Jack Siney Sprints Past Outdated Sales Management with AI-Powered FrontRace

Episode Transcript
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SPEAKER_00 (00:01):
Hello and welcome to the Bald Ambition Podcast.
I'm your still very bald host,Wookiee Spitz, and the one with
tons of ambition today is Mr.
Jack Steady.
Welcome to the podcast.

SPEAKER_02 (00:18):
Thanks, thanks for having me.
By the way, if you go to myLinkedIn profile, this is all
new.
This hair, I've my LinkedInprofile is a very, very short
hairdo as well.
So I this is all this this hairon top of my head is about
seven, eight months old.

SPEAKER_00 (00:30):
Nice, nice.

SPEAKER_02 (00:31):
I'm in the bald ambition, yeah.
Amen.

SPEAKER_00 (00:33):
Yeah, you got that ambition too.
You got a little crop top.
So if you're just listening onSpotify, Apple Podcasts, uh, you
know, I'll put a you'll you'llbe in the little thumbnail with
your crop top.
And if you're on YouTube, wave,say hi.

SPEAKER_02 (00:46):
Yeah, just enough that I have just enough hair to
be dangerous.

SPEAKER_00 (00:48):
So I'm still jelly though.
You are the co-founder and chiefrevenue officer of Front Race.
And you're using AI, plugging itinto sales, and uh, I can't wait
to hear about your value prop.
Everyone's talking about AI, AI,AI.

(01:09):
It's genuinely a revolution,whether we like it or not.
A lot of changes.
When I when I think sales, Ithink numbers.
Show me the money.
If you're a sales manager, ifyou're a selling person, it's
all about the books.
So data and sales have beenentwined since the caveman.

(01:32):
Tell me, tell our audiencewhat's what's new now with AI
and what are you doing with AIto help it out a little bit?
Grease the wheels.

SPEAKER_02 (01:44):
What a lead-in.
What a lead in.
I I guess as perspective, I'dI'd step back just a little bit
and say, hey, if you in this,especially forget the tech side.
I'm not I'm not a tech.
So is AI revolutionizing thetech world unquestionably?
What it's doing in tech worldtoday is unprecedented, it's
amazing.
You know, my perspective isreally on the business
commercial side.

(02:05):
And I would just say asperspective, we started or the
the businesses started about 40years ago to put this tech stack
in place on the commercial side,right?
CRMs and and then callintelligence and pipeline
management and and uh RevOps andnow AI and what's crazy, you
don't have to take my opinionfor it.
If you put in Google or orChatGPT or Club, whatever, and

(02:27):
ask, are we any better today atforecasting and developing our
sales teams than we were 40years ago?
The numbers, they're either thesame or they're actually worse.
And so that to me is such aninteresting paradigm.
And so that's what we try tolean into.
It's like we spent all 40 years,CRM at all, all these systems,
millions and millions ofdollars, all these dashboards

(02:50):
and KPIs, and we're no bettertoday.
Public companies miss today asmuch or more as they did 30, 40
years ago.
It's shocking that we we haveall this data now, but we're not
much better at delivering whatwe say we're gonna deliver.
And and and how that could thatbe?
And so uh I would just one onereal world thing is somebody's
led big teams.
We we've all had the momentwhere our dashboard, we had all

(03:12):
the numbers, we had alleverything on our we look great
and we miss, we miss for themonth of the quarter, and then
we had the month where we misseverything and we're 30% over,
right?
And then inevitably the board orthe CEO walks in your office,
he's like, What happened?
Or she walks, you know, whathappened?
And you're like, I don't know,you start juggling, you know,
you're like I don't know.
You know, you start making stuffup.
And so to me, that's an amazinguh paradigm, amazing construct

(03:37):
that we built so much money, somuch time, so much in our tech
stack, and are honestly nobetter today at forecasting.

SPEAKER_00 (03:45):
So with that as a is that as a premise, uh whether
you believe it or not, again,Google it's Wall Street believes
it because the SaaS companieshave getting their ass kicked by
AI.
So sales, all the way fromSalesforce to Docu signed.
Amen.
Yeah.
They're they're tanking becausethe agentic technology holds the
promise of replacing the buttsand chairs with autonomous

(04:10):
agents.
And to your point, a lot ofthese services suck, not to be
disparaging about Oracle, butthere's tons of tech, the stack
is thick, and the practicalapplication and impact to
business, specifically salesteam, has been uneven, has been

(04:32):
kind of broken.

SPEAKER_02 (04:33):
It's so true.
Listen, listen, and and ifanyone's sitting there
listening, they're like, I don'tbelieve that, or it's not true.
Look, we've all had this.
If you've run a sales team,you're VP of sales, CRO,
treasure of sales, we've all hadthis experience, okay?
You have two reps, okay?
Their numbers look prettysimilar.
Uh their metrics, calls, uh,outreach, emails, pipeline look

(04:54):
very similar.
One is outselling the other 3x,right?
Susan is selling 3x Bob, right?
And so when that happens, onevery sales team in America,
again, the CEO or board comes inand goes, can't we just why
can't we?
What's Susan doing?
And again, the head of salesmakes something up.
Well, Susan's just good atclosing, she's a better demoer,

(05:16):
she has a better network.
We make something that's totallyfake, but that happens today.
We're we're no better today.
Every sales team, 80-20, 20% ofyour people are crushing it.
The other 80%, you're you're onsome kind of pit plan or
developmental plan or some kindof how can I get them to all be
Susan?
And we don't know.
It is it's shocking.

(05:38):
It's not that it happens, butthe fact that we spent so much
time and energies Salesforce andHubSpot have is look at all the
metrics and monitor all theirstuff.
And if you just look at itenough, you'll and the sidebar
to that is it's like the um insports, it's the Michael Jordan
complex.
Like Michael Jordan's amazing,but you can't teach what he did,

(05:58):
right?
He's like, I was just the bestplayer, or LeBron, whoever you
know, and so they can't teachit.
So Susan crushing it, you haveher come into your sales team,
do a training, and she can'texplain it, right?
She's like, I just do what'snatural, I just it's who I am,
right?
And so the team doesn't get anybetter, and so that's ultimately
the paradigm.

SPEAKER_00 (06:18):
They pretend tend to promote Susan, so right.

SPEAKER_02 (06:21):
She may not be a good manager, all right.

SPEAKER_00 (06:23):
So she usually sucks as a manager because all the
qualities that make Susan agreat salesperson hyper
competitiveness, relentlessdrive, those are exactly the
opposite qualities you want froma manager.
You want empathy, you want teambuilding.
They're not gonna do that,they're not gonna teach.
That's because they're out therebusy kicking ass.

SPEAKER_02 (06:46):
I can't tell you how many times I've had the best
person over 25 years try to do atraining.
And it's no offense to thosepeople.
They're amazing what they'redoing.
They're the worst trainers.
You try, can you just share,share what you're doing on
LinkedIn or share what you'redoing to close?
Or and then they they puttogether a little PowerPoint or
you know, whatever they do onGoogle Slides.
It's awful.
And the team, the team actuallyleads worse, right?

(07:07):
They're like, I just got dumber.
I'm gonna try something that'sout of the box that I'm not good
at, that that person happens tobe very good at.
And so NetNet, that's reallywhat uh we lean into at front
rage, trying to shine a light onwhat's been this black hole of
sales for 40, 40 plus years,which like, hey, what what makes
Susan so great?

(07:27):
And that's that's really whatwe're focused on.

SPEAKER_00 (07:29):
But it's an employee development kind of HR angle on
AI.
And and I've talked to otherfolks who do who do similar kind
of stuff, and the angle on thatis there's tons of data.
Yeah, you know, and anotherpundit called it data exhaust.
I saw that.

SPEAKER_02 (07:46):
I saw that in one of your other shows.
Yeah, yeah, yeah.

SPEAKER_00 (07:48):
We got a ton of data and it and it all gets
vaporized.
It's often to the ether.
And if you have an opportunityto measure it, analyze it, and
AI at its core is machinelearning and deep learning.

SPEAKER_02 (08:04):
You got it.
You you got it, 100%.
That so so a little commercialfront race.
I totally agree.
That you are uh a hundred bucksafter the show.
Like what we do for companies,which is the AI I think everyone
wants today, like like AIsidebar, we're in the first days
of AI, just like AOL.
If you remember AOL, like AOLwas great, but if you were still

(08:25):
in AOL today, you were wildlyYou've got mail.

SPEAKER_00 (08:30):
Now it's you've got a bot.

SPEAKER_02 (08:33):
Right, totally, right?
So we're in the earliest,earliest days.
So committing to an AI anythingis can be challenging, right?
And so what we do for companiesis we say, continue doing what
you're doing, don't stop.
We put a little layer on top oftheir tech stack, which connects
all the data, normalizes, as yousaid.
And then we take AI and non-AIanalytics and start to tell the

(08:58):
company, as you said, theyalready have a bunch of data,
but the challenge is for yearswe haven't been able to measure.
I believe the difference betweenthe person selling 3x and the
person selling 1x.
It's 20 little things.
It's not the big things.
Everybody knows the big things,everybody knows their pricing,
the demo, the FAQs, everyonealready knows that.

(09:18):
It's 20 little things thathappen that historically we have
not been able to measure.
And so I'll give you someexamples.
Those things would be likeeveryone has a sales manual.
Here's our 22 steps, like thisis how we sell.
The reality is your best peopletypically move that all around.
They're they're doing itbackwards, they're they're not
doing it the way whatever's inyour sales manual, they're not
following the sales manualtypically.

(09:40):
So, what is the order thatactually works?
What's the order that actuallycreates deals?
Then it's not only the order ofthings, but how much time
between those activities?
What it's not that you send anemail, we've all had this
experience.
You connect with somebody onLinkedIn, and like six minutes
later, they're pitching you,right?
That makes you feel a certainway, it makes you it feels

(10:00):
yucky.
And so it's it's what's done,the timing between it as
examples.
There is the measuring thequality of how it's done, which
can be subjective, but we tiethat to did they win.
There is uh when you bring upcompetition, how you bring up
competition, when you broughtwhen you first start talking
about pricing, did you movepricing?

(10:20):
All of those variables add up tothe difference between why
somebody is selling 3x oversomebody else.
And historically, as youmentioned, we've had a ton of
data.
We haven't had the abilitybefore AI to measure those at a
such a voluminous way that'sneeded.
And so that's what Front Racedoes.
We literally uh connect yourdata, start to provide uh uh

(10:42):
standardization to an apple isan apple is an apple across all
your systems, and then we usesome analytics to give you new
metrics that actually tiesuccess and tell you what the
best next step is for each oneof the deals pending.

