In this episode of the Customer Success Playbook podcast, we're excited to host Samuel Cummings, CEO of DataPlant, who is at the forefront of revolutionizing customer success through AI and data science. Samuel shares his fascinating journey from being a data scientist at Gainsight and LinkedIn to founding DataPlant, a company dedicated to tackling the challenge of scaling personalized customer engagement.
Join us as Samuel delves into:
- The evolution of customer success from reactive support to proactive, data-driven strategies.
- How DataPlant synthesizes complex data to create hyper-personalized messaging.
- The critical importance of digital empathy in managing a growing number of customer accounts.
- Real-world examples of DataPlant's impact, including their work with Restaurant 365.
- Predictions for the future of customer success and the transformative role of AI.
Samuel's deep expertise and innovative perspective offer valuable insights and practical strategies for anyone involved in customer success. Don't miss this engaging episode that explores how DataPlant is changing the game and what the future holds for customer success.
HTTP://www.dataplant.com
https://www.ai4diversity.org/podcast/episode/1a1b4367/ai4diversity-meets-aura-or-ai4diversity-podcast-32
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Roman Trebon (00:30):
all right.
Welcome to the customer successplaybook podcast.
I'm Roman Trebon and with mealways is my cohost, Kevin
Metzger.
Kev, how are you doing today?
Kevin Metzger (00:41):
And I'm doing great.
I'm excited about today's guestsand excited about plan hat and
learn more about it.
So what do you think?
Roman Trebon (00:48):
wild.
Without, well, I'm also excitedabout these awesome new Polish
shirts we got, which, you know,again, if you're, if you're on
our LinkedIn page or our YouTubechannel, not only do we got Sam
coming on, we got the new gear.
We're repping as well.
So.
Yeah, this will be a greatepisode.
Kev, why don't you introduce ourguest and our topic for the day?
Kevin Metzger (01:05):
Yeah.
So we've got Sam with us.
He's a data scientist who's beenin customer success at Gainsight
and LinkedIn and now started acompany focused on AI and
customer success calledDataPlant.
I've got I've got to listen toSam at the Gainsight conference
and I'm really excited to haveSam on the show with us today.
(01:26):
Sam, can you give me, maybe giveus a little bit more about your
background and how you gotstarted with DataPlant?
Sam Cummings (01:32):
Well, gentlemen, I'm going to need one of those
shirts.
Let's start there.
That shirt is, there we go.
Merch game is popping.
Well, Sam Cummings, CEODatalant.
I just cut my teeth at a timewhen customer success was in his
second wave.
Gainsight was founded.
There were companies out therelike churn zero to tango, et
(01:52):
cetera, but companies werefiguring out how do we build a
data driven practice?
And that's why I help companiesdo.
And so I traveled the world, Iwas head of data science at
Gainsight.
So I work with companies likeDocuSign, Marketo you know,
RingCentral to realize theapplication of data science in
the customer success space.
So everything from predictivemodeling applied to revenue
(02:15):
retention, you know, reducingchurn producing opportunity
reports, the whole nine yards.
And I tell you, I learned a lotof gems, and I took those
things, then I went and Iactually became a CSM.
And I tell this to anyone who'sbuilding something, walk in the
shoes of the people you want toserve.
And that's what I did with mycareer.
I then went on to LinkedIn tobecome a CSM, and I tell you,
(02:39):
everything I learned as the dataguy.
I put the practice as an actualCSM, and that gave me a wealth
of, a wealth of experience thatdoubled down on what I had
before from a data perspective.
And then I went on to go toIndeed to become Director of
Implementation.
So I've been in every seat fromindividual contributor,
technical person, data sciencedirector, and that gave me such
(03:02):
a perspective that I used tobuild this guy.
Datalant.
Roman Trebon (03:08):
You got, you got, you got some polo game as well,
Sam.
I like that polo shirt as well.
We're all, we're all lookinggood here.
Sam Cummings (03:14):
Just go to the website, get the shirt y'all,
subscribe.
Roman Trebon (03:18):
So Sam, talk to us a little bit about Datalant.
What is data plant?
What do you do?
How are you helping people inthe customer success world?
Sam Cummings (03:25):
Yeah.
And this is really just a partof what's going on now in our
time.
There's been this premise, theidea and the way customer
success teams generally approachis.
I'm going to lose business in mylong tail.
So my smaller accounts in my midmarket accounts, and I'm going
to make those gains with myhands on enterprise accounts.
That's the nature of thebusiness.
(03:45):
Everyone expects to have higherchurn in that segment.
That was great when there wasmoney printer season, and you
could just try to make it toyour next round.
Now, companies are being forcedto grapple with that problem.
And if they want to really drivetheir business to the next
level, Everyone has to solvethis scale issue of being able
to engage in their smallersegments.
(04:06):
And that's what we're born outof.
It's solving really one of thebiggest problems of our time.
Digital empathy.
As companies are looking at morecustomers that they're managing
per CSM.
I think a phenomenal speaker Iknow as well named Irit.
She has a CSM practice.
If you don't know her, check outher podcast.
She has shared some insightswhen she was speaking at a
conference in Israel about howthere's just a ton of innovation
(04:29):
that's happened.
But one of the biggest metricsthat shifted over the last
decade is the number of accountsper CSM.
We've gone from 20 to 30accounts per CSM to 40 to 60.
And now we expect it to get upeven higher.
And so regardless of if it'senterprise, mid market or S and
B.
There's going to be a need toscale like never before.
And the problem underneath thathood is when you're one to one,
(04:52):
you can empathize with yourcustomers because you're talking
with them, you're engaged.
But when you lose that one toone, you become one to many or
one to a lot.
That's where you have achallenge of, can I empathize
through these digital channels?
And that's what data planshelping companies with is not,
Hey, I can't hire a bunch ofCSMs to engage all these people.
(05:13):
Even if I wanted to, I don'thave enough to hire a hundred
people.
So how am I going to allow forthe people I have to be great?
And that's what data plan isdoing.
We're helping companiessynthesize their experience of
their clients and create hyperpersonalized messaging so they
can no longer just betransactional at their segments
that are smaller.
I
Kevin Metzger (05:33):
love it.
Yeah.
So going down that path, whatkind of information is it that
you, what kind of data is itthat you're gathering in order
to try and basically get theinformation you need to have and
create the digital empathy?
Sam Cummings (05:48):
Yeah.
So I conducted a study and thisis kind of going into my role
here.
Before we started, we werestarting and synthesizing what
data plant would be.
We did a study of and reachedout to about 200 COOs.
CCOs and then CROs of like whatkind of data they have.
And you'll be surprised.
The main data that people areusing today is two sources.
They're using support ticketdata, which represents the first
(06:09):
wave.
The first wave of customersuccess was all reactive and it
evolved out of going beyondcustomer support.
And then essentially the secondwave was product usage.
So being able to look at productusage data, whether that's the
Google Analytics, Pindo, youknow, Mixpanel, all these tools
kind of arose during that timewhen it was about, okay, what
are people doing in theplatform?
