A Visit With Tim Berry

In this episode of "In the Interim…," Dr. Scott Berry interviews Tim Berry, co-founder of Blend360, detailing a career that demonstrates the practical application of statistical and analytical methods within large-scale business environments. Tim outlines his quick shift from earning a master’s at the University of Minnesota to industry positions, starting at AT&T Bell Laboratories, where he built and tested retention models on millions of consumer records. He recounts his time at Rapp Collins, where analytics had limited organizational impact, before joining Merkle and transforming analytics into a key business component through growing a team from two to over eighty, contributing to hundreds of millions in revenue. At Blend360, Tim discusses acquiring Consultants To Go (C2G) to build new capabilities, focusing on hiring and developing young analytics talent through programs like All-Star. The conversation addresses the evolution from traditional statistics to analytics, the rise of AI and agentic AI for workflow automation and calls out media overstatement of AI-driven disruption. He concludes with pointed career advice to his nephew and other quantitative students: prioritize adaptability, industry experience, and continuous learning over chasing credentials.

Key Highlights:

• Graduate thesis using the Bradley-Terry model for baseball outcome prediction
• Mainframe-driven, large-scale retention modeling at AT&T
• Analytics as a peripheral function at Rapp Collins versus central driver at Merkle
• Rapid talent expansion and analytics leadership at Merkle
• Founding Blend360 and institutional talent development
• AI advancements, agentic AI for business, skepticism on AI hype
• Concrete advice for quantitative undergraduates

For more, visit us at https://www.berryconsultants.com/

Creators and Guests

Host

Scott Berry

President and a Senior Statistical Scientist at Berry Consultants, LLC

What is In the Interim...?

A podcast on statistical science and clinical trials.

Explore the intricacies of Bayesian statistics and adaptive clinical trials. Uncover methods that push beyond conventional paradigms, ushering in data-driven insights that enhance trial outcomes while ensuring safety and efficacy. Join us as we dive into complex medical challenges and regulatory landscapes, offering innovative solutions tailored for pharma pioneers. Featuring expertise from industry leaders, each episode is crafted to provide clarity, foster debate, and challenge mainstream perspectives, ensuring you remain at the forefront of clinical trial excellence.

Judith: Welcome to Berry's In the
Interim podcast, where we explore the

cutting edge of innovative clinical
trial design for the pharmaceutical and

medical industries, and so much more.

Let's dive in.

Scott: All right.

So welcome everybody
back to In The Interim.

I'm your host, Scott Berry, and I have
a, a, a guest today, a statistician,

and, um, it's not uncommon on
this show, we have other Berry's.

So I, I

Tim Berry: Yeah

Scott: another Berry you've, you've
probably not been introduced,

but another statistician who
is not at Berry Consultants.

Uh, most of the Berry's you
meet are at Berry Consultants.

So my brother, Tim Berry, is a ho-
is a guest today on In The Interim.

Welcome, Tim.

Tim Berry: Yes.

Thank you, Scott.

Uh, it's the first time I've been called
a statistician in a while, so, uh,

Scott: Yeah

Tim Berry: exciting.

And I happen to be actually
right down the lake from Scott

here, so, um, in a good spot here

Scott: Yep, yep.

Uh, you can almost see Tim's house
through the window, uh, there.

Um, okay, so let's, let's y- so
you said it's a long time since

you've been called a statistician.

Um, let- let's talk about the
career, his, a- and what more people

call him nowadays, uh, in that.

So your, your career as a statistician
started, we were both at the University

of Minnesota, and you got your master's
degree at the University of Minnesota,

and you you took a class from your father.

that right?

Tim Berry: Yes, that's, that's right.

That's right.

It was, uh, a weird…

It was weird.

Uh, I s- I, I saw my dad, Don, in
action and, uh, I, you know, in,

in, um, some of the sports he would
coach me in, he would always be a lot

harder on me in those sporting events.

But, uh, actually in this class,
he took it r- nice and easy on me,

and so I was surprised for that.