SPEAKER_00 (10:54):
How do you deal with personalization?
So oh, I love it, that'sperfect.
Norm Norm McDonald Norm McDonaldmade people laugh.
Yeah, so does Bill Burr.
And Norm McDonald had thatdeadpan humor, and Bill Burr is
the Boston guy who's just goingnuts on you.
Amen.
You mentioned sequencing, so youcould you could find an optimal

(11:18):
sequence, hypothetically, but itmight not work for Norm McDonald
because he's Bill Burr.

SPEAKER_02 (11:25):
But that's the whole thing.
Listen, that's the whole thingin sales.
We hire a CRO or BP of sales, wesay, figure it out, monitoring.

SPEAKER_00 (11:33):
Get a system together.

SPEAKER_02 (11:35):
Totally.
Uh go to Sandler, go to MillerHyman.
Here's our 22 steps, here's our28 steps, whatever it is.

SPEAKER_00 (11:41):
An objection handler, X to Y.

SPEAKER_02 (11:44):
Oh, you are preaching.
Uh preaching.
So we say, hey, let's here's ourprocess.
Meanwhile, as you just said,every sales rep has different
strengths, no doubt.
And every prospect is different.
They're in different verticals,they're of different sizes, they
have different money, they havedifferent number of licenses or
different problems.
And then we're like, jam these22 steps into every single

(12:08):
thing.
And if you don't, uh you'refired, or we're gonna put you on
a pip.
And it's like, oh, no.
That's again for 40 years whywe're struggling.
And so, with that said, what'samazing about AI, it starts to
enable you to do this personal,highly personal.
Hey, Bob is great on the frontend.
Let's make sure he is that.

(12:29):
But hey, when we get the demo,we might need some help.
And when we close, definitelyneed help.
Mary, horrible on the front end,great demo, can't you know?
So it lets you give everybody uhthe things they're great at, and
then starting to develop wherethey're not, andor get them
resources where they're not.
So again, we can have the bestnext step for each opportunity

(12:49):
instead of saying, everybody dothese 22 steps, and if it
doesn't work for you, you'refine.
Exactly.

SPEAKER_00 (12:55):
And and it also recognizes the individuality of
the salesperson.
And if you're in sales for ananosecond, you realize that
those stars, the three X people,are all kind of doing their own
thing.
They are some of them might belike, Hey, how you doing?
They're instantly engaging andfun.

(13:16):
You could have a beer withanybody, and it's one big party
all the time.
Amen.
But you've got salespeople,three X salespeople are like,
Hey, how you doing?
Yep.
Can you tell me a little bitabout your business?
I'm really I'm really curiousabout it.
That they're they're both doingit their way.

SPEAKER_02 (13:37):
I had listen, I had a team and I in our last
company, I had a team of over100 people, and then we'd work
in teams, and then each team, solet's say we had 20 to 30 teams,
they all had a miniatureculture.
And so we hired it was aligningnew people on the right team
because as you just said, I hadcutthroat New Yorkers, I had
sweet Mary Poppins, I had theanalytical person, I had the dry

(13:59):
person, I had the party person,I had the social media person,
and so it success begets successif you align the right people.
And so I, God bless, I that iswhat we are leaning into and be
like, stop taking yourstandardized process and
demanding that everyone on yourteam does it and it works for

(14:19):
every client because nothingcould be farther from the truth.
And so now that we have someability to figure out what
really works and share that andmeasure it, that's that's uh in
my mind, the future of how we'lldo sales much more productively.

SPEAKER_00 (14:36):
You know, I see where this is going.
This is rapid-fireimplementation, too.
You don't you don't spend sixmonths putting together a sales
methodology necessarily becauseyou're immersed in data and
you're also analyzing eachindividual salesperson on a
team.

SPEAKER_02 (14:54):
Yeah, you you know what happens is um we we think
the a process should be acertain way.
Everyone's biased, right?
If you hire a new team lead orpromote somebody, as you said,
they come in with their biasesof like, here's what we here's
what I like.
I like, I like gong, and I liketo really analyze the calls, and
uh, or somebody else lovespipeline management.
And what's great about humblywhat I what we do, what Front

(15:18):
Race does, we we don't careabout anyone's opinion or your
systems.
We give you the facts of whatactually is happening, and you
can do it that way you want.
It's it's like um if you're asports fan, the money ball, that
movie money ball and baseballthat analyzes all the stats.
It's telling you factually, hey,here are the things that look
like winning, here are theactivities that drive winning,

(15:39):
because it all looks a certainway typically within some
bandwidth.
And by the way, most peopledon't want to do this.
Here's what losing, here's whatwe do when we lose, here's some
consistent traits.
Because most people lose a dealand they get irritated and they
throw it away and they're like,whatever, I'm I don't want to
talk about it.
But if companies have,especially if they have some
significant legacy data, we'reable to analyze and go, hey,

(16:01):
well, when you lose, here's whathappens.
But by the way, when you win,here's what happens.
And inevitably, when we provideour first analytical report for
a company, it's like they'reshocked about two or three great
things.
They're like, oh my gosh, weneed to share this with
everybody.
And then two or three thingsthey inevitably are bad, and
they inevitably go, that's nottrue.

SPEAKER_01 (16:19):
And you're like, Yeah, that's true.
That's that's what's happening,right?

SPEAKER_02 (16:23):
And so it's it's amazing that a lot of people
don't really understand theirdata.
In fact, um, Larry Ellison, uh,just a couple weeks ago, if you
saw, he said, pick pick whateverLLM you want.
You they're all using the sameopen public data, right?
What if they're all using thesame public data?
The magic is in your data.
The magic is in your privatedata.

(16:43):
If you have legacy data of winsand losses, what activities your
team has done, that's where themagic is.
You need to get that data, startto mine it, because that the
answers are there.
AI will help provide analyticsand give you some accretive type
stuff, but the magic is in yourdata, not in Cloud writing some
generic marketing one pager foryour company.

(17:06):
It's that's just an aggregationof all your competitors.

SPEAKER_00 (17:10):
This is where big SaaS companies like Salesforce
are now getting their asskicked, too.
It's not just redundancy withthe agentic technology, but it's
an antiquated approach to salesoptimization for all the reasons
that you described.

SPEAKER_02 (17:26):
I am shocked.
By the way, I love Salesforceand HubSpot.
They're they're we we work withall of them.
They're they're yeah, they theydo a lot of amazing things, but
ultimately internally, you'relike, again, where we started
the show is is it helping us winmore?
Like, is it helping us theforecast is X?
We it's shocking.
Almost uh, you know, most bigcompanies have one of those two,

(17:49):
and you're like, we still don'tget to the we still don't
reliably get to the number, nordoes your team.
And no one asks no one asks whyor how.
It's like you, but if you sayyou have a CRM, you're almost
derelict of duty, right?
If you you're all like, youdon't have a CRM, you're fine.

SPEAKER_00 (18:05):
I love this.
And there's a strong parallelwith healthcare.
So I've been a digitalstrategist on the healthcare
side, the digital health sidefor quite some time.
And the term that's been tossedaround for decades is the
quantified self.
Okay.
So we've got 37 trillion cellson average in a human body.

(18:26):
This is good.
And we don't we know nothing,right?
We are they're gonna look back,Star Trek triquarter years
ahead, they're gonna look back,and we're like cavemen wearing
pelts at where we're at rightnow in terms of understanding
the human body.
And the big deficiency isdata-driven too, because the
human body is just socomplicated, it's so personal.

(18:49):
And if if only we could measureall of this and analyze it, we
could not only manage butprevent disease, we could do all
these amazing things, and you'redoing something similar for a
salesperson.
Did you see?

SPEAKER_02 (19:05):
Look, by the way, I comment to what you said.
Did you see Elon said a coupleweeks ago about aging?
You know, when he was, I thinkhe was overseas, and he was
talking about, hey, saying wehave 37 million cells, and like
he's like, we're gonna solveaging because all those cells
age at the same time.
He goes, Your arm's not yourarm's not 90 and your face is
22.
He goes, There's a trigger, he'slike, we're gonna figure it out
and we're gonna address aging.

(19:26):
And you're like, you know, likeso it's so good.

SPEAKER_00 (19:29):
It's uh what Elon is saying is I'm addressing aging
because not only do I want to bethe richest man on the planet,
but the oldest one.

SPEAKER_02 (19:38):
Yeah, I want to be alive when we get out to Saturn
or Saturn.

SPEAKER_00 (19:40):
Yeah, we get to Mars.
I want to be the guy on Mars.
So there you have it.
But uh, love him or hate him,Elon's always leading the
charge.

SPEAKER_02 (19:48):
He's interesting to listen to, boy.
That guy, he is there's a lotgoing on there.

SPEAKER_00 (19:53):
So spectrum-y people who are changing the world for
for better or for ill.
It's uh he's he's in that SamAltman, Zuckerberg.

SPEAKER_02 (20:03):
Yeah, amen.
They're uh we were talking aboutbefore the show.

SPEAKER_00 (20:05):
There's a couple, yeah, there's a couple hashtag
awkward, but hashtag genius.
It's almost like you can't haveone without the other.

SPEAKER_02 (20:14):
It is.
It's it's like in theentertainment field.
The best singers we all knowwere all weirdos.
You know, any a lot of peoplecan sing, but if you want to
become famous, you gotta docrazy tour around the world,
live on nothing, you know, andso a lot of people can can sing
or rap, but you gotta have theother kind of weirdo part of you
to travel and do all the time.

SPEAKER_00 (20:31):
You need an agent or a manager, you know,
domesticating that stuff.
Yeah, amen.
That's so true.
Going back to the quantifiedself, you you you are
quantifying the sales force.
Yeah.
And analyzing it.
Well, you know what's happening?

SPEAKER_02 (20:46):
It before COVID, if you had if you had a sales team
of 20 and you hired a goodperson, so just let's assume
they're good.
Forget you had a bad hire.
They're good.
You would say to them, hey, gofollow my top three people,
right?
Go shadow Bob, Mike, and Susan.
And and from each one of them,pick a couple things, right?
You'd be like, get three thingsfrom John, or Mike, get three

(21:07):
things from Susan, da, da, da.
And they would develop andevolve and watching them.
Well, we can't do that anymore,right?
Every most people work fromhome.
You can't say, go sit in Bob'shouse.
You can't, you know, you can't.
So we are really at a deficit oftrying to how do you develop
people?
If you hire a good person today,what how do you share best of?

