, an analyst that's looking
at, you know, an an economist, a PhD,

like literally looking at the market and
saying, where should you focus today?

Like, where are the
risks in your pipeline?

And then the transparency piece that
gets coupled with it, meaning like,

can we show people in a feed everything
that's happening related to an

opportunity alongside of these insights
that this AI assistant has produced.

The idea behind that is to say, okay,
if a lawyer, for example, is sitting

on a contract for a week, right?

And we're sitting there telling the
seller, time kills all deals, time

kills all deals, but this other person
is adding time to the sales process.

Like is there a way that that can
become transparent to everyone and

you can see its impact on the odds
so that everybody gets enlisted

in the process of bringing things

Warren Zenna: to the finish line.

Got it.

This is really cool.

So, I mean, I, I'm curious cuz you and
I talked about, we spoke last, my, you

know, sort of, I guess maybe curmudgeonly
sort of general attitude about new things.

I love new things.

I love tech.

I always download the new app.

I always play around with
the new software, but I.

I'm also skeptical when I see a lot
of people excited about something.

Every time I see everybody excited about
something, the first thing I think is,

oh, okay, this is the new, the new thing.

You know?

So we'll see what it really goes.

You know, a lot of it is, as you
know, AI has offered the media

something to talk about, right?

It's really interesting
conversation to have.

It's provocative.

It's also something that a lot of
people don't know about, so you could

fill the topic up with any opinion you
want and sort of be right, you know?

And so it's an interesting
place where I see.

Maybe I'm, and what you and I also
discussed, and I agree with this, is

that we're at a different time, right?

I mean, when the internet came out
and when the iPhone came out, things

weren't moving as fast and they were
moving, moving fast, relatively,

but they're moving even faster now.

I mean, I see a new AI system
come out every two weeks that

seems to be better than the last.

And so I'm, I'm aware of how these sort
of, you know, Moore's law stuff works.

So I'm curious to know where
you think this is really headed

and what, what's the adoption?

What are some of the things that you're
hearing that people are resisting and how

do organizations overcome that resistance?

What's the actual real use that
an organization can have today

with this stuff instead of just
like poking around with a bunch of

new dashboards or make it really

Heidi Messer: useful?

It's such a good question, Warren.

So I think first of all, uh, your first
question, you know, sort of understanding

the nuances between different versions
of artificial intelligence, so, As

I mentioned, deep learning is, is a
subset of machine learning, right?

But it's a much, um, more advanced
version of, of what older forms of

machine learning could, could actually do.

So the first thing is understanding
that not all AI is the same.

And one of the questions you're
gonna wanna ask is what is the

training set of data that you're
using to produce these insights and.

Here's, here's the downside of ai.

If the training set of data that
you're using is biased, then the

outcomes it predicts will be biased.

Yep.

So if I could, if I, if you could
indulge me in the forecasting

processes, like mm-hmm.

I think you and I had a mind, Mel
on this, which is, if you look at

today's forecasting process in most
traditional organizations, it's very

much based off of human opinion.

Um, even the words behind
it, you know, commits.

Stages.

So a seller commits to closing a deal.

It pick, they pick the
stages that it goes with.

Then the stages, um, become the predictor.

So if you wanna talk to any seller
who's manipulating a forecast, whether

they're using it with, um, a SAS piece
of technology to help them, or they're

doing it through Excel spreadsheets,
literally they just changed the stage.

In crm, and suddenly the odds
of that deal closing go up.

If it starts to go south, they've
got in the back, their back pocket,

two deals they're gonna swap it with.

Mm-hmm.

Right?

So now think of you're the manager
and you have to go to that board.

You're the cro.

You have to commit to a number, and
you're going to get opinions from all

your salespeople, what they're likely
to do, and you're gonna sit there

and say, is that person a sandbagger?

Does that per, does that
person have happy ears?

I mean, this whole process is
literally, it's all emotional.

It's all emotional, very emotional.

It's, and the process, it's emotional.

The process has become emotional.

And so to answer your question about
what can organizations do to modernize,

I think the first thing is to be
aware of when a process is built

around emotion bias, inefficiency,
and be willing to part ways with it.

And, and that is so hard.

I've, I've heard so many CROs say
to me, my forecast is my brand.

Yep.

You know, my ability, think about that.