But yes, that was a
great experience for me

Scott: Uh, so I've, so I've worked,
I've worked with Dad for, for many, uh,

years, but I never took a classroom.

I think both you and, and, uh, our
sister Jen, uh, actually had him as

a professor, which is, which is, uh,
an interesting experience for all.

Okay, so, uh, and, and your, your,
um, your, your thesis project

for your master's, I believe
was a Bla- a Bradley Terry model

predicting baseball games, even

Tim Berry: Yes

It was.

It was, it was, uh, one of my passions,
still is my passion, is, um, sports

and using the numbers and data and
analytics in, in the sports world.

And, uh, it was the, the model was,
uh, predicting whether one team

would beat another in, uh, baseball.

And, um, at the time,
there wasn't a lot of data.

There was, uh…

We-- I remember we had to ship out
and we had to go to a live sports

bur- bureau to get some of that data.

Now that data is readily
available across the, uh, the web.

Um, but anyway, used some unique
predictors and, uh, uh, built a

Bradley-Terry model to predict the odds
that one team would beat another team.

And then I actually applied it in real
life and to see how good I was and, uh,

that, and that's a theme throughout my
career and my passion in using statistics

is to see if I'm right, see the answer.

Um, and, uh, did a lot of kinda
back testing, actually pretended

to bet on games and, uh, uh, um,
uh, well, good news and bad news.

The good news is the model was, uh,
better than the bookies at predicting

who would win a baseball game.

The bad news is there's something in
sports and betting called the vig or

the, uh, you know, the, the gap between,
uh, the, the, the, uh, you know, just

because I was better than the bookie,
they put a, a 7%, 5% cushion on top

of that, and I think I was 2% better.

So I had to scrap that model and
realize I wouldn't be able to

make money in that, uh, um, thing.

But it was good.

I enjoyed that one

Scott: I, a- and, and, uh, v- very much
a, uh, precursor to, to your career.

So, a- and that was about
n- uh, 1990, I, I believe,

Tim Berry: Mm-hmm.

Scott: where, uh…

And, and by the way, Tim and I took
classes together at the University of

Minnesota, and I went off to graduate
school, and you got a job at that point.

Tim Berry: Got it, John

Scott: master's in statistics from
Minnesota, what's the first job?

Tim Berry: So the first job is w-one
is I was, uh, I was debating and, uh,

with, with, with Scott and my dad, um,
whether I would stay within academia

and, uh, whether I was, you know…

And, and I barely skated by with
my master's degree, got by my

thesis and, and I was very anxious
to get out in the business world,

to get out of academia and start.

Uh, I'd been learning my whole life.

I was, I was tired of learning, and
I decided it's time to get out there

and, and apply some of these wares.

So took me a while to, to…

I had a lot of opportunities.

There was, um, entry-level opportunities
within the pharmaceutical, uh, world.

I almost took a job, um, there.

I had three or four opportunities and,
uh, actually coaching from, uh, my dad

was, "Look it, as a master's degree
person, uh, engaging in pharmaceutical

companies, you might not get the
opportunity than if you had a PhD."

And so that was kind of a, uh, um,
kind of helped me to make a decision.

And then, and I, and I ended up taking a
job at AT&T Bell Laboratories, and this

was back in the day that, um, AT&T was
a monopoly in the long-distance world.

Cellular was just coming on.

And, uh, the job there was to how do
I help use the data that we had within

AT&T to drive business decisions?

Better business decisions and, and
more informed business decisions.

And it was…

I remember the first-- one of the
first projects I was working on

was the likelihood that, um, a AT&T
consumer would, would, uh, disconnect

from AT&T and go to MCI, right?

And, and back in those days, there
was, um, a battle for long-distance

subscribers, uh, to the point where they
were offering people money to come over.

And, um, so AT&T had a
big retention, um, effort.

And so we built a big logistic
regression model that predicted the

probability that someone would, um,
um, someone being the AT&T consumer,

would disconnect and go to MCI.

And, uh, actually, uh, I remember
we built it on big mainframes.