(21:28):
You know, you hire a goodperson, like, how do I, how do I
crush it?
And and the best I've seen isfictional.
Like, well, go listen to hislike this.
Go listen to their web call.
Uh follow their.
It's it's very, very challengingin today's day and age to become
great.
And so, as you just said, whatwe're trying to do for companies
is when you hire a good person,you can now share with them hey,

(21:50):
here's what greatness looks likeinside this company.
Here are the 22 things, here'sthe order of them, here's some
examples of those.
You will add your Flavor to it,so we will kind as as AI grows.
We all know this.
If you have Chat GBT or Claudeat your house, you know it grows
as it gets to know you, itstarts to send you stuff.

SPEAKER_00 (22:10):
It starts to blow smoke up your ass.
Oh, it always does that.

SPEAKER_02 (22:13):
No, it always does that.
Chat, this is the best.

SPEAKER_00 (22:16):
You are you are such a genius.
You know what?
This this is a funny side note.
I tend to be cynical andsarcastic.
Me too.
Me too.
So my my chatty bro, that's myname for him, Chatty Bro.
I even had him as a guest on mypodcast.
You know, he's he did prettywell.
So my chatty bro is now throwingit back and throwing it down

(22:38):
because that's his way ofkissing my ass.
Because he knows that that'swhat I like.
So he's throwing me shade andgiving me attitude.
Isn't that funny?

SPEAKER_02 (22:47):
Two things, two things I'll say.
These one one's one's mysoapbox.
One is it's so funny if you pushback, it then always tells you
you're right too.
You're right, we should do that.
You know, he kind of never saysyou're an idiot.
And then and then I the littlesoapbox, it's quick side bars.
We're we're gonna that's gonnabe an issue.
People are speaking into ChatGBT or Claude, whatever your

(23:08):
system of choice is.
That data's all being kept inmind.
We think we're having a littleprivate conversation, but it has
everything about you.
Like, no doubt that data isgonna get mined five years from
now.

SPEAKER_00 (23:19):
Oh, it already is.
It already is.
I mean, they they need the cap Xon this industry, it is
absolutely insane.
Oh they're gonna need togenerate some revenue here.
Now, Claude, you know, Anthropicis doing the best right now.
They went from a 1 billionestimate to 20.
They did 20x this year of whatthey forecasted.

(23:42):
They're doing pretty well.
But even at that rate, they'reuh they they say they might be
profitable in 2027, 2028.

SPEAKER_02 (23:51):
Well, that you see it's like it's like LinkedIn.
You know, LinkedIn had us allput our resumes for years free.
You're like, how's this gonnawork?
Everybody, you know, and and nowit's the lead gen.
They knew, you know, they knewyears ago.
I remember when LinkedIn cameout, you're like, it's free to
put my resume up there.
What why is that?

SPEAKER_00 (24:07):
And then they knew the endgame.
Well, that's why Microsoftbought them because they needed
a cohole in that.
But but but LinkedIn, I'm gonnaget on my soapbox.
LinkedIn sucks for the samereason that Salesforce, but even
worse, it's terrible.
It's the worst, it's the worstsocial media platform.
Not only is everyone all boasty,like I just I want to make an

(24:30):
announcement that I just becamesenior vice president uh of the
Dominican.

SPEAKER_02 (24:35):
If you if you go into LinkedIn, if you look if
you use LinkedIn as your socialmedia feed, you'd swear AI is
gonna end the world by thissummer.
You everybody, and I'm justgonna tell you, frontline deal
with hundreds of companies uhevery month.
AI on the business side, we areso in our infancy.
It is there are so few companiesthat are using AI at scale on

(24:58):
the business side.
Again, the tech side, it iscrushing.
It what it's doing on the techside, but for business
operations, sales, clientservice, retention, it is so
minimal.
They're like, fire your wholeSDR team and hire agents.
Not a chance, not a chance thatworks.
Not a I don't care how much youtrain it.

(25:18):
I don't care how much you thinkyou not.

SPEAKER_00 (25:22):
It's it's shambolic fraud for a glorified call it
out.

SPEAKER_02 (25:28):
It is, it's like I think one of your podcasts, it's
like glorified spell check.
You know what I mean?
It'll create verbiage, document,it's amazing, but real world
interact with somebody and andand do some strategic notion.

SPEAKER_00 (25:39):
And provide immediate or near-term benefit
in terms of optimizing aSalesforce or even optimizing
remedial functions without thatkind of human engagement
oversight.
It's just it's in its infancy.
Totally.
And people tend to gravitatefrom one extreme to another,

(26:00):
which is I need to eitherinstall this little widget or I
need to have Claude Methos runmy company.

SPEAKER_02 (26:08):
Well, look, I'll just say the thing right now is
this Claude co-work, right?
I'm gonna take everything, I'mjust gonna put it in Claude, and
it's just gonna connect all mystuff.
And again, I'm living it everyday.
I'm not a technologist, livingit every day.
Claude, God bless what it'sdoing.
It's amazing.
It's the way you can connectdata and run basic analysis, it
is great.
It's it's amazing stuff.

SPEAKER_00 (26:29):
But if you try to do it's science fiction, it really
is.

SPEAKER_02 (26:32):
But if you try to do any complex analysis, it does
not work.
And it will get you will loseyour job.
I promise you.
If you put your company data inClaude Cowork and think, oh, I'm
gonna, I'm gonna analyze my repsover the last quarter, pipeline,
and metrics.
I promise you with 100%certainty that will break.
It'll but it will tell youfactually, because it makes

(26:54):
stuff up, it will it will fillthe screen, you will make a
series of bad decisions that arebased totally fake, and you'll
get fired.
Because it can't do the joins,it can't do the analysis.
It picked one-to-one, who was mytop rep last month?
What you know, how many callsit's that's perfect.
You get to the third or fourthlevel of multi-y want to do any
strategic, it doesn't work.

(27:15):
And it it's not meant to work.
It's it's it's it's the datajoins and how the code, it's
very challenging.
And that's what we try todeliver for people.
We we built some technology thatputs flags and hard flags in the
sand.
So you can do some, you can doreal analysis, but without that,
folks are always like, I'll justgo put it in Claude and and
co-work.
You're like, all right, well,get your resume together because

(27:38):
that's gonna not come out right.
And no, no chance.
By the way, I don't I don'tforget if you get um front race
or not, but no chance that comesout well, so please don't do
that.
It's not ready.

SPEAKER_00 (27:48):
So we're we're halfway through this sales pod,
and you've sold me on it, allright.
We've got the value prop, we'vegot the pain points, we've got
the implication.
I got the contract.

SPEAKER_02 (27:59):
I'm gonna send you the contract, Louie.
I'm gonna sign on the contract.

SPEAKER_00 (28:02):
Now, now, how does this work?
So, all right, Jack, you'veyou've you've sold front race
in.
I've got, let's say, aSalesforce of a hundred
salespeople, yep, and I'm usingSalesforce or one of the other
CRMs.
You know my stack from beingfamiliar with all the different
ones.
Now, you mentioned you're anapplication.

(28:24):
So, how does this work?
I'm assuming that there's adiscovery process, so you learn
about my people and my legacyprocesses, and then there's
there's got to be a techintegration, and there needs to
be some kind of UI or newdashboard, or what exactly the

(28:45):
hell do I get?

SPEAKER_02 (28:47):
Amen.
So totally fair.
So, last part first, it's a webinterface.
So you log on a web page, it'sthere.
So ideally, you get up each day,you log into front race, it'll
tell you what to do today.
That's that's the netconclusion.
But to your full question, noregarding all of the I don't I
don't want to know anythingabout I don't there's no, you
know, in the old consultingworld, let me do a discovery

(29:07):
phase, let me map your process,and let's have a let's have a
team meeting, let's get all yourhitters in the room.
Uh, we're gonna spend six monthsdeploying.
None of that.
That's again some of the beautyof today's technology.
So when we onboard a client, wehave it, uh we spent a couple
years developing, and and a lotof AI companies have the kind of
this APIs now, these how toconnect the data, way easier

(29:28):
than when you and I wereyounger.
If somebody said it to you,you'd be like, that's a
six-month effort.
So the first thing we do isconnect the data for companies.
So a layer that goes on top,piece of technology, connects
all the data.
That's actually in today's worldnot that hard, to be honest.
A lot of APIs out there.
But the magic in the connectingthe data, the next step is you
have to normalize the data.

(29:50):
An Apple in your Ford systemshas to be an Apple.
It has to be an Apple in yourCRM, an Apple in your pricing.
And shockingly, it's not.

SPEAKER_00 (29:59):
For most companies, an Apple is in one system and
aren't interoperability problem,data inconsistency.

SPEAKER_02 (30:05):
Right.
So the two things we do forcompanies to start are uh uh
connect the data, normalize it.
And so that takes about three orfour days.
So we'll we'll get the data in.
So again, this project sometimesyears ago would be like that's a
three-year project.
So within a day or two, we haveAPIs, we'll start to normalize
the data for companies.
Then the second piece behind thescenes before they will log on,

(30:28):
we apply some analytics.
So there's two parts of ouranalytics that are really
unique.
There's a bunch, but two thatare stand out.
I mentioned one earlier.
One is a metric engine.
So we have a series of metricsthat are non-traditional.
We're not trying to give youyour company more metric, but it
starts to measure all the thingsI mentioned earlier.
All these little nuances ofwhat's the order of things,
what's the space of things, whendo you mention pricing, when do

(30:50):
you mention competitor, how didwe get the lead?
Did you have a relationship withthem prior?
What's the professionalismscore?
Your tonality, was it a qualityendeavor?
By the way, all those thingsmeasured by did you win the
deal?
Because who cares what youropinion is or my opinion is, did
we win the deal?
And if we won the deal, in someway, shape, or form it work.
So we start to measure on that.

(31:11):
So we give folks this set ofmetrics.
The metric engine starts to notonly create these metrics, but
measure them in time.

SPEAKER_00 (31:18):
Okay, hold on, time out for a second.
So I'm assuming that there's adelta that you're noticing right
away between all the data that'son whatever platform they're
using.
You could API, do a plug-in, andand slorp it up into your
application, but frankly,they're not even measuring some
of the things that are importantto measure.

SPEAKER_02 (31:38):
Right.
So great, that's a greatquestion.
So think about um, as ananalogy, think about if you were
going to go to your calendar.
I think we all have a calendarand you see what your day is,
right?
You see, oh, here are mymeetings every week, and there's
empty spots in it, right?
For when you're free or you'renot, you don't have a meeting
scheduled.
That's how the data is on almostevery company.
No one has perfect data, right?
But what we start to do when wedo a baseline for a company, we

(32:01):
say, here's the data on what youhave.
Here's the things we can speakinto based upon all the data you
have.
Inevitably, as you just said,there's gonna be three or four
things they have no data on.
We don't have data on texting,we don't have data on social
media, we don't have data on Idon't know, uh, after hours
stuff they do on their home,whatever.