My ability to predict the
future, which no one can do.

No one is my, is how I value myself.

Yep.

And that's actually, you know,
the real value in my view of a CRO

is they're an incredible coach.

They're an incredible strategist.

They're people that can look at changing
markets and help an entire team adapt.

They can bring out the best in the
people that they, you know, they manage.

Um, they can optimize revenue so
they can take their strategy and

convert it into the optimal results.

Mm-hmm.

And this process that has become
very emotional and embedded

in organizations has actually
superseded all of those human skills.

That are way more important than the
ability to go to and poll your people

and get an accurate result from a poll.

Yep.

Like

Warren Zenna: AI should do that for you.

Yeah, it's great.

I mean, it is fascinating
everything you're getting into

now, which I, I think is cool.

We're kind of getting into
philosophy, which I wanted to,

cuz this is really philosophical.

So the problem we're having with
business in general is it's people.

I mean, people, it's not machines, right?

And so people, of course,
they have all sorts of biases.

I mean, aside from survival, which I think
frankly is really primarily what's driving

most of this, it's survival, right?

Like you just said.

I mean, if I'm gonna build a brand
around a certain thing that I'm able

to do, That's my survival, that's
my sort of brand in the marketplace.

And I'm gonna try whatever I
can to burnish that and make it

even more prominence that I can
gain more followers or more, you

know, prestige around that thing.

Particularly if I tend
to get it right enough.

And I think, frankly, you and
I both, both probably know that

forecasting is like gambling.

I mean, if you get it right a lot, it
doesn't mean you're a better gambler.

Just means your luck was better.

You know?

And I'm not trying to denounce the skill,
but you know, there's so many variables.

It's really hard to predict this stuff.

And I see it happen all the time.

But what we're talking about too is,
you know, we're talking about people.

Who have motivations.

They made investments.

They wanna keep their jobs, they
wanna look good, they want to, I mean,

there's just so many factors here that
if you're having human beings that are

creating what you call these training
sets, I never use heard that term

before, but I understand what it means.

They're gonna be inherently biased
because people produce them.

So, I mean, I look at what's going
on right now with some little prompts

I've put into chat, G B T, just more
as like an experiment, you know?

And I ask it questions that I know.

Or provocative and you know, you
could see the output is biased and

you know, okay, so I understand that.

But how does that get
removed from the system?

How do you get people to, how do you
get a company to be self-aware of

its bias in order for it not to be?

I don't know.

It's almost saying, how do
you make a company not human?

Because I don't think it's possible
for human beings to not be biased.

Heidi Messer: Right.

Well, that, that's the problem with
forecasting off of sales history, right?

Because sales history is, A compilation
of what your organization has

done in the past, perhaps tempered
by people managing expectations.

Yep.

So was the forecasted revenue that you
produced the optimal amount of revenue,

or was it designed to attain a certain
number That was forecasted and 90% of the

organizations that we talk to, they're
measuring their forecasts on attainment

rates, which means, you know, people
are discounting at the end of a quarter.

People are swapping in deals, they're,
they're doing all sorts of things to Yep.

To hit a number.

Um, some of it is, is healthy behavior.

Some of it's less healthy behavior.

So I would say the big juncture where,
where we come in and, and change

that paradigm is there's what people
say and then there's what people do.

And all of the activities that our
system is analyzing to determine

a daily forecast is based on.

Patterns of what both buyers
and sellers are doing, not what

they're saying they're gonna do.

It doesn't have any commits
are irrelevant in our systems.

Stages are irrelevant in our system.

It's literally watching all the
interactions and saying, I'm observing

what's happening in the economy.

I'm observing what's happening between
this seller and buyer, and I'm observing

what this buyer's done in other selling
contexts, and here's what I predict.

Probabilistically is likely to happen.

What we then do is we say,
look, AI is not omnipotent.

It's, it's, it's not, it can't,
it can do the job of many humans,

but it can't do superhuman jobs.

Yep.

Right.

Like it can't do things that we can't do.

It can think on a much faster plane
than we can ingesting lots more data,

but it can't predict the future.

Mm-hmm.

So if it could, then, then all of
us would be retired living Yeah.

Beach somewhere.

Warren Zenna: So that's what everyone,
that's what everyone's afraid of.