I remember the first time I
submitted the model, 'cause it ran

on millions and millions of records.

It took two and a half weeks to run.

Now that same model takes less than
the le- sub-second timing to run.

Um,

Scott: Yeah, yeah.

Tim Berry: and, uh, and interesting
enough, by the way, that then when,

when we, we ran the model, we did
multiple iter-iterations of it.

We decided it was pretty good.

Um, it was better than the, the, the
treatment that they were applying.

And, um, we, we scored
everybody in the US population.

We put them into deciles, and
then the question was: what

do you do with those deciles?

Meaning if you knew, here's ten
percent of the people that are highly

likelihood of disconnecting and go
to MCI, um, where do you target and

how do you, how do you, um, how do
you keep them from, uh, uh, leaving?

So there was multiple iterations there.

Um,

Scott: But

Tim Berry: the…

Scott: experiment?

So are you, are you

Tim Berry: Yeah

Scott: that you give an incentive
to somebody in the 10th percentile

and you, you don't somebody else?

And like your baseball example, you
get to figure out are you right?

Tim Berry: Yeah.

Yeah, you get to figure
out where the right…

One, one of the things we missed
there though, interesting enough,

is we took the top decile, the mo-
people most likely to leave, and

we did a full experimental design.

We did multiple treatments, uh,
and then we watched to see the

impact those treatments had on
whether someone would leave or not.

One of the things…

And, and, you know, there was, I, I don't
remember, um, uh, um, off the top of my

head, but there was probably five or six
different treatments and, um, the, the…

There were-- there was only one
that worked, and that was a,

that was a, uh, a win-back check.

You had to offer the person money.

"I know…

Thank you for being a
valued AT&T customer.

Here's $100 for staying."

Right?

And that, that then moved the
needle to keep them from leaving.

But the one thing we did miss
is we didn't do enough testing

into deciles two and three.

And what we found later on is that
it's easier to save someone and the

incremental impact that you can have
on saving someone and staying at AT&T

was better in decile three than decile
one, 'cause decile one, at the time, we

would call them promo surfers, and you…

And, and they were gonna
leave no matter what you did.

Um, so anyway, that was, uh, that was a…

We, we missed that initially,
and then we came back and got it.

So it was

Scott: So, so

Tim Berry: interesting

Scott: Fascinating.

So you're, so you're using big
data to, uh, uh, be a statistician.

Uh, you wouldn't have been surprised,
I assume, to be called a statistician

Tim Berry: No

Scott: uh, in that.

So you're at, you're at, uh, for about
five years, and then where do you go?

Tim Berry: Yeah.

So the, um, uh, one is I had a, w- I had
a killer time at AT&T and I learned a ton.

So I was, you know, I talked about I'd
learned my whole life I wanted to get

out there and in the business world and,
and, and ironically, uh, that's where I

learned, uh, the most I've learned, and it
was around business and applying things.

And so, but at AT&T at the
time, you really had to go into

sales to move up the ladder.

And one of my ambitions was to
continue to progress my career and,

uh, apply some of these analytical
skills that I had to other areas.

And so I, I looked within AT&T but just
couldn't find the right opportunity.

So I ended up taking a job at a
marketing agency, a company called,

uh, Rapp Collins, who's still around.

They're part of, uh, Omnicom, which
is a large marketing advertising

agency, and they wanted me to
come, um, to, to join that group

and build their analytics group.

And, uh, um, so I joined, uh, Rapp,
went to, uh, Dallas and was really,

I was really the second person in
that group, and I was the, the, um…

at, at that time, either the
senior director or the vice

president of analytical services.

I can't remember which.

Um

Scott: But, but still, you, you're
still a statistician at that

Tim Berry: a statistician.

Scott: Okay.

Yep, yep

Tim Berry: And, and I, I, I, I, uh,
I didn't know if you wanted to, uh,

talk about my time there 'cause…

But anyway, my time
there was pretty short.

It was a y- a year and a half.

And, um, what…

And I didn't love the job.