(32:21):
So then what's great, we couldjust say to a company, uh, we're
unbiased.
Do you want to dive into thosethings?
Do you do you care about knowingwhat how somebody's generating
leads on social media, or do younot care?
Right?
So if they care, if there's ayeah, we've got to see the text
because that's part of therelationship thing.
Bob sends 92 texts.
Well, there's a myriad oftexting programs, right?

(32:43):
So then, okay, then let's startto monitor the text and we start
to creatively fill in the openspace on someone's calendar.
Does that make sense?
So then they can decide becausethey can say, you know what, I
don't care.
I don't, I don't, I don't wantto track the the the the text, I
don't care.
Uh you're like, oh, okay, likethat's on you.
Do you want to track theproposal?

(33:04):
They sent a proposal day one, itchanged 47 times.
Now we're down to half theoriginal value.
Do you care about that?
Uh no.
Okay, well, I okay.

SPEAKER_00 (33:15):
I I would want to.
I'd don't you push back though?
Because more data is moreknowledge, and more knowledge is
more power.

SPEAKER_02 (33:25):
We we do, but but what I also want to do to
companies is start to give thema baseline and so they can see
the holes they have, right?
And let and then and then youdecide.
That that's what's humbly, Ithink that's one of the great
things we do.
We don't tell people to go getsome, hey, what do you have
today?
Let's look at it.
Does it answer the questions youcare about?
Let's strategically look at theholes you have in your data.

(33:46):
Everybody has holes, and you youcan decide as a corporate
management team, do we want toinvest in this thing?
Instead of buying a new AI toolthat's not going to tell you
anything because it's analyzingstupid wrong data.
Why don't we buy a textingengine?
Or why don't we buy some peopledon't track any of their calls?
Why don't we buy a call systemthat can digitally you can see
your calls, or let's do a umtranscription service of your

(34:08):
web meetings?
Like let's let's start to fillout the data, but if they don't
want to, God bless, you know.
So, but we we we get the datathey have, normalize it, and
then start to give them, hey,here is what we believe are the
things really impacting yourdeals, good and bad, good and
bad, right?
And then the second part of thatis you said, hey, are we gonna

(34:30):
do this, figure out what ourprocess is?
Well, no, because that's what'sgreat about the tech today.
AI will when it has all theelements, it'll start to do that
on its own.
I don't have to ask, especiallyif they have legacy data, if
they have data over the last twoor three years, it'll tell you
in a week, here's what yoursales process is.
I don't I don't need yourprocess flow chart.
You're measuring every touchpoint.

(34:51):
Totally.

SPEAKER_00 (34:51):
I don't I don't need you know, I don't need you to
tell me.
It implicitly determines whatthe process and structure is
that underlies it.

SPEAKER_02 (34:58):
Totally.
And and what happens in thatmeeting, always the head of
sales, their mind explodesbecause they have their process
flow diagram of 22 boxes, acouple diamonds, yes or no,
right?
And inevitably, every singletime, whatever number of boxes
they have, it's twice as many.
The real world is it's alwaystwice as many boxes.
It's steps, proposals, documentsense, interactions, contacts,

(35:21):
questions.
It's twice every single company,it's twice.
So to connect some dots, why theAI agents for the past year have
really failed a lot of companiesis that you you you have a
process, you think you know, youput an agent in there, you tell
the agent what to do.
It's only doing half of whatreally results in success.

(35:43):
So guess what?
When it only does half of whatdrives success, it doesn't work,
then you're pissed, right?
Because you're like, we did anagent, it didn't work, our sales
went down, and you're like, Iknow, because you only told it
to do this, and your best peopleare doing twice that.

SPEAKER_00 (36:00):
Yeah, and you don't know what you don't know.
Totally.

SPEAKER_02 (36:03):
Yeah, amen.
Amen.
So those those are that's how wego through the process.
It's it's uh AI to me, is itgonna end the world?
I don't know.
But the one thing it will do iscause us to think differently.
So that that thing of, hey, do Ihave to ask you what your
process is or what you what areyou really doing?
I don't care.
I don't tell us anything.
That's it's like a magic show.

(36:23):
Let it let us have your data,we'll tell you what's actually
happening versus what youthink's happening.
And so that typically is areally fun process to go through
with companies that areopen-minded.
Some companies get irritated,but it's like, hey, here's
what's really happeningfactually.
Regardless of what your opinionis or what Miller Hyman told
you, or what Sandler, you know,you whatever.

(36:46):
Here's what's here's what'sactually happening.

SPEAKER_00 (36:49):
Out of the box, and by out of the box, I mean you
don't need any extrainformation.
You're looking under the hood,and it's not from a process
point of view.
There's no PowerPoint slideshere, it's just the raw touch
point data.
You get sales frequency, you getcall duration, you get all the

(37:09):
usual metrics.
Yes.
Okay.
Is that sufficient as baselineto determine why Susan is 3x
over Bob?

SPEAKER_02 (37:20):
Oh my god.
You know, I've been doing this alot.
No one's asking that question.
That's amazing.
Right.
No, the the short answer is no.
Uh, you know, uh, from a can wereplicate Susan perfectly?
But what does happenimmediately, it's quite obvious
Susan's process is so differentfrom the norm.

(37:41):
And then you start to go, whatwhat are those empty holes?
What like Susan's deals havethere's holes here.
What what's happening?
And then when you talk to her,she's like oh yeah, I'm on
social media, I'm LinkedIn, andthat or hey, yeah, see my phone?
Look, here's my 87 text, or hey,I don't, I have my own private
little kind of proposal app andI analyze, you know, inevitably

(38:04):
there's some tools there, andyou're like, okay, well, she
just told me we're gonna dothat.
So so typically day one, we getover 50% of like enlightenment.
People are like, it it istotally different than what we
think it is.
And then for us, we really enjoyuh the back and forth of like

(38:25):
helping companies work throughis it worth uh tracking the
social media piece?
Is it worth seeing the video?

SPEAKER_00 (38:33):
There's an implicit gap analysis that's taking place
off baseline, and then theclient, him or herself, comes
the realization well, you know,we've just got our standard
metrics.
We see we see some differenceswith Susan, and duh, we know
she's kicking ass.
Right, but but wow, look,there's there's some gaps in her

(38:58):
touch points, which we'reclearly missing.
And these front race guys aremaking recommendations on how we
could boost measuring more touchpoints, measuring them better,
adding dimensionality to Susanin ways we never thought about
before.

SPEAKER_02 (39:16):
She's typically doing the process different than
the 22 steps, right?
That the CRO swears this is oursteps, and inevitably you know
this.
You mentioned the eclecticpeople, it's opposite.

SPEAKER_00 (39:25):
Never I'm I'm in my experience, I'm sure in yours,
there's no 22-step salessuperstar, they don't exist.

SPEAKER_02 (39:34):
Well, they she does it, she does it almost opposite.
You know, it's like she'llsometimes she sends the pricing
meeting too.
And you know, the executivestaff's like, what?
What?
Yeah, yeah.
Second meeting before she evendemos.
Here's the price.
And they're like, What?
What that no, they don't getpriced till after the demo.
And she's like, no, no, no.
Here are my initial email, youknow, my my second introductory

(39:57):
email.
I tell them the range of pricingbased on your sization is
between 42 and$67,000.
So she already has a flag in thesand that is instrumental to her
clothes, where most of the repsdon't mention clothes till after
the demo or after they uh, youknow, they uh did a proof of
concept.
And so uh those things areshocking.
Again, the difference between,in my world, three X person and

(40:20):
one X person, it's 20 littlethings.
It's 20 little things.
And starting to day one, weprovide 10, and then you're
like, can we get to 11 and 12and 13 and start to fill that
gap?
What is it?
But it's not the big things,it's 20 little things and
starting to shine a light onthose so we can replicate.
So, so my my on my team, my Cplayers are B players, my B

(40:42):
players can be A players.
We're never we're probably neverreplicating Susan in totality.

SPEAKER_00 (40:46):
Yeah, it's just optimizing.
If you could get it off just afew basis points, the ROI of
implementing your service isalready covered.

SPEAKER_02 (40:54):
When you when you do the math, I'm not a math major,
but when you increase the top,and we all know this, I don't
funnel.
When you increase the top 20%,the net in the bottom is 3x.
I mean, it's it's because youhave such a bigger thing, it's
it's amazing.
And so really trying to providethat statistically, not
guesswork, not you hired a magicmanager, we hired more
superstars, we have more, it'sstarting to put some science to

(41:16):
something that we've kicked,we've punted on for 40 years,
for 40 years we've been puntingon and be like, hey, let's start
to look at this black hole ofsales and put some numbers to
it.

SPEAKER_00 (41:27):
Do some of those magical personal soft skills
come through in the raw matrix?
Yes, but and by that I mean Iwas on I was on a double date
recently, and the otherpartner's partner, she's a top,
she's a 5x Susan.
Okay, she sells for pharma andand the doctors love her, right?

(41:51):
She's got the ADHD.
She's like, she's got her phone,she's texting, she's doing all
this, and when you look at her,she's like the Tasmanian devil's
wife, you know.
She's involved, but she pours iton the doctors.
They love her, they know her.
She goes to ad boards, she doesthe clinical stuff.

(42:14):
And when you ask her about thedrug, she's very knowledgeable,
but she'll talk about it forabout 20 seconds, and then she's
off on something else.
And I guarantee you, she's thesame way with the doctors.
But the doctors feel thatthey're covered, they feel that
she's there for them, and theythey adore her, they adore her

(42:35):
attention and all that stuff.
So these are all soft skills,it's got nothing to do with her
knowing progression-freesurvival of the cancer drug.

SPEAKER_02 (42:46):
See what I'm getting at?
I am.
We have a whole in our metricengine, hundreds of these
qualitative, so kind ofsubjective measurements.
And so I'll give you one oneexample, real world example.
One of the CROs was adamant, hefelt like his sales reps would
talk over the clients.
He was in the in the he'd watchthe replay of the video

(43:07):
conference, he'd be like, Yougotta let them talk and blah
blah blah.
And so we had this patientscore.
So when at the time the prospectstopped talking, when did you
interact again?
So it was his it was his thing.
Eight months of data, completelyinconsequential.
He he he was so adamant, it wastotally inconsequential for all.