(06:30):
So we take those sources as wellas your CRM data and any other
sources that you might have,marketing, outcome data like
renewal history or churnhistory, and we bring all that
data together.
So if you have complex datarelationships, you have multi
layered hierarchies like regularchild accounts that roll up to
parent, that roll up tograndparent.
(06:51):
We stake our claim in working incomplex data scenarios and
really synthesizing that intouseful insight.
Roman Trebon (06:59):
So, so Sam, I don't need, I don't need to be a
data scientist to consume theoutput, right?
You're going to do all the heavylifting and get all that data
together.
And then you're going to helpguide me as a CSM.
You mentioned 60 to 1 ratio.
Oh, I'm getting, I'm almostbreaking out in hives hearing
that.
But this output is going to helpme be able to like, you know,
scale and touch all theseclients that I'm responsible
for.
Sam Cummings (07:19):
Yes, because this is a revolution happening under
to under between our eyes,right, right in front of us.
The real opportunity is notsomething that's in a vacuum.
We have been on a long arc ofinnovation from in the beginning
when I came out of college.
I'm a date myself, but I was S.
A.
P.
Certified.
And back then, you had the jobwith JavaScript, code the
(07:40):
relationship between objects,code the jobs that do
transformations.
So if I wanted to convert a leadto an account, or a lead to a
contact, I had to script that.
But then there's this tool orthis platform, some people might
know, there's a little bittycompany called Salesforce.
That really took the wave when Iwas graduating.
That was the same thing thatSalesforce did for technology
(08:02):
and ERPs and CRMs.
The same thing happened withWordPress for web development
and blogs, right?
The average person can now doit.
You didn't need a developer andthat is what we're doing in this
next wave that's happening.
Cause even now, when you want touse a Salesforce or anything
like a Gainsight, there's wholetrainings, there's whole
(08:24):
certifications because thesesystems are sandbox tools, which
again, is better than coding,right?
It's better than the JavaScriptdays.
But you still have to becertified in understanding how
to make these tools do any onething.
We're in a new age, andDataPlant is leading that age,
where the professionalsthemselves will be able to drive
their business.
And so imagine, you could justtalk to DataPlant and say, give
(08:45):
me a report on downloadperformance and put it in a bar
chart format.
And instead of like, writing theSQL, that's what most of the
tools are still doing today,even with AI.
They'll just write the SQL foryou.
But you still gotta know what todo.
Here is, you can just talk toDataPlant and it'll, since it
auto generates all the possiblecharts, think of it like a tree
(09:05):
with the fruit or the insightsand you're just curating with
your voice and what you look atand passively as you leverage
it.
This nice curated bush of fruitthat can bear fruit in your
business.
Kevin Metzger (09:17):
Sam, from, can we take that down from an analogy
level, a little deeper intowhat's actually happening with
the data and how you're, howyou're synthesizing it?
Sam Cummings (09:26):
Yeah, so real example, customer we work with
called Restaurant 365.
40, 000 locations, so they're apoint of sale system for
restaurants.
We're helping them analyze theircustomer usage data because they
have a lot of products.
So think about it.
You have maybe four to sixproducts that you might have.
Each customer might be in one ofthree segments.
So it could be an SMB client, amid market, and an enterprise.
(09:50):
Each of them have differentdesired outcomes based on who
they are and what they need fromyour platform.
And so we're able to analyzethat based on what the size of
the company, so if it's an SMBclient and then what they
bought.
So do they buy the operationsfeature or the accounting
feature?
And so that tailored marketingof being able to engage them and
(10:11):
communicate is what theyleverage our tool for.
So they can use, their CSMs canreach out to a restaurant and
say, compared to all otherpizzerias.
You are spending 25 percent moreon labor.
If you leverage our payrolltool, you can lower that burn
and really get a more efficientbusiness and put yourself in the
top performers.
To do that in any other CSplatform is impossible.
(10:33):
The nature of the data is justtoo complex.
You know how many restauranttypes there are?
Seafood, pizza.
So imagine a rule chain that youwould build to really automate
that, which again, that's theunderpinning of all modern CS
software, which is rulebuilding.
And so we saw it like thatitself was the barrier to
unlocking these personalizedinteractions and these tailored
(10:55):
insights.
And that's what we did.
We moved away from that andreally transformed how it works
to where now you can, as anindividual contributor, as a
leader, you don't have to paythe tax.
That was required before.
Before you had to pay a taxessentially of building out
everything, going and findingout what's working, then
defining the playbook androlling out.
(11:16):
We're duty free, baby.
Roman Trebon (11:19):
I love, I love that example.
And like you said before, itwouldn't have happened.
You didn't have the time.
You didn't have the resources.
You didn't have it all there tosay, Hey, you know, your, your,
your labor cost is up in X, Y,I, you know, look at these
solutions.
So for our audience, if youhaven't gone to the website yet,
you have to check it out.
So you have to go to theDatalant.
Com.
Sam, you already have anamazing.
(11:39):
Set of free tools on there.
So I found this, what we had,we're getting new year and have
you on the show, checking youout.
You have these free chat, GPTbots on there.
I've, I've already sent them toa whole bunch of people.
I absolutely love them.
They're game changers.
How, how'd you guys come up withthese?
What was the concept behind thisand tell the audience a little
bit about what they are andwhere they can find them.
Sam Cummings (11:59):
So this is where we really saw the future coming
forward.
And I tell you what's comingnext is going to be even more
fun.
I'll give you a sneak peek.
I'm dropping the sneak peeks onthis podcast only.
So if y'all didn't hear breakingnews, this is exclusives.
So that's it in the background.
So we saw that the main approachto this has been, we're going to
create these all knowing bots.
(12:21):
So if you look at most of theother companies, everyone's got
a GPT out there, but their ideais like a chat GPT tool can do
all things.
What that leads to is they don'tdo anything.
Well, And so what we saw waswhat's better suited is have
smaller, more targeted modelingtools that solve the top
problems.
And we went and did someresearch as well.
(12:41):
Big, you know, data guy,surprise.
But I researched what were thetop use cases of CS teams.
And then we look to build theGPT around each of those.
And we really tailored them.
And then I know a lot of cheatcodes around building GPTs, the
Beck perform higher, once hecaught, I'll give you two of
them.
If you tell a chat GPT to take abreath, that alone will allow it
(13:03):
to be more diligent.
I know it's not human.
It's not going to literally gobreathe, but that'll slow it
down and make it think moredeliberate.
Another trick.
If you tell it to proceedwithout instruction, it will
allow it to make moreassumptions.
Based on what he knows in hispremise, that it probably
wouldn't have done beforebecause he didn't trust that it
would be accurate.
And so those are just some ofthe cheat codes coupled with the
(13:24):
learnings that we had over thedecade of working in this space.
That's why our bots are morepowerful.
And then we built them into themain core use cases like
customer success transitions,big problem transitioning
account.
Being able to do customersuccess strategy planning as a
leader.