I don't think people
think about being retired.

I think people think
about not being employed.

I, I think that's what people are afraid

Heidi Messer: of, you know?

Yeah.

And, and look, I think I'll, I'll,
I'll steal an internet meme, which

is, you know, AI's not gonna take
your job, but someone using AI will.

Mm-hmm.

Which means if you rejected,
you know, the help of.

All those analysts and all those
assistants, and you said, Nope,

I'm gonna be a lone ranger.

I'm gonna go it alone.

You're gonna be slower and less
informed than somebody who has the help.

Mm-hmm.

Warren Zenna: You have to become
a cyborg or else cyborgs are

gonna take your job, basically.

Yeah.

Heidi Messer: Yeah.

Well a lot of people refer to it as
like giving people superpowers, right?

Yeah.

So like, sure.

I don't know.

Do I wanna go try to lift
weights next to Superman?

Like I'm gonna lose, you know?

Cause Superman has superpower so.

So I would look at AI more as a superpower
and less as like threatening job security.

Because what it really does well, and
this is where I think CROs in particular

benefit from ai, there are certain
jobs where, you know, I think if you,

you're doing rote tasks all the time.

Yes.

Like, then it's likely that
AI is gonna replace you.

But for a cro, so much of sales is, is
about that like layer of skill that.

CROs very rarely get to exercise.

Um, you know, the, the best CROs I know
could coach a professional sports team

like they're that good at coaching.

Sure.

Agreed.

Um, you know, they're, they have
a network of relationships that's

worth more than, you know, any
sort of line item on their resume.

They just know people, people
trust them, people follow them.

They have good strategic skills.

How much of that, and, and I, I'd
ask this back to you, how much of

that do you think gets exercised
on a daily basis when you're a cro

versus how do I prepare a board book?

How do I generate a forecast?

How do I make sure that I understand
where, what surprises lurk in the

pipeline that I'm not aware of?

Most of their work now is

Warren Zenna: right, maybe 30, like
same thing like 30% of the time.

And, you know, you're so right about this.

It's, it's great you're talking about
this because, you know, you look at the

CRO competencies and when I, you know,
you say this, and the people I speak to

agree that you need to be a great leader,
and no one doubts that the job comes

with incredible amount of, you know,
communication skills and persuasion skills

and motivation skills and coaching skills
and empathy and all this other stuff.

But at the same time too, the cro.

Is responsible for growing revenue
of the company, and that always

takes precedent over everything else.

And there's always fire drills and there's
always data that needs to be looked

at or things that need to be fixed.

And so, All those skills that would
value or benefit the company are not

being taken advantage of because the
person's mired and all these other things

that people think are more important
because they're more urgent, you know?

And so this is where I think the
opportunity lies potentially in tools

like this where if I as A C R O knew
that I had something that could assist

me in me having to do less of that stuff
instead of hiring a whole staff to do it

for me, which might be another solution.

So that I could stick to the
things I know that really matter.

That would be amazing.

So again, sort of like the, the question
I asked before is, I'm curious to know

like what are some ways that that, is it
something that can be done today or is

it still sort of, we're not there yet?

Like what are some things that
are actually achievable now?

As opposed to what might be achievable.

And your second part of the question
is how soon do you think this

stuff really will be ubiquitous?

Like what, what's the, in your prediction,
I don't want, now you're not in your, an

oracle, but you, you're in this, so you
might have a sense of what that looks

Heidi Messer: like.

No.

So I think, um, is this ready today?

It is.

Um, it's here, it's now.

I would say if you have not
automated CRM data, capture an

automated forecasting you're behind.

Um, You know, if you're spending a day
a week forecast Fridays and you've got,

you know, competitors who are literally
clicking a button and getting an up to

date scientific AI enabled forecast,
you're eight hours behind in execution.

Mm-hmm.

They're, they're spending those time, that
time training their people better or being

in front of buyers more or doing something
that is not gonna help your cause.

So I think.

Getting to basic automation and then
starting to do the change management

that comes along with ai, which is
okay, maybe my forecast isn't my brand.

Maybe my brand is how well
trained my salespeople are.

Mm-hmm.

Maybe my brand is how
satisfied my buyers are.

Right?

I don't have 83% of buyers who are
unhappy at the end of a process.