(43:31):
It did not that did not matter,but we have a litany of those,
and what you're talking about, Ijust I want I want to be I want
to be really clear with youraudience because there's two
things here that are I I thinkreally, really important.
I'm not we're not selling somepithy mixy duck pixie dust of
measure quality or measureprofessionalism or measure
tonality.
What what we're talking about isAI can start to put scores to

(43:54):
those things, okay?
But the magic, it's like NPS.
If you have you done an NPS.
Survey, it's not what the scoreis of your company because it's
the score, how does it move overtime?
Right?
That magic of NPS is not whenyou do your NPS and national
promoters or uh uh is itnational promoter score?
Net promoter score like whateverit is, it they tell you who
cares what your first one is,you just want to keep getting

(44:15):
better, right?
And because it's people thatlove you versus attractors and
blah blah blah.
Same thing.
The the software starts tomeasure the tonality, which is
different for everybody, andthen it starts to measure did
you win the deal?
Right.
And so we have a myriad of thosewhere it starts to give you a
score, and then I haven't talkedanything about it yet, but the
second part, the metric engineis what we do, but then we have

(44:37):
this thing called time machinethat can track it over time
because believe it or not,shocking.
Salesforce today, loveSalesforce, God bless you.

SPEAKER_00 (44:46):
We keep playing that so we don't build up bad
Salesforce karma, but it's itdoesn't exist.

SPEAKER_02 (44:53):
So Salesforce today, if you had a proposal and it
changed over six months, youcan't go back and see how it
changed, when it changed, andwhat variables change.
You can't.
It does not exist.
If you have a proposal, theycreate it, and what we rely on
today, we tell the sales directto change it.
We're like, hey, you tell uswhen it's gonna close, what's
the probability it's gonnaclose, change the value of it,
and then we're pissed when thepipeline sucks.

(45:15):
And you're like, that's done,what they're so biased.
And so we have this time machinethat not only starts to measure
these metrics, but then tracksthem over time, and you can
start to see is it not not is isMookie's better than Jack, it's
Mookie versus Mookie.
Hey, here's how you did it lasttime.

SPEAKER_00 (45:35):
It gets rid of this standardization, right?
And it gets rid of erroneousvariable analysis, such as
talking over someone.
That woman I just described, the5x pharma sales dynamo, she's
literally yelling at everybody.
Yeah, sometimes can't even get aword in, and I guarantee you

(45:56):
she's the same way with herdoctors, like hey, hey, hey,
hey, hey, but but it works.
Get to what works, don't obsessover something that's otherwise
irrelevant because you thinkit's gonna impact the sales
negatively, right?

SPEAKER_02 (46:10):
And we mess people up all the time.
Listen, we we see it incompanies where they put a new
system in place, right?
We we just bought Clarity or webought Gong, and we gotta follow
the the or we have a newprocessing, like you're like we
see it in the data.
We're like, hey, what happenedFebruary of 2025?
Oh, that's when we bought blahblah blah.
And you're like, Yeah, sincethen everything sucks.

(46:33):
Like you either and you addedthat system, you forced everyone
to use it, and everything wentdown 32%.
Like, why are you doing that?
What like well Bob really likedthat system, and he thought we
really needed to, and you'relike, I know, but every time
someone uses a system, yourclose rate goes down.
So again, no opinion, nopersonal opinion, it's all the

(46:56):
stats.
It's it's what are the youstarted the show with it.
What do the numbers say?
And the more data you have,especially legacy data, and
going forward, it'll get smarterand smarter and smarter.
Your LLM and what we do getssmarter each time as we start to
measure it, measure metrics, andthen track it over time.
It's amazing the impact that hason companies.

SPEAKER_00 (47:15):
How does it impact the salesperson?
Because I want to dovetail offof what you just said, which is
really great for employeeempowerment, salesperson
empowerment.
Because the old school is we gotthe 22 steps, buddy, and you're
talking over people, so shut thehell up and listen more.
And when you're from 18 to 19,we notice that you drop the

(47:39):
ball.
And they're like, what theactual F does that have anything
to do with what I'm trying to dohere?
Amen.
So you're you're reading betweenthe lines, you're also passive,
not active.
You're you're what you'rewatching with data and you're
taking it in, and then you'reable to provide recommendations

(47:59):
that make sense for theindividual seller.

SPEAKER_02 (48:02):
That's that's it.
Each in our app for everyopportunity, you bring up an
opportunity, it ranks theprobability it's gonna close
statistically, not somebody'sopinion, the date we think it's
gonna close.
What is it in critical ornon-critical stage?
Like, is it we need to getsomething needs to happen right
now?
And then starts to tell you whatshould be done, and then it does

(48:23):
a myriad of other really coolthings.
It'll tell you exact message tosend, what you should say, why
it's important, how to move theprice, a lot of things.
But for for a rep, for eachthing you're working on it,
it'll literally be like, youmight think this, but here's
statistically where it is, whatit's worth.

SPEAKER_00 (48:39):
Tell me more about your UI.
As you've been describing moreor less the user interface for
the sales manager, which hasthis other stuff.
I'm curious also about the UIfor the sales person.

SPEAKER_02 (48:53):
It's the same, it's the same web interface.
Everybody loves it's you loginto front race, you have a
login, and it it literallydiscriminates between typically
a management personnel.
If you're a CRO, here are whatexactly what you think
management level, hey, your yourdelay uh in closing, there's a
big gap.
Um deals have been it used to be67 days, it's now 92.

(49:14):
Hey, two deals are scheduled toclose today, but both the people
closing are not good at closing,right?
At executive level, it tells youhottest action items constantly,
but the same interface when youlog in if you're a sales rep,
it'll give you your deals andyour stuff and start to share it
with you.
Be like, hey, today, here arethe signals for what you should

(49:35):
do today to have the biggestimpact on your day.
And if you want to go look atall your deals that are pending,
here per deal is what you shoulddo with each deal, if anything,
per today.
And so it's it is humbly, it isI again, I it's the thing that
people want from AI.
Can you make me smarter?
I'm not purposely screwing up mydeals, but that's what people

(49:56):
do.
They send the wrong thing, theygive the wrong pricing point,
they send another text, and theyshouldn't.
They should literally do anotheraction item.
And so we've really helpedpeople maximize how granular is
that experience.

SPEAKER_00 (50:10):
So if you've if you're a sales rep and you've
got the rep plugin forSalesforce, you've got your own
dash, and it gets reallynitty-gritty.
It's like number of calls.
It's got a CRM built in too,right?
So it's like I sent the email onApril 20th at 8 20, and I did or

(50:31):
didn't get a response within twodays.
And then you got the littleyellow and red going, you need
to follow up, and it's basicallyyou're a spreadsheet jockey.

SPEAKER_02 (50:42):
Right.
I but but you know what's funny?
As you asked that question,which is a very fair question,
lovingly.
It's so funny.
When we when I'm dealing withthe CRO who said something like
that, I got all this inTailsports.
You're like, okay, well then,well then do you hit your goal
every quarter?
Or how how yeah, how is it howis this and inevitably the
result's not good, but the butbecause here's the issue.
One, you're missing somemetrics, but two, we you and I

(51:06):
could look at the exact samedata on a dashboard and take
totally different actions,right?
You you would look at a set ofnumbers and go, oh, you know,
from that, I would then callthem and reduce the price.
And I might say, oh no, don'tcall them at all.

SPEAKER_00 (51:20):
That's what I'm getting at.
So you're does does front racereplace, in a sense, their CRM
experience that they've hadbefore you?
Does it augment it?
Right.
We we don't we're not a CRM.
What is what is the actualexperience of you coming on
board like?

SPEAKER_02 (51:41):
Yep, no, totally fair.
We do not, again, we do not havea kept replacing any of your
stuff as we sit here today.
What is that?
You know, like Salesforce just,I think this uh oh the sun just
came out my asking.

SPEAKER_00 (51:53):
I know, you're getting a you're getting a tan
there.
My lord.
For listeners on Spotify andApple, Jack, Jack in the last
few minutes started glowing.
And you know what that is?
It's like the movies.
He's he's nearing the close.

SPEAKER_02 (52:08):
No, no, God, God, God, I must say something.
I must say something good.
Like God's like, all right,now's the time.
Spotlight on.
Yeah, so that's so funny.
But what we wanna we want tohelp them get through the deal
and provide them that data.
But if you saw Salesforce thisweek announced, hey, they're
have no UI, they're just gonnahave an API, right?
They're gonna they're gonna losetheir front end and start to

(52:30):
just let agents plug into it,right?
So that would have beenshocking.

SPEAKER_00 (52:34):
That's because they shit the bed uh on the stock
market, right?

SPEAKER_02 (52:38):
That that would have been shocking, though, two years
ago.
If you said no UI, so could arewe moving to a time, I believe,
where the traditional CRM andthese these sales enablement
tools are not the answer.
I I think we are.
I I think it's evident 40 yearsin.

SPEAKER_00 (52:54):
It's been a clunky disaster.

SPEAKER_02 (52:56):
Yeah, I don't want to say failure, but it's we have
more data.
It's a pain in the ass to use,and it's kind of we're no
better, but but if it was but ifit was better, if we hit the
number, if we delivered on goal,if 100% of the people on your
team or 90% were getting togoal, you'd be like, it's so
worth it.
Let's record all the calls,let's let's analyze all the

(53:18):
stuff, let's have metric, let'shave a dashboard of every single
person in terms of both worldsnow.

SPEAKER_00 (53:22):
It is.

SPEAKER_02 (53:22):
Amen.
We are data crushing our people,and none of it, humbly, none of
it, is having a difference onthe bottom line.
Not not, there's not a I've meta CRO yet.
Like some companies are hittinggoal, God bless.
But it's not because of theirdata elements.
It's uh when they miss goal,they have no idea why.
And it's I believe it's forthese things.
It's it's the difference betweengood and great, or 20 or 30

(53:45):
little things we've historicallynot been able to measure.
You mentioned some of them beingqualitative, definitely, some of
them being in the weeds type oftiming and logistics items.
We again we haven't been able tomeasure at a uh a macro and a
it's with so overwhelming theamount of data which AI makes.

SPEAKER_00 (54:03):
Like level of grit.
Like how determined are they?
And that might seem kind ofobvious, but but how do we
measure grit?

SPEAKER_02 (54:13):
That's that that is I've never had that question.
That's a really interestingthing.
I mean, that can lose thefoundation.
I'm going back, look, I'm goingback to the tech team and be
like, oh, add add grit.

SPEAKER_00 (54:24):
Relentless determination.
Are you zero to ten?
And and it's of a virtualcertainty that your three X
people are all eight and abovein terms of the grit, right?
Sonacity, that's it.