How do I plan how I'm going toapproach my business?
So we targeted these very nicheproblems and that's why you see
(13:45):
that our free tools are way morehigh performing But i'll stop
there before I give you guys thecheat codes for the future
Roman Trebon (13:51):
Oh, i'll tell you what.
I so just just our audienceknows I went there, but you have
a success a success plan boughtIt's on there.
Like you said, it's just that wehad just met with the client
first time.
Tell us about your business Dada da Put in the transcript, put
it into your success bot plan,spit out an amazing first, I
mean, draft, I mean, an amazingsuccess plan that we had within
(14:14):
minutes that would have takenus, you know, you know, before,
before the bot or even trying todo it ourselves, like you said,
it would have been iteration,iteration, iteration.
So Sam, you already saved mesome time.
So I'm all in.
I can't wait to hear what'scoming next.
This
Sam Cummings (14:27):
is where Sci-fi kicks in.
Kevin, let me share yourthoughts before I jump into it.
Kevin Metzger (14:30):
I was gonna say, I want to take a guess.
Tell me, if I, tell me how closeI am, you're gonna have the, so
with the bots, as you build outindividual bots, is it that they
get to, they'll startinteracting with each other to
basically build out the plansand the functions going forward?
Sam Cummings (14:46):
You got it.
This is why this man is really,I'm telling you, I wouldn't have
came to no other podcast, butthis one, I mean this sci-fi
kicks in gentlemen.
Let me just take a step back inthe concept.
There is no rules.
This is the wild wild west.
There's no one that can tell youdo this, do that, do this, you
get that.
And this is what this lookslike.
(15:06):
What we're going to have, andagain there's some challenges
with the price and the cost ofthese bots to be able to make it
scalable, so there's going to besome time to this, but the other
side of this is you're going toessentially pose goals To a chat
room.
In that room, there'll be six orseven different GPTs, and
they'll all work together.
And I've been experimenting withthis technology already and
(15:27):
working what we're doing withDatalant.
And what we found was crazystuff.
So I put all those bots that wehad already in the room to solve
a problem.
And again, the statistics haveshown us over the last two or
three years, the first versionof GPT, they had about 80
percent efficacy across a seriesof human tasks.
The more tailored version thatpeople have been working on
(15:48):
lately.
Up in the maybe 90s.
These new community approachesof bringing in multiple GPTs
together.
98 efficacy above.
Think of it like ants, right?
Any visual ant can be smart,somewhat, but together they have
emergent behavior.
That's happening right now.
Now what's crazy, we're puttingin different types of GPTs that
(16:09):
have no correlation with theother ones.
So for example, I put a policebot that does interrogations in
there, and the performance wentup.
So what that means is, there'sno rules in what kind of bots
you can combine.
You can bring a bot that's acounselor bot.
And have it talk to the otherbots and it might actually
improve their performance.
And so that's the world that'sgoing to be brand new here is
(16:31):
it's not just like, I need allmy bots to have some similar
relationship, meaning becustomer success.
You can put six bots in therethat all have a similar
relationship and it put a wildcard in there and get a 10 time
better performance.
The game just changed,gentlemen.
Kevin Metzger (16:47):
It's effectively, you're kind of driving an AI
hive mind, is really what you'retrying to, you're putting
together.
Is that accurate?
Sam Cummings (16:54):
Yeah.
Think of it like one of thosethat might not be as, you know,
they're like, well, that's soethereal.
You've, anybody ever played Uno?
Roman Trebon (17:00):
Yeah.
Oh yeah.
Oh yeah.
Sam Cummings (17:01):
Same thing.
Each bot puts down his card.
So it puts down what it thinks.
Every other bot knows thehistory of what's happened.
So they can all pick up fromeach other, edit it, and then
drop the next card.
So we're all essentially doingit essentially in the box and
it's getting super fast speed oflight, but they're playing UNO
together.
And from that, you get thisemergent, better game.
Roman Trebon (17:21):
Love it.
So Sam, you've been, like yousaid, you, you've been in,
you've been in customer successfor a while.
You know, you're, you're in, youknow, Gainsight, LinkedIn,
you've been all around, right?
So we talked about AI a littlebit.
Where do you see, what iscustomer success look like five,
10 years from now?
I mean, what are we looking at?
What do you think is you have toget your crystal ball out and
dust it off a little bit.
Sam Cummings (17:43):
So for those working and I'll talk to a
specific sub audience herebecause you are going to be the
heroes of our future customersuccess operations.
Those are the folks that have anopportunity to make sci fi real
the way we've been thinkingabout customer success, not
going to move the needle.
We're going to have to go to anew level.
The reason why is the thresholdshigher.
(18:05):
We're already seeing this insales and marketing code
outreach dying.
Email spamming, not working.
Good luck.
Even reaching out on LinkedIncold.
Try it, see what happens foryou.
The issue is, that cold outreachtype of mindset without
personalization doesn't breakthrough.
And so that's where the futureof this, what I see is, Is these
(18:28):
hyper personalized engagementswhere again, I'm reaching out to
people on a way that know fromtheir perspective, they know
that that message was tailoredfor them.
If you can't tailor your messagein today's age, when everyone's
using these chat GPTs and ouremail inboxes have been blown up
for the last 20 years, you arenot going to be able to break
through the noise.
(18:48):
So as CS ops teams, that's thefirst level of where I see this
innovation happening, wherethey're going to be the creative
ones, they're going to becomerock stars.
Because they're going to havemore ability to touch their
clients than anybody else in thebusiness.
If I'm a CSM, I might own 20accounts, 60 accounts, etc.
Through digital customersuccess, these folks are going
(19:09):
to impact millions of people.
And so the way that the industrygoes, if we decide to go further
and be a little bit more risktaking in how we approach this,
oh man, we're going to be thereal, real heroes.
So that's the first group that Isee really benefiting from the
innovations today.
People that are going to beleaning into digital scale.
Now, the other group that Ithink is really going to be the
(19:29):
beneficiary on the broader senseis the medium to low performers.
There was a big gap betweenthose that were great at doing
their job in CS versus thosethat weren't, that was really
bucketed into the soft skills.
Can you take good notes?
Can you do good recapping?
Can you distill your points intoreally clear messages?
(19:49):
What's your follow up game like?
Those all are now going to bedemocratized.
So the distance between someonebeing really good on those skill
sets and the average person isgoing to be way shorter.
So that's going to allow for allteams to have really a higher
performing group across theboard.
I'm excited for that time.
And again, I'll share more as wego forward on some of those
(20:11):
unique niches where theopportunity is.
But overall, CS Ops is going tohave more touch and become more
of a hero and individualcontributors are going to be
more democratized in theirperformance.
Kevin Metzger (20:21):
Yeah, and I think the ops right now is huge.
I think that's where you're,you're, you're getting a lot of
the games and allowing the CSMteams to basically expand what
they're serving.
From we hear a lot about it, youknow, it's not those who Use a
error.
It's not going to replacehumans.
It's humans using AI.