And I didn't love the job because
at that time, I, I was treated

more like window dressing, which
was, "Hey, analytics is…"

Uh, it, it's a lot…

Scott: So,

Tim Berry: I, I was…

Scott: So you're,

Tim Berry: yeah

Scott: AT&T, you're, you're helping AT&T
by doing data analytics for internal

Tim Berry: Yeah.

Having a real impact,
real impact it feels like

Scott: Okay, so they, they, they're
living, they're, they're running

your models, they're trusting.

Now you go to Rapp Collins.

Are you selling this data
analytics to clients, or are

Tim Berry: Yes

Scott: it internal for Rapp Collins stuff?

Tim Berry: Oh, no, no, sorry.

Scott: this, you're selling this to

Tim Berry: They have clients.

Those clients are-- those clients,
what, what RAP, what RAP would do is

they would, uh, run marketing programs.

A lot of how they made their
money was buying media, right?

And so they would go work for clients
like Hyatt, um, Hotels and, and they

would help, um, do better marketing
across Hyatt's, uh, landscape, and

they would be, uh, paid to do that.

And, um, so I don't know if you wanted to
add something to that, but I know you and

I did some work at, uh, Hyatt at, at RAP.

And, uh, but, uh, but I should probably,
uh, finish the point around why I

didn't spend a lo- uh, last long there
and, and the whole window dressing is

just because analytics was not really
core to what they were trying to do.

They weren't really committed to it.

Um, a client would say, "Hey, do
you guys do analytical services?"

And they'd trot me out there and they'd…

You know, I'd, I'd, I'd give them
a little song and dance, and then

I'd get put back into my room.

Now, that's a little bit of an
exaggeration to some extent, because

I did do some real work there.

But I knew that it wasn't really, um,
ultimately what I was looking for there.

Um

Scott: I remember we did
a really cool change point

Tim Berry: Yeah.

Scott: predict if somebody hadn't stayed
within a certain time, are they, are, are

they going to a different hotel chain?

Tim Berry: That's right.

Scott: yeah, yeah

Tim Berry: another version of a retention
model with a different frequent stair.

Frequent stair

Scott: Yep, yep.

Okay.

So you're not having the impact you'd like
to have, so you're there a couple years.

So there, then you leave
and you go somewhere else.

And yep

Tim Berry: Yeah.

I, I, I leave and I go back,
I, I go to a place in Maryland.

Um, at the time it was called
Merkle Computer Systems.

It was a, oh boy, 80, 90 people
person company, so really pretty

small, about 10 million in revenue.

And they, they were great at, at, um,
at the time was called merge-purge,

which merge-purge today is called data
engineering and bringing data together.

But they were experts at combining data.

Uh, merge-purge being that they were,
they were really good at taking name

and addresses, combining them together.

So if you have Scott Barry living at 123
Main Street, and Scott David Barry living

at 12 Main Street, realizing that that's
the same person, bringing that together

so that when you market to that person,
you're not sending two messages out.

Sometimes you even see that today where
you get emails, the same emails come

to your address 'cause they haven't
done proper deduping, so to speak.

So that's what w- that's what Merkle was.

And, uh, they, they, uh, the CEO of
that company really saw an opportunity

in bringing analytics to that data
environment for their clients and, uh,

were looking for someone to lead and build
their analytical services, um, capability.

And so,

Scott: So, so, so still you're, you're
now at a service company that is

selling data analytics to clients,

Tim Berry: Yep

Scott: you're leading that,
that group, uh, at, at

Tim Berry: A pretty brand new.

Our clients weren't expecting that.

Uh, the organization didn't
know much about analytics.

Um, and so it was a, it was an exciting
opportunity, but it also meant, um,

you know, I did a lot of education.

There's a funny story.

I was…

I get to, I get to Merkle and, um,
uh, I, I know you guys are gonna be

surprised at, at this, uh, story.

But I get to Merkle and it's
about two months in and I get the

opportunity to address the company
on what analytical services is.

And so there's a…

And again, it's not a big company.