They felt like they encountered a trusted

Warren Zenna: advisor.

I would assume though, the only
way that somebody would be able to.

Uh, shed, that forecasting brand
would only be if they knew that they

could substitute their forecasting
with something else that was equally

or better because they'd have to hold
onto it because it's so important.

Right.

So that's why you, you need to substitute,

Heidi Messer: you know, so we, it takes
two to four weeks to get up and running

with Collective Eye, and, and one of
the things we do is we say, look, if

you're, if you're nervous about that,
Run your traditional forecast for another

quarter and look at what happens with
ours and look at what happens with yours.

And then when you feel
comfortable that you can trust

the machine generated forecast.

It's a lot like we, we often
use the analogy of ways.

Um mm-hmm.

I don't know if you've used,
you know, ways or Google Maps.

Of course I

Warren Zenna: use wa almost every day.

So

Heidi Messer: you, you probably
can remember a time when you

said, you know what, screw it.

I know how to get where I'm going.

I'm not gonna turn ways on.

I'm just gonna use my own intuition.

And and then all of a sudden you
get in the middle of a traffic jam.

Warren Zenna: Yep.

Yep.

Happens all the time.

Right?

And my wife and I argue all the time.

Cause if I'm in, if I'm using
Waze and I'm in a traffic jam,

then she says, Waze sucks.

You shouldn't have used it.

You see?

And I'm thinking, yeah, but the odds
are that if we didn't use it, we'd be

in traffic jams more often, you know?

So it's, it's, it's, it's
funny how these things work.

I I But you have the mindset for this.

You, I mean, I love it.

Like, for me it makes sense cause
I'm willing to take that risk and

have that traffic jam every once in a
while knowing that it'll happen less.

Like to me it, that's what's
really more important.

It's not avoid, and

Heidi Messer: that's really way
to think about an AI forecast.

Totally.

So, so my point is, if
you're, if you're worried.

You know, take both sets
of directions with you.

Like look at the forecast that
you did manually and look at

the forecast that ai your,

Warren Zenna: your, your, your
science experiment would be great.

It'd be like my wife and I taking two
different cars and I use ways and she uses

her and we'll see who gets there first.

You know, and yeah, probably most of
the times I would get there before her.

But, you know, whatever.

That's, that's, that would be a
really good experiment that might

end the argument right there.

So I'm glad you in

Heidi Messer: that vein,

Warren Zenna: but, Yeah, no, it's fine.

I, I know I have to work that out myself,
but I like the idea, it's brilliant.

It's saying, I'll tell you what,
like, let's run your experiment

against our experiment side by side.

And then the only kind of
barrier to that is like, well,

what if this was an anomaly?

You know?

Cause it was sort of 50 50 thing.

But I think that there's probably
ways you can indicate as to how your,

your system was able to cease it.

What's funny about that,

Heidi Messer: Warren, I'll just
say is, so, you know, when, when

you say about the anomalies, like,
yeah, AI gets it wrong sometimes.

For sure.

Sure.

I mean, everything does, yeah.

But everyone's assumption that humans
don't like, suddenly we, we trust people

all of a sudden in a way that we never do.

And there there is no CRO that
I've met that feels good week one

of a quarter with their forecast.

Yep.

Not week two, not week three.

You know, where they start to feel
better is like, I don't know, week

seven or eight when they are kind
of can see the line of sight until

they're gonna make their quarter.

AI should help you feel better about
that because the time that you're

taking and feeling insecure for almost
half of the quarter plus, mm-hmm.

It's removing that time and allowing
you to actually be executing during

that time instead of spending time
and still feeling like you don't know

Warren Zenna: what's gonna happen.

So, you know, that's really
interesting cause it seems to me

then that the adoption of something
like this, a predictive modeling

ai, well, Accelerate when it's
done enough times, like ways, okay.

That it just works more often
for more and more people.

So collectively, everyone realizes
that, you know, this is just

better and there's enough data
for everyone to agree that, yeah.

You know, I guess on average, If I go
to a party and I do a little, you know,

uh, like a poll, everybody at the party,
and if 90% of them say, yeah, Waze gets

me there, usually better, then Okay.

That's sort of proven, right.

So what's the timeframe for this?

Like, cuz so I'm a company,
I've been in business for like

eight years, you know, whatever.