That's going to replace humansnot using AI.
(20:43):
But I think as we talk about it,the AI is going to allow a human
to scale much work.
Right?
So, and ultimately, it'shopefully as humans continue to
scale.
Outreaches and what they're ableto do and how they think they'll
be able to keep up with it andwe'll grow more customers.
(21:05):
And by growing more customers,we're going to expand.
You're going to grow.
You're going to grow the pot.
And I think that's where there'sthere's an advantage.
I do think that.
As CSMs or CS ops folks, you gotto start thinking about how you,
what, where you're bringingvalue.
What do you think on that?
Like, where do you recommendpeople start thinking about how
(21:27):
to think differently?
So they're ready for the future.
Sam Cummings (21:30):
Yeah.
Just throw away everythingyou've heard.
All that stuff that worked in2012 is not going to work today.
You can show up with the samethings you were doing then and
be walked out of the room.
And what I mean by that is.
The approach to customer successwas top down.
The idea was we could justfigure it out, we know what
(21:51):
works and then we're going tomake essentially and the reflex
is the system has a rule thatsays if less than six logins do
this, if more than 12 you know,interactions do that, like that
was the core idea.
Top down.
The CSM was essentially was themeat bag executing on the
systems intelligence.
Innovation is too fast for that.
(22:12):
We saw that in COVID.
I tell you, I was in the seat,so I got the seat as first hand.
When COVID hit, all these folkshad all these rules that all of
a sudden broke.
If you looked into theircockpits, whatever tool they're
in, into where they had thesenotifications for their teams,
they had thousands of upsellrecords that said, this client's
ready to upsell.
Did everybody all of a suddenready to upsell their business?
(22:35):
No, since people were indoors,they logged in more.
And so that was a perfectexample of like the idea of I'm
just going to hard codeeverything and then have it be
top down in my business onlyworks when stuff doesn't change.
Now we might not always get aCOVID again, again, something
like that, thattransformational, but what is
true products teams are evolvingtheir products faster than ever.
(22:55):
So say I change how a featureworks, all the rules I built
around that now are no longerthe same.
And so there's just an infiniteproblem that we're going to deal
with regardless Which is therate of change in our markets.
That means we need to switchthat premise of top down CS to
now a more agile version.
And that's what data plan isenabling.
(23:16):
Think of it simply put with ananalogy here before it was turn
based, we configure everything,we execute that configuration,
what data plan switching to isreal time strategy.
Since we've removed the duty topay the tax.
Now, there's no issue for you toexecute.
If you on the Monday morning,think about, hey, we want to
drive improvement of thedownloads feature, you can
(23:38):
execute on that same day versushaving someone go build out all
the reporting and then doanalysis of what's working, then
define the strategy, what we'regoing to deliver.
We're able to deal with that inminutes.
So now you as a leader canexecute.
So what was thought notpossible, I can only run one
campaign a quarter because it'sgoing to take so much to
(23:59):
mobilize or I can't run what wecall perishable campaigns
because we don't get certainthings are perishable, meaning
it's so short in the moment thatit wouldn't make sense to
automate it.
It's like, for example, if Ihave an upcoming webinar this
coming week.
To put a lot of time to reallycustomize a message around that
and build that out for oneoutreach, the values not work to
(24:21):
squeeze.
But if I can move like this, nowI'm able to do that.
And that's the big differencehere.
The value and personalization isin the perishables.
That's where you break through.
And if you can't leverage that,which you can't today, you're
going to miss out on theopportunity to really capture
those people's attention anddrive outcomes.
Roman Trebon (24:39):
Yep.
The speed and thepersonalization, Sam, you know,
that gets me excited.
As a client, not even someonewho's helping serve clients, but
as a client myself, like yousaid, what, what, you know, what
are we doing?
Where's their opportunity tomeet for me as a client?
How do I compare it againstothers?
And it, you know, you said thattailored towards me as my
organization, that's real value,right?
(25:00):
That's real value.
And again, right now it's, it'sslow.
It's, you know, it takes time.
It's, or, or you get somethingand it's you see a benchmark and
it's, You know, i'm one of asegment.
It's not me as an individualclient.
It's like, oh You're in thissegment and here's how you can
compare not me as an individualclient.
That's a real game changer.
I love that It's gonna be sohappy.
(25:21):
We're either we ready to hit samup with the the hard hitting
questions here
Kevin Metzger (25:26):
Yeah, I think it's time.
Let's go.
All right, sam We got some rapidfire stuff for you.
So i'm rubbing you want to takeit or you want me to?
Roman Trebon (25:34):
Well, we can go back and forth here.
Let's see.
All right, sam All right, buckleup early bird or night owl night
owl night out.
All right movie Oh, I love it.
You like, what about thesequels?
You like, you're a sequel guytoo, or just the original?
Sam Cummings (25:50):
Oh, yeah.
The original is great.
The sequels are marinade on you.
You know, you got to sit intothem.
Roman Trebon (25:55):
All right.
You got a favorite sport thatyou watch or play?
Boxing.
Boxing.
Who's your you got a boxer?
You that's me.
That's, that's my, you got,you're speaking my language.
You got, you
Sam Cummings (26:05):
got a favorite boxer?
Yeah.
Yeah, so my favorite boxer nowin this current time, Javante
Davis, y'all see that fight thisweekend?
Roman Trebon (26:14):
Let's go.
Unbelievable walkout.
The guy always delivers.
So yeah, we will.
I watched.
So unbelievable.
Kevin Metzger (26:19):
Oh yeah.
All right.
So Roman's in the process ofbecoming a professional ref, I'm
a
Roman Trebon (26:26):
judge, judge, professional judge here in
Georgia.
So I, I'm, I'm, yeah.
So I, anyway, that's a wholedifferent conversation.
So.
Sam Cummings (26:34):
Get in the game.
We need you.
Roman Trebon (26:35):
That's why I got into game.
Sam, you've seen it.
You're a box of being, you'veseen enough bad scorecards.
You know, there's help needed somuch politics.
Can't wait to see you there.
Maybe I'm ever going to tripover to Saudi Arabia too.
Who knows?
All right.
Book recommendation, favoritebook or book recommendation for
our audience.
Smart brevity, smart brevity.
Sam Cummings (26:57):
All right.
Priceless book.
I think the biggest thing youcan look up the author and all,
but why I recommend it is we arein a time when you can
communicate pointed and it'slike being on CNN when they have
eight boxes, you got to hit.
Roman Trebon (27:10):
A place you'd want to travel to, Sam, you've never
been Bali, Bali.
Nice.
Nice.
All right.
And then to close out here,where can our audience find more
about you and more about a dataplan?
Sam Cummings (27:24):
Check us out on LinkedIn, Samuel J 3 1 4.
That's my personal LinkedIn andthen Datalant.
You can just look us up onLinkedIn under D a T a P L a N
T.
And then also run a podcastcalled AI for diversity.
We have 500, 000 followers onLinkedIn.
You can check us out there.
Roman Trebon (27:42):
Awesome.