SPEAKER_02 (54:36):
You know what I was thinking of too?
This will get me in trouble.
But I think too, there'ssomething about, especially in
the face-to-face sales world,appearance, look and feel, uh,
your your you know, how do youhow you right?
So being able to take some ofthose things and and start to
quantify them.
But that, but that those arereal things.
We we can keep trying tosidestep those, but those have

(54:57):
those you can send two peoplein, forget they're whether
they're pretty or not, orwhatever, but one looks slightly
disheveled, one looks together,you know, but based upon
whatever you require that theywear it.
And those are all real thingsthat affect the outcome.
That we've never measured that.
Have you ever seen a Salesforcedashboard on on their appearance
or look and feel or even likeattention getting magnetism?

SPEAKER_00 (55:19):
Right?
Going back to Noam McDonaldversus Bill Burr, all right?
They're the opposites.
You know, Noam McDonald's like,you know, I was taking a walk
and the dog took a dump, youknow, and then Bill Burr is
literally screaming at you atthe top of his lungs.
They both have magneticpersonalities, but in their own

(55:40):
way.
Yep.
So how do you reduce that to asingle key variable, which is I
can't take my eyes off thisperson and I want to keep
talking to them, I want to keeplistening to them, right?
Amen.
That's the kind of essentialmetric that makes the
difference.

SPEAKER_02 (55:56):
Amen.
I and I and and just in allhonesty, when you talk about 20
different things making adifference, will we ever have
all 20?
Will you be able to maybe withrobots?
If Elon gets us to robots withAI, maybe we're you know, match
all 20.
But in in the world of humans,our the goal is to let can we
get to 15 out of 20?
And and there'll still be five.
You'd be like, it is magnetism,it's their appearance.

(56:17):
There could be some things.

SPEAKER_00 (56:18):
There's one other element I want to toss at you,
which I think is verysignificant, and salespeople
make this mistake, especiallysales managers.
There's this idea that you needto be awesome for everyone all
the time.
And if you're not, you suck.
So, for example, my 5x pharmagal, okay?
She's ADHD and she's runningaround.
They love her, but by they, I'mtalking about 80, 85% of those

(56:43):
doctors.
And I'll put equal money, the 10to 15 percent cringe when she
walks.
Every time I see her face,they're like, no! Oh, not her
again, not again, right?
And she's a 5Xer.
So that's a critical decisionthat has to be made.
And it goes all the way to theessence of branding, which is
pick your lane, find yourpositioning, pick your identity

(57:06):
that works best and isreflective of who you are, and
understand that it's not gonnawork for everybody.
Right, you're you're so right.

SPEAKER_02 (57:14):
Listen, we and you know what's interesting?
We do that, and any good managerdoes that.
When I have my sales team, Icouldn't take Mary Poppins and
send her into New York, justlike I couldn't take the New
Yorker and send him out toIreland, right?
It doesn't work that way.
And so we put measures topeople's personalities and how
they sell professionalism, uh,the extrovert, are the

(57:34):
introvert, are the relationshipbuilder.

SPEAKER_00 (57:36):
You gotta align with the prospect.
The prospect is a key variablein totally, and but but think
about just reality check.

SPEAKER_02 (57:45):
How many times have you ever seen a dashboard that
says that?
Hey, client profile, andwhatever range you want to put
in it, one to ten, just just asa just as a range.
New Yorker to country quiet.

SPEAKER_00 (57:58):
Never, you know what I mean?

SPEAKER_02 (57:59):
Right, none, none, none.
I mean it's so important.
It's um it's unbelievable.
It's unbelievable.
McDonald Bill Burr.
Right.
Or or one that we measure todayis is how did we get the lead?
Not all leads are the same.
Like, did we get it as areferral?
Did we get it off social media?
Did we get it as a cold leadfrom a marketing campaign?

(58:22):
Did we get it because uhsomebody drove by a truck and
the truck had a big average?
That affects the outcome somuch.
There, you ever seen a score forthat?
You ever you ever seenSalesforce be like lead, not
lead quality of lead, but likewhere it came from and how that
much that impacts the endresult?
And I'll give you another one,I'll give you another one
measure.
This is a real one.
This this uh this just changedme.

(58:44):
This is a real one that no onetalks about.
Hey, are we discounting thismonth?
Well, what's the biggestvariable are we gonna discount
this month?
What is it?
How are we doing?
How how are we doing?
How how are we doing thisquarter?
If we are crushing it, we're notdiscounting.
But back, you you need an armand leg.
Oh, we're 30% under.

(59:06):
Oh, listen, we're running a twofor one sale.

SPEAKER_00 (59:08):
Do it so does it blow up your freaking brand?
Remember the iPhone, uh, I don'teven remember, iPhone 6 back in
the day.
It was it was a huge mistake,Tim Cook's biggest mistake.
He did a like the$300 plasticiPhone, okay, with like a shitty

(59:28):
non-glass cover.
It almost destroyed the brandbecause they were competing with
the cheaper Androids.
So if you're discounting, areyou underselling and destroying
your brand because you're notselling based on value, or are
you taking a great opportunityto get in a wedge and build a

(59:49):
new client?
Right?

SPEAKER_02 (59:50):
Well, listen, I I I I yes, but I would just say very
few brands, very few, have theluxury of being like we never
move all price.

SPEAKER_00 (59:58):
Like, I'm not saying it's monolithic, but to your
exact point, when to do it andhow much.

SPEAKER_02 (01:00:03):
Right, right.
Because people, when I deal withcompanies, they're like, well,
Google does that or Apple doesthat.
And you're like, you're notGoogle or Apple.
You're what are you talkingabout?
You're barely in business.
What don't compare yourself toGoogle.
Like, so so in that pricingworld, it's like, how much
you're gonna move on price, it'sso affected by how you're doing
as a company.
That's that's the point.
It's like you I don't care whoyou are, because we've seen

(01:00:26):
we've all seen Louis Vuittonwent down as far as we go came
back, God bless.
But when they were down, theywould very different to what
they would do today.
So, right, when you're ridinghigh and everything's great,
you're like, listen, the priceis the price.
You don't want it.

SPEAKER_00 (01:00:39):
Don't thin it out.
And then if you really want tostay competitive in a
commoditized atmosphere, whetherto your point, whether it's
five, ten, fifteen percent, thatcould make you or break you for
some of these similar reasons,because you don't want to
undercut value.

SPEAKER_02 (01:00:55):
But how have when have you ever seen a CRM
dashboard that said, oh, here'sour pricing based on where we
are in the month or the quartertoward our goal?
There, the correlation betweenthose two is so high.
It is, especially if you're acompany that does a lot of stuff
at the end of the quarter or theend of the month, the
correlation between where youare uh to goal and where you are

(01:01:18):
at uh time of the year isenormous.
Your ability to discount orchange price or move on price.
Highly correlated, highlycorrelated, unbelievable.

SPEAKER_00 (01:01:30):
Exciting stuff.
So, all right, I am uh baldambition l t and I've got uh
source of a hundred guests andthey're they're ready to rock.
Amen.
How does this process work?
Because you describe a very,very aggressive onboarding

(01:01:51):
process, and there's there'sminimal due diligence when it
comes to process andconversation.
You guys aren't a McKinsey, it'sthe opposite, and it sounds like
there's a technical aspect.
So there's a couple things therebeyond just the immediacy of the
timing, and the thing that popsup immediately into my bald head

(01:02:12):
is privacy and security.
You're APIing my ass all overthe place.
Uh, where's my data going?
Uh give me some assurances.

SPEAKER_02 (01:02:23):
Sure.
API, we're we're uh uh AWS or orGoogle Web, right?
So all super secure, stock two,all those necessary things.
But you bring up a really,really good business point,
which is uh, I don't know, I'llget myself in trouble here, but
I would just say that executivesthat we're like where you and I
are in life, when we starttalking about what we do, they

(01:02:46):
start fibrillating because wehave the old construct of what
data project and connecting dataand normalizing it.
Like we we're in our, you know,we we just tried to connect our
CRM to something five years ago.
You'd be like, oh my gosh, I'llsee it next year.
And so it's amazing the uhwhat's happened and and how uh

(01:03:06):
more fluid that process is.
And so I our biggest thing is wemake sure that we're more
diligent.
Sounds weird, but the moremature an executive is, we want
to make sure that we go throughit because uh sometimes it gets
killed when they take itsomewhere else because, like,
how are they gonna do that?

SPEAKER_00 (01:03:21):
I I gotta relate some of my own experiences too
with my own clients on not thepodcasting side of the house,
but on the consultancy side ofthe house.
And and it's amazing the extentto which sometimes very senior
people in organizations acrossmultiple verticals don't get it.
And that's no insult to theirintelligence or their

(01:03:43):
experience, but to your exactpoint, there's an old school
mentality about what technologyis and does, and the
flexibility, fluidity, theplug-in nature of AI, which is
really one of its key strengths,isn't really made aware to a lot
of people who are used to thisJurassic Park.

(01:04:06):
I need, I need a team, I needsix months, I need half a
million dollars before anythinghappens, and I need to shut
everything down while I mightremember data migration.
I need to migrate, I need tomigrate the server, right?

SPEAKER_02 (01:04:28):
You see what um Claude actually put out co-work.
I think today um uh uh uh OpenAIreleased their connector.
There is so much happening in,and I don't have this, like we
all gonna be out of a job, butit's gonna be different.
We you have to we have to startthinking about our issues
differently.
Like if somebody would have saidto you the day the internet
started, oh, we're gonna watchmovies on our phone, or they're

(01:04:48):
gonna be, they're gonna be,we're gonna stream TV, you're
gonna get rid of cable andyou're gonna stream.
You'd be like, there is not achance.
I can't even download a picture,you know.
And so again, we're in ourinfancy of this AI thing.
And I just I just want toencourage your listeners or
folks that are watching.
What we try to do for companiesis give them a foundation of
analytics so that again, thislast mile, it's gonna keep

(01:05:11):
changing.
The solutions that work, AI iscannibalizing itself every
single month.
And so we want to give you a setof analytics so that as you plug
different AI tools in, you canmeasure is this helping us?
Is this hurting us?
Is this good?
Is this bad?
You've got to have a way tomeasure what's happening.
That's what we're trying to dofor companies.
I don't want to be out here onthis last mile.

(01:05:33):
This this is so, God bless Elonand and whoever Sam, whatever
they want to do.
But your company needs tonormalize the data, be able to
know what the process flow is,test different things, test AI.
And what works today, I promiseyou, will not be the thing
you're gonna want 18 months fromnow.
There's no way the waytechnology is advancing.
So you want to have something tobe able to measure and

(01:05:54):
constantly track hey, is thisgood?
Is this bad?
When do we start declining?
It's time to replace that.
Do you You know that uh thatwe're paying for this, now this
part's free.
And so that's what we're tryingto do for companies is give them
a baseline that they can makegood decisions over the next
couple years because it'schanging constantly.