And I've been running my traditional
predictive models that I do now, the, you

know, the traditional stages, et cetera.

And then I adopt an AI program.

How much time do you think it
needs to take for a company to

go, okay, we're bought in, this is
something we're gonna do from now on?

Is it like a quarter?

Is it

Heidi Messer: a year?

So there's, there's the time it
takes to implement our technology,

which is two to four weeks.

Yeah.

Then there's the time to address
the change management piece of it.

And that, I think is, this is where
AI does require change management.

Um, you know, I think more
traditional selling orgs that

are, are heavily wedded to.

Older forms of forecasting
processes, there's a lot of other

things that go with that, right?

That they'll, they'll be very much
like, um, we've, we've, we've mapped

out a buying process for every buyer
and we treat every buyer the same way.

So at Stage Discovery, they get
this collateral at stage proposal,

they get this collateral, here's
the script that you have to read.

Here's the playbook.

The thing that AI changes that does
take a little bit longer culturally

to change is we're actually moving in
a much smarter direction for selling.

So I, I think you would really, um,
based on our conversations, you would

vehemently agree with me on this, which
is if we can come to buyers as trusted

advisors, as a smarter version, you
know, people who are coming to help

them solve problems, not read scripts.

Not force them into a journey that maybe
is not the journey they wanna be on.

We're gonna find a lot more sales
happen and a lot more satisfied

buyers on the other side of that.

No,

Warren Zenna: no doubt about that.

No

Heidi Messer: doubt.

AI should enable that.

And, and that requires shifting.

All that time you're saving, getting
that massive productivity in less than

a month to higher value management
activities, like things like.

Can I convert my pipeline review
session into a strategy session

where the AI has told me the
three deals I need to worry about?

We spend our entire hour
talking about those three deals.

Um, can I convert my forecast Fridays
into training sessions where I help

people get elevated sales skills, um,
you know, strategy, training, coaching.

Converting an organization to that and
then getting the rest of the organization

on board to say we're all accountable.

Like, Hey, you lawyer, you
can affect the odds too.

So like, how can we
enlist you in the process?

That's, that's a little
bit longer of a journey.

Um, that I would say is more
like two quarters to three

Warren Zenna: quarters.

Yeah, I get that.

I could see that humans adjusting to
this new world will be interesting.

Right.

Um.

So a couple things come up for me
when you just said those things.

So interestingly, I think what a lot
of managers don't know is how many of

their salespeople hide behind process
because they don't know how to sell well.

But they're really good at process,
so they satisfy these needs and they

get a pat on the head all the time
for being the most buttoned up, you

know, or robust, you know, person.

But if they didn't have to do that
anymore, they wouldn't know what the

hell to do with themselves, you know?

I mean, I, I, I actually, it's
weirdly, but I usually tell, I, I,

I, as you know, I do this a lot.

I'm like, you know, the, the people
in your organization whom as you or

avoid or are sort of almost allergic
to, You know, CRM systems are

probably the best people you have.

They're likely the ones that have
the skills that you really need.

They're the ones that wanna
break free and go do the job

the way they're born to do it.

Not the way you're constraining them.

And you know, if you look at people
in the way that they enter data and

you're rewarding people for doing that,
you're gonna end up with a bunch of

lousy salespeople, but really great
data scientists, which you don't

really, you didn't hire them for that.

Right.

You know?

And so I think that what this
system is gonna do, and I think

it's good, is like, to your point,
it's gonna start to surface.

We need people, we
don't need that anymore.

You know, we now need people who can
go have conversations and be persuasive

and like you said, like ask good
questions and inquire people's needs

and listen to them and relationships
and empathy and, yeah, I mean, one of

the reasons that this, I started this
company, it's really interesting story.

It's co-related to this.

So I was a buyer.

I was, I had, I ran a p and l at an
agency, and so a lot of technology

companies were coming to our offices
on a weekly basis, selling me stuff.

You know, I was the decision
maker, you know, and so what I,

what I felt, it was palpable.

Palpable how these salespeople were
trained to ask me questions for

the purposes of getting my answers
so they could put them into some

bucket that they needed to satisfy
when they went back to their office.

It had very little to do about
the conversation we were having.

It had more to do with the
conversation that they had to have

when they went back to the office.