Sam, thanks so much for comingon the show.
Love the polo shirt.
We'll have to find, well, you'rethe first up for our our guest
shirts that we're going to queueup here soon.
So really, really appreciate it.
And for our audience, make sureto check out Sam.
His stuff's amazing.
Definitely check out hiswebsite, the data plant great
stuff on there.
And for everyone, thanks forlistening.
(28:03):
You can follow us on LinkedIn atRoman Trebon at Kevin Metzger,
make sure to check out ourcustomer success playbook page
as well on LinkedIn.
And Kevin, awesome.
all right.
Welcome to the customer successplaybook podcast.
I'm Roman Trebon and with mealways is my cohost, Kevin
Metzger.
Kev, how are you doing today?
Kevin Metzger (00:41):
And I'm doing great.
I'm excited about today's guestsand excited about plan hat and
learn more about it.
So what do you think?
Roman Trebon (00:48):
wild.
Without, well, I'm also excitedabout these awesome new Polish
shirts we got, which, you know,again, if you're, if you're on
our LinkedIn page or our YouTubechannel, not only do we got Sam
coming on, we got the new gear.
We're repping as well.
So.
Yeah, this will be a greatepisode.
Kev, why don't you introduce ourguest and our topic for the day?
Kevin Metzger (01:05):
Yeah.
So we've got Sam with us.
He's a data scientist who's beenin customer success at Gainsight
and LinkedIn and now started acompany focused on AI and
customer success calledDataPlant.
I've got I've got to listen toSam at the Gainsight conference
and I'm really excited to haveSam on the show with us today.
(01:26):
Sam, can you give me, maybe giveus a little bit more about your
background and how you gotstarted with DataPlant?
Sam Cummings (01:32):
Well, gentlemen, I'm going to need one of those
shirts.
Let's start there.
That shirt is, there we go.
Merch game is popping.
Well, Sam Cummings, CEODatalant.
I just cut my teeth at a timewhen customer success was in his
second wave.
Gainsight was founded.
There were companies out therelike churn zero to tango, et
(01:52):
cetera, but companies werefiguring out how do we build a
data driven practice?
And that's why I help companiesdo.
And so I traveled the world, Iwas head of data science at
Gainsight.
So I work with companies likeDocuSign, Marketo you know,
RingCentral to realize theapplication of data science in
the customer success space.
So everything from predictivemodeling applied to revenue
(02:15):
retention, you know, reducingchurn producing opportunity
reports, the whole nine yards.
And I tell you, I learned a lotof gems, and I took those
things, then I went and Iactually became a CSM.
And I tell this to anyone who'sbuilding something, walk in the
shoes of the people you want toserve.
And that's what I did with mycareer.
I then went on to LinkedIn tobecome a CSM, and I tell you,
(02:39):
everything I learned as the dataguy.
I put the practice as an actualCSM, and that gave me a wealth
of, a wealth of experience thatdoubled down on what I had
before from a data perspective.
And then I went on to go toIndeed to become Director of
Implementation.
So I've been in every seat fromindividual contributor,
technical person, data sciencedirector, and that gave me such
(03:02):
a perspective that I used tobuild this guy.
Datalant.
Roman Trebon (03:08):
You got, you got, you got some polo game as well,
Sam.
I like that polo shirt as well.
We're all, we're all lookinggood here.
Sam Cummings (03:14):
Just go to the website, get the shirt y'all,
subscribe.
Roman Trebon (03:18):
So Sam, talk to us a little bit about Datalant.
What is data plant?
What do you do?
How are you helping people inthe customer success world?
Sam Cummings (03:25):
Yeah.
And this is really just a partof what's going on now in our
time.
There's been this premise, theidea and the way customer
success teams generally approachis.
I'm going to lose business in mylong tail.
So my smaller accounts in my midmarket accounts, and I'm going
to make those gains with myhands on enterprise accounts.
That's the nature of thebusiness.
(03:45):
Everyone expects to have higherchurn in that segment.
That was great when there wasmoney printer season, and you
could just try to make it toyour next round.
Now, companies are being forcedto grapple with that problem.
And if they want to really drivetheir business to the next
level, Everyone has to solvethis scale issue of being able
to engage in their smallersegments.
(04:06):
And that's what we're born outof.
It's solving really one of thebiggest problems of our time.
Digital empathy.
As companies are looking at morecustomers that they're managing
per CSM.
I think a phenomenal speaker Iknow as well named Irit.
She has a CSM practice.
If you don't know her, check outher podcast.
She has shared some insightswhen she was speaking at a
conference in Israel about howthere's just a ton of innovation
(04:29):
that's happened.
But one of the biggest metricsthat shifted over the last
decade is the number of accountsper CSM.
We've gone from 20 to 30accounts per CSM to 40 to 60.
And now we expect it to get upeven higher.
And so regardless of if it'senterprise, mid market or S and
B.
There's going to be a need toscale like never before.
And the problem underneath thathood is when you're one to one,
(04:52):
you can empathize with yourcustomers because you're talking
with them, you're engaged.
But when you lose that one toone, you become one to many or
one to a lot.
That's where you have achallenge of, can I empathize
through these digital channels?
And that's what data planshelping companies with is not,
Hey, I can't hire a bunch ofCSMs to engage all these people.
(05:13):
Even if I wanted to, I don'thave enough to hire a hundred
people.
So how am I going to allow forthe people I have to be great?
And that's what data plan isdoing.
We're helping companiessynthesize their experience of
their clients and create hyperpersonalized messaging so they
can no longer just betransactional at their segments
that are smaller.
I
Kevin Metzger (05:33):
love it.
Yeah.
So going down that path, whatkind of information is it that
you, what kind of data is itthat you're gathering in order
to try and basically get theinformation you need to have and
create the digital empathy?
Sam Cummings (05:48):
Yeah.
So I conducted a study and thisis kind of going into my role
here.
Before we started, we werestarting and synthesizing what
data plant would be.
We did a study of and reachedout to about 200 COOs.
CCOs and then CROs of like whatkind of data they have.
And you'll be surprised.
The main data that people areusing today is two sources.
They're using support ticketdata, which represents the first
(06:09):
wave.
The first wave of customersuccess was all reactive and it
evolved out of going beyondcustomer support.
And then essentially the secondwave was product usage.
So being able to look at productusage data, whether that's the
Google Analytics, Pindo, youknow, Mixpanel, all these tools
kind of arose during that timewhen it was about, okay, what
are people doing in theplatform?
(06:30):
So we take those sources as wellas your CRM data and any other
sources that you might have,marketing, outcome data like
renewal history or churnhistory, and we bring all that
data together.
So if you have complex datarelationships, you have multi
layered hierarchies like regularchild accounts that roll up to
parent, that roll up tograndparent.
(06:51):
We stake our claim in working incomplex data scenarios and
really synthesizing that intouseful insight.
Roman Trebon (06:59):
So, so Sam, I don't need, I don't need to be a
data scientist to consume theoutput, right?