There's about 100 people there.

And, uh, I knew one of
the ladies in the, uh…

She worked with me.

She was one of the salespeople,
and she had a, she had a, uh,

sister that also worked there.

And while I'm up there talking,
uh, the sister's kinda…

She's, she didn't know much
about, um, um, analytics.

And so finally she says, "I don't get it.

Who is that?"

And, and the lady that I knew,
she says, "Oh, that's Tim.

He's our, he's our modeler."

She thinks a minute, looks back and forth,
says, "Boy, he's not that good-looking."

And so, uh, that, that…

I, I spent a ton of time
edgiting- educating people on

what analytical services were.

They had a lot of nonprofit
organizations, and we started to

build predictive models for those
nonprofits, which is who's likely to be

a donor and how much they might give.

And there was tons of pushback.

This is early '90s, mid '90s,
on you can't model emotions.

So even though you think you can predict
who's gonna make a decision, we as

marketers, um, um, um, use emotions to…

And, and they still do it
in the fundraising world, to

use emotions to get donors.

How can you predict that?

How can you model that?

So, um-

Scott: it almost sounds a little bit
like Moneyball, where it's the fight

Tim Berry: Sure

Scott: the analytics
people and the scout who

Tim Berry: In the gut,

Scott: at

Tim Berry: for sure

Scott: you know, uh, his muscle
tone and tell you whether he's

gonna be a good baseball player
as opposed to the numbers."

you're, you- a company, and a huge
part at, at this time was getting data.

It, it was who had data, selling data,
and so you're, the company has this

amazing data, and it curates it, and
it, it, you know, so it has good data.

But now you're selling the
service of using the data to make

better decisions to companies.

Now does this catch on?

Does, does, does data
analytics catch on for Merkle?

Tim Berry: Yes.

Yes, it does.

It, it, it did.

It, it took a while.

It took…

You know, I, I, I talk about building
a business, and we were really

building a business within, uh, Merkle.

Um, and, uh, it, it's
like pushing a flywheel.

You start to push it, and it's
heavy, and it's not going anywhere.

And you push it, still
it's not going anywhere.

And you push it.

It finally starts to turn a little
bit and starts to turn a little bit.

And then once, after or after you
continually push it, then it's,

uh, it starts going on its own.

And it-- I remember when we hired, it took
me about a year to hire two people, and

then after three years, we had almost,
uh, 80 people in that group, right?

Just because it…

They had data and analytics and
statisticians and hiring people with

master's degree, some with PhDs,
um, some with bachelor's degrees.

But, but about 80% of who we were
looking for were people with master's

degrees and, uh, PhDs degree, PhDs.

And then that continued.

That was just three years into it.

Um, and, um, so that continued to
grow and scale within Merkle and

became a huge part of its value
proposition, um, in the marketing arena.

Um, so

Scott: Okay, so you're still
a statistician, but now

you're leading this group.

You,

Tim Berry: leading the group

Scott: founded, you're leading the group.

Um, and then, um, this grows to a
size that your position changes, uh,

Tim Berry: Yeah

Scott: from statistician
to president of MERCKLE.

Co-president?

Yep.

Yep.

Tim Berry: There was a couple
steps in between that, but

yes, that's what happened.

And, um, important…

It would be important to, to note.

So we-- I was out, you know,
I spent a lot of time doing,

uh, customer pitches, right?

And, uh, I think that's one of
the, you know, the, the, uh…

Scott: data analytics.

So, uh,

Tim Berry: Selling data analytics to our
clients, trying to convince them that

they should spend money on analytics
to help them do better things, right?

And so, um, as part of that, I, I,
I, um, connected with a, um, uh,

a partner, uh, Patrick Hennessy,
who was a salesperson at the time.

I was the analytics guy.

He would-- He and I would go out
and do customer pitches together.

And we just realized that a lot of
the skills that I brought to the table

helped him, and a lot of the skills
he brought to the table helped me.

And so we started to combine, um,
w- uh, you know, we combined our

pitches, and then all of a sudden we
decide, let's build a group together.