I, it was clear to me and I was thinking
like, are you, what are you doing?

And I, I knew it wasn't their fault, it
was the way they were being dispatched

and the way they were being managed was
come back with the right answers for me.

So I can measure that
meeting appropriately.

And that's horrible.

And that's what's going on right now.

And I do see the future where
if those sort of things, this

is the training thing as well.

But more it is, and
we're in agreement here.

It's a constraint thing.

It's because the businesses have
become data collection centers.

They haven't become, they're, they're
not sales, uh, problem solvers, you know?

So that leads me to the second question,
which is, Is a platform like yours, what's

the difference between using AI in a, in
a, in a company that's more transactional,

like a company that that has lots of
sales, short sales cycles as opposed

to longer term, more complex sales.

Like where do you see AI being
used differently within those

two different types of cultures?

Heidi Messer: Yeah.

Well, so I think there's some
commonalities, which is much

of sales is about focus, so,
Imagine if you are a seller.

And by the way, I love the inspiration
for your business because it's true.

I mean, any of us who've been on
the other side have been buyers

who've trained salespeople.

You can reverse engineer Oh yeah.

Training by the questions you're
getting, you know, when, when you

want, you have a specific question you
want answered and well, can you walk

me through your problem statement?

Like, can you, like, it's crazy.

It's crazy.

So, um, so I think when you talk about
highly transactional versus complex

selling, Um, you know, someone who's
in a highly transactional sale is

likely to have a very large pipeline
of opportunities that, um, that are,

are meant to move through the pipeline
quickly, but more than they can

work on in any given period of time.

And so, um, AI is gonna provide you
with odds, machine generated odds to

say like, here are the ones, here are
the people who are popping their heads

up and, and showing or exhibiting
interest in a way that you should be

paying attention to at this moment.

Mm-hmm.

In a more complex sale.

I think it's very important that
you couple AI with transparency.

Um, meaning, you know, you're
gonna have the odds which are

much more difficult to calculate.

Uh, so there, so there's the
volume on the transactional side.

That's what makes it difficult
to calculate the odds, just cause

people don't have time to do it.

In a complex sale, there's
a lot of people, so.

I'll often ask, you know,
sales managers, I'll say, all

right, here's, here's a deal.

You have in your pipeline, there's
10 buyers on one side and eight

people on the selling team.

You know, they've got their
procurement people, you've got your

security people, blah, blah, blah.

I'm like, okay.

Now tell me what stage this is.

Well, you know, this person has given
me a verbal yes, but they've told

me they need to bring in these three
other people to agree with them.

Or the business person has said yes, but
now it needs to go through procurement.

Is that discovery is that
proposal is that closing is.

So what AI can do is AI can tell
you as these people enter the sale,

are they positively or negatively
impacting your likelihood of closing?

It can then help you figure out who
think, I think of the, the seller

as the conductor and the orchestra.

Like who do you need to bring in on your
side to help you at any given moment?

Like at some point you need
to bring in the lawyers.

At some point you need
to bring in sometimes.

Senior management to say like, I need
somebody senior to help me close this

deal and get it over the finish line.

So the AI will let you know what
are the output of your interactions.

So, Is it furthering the outcome you want
or is it deterring the outcome you want

Warren Zenna: from happening?

Got it.

It's great.

Interesting.

So, um, what's, uh, the right size
of a company that could use this?

I mean, is there a point at
which it's just too small

and there's not enough data?

Like, where's your sweet spot in terms
of when a company should start thinking

about implementing something like this?

So, I,

Heidi Messer: we typically, we've,
we've worked with larger companies,

but now we're expanding into
mid-market and moving, you know,

Sort of to smaller organizations.

Warren Zenna: And how do you,
how do you define mid-market in

Heidi Messer: your world?

Um, anyone who has 50
sellers or more would be

Warren Zenna: mid-market.

So its number of sellers is
really ultimately the number,

Heidi Messer: yeah, the number of
salespeople that they're managing.

Um, but anyone can use ai.

I mean, it's just like chate pt, you
know, you could be a small organization

and need to produce content blogs
and don't wanna spend five hours of

human time producing a content blog.

Um, same thing with a smaller organization
that just wants to know the odds and where

to focus and a forecast on their business.

Like these are sort of common needs
across every size of business.