You're going to do all the heavylifting and get all that data
together.
And then you're going to helpguide me as a CSM.
You mentioned 60 to 1 ratio.
Oh, I'm getting, I'm almostbreaking out in hives hearing
that.
But this output is going to helpme be able to like, you know,
scale and touch all theseclients that I'm responsible
for.
Sam Cummings (07:19):
Yes, because this is a revolution happening under
to under between our eyes,right, right in front of us.
The real opportunity is notsomething that's in a vacuum.
We have been on a long arc ofinnovation from in the beginning
when I came out of college.
I'm a date myself, but I was S.
A.
P.
Certified.
And back then, you had the jobwith JavaScript, code the
(07:40):
relationship between objects,code the jobs that do
transformations.
So if I wanted to convert a leadto an account, or a lead to a
contact, I had to script that.
But then there's this tool orthis platform, some people might
know, there's a little bittycompany called Salesforce.
That really took the wave when Iwas graduating.
That was the same thing thatSalesforce did for technology
(08:02):
and ERPs and CRMs.
The same thing happened withWordPress for web development
and blogs, right?
The average person can now doit.
You didn't need a developer andthat is what we're doing in this
next wave that's happening.
Cause even now, when you want touse a Salesforce or anything
like a Gainsight, there's wholetrainings, there's whole
(08:24):
certifications because thesesystems are sandbox tools, which
again, is better than coding,right?
It's better than the JavaScriptdays.
But you still have to becertified in understanding how
to make these tools do any onething.
We're in a new age, andDataPlant is leading that age,
where the professionalsthemselves will be able to drive
their business.
And so imagine, you could justtalk to DataPlant and say, give
(08:45):
me a report on downloadperformance and put it in a bar
chart format.
And instead of like, writing theSQL, that's what most of the
tools are still doing today,even with AI.
They'll just write the SQL foryou.
But you still gotta know what todo.
Here is, you can just talk toDataPlant and it'll, since it
auto generates all the possiblecharts, think of it like a tree
(09:05):
with the fruit or the insightsand you're just curating with
your voice and what you look atand passively as you leverage
it.
This nice curated bush of fruitthat can bear fruit in your
business.
Kevin Metzger (09:17):
Sam, from, can we take that down from an analogy
level, a little deeper intowhat's actually happening with
the data and how you're, howyou're synthesizing it?
Sam Cummings (09:26):
Yeah, so real example, customer we work with
called Restaurant 365.
40, 000 locations, so they're apoint of sale system for
restaurants.
We're helping them analyze theircustomer usage data because they
have a lot of products.
So think about it.
You have maybe four to sixproducts that you might have.
Each customer might be in one ofthree segments.
So it could be an SMB client, amid market, and an enterprise.
(09:50):
Each of them have differentdesired outcomes based on who
they are and what they need fromyour platform.
And so we're able to analyzethat based on what the size of
the company, so if it's an SMBclient and then what they
bought.
So do they buy the operationsfeature or the accounting
feature?
And so that tailored marketingof being able to engage them and
(10:11):
communicate is what theyleverage our tool for.
So they can use, their CSMs canreach out to a restaurant and
say, compared to all otherpizzerias.
You are spending 25 percent moreon labor.
If you leverage our payrolltool, you can lower that burn
and really get a more efficientbusiness and put yourself in the
top performers.
To do that in any other CSplatform is impossible.
(10:33):
The nature of the data is justtoo complex.
You know how many restauranttypes there are?
Seafood, pizza.
So imagine a rule chain that youwould build to really automate
that, which again, that's theunderpinning of all modern CS
software, which is rulebuilding.
And so we saw it like thatitself was the barrier to
unlocking these personalizedinteractions and these tailored
(10:55):
insights.
And that's what we did.
We moved away from that andreally transformed how it works
to where now you can, as anindividual contributor, as a
leader, you don't have to paythe tax.
That was required before.
Before you had to pay a taxessentially of building out
everything, going and findingout what's working, then
defining the playbook androlling out.
(11:16):
We're duty free, baby.
Roman Trebon (11:19):
I love, I love that example.
And like you said before, itwouldn't have happened.
You didn't have the time.
You didn't have the resources.
You didn't have it all there tosay, Hey, you know, your, your,
your labor cost is up in X, Y,I, you know, look at these
solutions.
So for our audience, if youhaven't gone to the website yet,
you have to check it out.
So you have to go to theDatalant.
Com.
Sam, you already have anamazing.
(11:39):
Set of free tools on there.
So I found this, what we had,we're getting new year and have
you on the show, checking youout.
You have these free chat, GPTbots on there.
I've, I've already sent them toa whole bunch of people.
I absolutely love them.
They're game changers.
How, how'd you guys come up withthese?
What was the concept behind thisand tell the audience a little
bit about what they are andwhere they can find them.
Sam Cummings (11:59):
So this is where we really saw the future coming
forward.
And I tell you what's comingnext is going to be even more
fun.
I'll give you a sneak peek.
I'm dropping the sneak peeks onthis podcast only.
So if y'all didn't hear breakingnews, this is exclusives.
So that's it in the background.
So we saw that the main approachto this has been, we're going to
create these all knowing bots.
(12:21):
So if you look at most of theother companies, everyone's got
a GPT out there, but their ideais like a chat GPT tool can do
all things.
What that leads to is they don'tdo anything.
Well, And so what we saw waswhat's better suited is have
smaller, more targeted modelingtools that solve the top
problems.
And we went and did someresearch as well.
(12:41):
Big, you know, data guy,surprise.
But I researched what were thetop use cases of CS teams.
And then we look to build theGPT around each of those.
And we really tailored them.
And then I know a lot of cheatcodes around building GPTs, the
Beck perform higher, once hecaught, I'll give you two of
them.
If you tell a chat GPT to take abreath, that alone will allow it
(13:03):
to be more diligent.
I know it's not human.
It's not going to literally gobreathe, but that'll slow it
down and make it think moredeliberate.
Another trick.
If you tell it to proceedwithout instruction, it will
allow it to make moreassumptions.
Based on what he knows in hispremise, that it probably
wouldn't have done beforebecause he didn't trust that it
would be accurate.
And so those are just some ofthe cheat codes coupled with the
(13:24):
learnings that we had over thedecade of working in this space.
That's why our bots are morepowerful.
And then we built them into themain core use cases like
customer success transitions,big problem transitioning
account.
Being able to do customersuccess strategy planning as a
leader.
How do I plan how I'm going toapproach my business?
So we targeted these very nicheproblems and that's why you see
(13:45):
that our free tools are way morehigh performing But i'll stop
there before I give you guys thecheat codes for the future
Roman Trebon (13:51):
Oh, i'll tell you what.
I so just just our audienceknows I went there, but you have
a success a success plan boughtIt's on there.
Like you said, it's just that wehad just met with the client
first time.
Tell us about your business Dada da Put in the transcript, put
it into your success bot plan,spit out an amazing first, I
mean, draft, I mean, an amazingsuccess plan that we had within
(14:14):
minutes that would have takenus, you know, you know, before,
before the bot or even trying todo it ourselves, like you said,
it would have been iteration,iteration, iteration.