So he joined me in helping
build the analytics group.

And then, um, uh, realizing that we
were, you know, we were more effective

as what we like to call two in a box.

And so then we just continued to
progress as Merkle grew, and Merkle

ended up scaling to almost a billion
dollars in revenue by the time I left.

We came at, at, uh…

When it was less than 10 million.

And then he and I stayed working
and leading groups together.

And to Scott's point, we…

By the time I had ended up leaving,
uh, Merkle after about 20 years, I

was co-president with Patrick Hennessy

Scott: How big did the data analytics
group come by, by the end of this?

Um, uh, what fraction of the
billion dollars is data analytics?

Tim Berry: Yeah.

It was a, it was about, there
was a couple of things in there.

Um, so we, as you mentioned,
we had data that would help…

We, we would sell some data
to our clients that would help

them get more predictive data.

You know, like we had age and income
and occupation and things like that.

So that was included in that.

But it was about $350 million of
the billion dollars in revenue.

Um, so about 35% of that.

Uh

Scott: Uh, amazing.

Now, i- at this time, and this is…

I, I think you're at Merkle
till 2018-ish, uh, uh,

Tim Berry: Yep.

Scott: I think you go to…

Tim Berry: So

Scott: you spend time in London,
you lead Europe Merkle and all that.

But is there any AI at Merkle before 2018?

Tim Berry: Wow.

Well, um, in, in, in a word, not as we
know AI today, but the way we define

AI today, there was tons of it, right?

And, and, uh, but it was,
it wasn't defined as AI.

So the…

And again, so it boils down to the
definition of how you wanna define AI.

But there was really an evolution in my
life cycle as a business person from when

I first started to even as I sit here
today, which was there was statisticians,

then the-- then, then they became
analysts or analytical folks, right?

And, and back in my day when I was
a statistician, if you called me an

analyst, I'd be upset with you, right?

Um, and then, but it just
became a common term.

So there was an evolution from being a
statistician to then being in analytics

to then being a data scientist.

And then as we sit here today, data
scientists are now AI people, right?

AI engineers, AI scientists, um,
because it's the way of the world.

But a lot of the things that are
going on that we call AI today is

fundamental data engineering, data
science, sta-statistical work.

Uh, so

Scott: Okay, interest.

So, and now Merkle, Merkle gets bought
and you, you stay on there, but then

you leave Merkle, uh, at this point.

Um, and, and what next?

Tim Berry: Yep.

Yeah, so, so I'll bring
Patrick in again on that.

And so he and I were in the boardroom
of, at, at Merkle about 2010.

With the…

Now Merkle got sold in 2018.

We had…

And if you look at what Merkle
as a company, there was a lot of

different evolutions of Merkle.

We, and, and it's an incred- incredibly
important lesson that I learned in,

in, and at a company called Blend,
in building and growing Blend, is

making sure you evolve and change.

And so we were going through one of those
changes, and we, we, as I mentioned, we

were a data company that brought analytics
in, that had technology, and we were…

Our secret sauce was data
analytics and technology.

And most of our clients
were in the marketing world.

And we had a decision to make that
was, "Hey, um, where do we go next?

Do we wanna then stay engaged in
the marketing world and evolve our

services around what, what we were
calling a addressable marketing agency?

Use that superpower of data
analytics and technology, and apply

it in the agency world and apply
it in marketing for our clients."

We were debating whether we should do
that or we should, we should use data

analytics and technology and apply it
outside of marketing into all kinds

of other areas, from healthcare to HR,
to, to manufacturing, to supply chain.

And Patrick and I were voting
and arguing that, "Let's do that.

Let's bring that superpower to
other domains of the business."

And there was a he- heated debate and back
over, over a couple weeks actually, and in

the end, our CEO, David Williams, got to
make that decision, and he decided, "Nope,

let's stay within the marketing space."

And that was always a point
that we said, "Boy…"

And it, by the way, that was a good
decision for Merkle, and in the end,

everybody benefited from that decision.