Warren Zenna: Is there anything that we
haven't covered in this conversation that

you think would be left out if we ended it

Heidi Messer: right now?

I mean, I would say that the biggest thing
is something that you asked me before,

like when is the right time to start?

Yeah.

And it's now, um, you know, there
may be fits and starts where

there's some applications that
don't live up to their potential.

But I would say anyone who is a CRO
today, given the productivity gains

that you can get within a matter of
weeks, um, given the transparency into

your business, it should start today.

And then the second thing is to really
think about how do you get people

on the bus like, CROs, I think CROs
are gonna be the next, um, farm team

for CEOs because there's no doubt.

I, I do.

Because Yep.

If you can understand deeply and
scientifically how revenue is generated,

which requires you then understanding
your customers and how market shifts are

happening, you have the majority of skills
that a great ceo and, and by the way, you

can lead people through disruptive times,
which is what AI is about to usher in.

You have the skill, the
makings of an amazing ceo.

So I think the people who are early
adopters in this space are gonna end

up sitting really in the C-suite and on
the boards at a very, very senior level.

Warren Zenna: That's great.

So let's tap into that for a second.

So the CROs who are listening to this
right now, even the aspiring CROs who are

listening to this right now, what can they
actually do today to start, like what's

the right, because it's overwhelming.

I mean, they're getting all
these inbound emails and they're

probably have a chat G P T or.

You know, or an open AI account
and they're looking, staring at it,

wondering what the hell they're doing.

What's the way that they
can practically take steps?

Should they be reading certain
things there to, is there a

place that they should go?

What are the things they can
actually do that maybe, let's

say in a month they'd feel more?

Advanced in terms of their
ability to understand this and

be able to use it in some way.

Yeah.

Heidi Messer: Well, if, if you don't mind
me saying, we, we do have, um, a weekly

event on innovation that I would love
for your audience to, to participate.

Sure.

What

Warren Zenna: is it?

Lemme know what it's, so why don't you

Heidi Messer: just talk
about it right now.

It's, it's called Collective I
Forecast and, uh, collective I

Forecast Collective I Forecast.

If you go to ci forecast.com.

Okay.

You'll see we routinely have speakers
across different sectors of innovation.

Um, we always have AI
people coming to speak.

We have the CEO of DeepMind coming.

Um, we have the CEO of
stability.ai, who, who spoke.

Um, and it's, it's q
and a community q and a.

So you can come and listen to other
people's questions or you can submit

one of your own to, and I'd love to
have CROs represented there because

they ask tremendous questions.

Um, we also have an upscale section
in our application, and one of

the reasons I believe that's so
important is any great AI company

is gonna be very interested in you
understanding where the world is headed.

So, you know, how do you spot a fake?

Um, it's the people who tell you
that, you know, process is king.

Um, that, that you should, here's a
piece of technology that's using AI

to help you improve your process.

Uh, AI destroys Eachs process for lunch.

Um, the idea behind AI is to
eliminate process so that you can

actually get straight to action.

So you should be looking for companies
like Collective Eye who are willing

to take the time to educate you on the
technology itself, who are transparent

about their mission, and who are open
about the fact that you're gonna have

to make some changes in the way people
operate in order for it to be successful.

So I would say that CROs
should definitely be.

Look, looking and listening to podcasts
like yours, they should be, you know,

joining in sessions like cai forecast.com.

Um, and then they should be looking for
partners who have positioned themselves

as thought as thought leaders because
they are, uh, because they're really out

there to help their clients get to the

Warren Zenna: next level.

Got it.

So in the ecosystem for someone who.

Is dabbling right now.

They have the wherewithal to
recognize that this is something

that they need to learn.

It's a skill set that I need to
upscale on now to be competitive.

And they have a chat, g p t or an open
ai, um, account, what are some things

that you suggest they do to start just
making small applications that they can

start to develop an acuity around how to
use these things in a more practical way

instead of it being some mystery, which
I, I'm having, I'm actually asking this

for myself cuz I'm playing with this a
lot, but, You know, it's just like, it's

an overwhelming amount of things and I
dunno if I'm doing it properly, you know?

Heidi Messer: Yeah.

Um, great question.

So, you know, start small, start
with like, what are things that

take me a long time to produce?

So, um, you know, maybe it's,
maybe it's emails, right?