So Sam, you already saved mesome time.
So I'm all in.
I can't wait to hear what'scoming next.
This
Sam Cummings (14:27):
is where Sci-fi kicks in.
Kevin, let me share yourthoughts before I jump into it.
Kevin Metzger (14:30):
I was gonna say, I want to take a guess.
Tell me, if I, tell me how closeI am, you're gonna have the, so
with the bots, as you build outindividual bots, is it that they
get to, they'll startinteracting with each other to
basically build out the plansand the functions going forward?
Sam Cummings (14:46):
You got it.
This is why this man is really,I'm telling you, I wouldn't have
came to no other podcast, butthis one, I mean this sci-fi
kicks in gentlemen.
Let me just take a step back inthe concept.
There is no rules.
This is the wild wild west.
There's no one that can tell youdo this, do that, do this, you
get that.
And this is what this lookslike.
(15:06):
What we're going to have, andagain there's some challenges
with the price and the cost ofthese bots to be able to make it
scalable, so there's going to besome time to this, but the other
side of this is you're going toessentially pose goals To a chat
room.
In that room, there'll be six orseven different GPTs, and
they'll all work together.
And I've been experimenting withthis technology already and
(15:27):
working what we're doing withDatalant.
And what we found was crazystuff.
So I put all those bots that wehad already in the room to solve
a problem.
And again, the statistics haveshown us over the last two or
three years, the first versionof GPT, they had about 80
percent efficacy across a seriesof human tasks.
The more tailored version thatpeople have been working on
(15:48):
lately.
Up in the maybe 90s.
These new community approachesof bringing in multiple GPTs
together.
98 efficacy above.
Think of it like ants, right?
Any visual ant can be smart,somewhat, but together they have
emergent behavior.
That's happening right now.
Now what's crazy, we're puttingin different types of GPTs that
(16:09):
have no correlation with theother ones.
So for example, I put a policebot that does interrogations in
there, and the performance wentup.
So what that means is, there'sno rules in what kind of bots
you can combine.
You can bring a bot that's acounselor bot.
And have it talk to the otherbots and it might actually
improve their performance.
And so that's the world that'sgoing to be brand new here is
(16:31):
it's not just like, I need allmy bots to have some similar
relationship, meaning becustomer success.
You can put six bots in therethat all have a similar
relationship and it put a wildcard in there and get a 10 time
better performance.
The game just changed,gentlemen.
Kevin Metzger (16:47):
It's effectively, you're kind of driving an AI
hive mind, is really what you'retrying to, you're putting
together.
Is that accurate?
Sam Cummings (16:54):
Yeah.
Think of it like one of thosethat might not be as, you know,
they're like, well, that's soethereal.
You've, anybody ever played Uno?
Roman Trebon (17:00):
Yeah.
Oh yeah.
Oh yeah.
Sam Cummings (17:01):
Same thing.
Each bot puts down his card.
So it puts down what it thinks.
Every other bot knows thehistory of what's happened.
So they can all pick up fromeach other, edit it, and then
drop the next card.
So we're all essentially doingit essentially in the box and
it's getting super fast speed oflight, but they're playing UNO
together.
And from that, you get thisemergent, better game.
Roman Trebon (17:21):
Love it.
So Sam, you've been, like yousaid, you, you've been in,
you've been in customer successfor a while.
You know, you're, you're in, youknow, Gainsight, LinkedIn,
you've been all around, right?
So we talked about AI a littlebit.
Where do you see, what iscustomer success look like five,
10 years from now?
I mean, what are we looking at?
What do you think is you have toget your crystal ball out and
dust it off a little bit.
Sam Cummings (17:43):
So for those working and I'll talk to a
specific sub audience herebecause you are going to be the
heroes of our future customersuccess operations.
Those are the folks that have anopportunity to make sci fi real
the way we've been thinkingabout customer success, not
going to move the needle.
We're going to have to go to anew level.
The reason why is the thresholdshigher.
(18:05):
We're already seeing this insales and marketing code
outreach dying.
Email spamming, not working.
Good luck.
Even reaching out on LinkedIncold.
Try it, see what happens foryou.
The issue is, that cold outreachtype of mindset without
personalization doesn't breakthrough.
And so that's where the futureof this, what I see is, Is these
(18:28):
hyper personalized engagementswhere again, I'm reaching out to
people on a way that know fromtheir perspective, they know
that that message was tailoredfor them.
If you can't tailor your messagein today's age, when everyone's
using these chat GPTs and ouremail inboxes have been blown up
for the last 20 years, you arenot going to be able to break
through the noise.
(18:48):
So as CS ops teams, that's thefirst level of where I see this
innovation happening, wherethey're going to be the creative
ones, they're going to becomerock stars.
Because they're going to havemore ability to touch their
clients than anybody else in thebusiness.
If I'm a CSM, I might own 20accounts, 60 accounts, etc.
Through digital customersuccess, these folks are going
(19:09):
to impact millions of people.
And so the way that the industrygoes, if we decide to go further
and be a little bit more risktaking in how we approach this,
oh man, we're going to be thereal, real heroes.
So that's the first group that Isee really benefiting from the
innovations today.
People that are going to beleaning into digital scale.
Now, the other group that Ithink is really going to be the
(19:29):
beneficiary on the broader senseis the medium to low performers.
There was a big gap betweenthose that were great at doing
their job in CS versus thosethat weren't, that was really
bucketed into the soft skills.
Can you take good notes?
Can you do good recapping?
Can you distill your points intoreally clear messages?
(19:49):
What's your follow up game like?
Those all are now going to bedemocratized.
So the distance between someonebeing really good on those skill
sets and the average person isgoing to be way shorter.
So that's going to allow for allteams to have really a higher
performing group across theboard.
I'm excited for that time.
And again, I'll share more as wego forward on some of those
(20:11):
unique niches where theopportunity is.
But overall, CS Ops is going tohave more touch and become more
of a hero and individualcontributors are going to be
more democratized in theirperformance.
Kevin Metzger (20:21):
Yeah, and I think the ops right now is huge.
I think that's where you're,you're, you're getting a lot of
the games and allowing the CSMteams to basically expand what
they're serving.
From we hear a lot about it, youknow, it's not those who Use a
error.
It's not going to replacehumans.
It's humans using AI.
That's going to replace humansnot using AI.
(20:43):
But I think as we talk about it,the AI is going to allow a human
to scale much work.
Right?
So, and ultimately, it'shopefully as humans continue to
scale.
Outreaches and what they're ableto do and how they think they'll
be able to keep up with it andwe'll grow more customers.
(21:05):
And by growing more customers,we're going to expand.
You're going to grow.
You're going to grow the pot.
And I think that's where there'sthere's an advantage.
I do think that.
As CSMs or CS ops folks, you gotto start thinking about how you,
what, where you're bringingvalue.
What do you think on that?