I'm glad that decision was made.

Uh, but at the time, Patrick and I were
saying, "When our run at Merkle is over,

we wanna create that type of company."

Um, and so that's, as,
as our run at, uh…

When, when Merkle got sold, um, we,
we both said, "This is the time.

Step out and s- and start what
we now, what we now call Blend."

Scott: Okay.

So you, you keep with the two in the box.

Tim Berry: Keep with the two in a box

Scott: you and Patrick go
out and you acquire a group

Tim Berry: Yeah

Scott: to build into this much
broader business outside of marketing.

Maybe you'll do marketing, but largely a
much broader business, uh, aspect of it.

Uh, and, and Consultants
To Go, I think, is a, is

Tim Berry: Hmm

Scott: company C2G, uh, acquired, and
this becomes Blend, uh, at this point.

So now you … By the way, you went
from sta- statistician, analyst,

data scientist, co-president.

Now you're co-founder.

Tim Berry: Koffar.

Scott: co-founder.

Uh, and, and this is Blend360.

And so what is Blend360?

Tim Berry: Yep.

Yes.

So we, um, we, we had a decision make
is, is we wanted to start this data

analytics technology company and apply it
to different areas of the business, so we

had to decide how were we gonna do that.

And one way to do it is you
plant the flag, say, "I'm open

for business," and you find…

you know, you start
finding clients, right?

Another way to do it is buy a company.

Gives you a foundation to build off of.

You got people who can do billing,
you got some clients, you've got…

And so, um, our thinking was, let's see
if we can find a company that we could

buy that could give us the platform
to build the company that we now call

Blend or the company that we were after.

And we did.

We came across this company called,
uh, I, I haven't heard that name,

Consultants-To-Go, in a while, because
we- as soon as we, as soon as I heard

that term, I said, "I'm not buying
a company that's Consultants-To-Go.

I get a hamburger, I get a consultant."

Scott: Yeah.

Tim Berry: and so we typ- we,
we immediately changed it.

We changed it's, uh, a strong point.

We changed it to C2G, and people
would say, "What does C2G stand for?"

And, uh, we would say,
"Committed to growth."

And so, uh, but it was e- but if
I go back to the Consultants-To-Go

point, it was about a, it was
about a $8 million company.

It was…

It had consultants.

It w- it had a unique value proposition
of it was largely 80 to 90% of the

consultants were women, and they were
s- it was started by two women coming

out of AT&T and Amex that, um, um, the,
the, a lot of the women had stepped out

of the workforce, wanted to get back
in, engaged in the business, wanted

flexibility, wanted to work on their
terms, and, and, and had great skill sets.

So they got started to get a Rolodex of
these people, and they would then go in

and offer them to their, um, clients.

And they, they had some good
clients in Citibank, um, AT&T, Amex.

And, um, and so that gave us a
foundation to build off of and, um, um,

and then we started Blend using that.

And, um, we, we were looking
to buy some analytic strength

Scott: Hmm

Tim Berry: Um, and we just couldn't
find anything that we were excited for.

One of our key value propositions
still today is we wanna just be

known for exceptional people, and a
lot of time we just didn't find the

quality that we wanted, so we just
decided to build that on our own.

And, um, and, and so we started the Blend
endeavor in 20-- late 2017, early 2018,

and, uh, we scaled, um, um…

Really it's, it's a broad value
proposition, but we would just go to

our clients and say, "You have data
here that can help you be smarter.

Let us come in there, let us understand
what your business problems are, and

then let us apply solutions there
that are gonna impact your results."

And you know what?

Sometimes they would say, "I don't
know," and say, "Just give us a shot.

Don't even pay us.

If we don't-- If, if we're not able to
impact your results, you should fire us."

And so we would get in there with
a pretty aggressive, um, approach

there and then, uh, just be able to
make sure that we could demonstrate

our ability to drive results.

And that, that, that
mentality exists today, um,

Scott: I, I mean, it's fascinating
from the time you're at Rapp Collins

where, uh, it's an afterthought.