Like run, run a couple emails through
chat to ET and see how it helps

your editing, how maybe it makes the
first draft of a blog post that would

better describe your technology.

Um, you know, I have one of my co-founders
is using it to produce a board book.

Um, literally he's, you know, figured
out a way to have it produce Python

code that can analyze data, that
can put things together for him in,

you know, minutes versus the days
it would take him to do things.

Um, just start to, to play around
with it for smaller applications.

Um, you know, a lot of it is for chat.

PT is in the prompts that you use.

What do you ask the application?

You know, there's lots of resources
that you can find that will teach

you how to do better prompts.

Um,

Warren Zenna: are there particular prompt,
um, applications or resources that you

prefer that you think are particularly

Heidi Messer: good?

I have to think.

I don't know off the top of my head, but,
um, as a follow up to you and anyone in

your audience who's interested, they can
actually just, um, tap me on LinkedIn.

Sure.

And I'm happy to, to share, um,
you know, what ones I've found that

I can't think of at this moment

Warren Zenna: that I, yeah, I just
think it's just so overwhelming.

I mean, I, I look at Twitter and
every third tweet is a thread about

new, you know, Prompt, you know,
tools, and they all look great.

I, I have no way to evaluate whether
or not they're, you know, charlatans

are, they're really brilliant.

So what I do, what happens when people
get overloaded with information?

They just ignore all of it because
they don't know what to do.

That's, that's

Heidi Messer: what's tough.

But you need to find communities where
you feel safe and secure, and you can

ask questions and you can share notes
because, you know, part of the impetus for

our name Collective Intelligence is that.

This is moving so fast, no
one person can do it alone.

Yeah.

You know, I only know what I know,
so I need my community to make sure

that I have, um, the most knowledge
that I, that I possibly can.

The other thing I would say
is what's really important

about AI is to have a strategy.

So, you know, for people who
use Collective Eye, we are their

AI strategy for sales, right?

Like we are, we're coming to
them and saying, we're here to

help you use AI to optimize every
aspect of your sales process.

You can take that same concept for
every function in an organization,

and I guarantee you'll, you'll be
able to find the collective eye

or the open AI of that function.

Um, and those companies should help you.

Like they should.

They should see it as part
of their mission to make it.

To PR to enter you into a community.

Like we have community, we have a Discord
channel, we've got, you know, forecast.

Uh, we've got a community
called Special Ops.

Like all of these are designed to help
people share information quickly because

the world is changing so much faster
than any one person can keep up with.

Great.

Warren Zenna: Well look.

This has been amazing as I
suspected it would be this really

fascinating and very pertinent topic.

I'm getting a lot of people ask about
this, so I have a feeling this is

gonna be a, uh, uh, well listened to
and I'm gonna get a lot of feedback

on this particular, uh, conversation.

So how do people get ahold of you?

What are the ways that people
can find out about you?

What are some other things that people
need to know before we, uh, we sign off?

Heidi Messer: Um, you can get
ahold of me on, uh, LinkedIn,

so you can look at my profile.

Heidi Messer on LinkedIn.

Um, very happy to connect there.

Um, sign up for the wait list for
intelligence.com cuz that's gonna be

a cool network, but I'm not at liberty
to speak a lot about right now, but

I'm gonna ask you to come back on this
amazing podcast and talk about it Sure.

When that launches.

Sure.

Um, but in short it's a community
of connectors that, um, we've.

Uncovered through our breath as
being very, very influential in

helping, uh, opportunities happen.

Um, and, uh, my email, uh,
h messer collective eye.com.

Happy to, you know, I'm a little
slower on email cuz of the volume

I get every day, but, um, but
always happy to hear from CROs.

Always.

That's good.

And Lauren, I can't thank you
enough for, for hosting me.

You're somebody I've really admired.

Um, the first time we met.

I was so impressed by how open-minded
and what a growth mindset you have about

learning about technology and new things,
and you're just incredibly talented.

Warren Zenna: Gracious.

Well, thank you.

I mean, means a lot to me.

Thank you so much.

Um, and this is helpful.

I mean, this is so pertinent to what
the people listening to this need.

So, um, I'm, no, we'll be talking again.

I know that.

So well, thank you so much for
doing this and, um, everyone,

um, we'll see you next time.