Like, where do you recommendpeople start thinking about how
(21:27):
to think differently?
So they're ready for the future.
Sam Cummings (21:30):
Yeah.
Just throw away everythingyou've heard.
All that stuff that worked in2012 is not going to work today.
You can show up with the samethings you were doing then and
be walked out of the room.
And what I mean by that is.
The approach to customer successwas top down.
The idea was we could justfigure it out, we know what
(21:51):
works and then we're going tomake essentially and the reflex
is the system has a rule thatsays if less than six logins do
this, if more than 12 you know,interactions do that, like that
was the core idea.
Top down.
The CSM was essentially was themeat bag executing on the
systems intelligence.
Innovation is too fast for that.
(22:12):
We saw that in COVID.
I tell you, I was in the seat,so I got the seat as first hand.
When COVID hit, all these folkshad all these rules that all of
a sudden broke.
If you looked into theircockpits, whatever tool they're
in, into where they had thesenotifications for their teams,
they had thousands of upsellrecords that said, this client's
ready to upsell.
Did everybody all of a suddenready to upsell their business?
(22:35):
No, since people were indoors,they logged in more.
And so that was a perfectexample of like the idea of I'm
just going to hard codeeverything and then have it be
top down in my business onlyworks when stuff doesn't change.
Now we might not always get aCOVID again, again, something
like that, thattransformational, but what is
true products teams are evolvingtheir products faster than ever.
(22:55):
So say I change how a featureworks, all the rules I built
around that now are no longerthe same.
And so there's just an infiniteproblem that we're going to deal
with regardless Which is therate of change in our markets.
That means we need to switchthat premise of top down CS to
now a more agile version.
And that's what data plan isenabling.
(23:16):
Think of it simply put with ananalogy here before it was turn
based, we configure everything,we execute that configuration,
what data plan switching to isreal time strategy.
Since we've removed the duty topay the tax.
Now, there's no issue for you toexecute.
If you on the Monday morning,think about, hey, we want to
drive improvement of thedownloads feature, you can
(23:38):
execute on that same day versushaving someone go build out all
the reporting and then doanalysis of what's working, then
define the strategy, what we'regoing to deliver.
We're able to deal with that inminutes.
So now you as a leader canexecute.
So what was thought notpossible, I can only run one
campaign a quarter because it'sgoing to take so much to
(23:59):
mobilize or I can't run what wecall perishable campaigns
because we don't get certainthings are perishable, meaning
it's so short in the moment thatit wouldn't make sense to
automate it.
It's like, for example, if Ihave an upcoming webinar this
coming week.
To put a lot of time to reallycustomize a message around that
and build that out for oneoutreach, the values not work to
(24:21):
squeeze.
But if I can move like this, nowI'm able to do that.
And that's the big differencehere.
The value and personalization isin the perishables.
That's where you break through.
And if you can't leverage that,which you can't today, you're
going to miss out on theopportunity to really capture
those people's attention anddrive outcomes.
Roman Trebon (24:39):
Yep.
The speed and thepersonalization, Sam, you know,
that gets me excited.
As a client, not even someonewho's helping serve clients, but
as a client myself, like yousaid, what, what, you know, what
are we doing?
Where's their opportunity tomeet for me as a client?
How do I compare it againstothers?
And it, you know, you said thattailored towards me as my
organization, that's real value,right?
(25:00):
That's real value.
And again, right now it's, it'sslow.
It's, you know, it takes time.
It's, or, or you get somethingand it's you see a benchmark and
it's, You know, i'm one of asegment.
It's not me as an individualclient.
It's like, oh You're in thissegment and here's how you can
compare not me as an individualclient.
That's a real game changer.
I love that It's gonna be sohappy.
(25:21):
We're either we ready to hit samup with the the hard hitting
questions here
Kevin Metzger (25:26):
Yeah, I think it's time.
Let's go.
All right, sam We got some rapidfire stuff for you.
So i'm rubbing you want to takeit or you want me to?
Roman Trebon (25:34):
Well, we can go back and forth here.
Let's see.
All right, sam All right, buckleup early bird or night owl night
owl night out.
All right movie Oh, I love it.
You like, what about thesequels?
You like, you're a sequel guytoo, or just the original?
Sam Cummings (25:50):
Oh, yeah.
The original is great.
The sequels are marinade on you.
You know, you got to sit intothem.
Roman Trebon (25:55):
All right.
You got a favorite sport thatyou watch or play?
Boxing.
Boxing.
Who's your you got a boxer?
You that's me.
That's, that's my, you got,you're speaking my language.
You got, you
Sam Cummings (26:05):
got a favorite boxer?
Yeah.
Yeah, so my favorite boxer nowin this current time, Javante
Davis, y'all see that fight thisweekend?
Roman Trebon (26:14):
Let's go.
Unbelievable walkout.
The guy always delivers.
So yeah, we will.
I watched.
So unbelievable.
Kevin Metzger (26:19):
Oh yeah.
All right.
So Roman's in the process ofbecoming a professional ref, I'm
a
Roman Trebon (26:26):
judge, judge, professional judge here in
Georgia.
So I, I'm, I'm, yeah.
So I, anyway, that's a wholedifferent conversation.
So.
Sam Cummings (26:34):
Get in the game.
We need you.
Roman Trebon (26:35):
That's why I got into game.
Sam, you've seen it.
You're a box of being, you'veseen enough bad scorecards.
You know, there's help needed somuch politics.
Can't wait to see you there.
Maybe I'm ever going to tripover to Saudi Arabia too.
Who knows?
All right.
Book recommendation, favoritebook or book recommendation for
our audience.
Smart brevity, smart brevity.
Sam Cummings (26:57):
All right.
Priceless book.
I think the biggest thing youcan look up the author and all,
but why I recommend it is we arein a time when you can
communicate pointed and it'slike being on CNN when they have
eight boxes, you got to hit.
Roman Trebon (27:10):
A place you'd want to travel to, Sam, you've never
been Bali, Bali.
Nice.
Nice.
All right.
And then to close out here,where can our audience find more
about you and more about a dataplan?
Sam Cummings (27:24):
Check us out on LinkedIn, Samuel J 3 1 4.
That's my personal LinkedIn andthen Datalant.
You can just look us up onLinkedIn under D a T a P L a N
T.
And then also run a podcastcalled AI for diversity.
We have 500, 000 followers onLinkedIn.
You can check us out there.
Roman Trebon (27:42):
Awesome.
Sam, thanks so much for comingon the show.
Love the polo shirt.
We'll have to find, well, you'rethe first up for our our guest
shirts that we're going to queueup here soon.
So really, really appreciate it.
And for our audience, make sureto check out Sam.
His stuff's amazing.
Definitely check out hiswebsite, the data plant great
stuff on there.
And for everyone, thanks forlistening.
(28:03):
You can follow us on LinkedIn atRoman Trebon at Kevin Metzger,
make sure to check out ourcustomer success playbook page
as well on LinkedIn.
And Kevin, awesome.
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