SPEAKER_00 (01:06:12):
The point that I think I'm hearing, and I just
want to bring it back to you, isis is is limited barrier of
entry.
So so so it's not as easy assnapping your fingers, but as
you mentioned, within a few daysyou can plug in.
And by plugging in, it'sunobtrusive.
Your systems remain yoursystems, you're doing your

(01:06:34):
thing, nothing changes, andyou're an add-on application.

SPEAKER_02 (01:06:39):
It's a layer, right?
A little layer on top.

SPEAKER_00 (01:06:40):
It's a little layer on top, and then you're given a
scalable option.
Dig in a little, dig in a lot.
Have your salespeople focus alot on this information, have
them do whatever they need todo, but it's crawl, walk, run.

SPEAKER_02 (01:06:56):
Oh, that's great.
We always say that.
We always say that internally.
We say that all the time.
Yep.
Crawl, walk, run.
Do not go out and spend$250,000on some AI solution.
You don't, you're not gonna wantit a year from now.
And I say this all the time.
There's no AI expert.
If somebody shows up your door,gets on a uh one of these boxes,

(01:07:16):
you're on a web conference,we're the AI experts.
No one, there's no one has anyidea what 2030 looks like, and
you're trying to do 2030, theyhave no idea about 2027.

SPEAKER_00 (01:07:27):
Yeah, yeah.
My my favorite, my favorite,talking about you know, my most
and shitified platform, which isLinkedIn, I hate it, is like you
got you got people who aregiving these deep seminars on AI
and certain stuff, right?
They're laying it out, this isAI.
When like by the time you get tomodule number six, change out

(01:07:53):
from under you.
I mean, if you're not talkingabout immediate pragmatic
application through the lens ofbusiness rules and business
application, what are youtalking about?

SPEAKER_02 (01:08:06):
Listen, for you for for people listening, get
everybody wants to jump into AI.
Listen, my best advice, besthumble advice.
Yeah, work on your data,standardize your data, get it
cleaned up, and get your processflow, understand what they are.
Before you invest in any AI.

SPEAKER_00 (01:08:20):
That's 95% right there.
And then that's never gonnachange because you got data from
multiple sources.
You need it to all beinteroperable and normalized,
and then most significantlyunderstand your own workflow,
your own word stream.

SPEAKER_02 (01:08:36):
Yeah, garbage in, garbage out is so applicable.
AI is going to expedite that,like you said.
In the years past, you'd migrateyour server six months, you
didn't know you got burnedimmediately.
You go buy some of AR, AI, applyit to your in in incorrect data
elements.
You will make seven more harddecisions, wrong decisions, and
you'll be out of a job or yoursales will be down 30%.

SPEAKER_00 (01:08:56):
Your vendor who you brought in to fix stuff all of a
sudden breaks even more, theyown your ass, and then you're
actually one step forward, 10steps back, and in a Salesforce
kind of view or a salespersonkind of view, sales management,
your people are still notbenefiting at all because
they're running around notlistening to anything you're

(01:09:18):
saying anyway.

SPEAKER_02 (01:09:19):
Listen, and every everybody today, you uh you talk
that AI is making more work forthe people that I mean it's just
you you feel like, oh, I gottacheck, I can look at AI, I gotta
create it's like so many morecycles of everything.
Because you're like, did you didyou check that?
Did you did you put that in chatGPT?
Did you check that in Claude?
Do we yeah, do we?
You know, it's it's it'screating a thousand cycles now

(01:09:39):
on everything.
I'm really gonna miss you onLinkedIn when they when they
what do you what was the oldliberal thing?
When they cancel you, whenLinkedIn cancels you, like I
think uh they're gonna bot me.

SPEAKER_00 (01:09:49):
They're gonna stand it.
I'm gonna get kicked offLinkedIn, and then Salesforce is
gonna, you know.
That's it.

SPEAKER_02 (01:09:55):
They're gonna be like, they're gonna be like,
remember him?
He where is he now?

SPEAKER_00 (01:10:00):
No, we love you, LinkedIn.
It's not I've been on otherpodcasts saying Satya Nadella
has been one of the best CEOs inall of history.
So he'll forgive me for shittingon LinkedIn.
I do want to ask you oneimportant thing though, before
before we go, and that is, andI've seen this personally from
my consulting work in AI.

(01:10:21):
Employees, whether they'resalespeople, whether they're
part of teams in whateververtical, are freaked out.
If you bring in, if like an AIcompany comes in with a new
application layer and you'reasking anything of these people,
and it's especially true, I'mcurious your opinion of
salespeople, because they'rereally competitive and to the

(01:10:44):
point of paranoia.
Don't take my stuff, you knowwhat I mean?

SPEAKER_01 (01:10:48):
Totally true.

SPEAKER_00 (01:10:49):
A lot of people think that you're coming after
their job.
Totally agree.
Yeah, so how do you deal withthat onboarding for front race
coming across as a friendinstead of an enemy that's ready
to break them?

SPEAKER_02 (01:11:03):
Great question.
You know, it's and then it's anold old school like change
management, right?
It's it's not the facts, it'show you speak about it.
And um, I I would I would sharethe following, uh just being
super direct.
It's what's happening today,this is a long answer to your
question, which is what'shappening today in the old
school.
If you were a public company andyou fired people, right?
You did a mass layoff, themarket would crush you.

(01:11:25):
They'd say, You're a bunch ofidiots, you overhired, you just
did a riff.
You in fact, we want to fire allof you, and we'd start over.
Now, we've seen the last sixmonths, companies are rewarded
for doing a massive riff, theirevaluations go up.
All these public CEOs, Iguarantee you are all having
meetings.

(01:11:46):
Right, that's what they say.
It's never been done.
Right, it's never been done.
It's it's when you, a publiccompany did a mass riff, the
market penalized them becausethey said you managed the
business poorly.

SPEAKER_01 (01:11:57):
Right.

SPEAKER_02 (01:11:58):
Only in the last six months now we have this dynamic
where, oh my gosh, you guys dida mass riff and say it's going
to AI.
We love you guys.

SPEAKER_00 (01:12:06):
You're staffing goes down and then market cap goes
up, they're inverselyproportional.

SPEAKER_02 (01:12:13):
Correct.
So that is a wild card in what'shappening.
And I just want to say to peopleone, you you AI's coming, right?
And so what we try to do forcompanies, they're like, oh,
you're gonna share my stuff?
Think about what I said earlier.
You go work for a new company oryou work for a company, and you
want to know what what's thebest people doing.

(01:12:34):
How would you find that outtoday?
Like, forget uh like how whoyou're gonna learn, you're gonna
get trained by the CEO orthey're gonna hire a sales
training company.
How would you smart, goodwilled,32-year-old, starting with pick
a company you love?
Apple, Google, whoever, do it,whatever, and you want to be

(01:12:54):
great.
You want to be in the top 20,you want to be how what how
would you possibly get trainedup to be great at that company
on this in the sales world?
What what does that look liketoday?
It's a mess.
It you you go to corporatetraining, those people don't
sell.
We know that you don't payattention, you get drunk at the

(01:13:16):
hotel bar.
People training you don't sell.
The people that do the trainingdon't sell.

SPEAKER_00 (01:13:21):
Either they don't sell or they're a sales star to
our point earlier.
Oh, they don't know what tosell, right?
They don't know what they'retalking about.

SPEAKER_02 (01:13:27):
No, but you maybe get the sales star for two
hours, you don't get them fortwo weeks of training.

SPEAKER_00 (01:13:31):
Not for the whole seminar.
You get a teacher, you get ashower not a doer.

SPEAKER_02 (01:13:37):
Right, and they'll teach you some model, and you
have no way of doing it.
And so that's a long-windedanswer to your question of what
we're trying to do for people isshare.
Since COVID, particularly, noone really knows what success
looks like.
I believe that's one of thereasons we're still fibrillating
from COVID.
We no one knows what what is Tomand Susan and Mike doing at

(01:13:59):
their home office in theirliving room, blah, blah, blah.
We're gonna track what's greatabout you because, as we said,
everybody's created a part ofit.
And so for the AI to be able tolike say, you know what Mookie's
really great at?
I don't know about the, you'renot great at cold calling or
building a pipeline, but oncethey're in from demo to close,
superstar.

(01:14:20):
And you've been able to talkabout that, see it,
quantitatively look at it,because when people know your
skill set and you're gettingcoached and management sees it,
you then have more value.
If not, you better be great atthe whole thing, right?

SPEAKER_00 (01:14:35):
That that is such a terrific point because nobody
does everything well.
And the problem that you reallysuck at certain things is
exponentially increased with howgood you are at doing other
things.

SPEAKER_02 (01:14:47):
And and about and think about the clients.
Uh like a lot of people separateenterprise from maybe SMB, but
some companies don't say them,maybe you're great at big
company deals, maybe you'regreat at small company deals.
Like we are gonna help sharewhat's great about you.
You may suck, by the way.
Like, like, let's just discountpeople that suck.
Discount people that are aren'treally working, they are not

(01:15:08):
really back to grant anddetermination, right?
Right, they they're theyshouldn't be in sales.
Forget those people that are,but people that are in the bell
curve, good, solid people.
We start to be able to measurewhat they're good at, how to
make them better in other areas,share that with the company,
know that hey, you you'reentering an area of weakness, so
let's get another resource tohelp you because that's not what
you're good at, right?
So those are all things that wedeliver for I believe we're an

(01:15:31):
enabler to those people sothey're better, measure, share
what they're great at, becauseno one's great at everything,
but we have to pretend like weare, and so I believe we've
really helped that person standin the world.

SPEAKER_00 (01:15:44):
I love that on a deeply personal level because I
know I suck at things, and I'mI'm awesome.
Me too.
Me too.
Me too.
If I could do more almostentirely of the shit that I'm
great at, I will be happier andthe world will be happier if we
could if we could shake it up.
How many times have you seen anemployee doing whatever role,

(01:16:09):
and with one group doing certainthings for certain clients,
they're a rock star.
Yep, and then there's a companyreorg, or something goes on, and
this same person doing more orless the same stuff goes to a
different group, is assigned adifferent kind of client, and
they tank.
Yeah, nothing has changed,they're the same person, rock

(01:16:32):
star to bum.
Yep, and and the fault is nottheir own, the fault is
alignment issue, and and thatcan be circumvented with data,
with knowledge.

SPEAKER_02 (01:16:46):
Real world, only only people that run teams and
live this for a world are gonnaagree with the following.
But it's like you can't have alleight players.
We're there's not everyone'swired that way.
You be you need B players to bewith B players.
Like, like you need a lot of Bplayers, like you need
stereotype.
If if we have a single parentthat needs to pick up their kid
at three o'clock, and they'rethey're uh 70%.

(01:17:08):
You need those people.
Like to expect them to do whatwhat the uh the super energetic,
young go-getter, not young, butwhoever the go-getter is, like
who's working 12, you know, 15hours a day, they're different,
but they're not all the same.
And a company, you do do youneed those crazy 15-hour people?
You do, right?

(01:17:28):
But you also need a lot of supersolid, very good quality people,
and matching what they want,their earning potential with the
opportunities is so important.
Like expecting everyone to bethe it's not doesn't work, it
doesn't work.
And so matching what they wantto do with their skill set and

(01:17:49):
the company being happy withthat performance is all part
when B people do B work and arepaid B money, everybody's fine.
They really are.
When A people do A business,paid A money, everyone's fine.
It's when a it's when a B playerwants A money for B effort.
That's when you get sideways inthe HR world.
So again, we help kind of putpeople in buckets and let them

(01:18:12):
self-choose.
Like, hey, here's why yourearning is here, or here's what
it is.
It's cool.
Are you good with it?
We're good with it.
We we love that you're whatyou're doing.
We need it.
We need we can't we can't existwith the bell curve moving all
the way to the right.

SPEAKER_00 (01:18:27):
It's like the nightmare before Christmas.
Jack Skellington wants to beSanta Claus.
Part of this problem is the Bplayer is a B player.
They need to acknowledge theirstrengths and weaknesses, know
which swim lane they're greatat, put them with the other B
players, and there's no shame inthe hierarchy.
And most of them are, but mostof them are happy there.
And stop being aspirational to Awhen really it's B is fine.

(01:18:52):
Be the best B player you can be.
Right.

SPEAKER_02 (01:18:54):
Most people I know in that world are fine.
They're like, good, I don't Idon't want to, I don't, the
extra hours to me in my personallife, or to be able to go work
out, or to take care of my kids,or whatever it is, to be a uh
little league coach is way moreimportant than the other things,
and it's fine.
That's totally fine.
But if you don't have the dataand you try to treat everyone
the same and every opportunitythe same and the same sales

(01:19:15):
process, that's why we are 40years in with no true answer to
why is a public company with anincredibly complex tech stack
with every metric under the sun,how can they possibly miss quota
or or sales team only 30% get togoal?
How can that possibly be with noanswer?

SPEAKER_00 (01:19:37):
Sales team turnover.
You've got frustrated people,right?
And then and then and then yougot the A-list people who who
are just you know freaking outin their own way without proper
guidance and reinforcement.
I do want to end on a on aPollyanna note.
The the the thing that I lovemost about this conversation and
what you're bringing to theworld is the opposite of the

(01:19:59):
doomsday AI scenario.
And by that I mean everythingthat you've been talking about
shows the benefit of using thistechnology to better understand
ourselves, our jobs, yeah, andto bring benefit that way.
And to do it in a way that'sexpeditious, unobtrusive, and

(01:20:20):
cost effective.
Everyone's like, oh my God, AIis stealing our jobs.
Oh my god, AI is gonna end theworld, it's the Terminator, it's
Skynet, we're doomed.
The flip side of this is all ofour lives could be so much
better if we're better alignedto our own skills and the stuff

(01:20:40):
that we do in the world.

SPEAKER_01 (01:20:42):
Yeah.

SPEAKER_00 (01:20:42):
And and I think we're we're we're at this golden
age if we can go through thisdisruption, exemplified by a lot
of what you're proposing forsales teams, which is I want to
do the job best suited to who Iam as a person.
And the best way to do that isfor you to know as much as you
can about me and aligning me insituations where I can be a

(01:21:05):
star.

SPEAKER_02 (01:21:05):
Amen.
Listen, here's the sales, here'sthe sales dirty secret.
This is so true.
I think I've never had a persondisagree with this.
Here it is, here's sales, theentire history of sales.
Hey, I reach out to 100 people,I connect with 30 of them, 15 of
them will talk to me.
I demo seven, I sell three,right?

(01:21:28):
I mean, everybody's numbers area little different, but isn't
that true?
Like I reach out to 100, I onlyconnect with conversion funnel.
It's never contained.
Right, but but imagine if we'reable to use AI and do some of
the amazing things so that we weno longer have to talk to 100
people.
Imagine if we only talk to 20.
And of the 20, 17 are dialed in.

(01:21:50):
It changes the entire world.
You know how much time we spendin sales, for lack of a better
word, wasted just calls that areunreturned, emails that aren't
the like it's imagine if we'reable to self-actualize AI and be
like, imagine.
I'll use this.
I'm a huge sports fan.
I use this analogy all the time.
Jordan Belfer, the Wolf of WallStreet guy, said this.

(01:22:12):
It's so true.
If you came to me and said, Ihave a Tom Brady trophy or Tom
Brady helmet signed Patriots,hey, it's$500.
I couldn't sell it to me unlessI resell it, right?
If I was gonna be, but it's notgoing to my house.
I don't care, Tom Brady.
Not if there's not a chance.
I don't, it doesn't matter.

(01:22:33):
You could have Tom Brady made itout to Jack Sonny, love you,
you're the best guy.
I'd still be like, I don't wantit.
And I'm not paying for it.
Like, I don't want it.
And so, but if you had a I don'tknow, Baltimore Ravens helmet
who I grew up in Maryland,signed by Ray Lewis.
I what is it?
It's 900.
I'll pay you a thousand.
I'll tell you like we're we'reall that way.

(01:22:54):
So imagine everyone typicallyhates salespeople or hates the
sales people.
Imagine though, as you gothrough your day, you go through
your life, if the calls you got,we all get too many spam calls.
While we're on this podcast,I've gotten three calls.
Spam, spam.
You know, it's like imagine thepeople that reached out to you
were things you really caredabout.
Like, hey, you just whatever gota flat tire this morning, and it

(01:23:19):
knows you have a flat tire, andlike, hey, here's three tire
vendors in the area that'll cometo your house and replace the
tire on driveway.
I definitely want that, right?
So imagine our business world,if they knew from the signals
that we're giving, we eitherhired people or we fired people
or we moved over to the newmarket or we just got a new
office.

(01:23:39):
If you had people that were inline with how you're living
interact with you, think abouthow amazing that would be.
And that's to me where we'regoing to.
Well, we don't have to do 100anymore.
We reached out to 100.
Again, 80 of them, 70 of themwant to tell you to hang up on
you.
Please stop calling me.
You're annoying.
I hate your guts.
But that's how we waste ourtime.

(01:24:01):
Let's just get down to 20.
And of the 20 you call, they'reall like, Oh, yeah, I'm
definitely looking.
Uh, yes, thank you for calling.

SPEAKER_00 (01:24:09):
Same transparency and alignment that I was talking
about.
Your sales team is towards theprospects, they're exuding all
this data too.
So connect the dots.
That's the math.
That is the marketing challenge,right?
And and even the best platformsdo a half-assed job, you know.
Like if you watch this onNetflix, you might like that.

(01:24:32):
It's variations of theserecommendations because they get
to know what you're watching.

SPEAKER_01 (01:24:37):
Yep.

SPEAKER_00 (01:24:38):
If we can get that right, then to your point,
salesperson, prospect, and thesalesperson is getting better at
doing what they naturally do.
And the prospect is becomingmore and more transparent in
terms of their pain points andneeds and opportunities.
And AI with machine learning,deep learning, is perfectly

(01:25:01):
positioned to munch all thisdata, connect the dots, and
share best practices to do it.

SPEAKER_02 (01:25:07):
People are listening people are not, and especially
in the sales process, the higherdollar value you're buying are
not going away in the salesprocess because there's not a
person in the world that wantsto spend six figures plus and be
like, who'd you buy that from?
Oh, I bought it from anautomated agent and uh they're
gone.
Yeah, I mean, like, have youever have you ever had a problem
with your company and you try tocall Google, like they either
took down your page or thesearch results don't work, or

(01:25:28):
somebody said, There's no one tocall somebody on the other line.

SPEAKER_00 (01:25:31):
Right.

SPEAKER_02 (01:25:32):
You can't so imagine you do that with something
that's critical to youroperations or really important.
So the the more for for theaudience, the higher dollar
value what you're selling, thechance you go away is very small
because people want to be ableto call back and be like, hey,
Bill, Mike, Susan, you know,hey, this is going good.
This is not going good.

SPEAKER_00 (01:25:52):
Remember the qualitative attributes about
what makes the salesperson goodis human qualities.
We're making human connections.
And it's particularly importantfor a consultative approach for
higher value services.

SPEAKER_02 (01:26:07):
You need help.
You want help, you want support,you want nothing's perfect, and
so you you're not again try tocall Google or Facebook when you
have a problem.
Zip.
You get nothing, and you'relike, this is unbelievable.

SPEAKER_00 (01:26:22):
You're gonna get a bot, right?
Your value prop sounds uhintriguing.
I'm gonna put links in thedescription for the podcast and
the YouTube video.
How does it work reaching out toyou?
Is it a is it a consulting call?

SPEAKER_02 (01:26:40):
Sure.
My little uh a 15-secondcommercial, you can find Jack
Signy, S-I-N-E-Y on LinkedIn.
You can ping me.
Or but if you go tofrontrace.com, frontrace.com,
one word, frontrace.com.
In the upper right hand corner,it says join the race.
Um just fill out the form.
We'll let you have the software.
We'll put our money where ourmouth is, we'll set it up,
customize it for you.
Can have it for a month forfree.

(01:27:01):
So you don't have to take myword that it works.
Like, literally, not some uhwhat is it, proof of concept
where it's vanilla.
It'll be customized to yourstuff.
You will literally I we tried tocreate uh a company that is so
sought after.
It's like you even if you neverbuy it, you're gonna be so much
smarter a month from now,totally for free.
That's how much we believe inwhat we're doing.

(01:27:22):
You can just go hit join therace, fill out your name, uh, I
think it's email, phone number,put it in there.
We'll we'll set up the software.
You can use it for a month plus,make sure it works great before
you ever have to pay for it.

SPEAKER_00 (01:27:33):
So awesome.
And then my IT guys are notgonna flip out.
You can just the plug in.
API key.

SPEAKER_02 (01:27:42):
Just quick sidebar.
You mentioned the call that makesure your IC security, they all
want to make sure, you know, doyou have the right security?
But that's that's always thequestion.
Who who has it?
But it's all super secure.
You're you're good.
But there is that question.
But once it is API key data setup in about 10 minutes, you're
good to go.

SPEAKER_00 (01:27:58):
Thank you, Jack.
Like.
Comment, subscribe, share.

SPEAKER_02 (01:28:02):
Thank you, thank you.
Yes.
Thank you very much.

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