You're, you're a, you're a, a,
"Oh, yes, we have data analytics.

Somebody go find them."

Um, to, you know, data analytics
at the time, now it's, it's, uh,

you know, almost like AI is now.

E- everybody's talking about data
analytics, how do we use this data?

So an easier sell, but you're
still trying to convince companies

that the data they have is gold

Tim Berry: Yeah

Scott: and, and it can make
you better and smarter.

So, so now you're largely
building data analytics at Blend.

and I know you went out to various, uh,
schools and brought in whole classes

of, of master's students, for example.

And, uh, so you're getting-- You're,
you're, you're, you're hiring large

numbers of statistician data analysts
and, and this is growing rapidly at Blend

now, this, this part of the business.

Tim Berry: Yep.

Yep.

And yeah, 'cause when you think
of Blend, you know, if we're…

Right now we're about
$200 million in revenue.

We have about 1,500 employees.

And, um, um, the, uh, c- the…

If you think about Blend, we
really only have two big areas.

We have our people, and we have clients.

And if we don't have people,
our clients have nothing to buy.

So we're very much a
services-based company, right?

And, um, and so we, we, we, we said
very quickly, "We gotta make sure that

we hire people that people wanna buy.

We gotta, we gotta be,
um, producing talent."

And, uh, a b- you know, we would
go out and find very experienced

people that knew how to do that.

Um, but we also said to ourselves, "We
gotta start bringing in great talent,

training them up to some extent."

When I say training them up, it's a huge
part of what makes a statistician, I'll

go back to that term, um, data engineer,
analyst successful in our world, is

understanding the business side of things.

And so we would hire people
right out of multiple schools.

We'd put them in cohorts.

Cohorts from six…

We still do this.

So I said we'd, we, we would.

Um, we put them in cohorts of six to 10
people, and then for three months we,

um, expose them to the business problems.

They go through, they go through some
training around, uh, the different tools

and technologies that we use that, uh,
they might not have used in, uh, in

the universities or colleges, right?

So there's some of that.

There's some exposure to some of
the algorithms we have and some of

the platforms that we've created.

But a, a big chunk of it is the,
the business problems we're solving.

Some of the things I went on earlier in
the, the podcast around here's some of the

use cases and w- and, and have them work
through that as a team so they can get

better understanding of the business side
of things and how analytics can help that.

So we, we have that.

We call it our All-Star program.

They go through that three months, then
we deploy them, um, in three months,

after three months into our clients, and
they've got a three-month trial period.

And after six months, if they
m- grad- graduate, they make

it as full Blend employees.

That was, that's been a
really great program for us.

It's scaled ac- It's, it's now in LatAm,
it's in India, and it's in EMEA as well

Scott: And, but this is largely in
still the data analytics, which still

Tim Berry: Yeah

Scott: what fraction of
Blend today would you…

I, and I know it may be hard,
but i- is roughly data analytics?

Tim Berry: Yeah.

So we, i-i-i-it, it, it's better to say
what are the capabilities of the people?

And so pre-AI, pre-AI,
about 80%, and it's about…

It was if I use 100%, 40% of
our people were data scientists.

40% of our people were data engineers.

All right?

So, you know, in, in your
guys' world, the statisticians

more than the data scientists.

Um, and that's made up blend, and
then the other was, was, was client

people or, um, marketing people or
domain expertise people and so forth.

But a large portion was our, the
data and analytics coming together.

Um, as you sit here today, that's
now transformed into, you know,

AI engineers and context prompt,
prompt engineers, context engineers.

We still have data engineers.

We still have some data scientists,
but that world's evolving, um,

into new titles and new areas.

Um, but,

Scott: so, and you're at, yeah, I
mean, it's this interesting thing.

I asked the question about AI at Merkle
because largely, well, you had data

engineer- engineers, and they were doing
things that you might think of that, that

this whole jumping into the AI bandwagon.

Now, now Blend is, is right where
this explosion sort of happens.

Are you having similar discussions
that you had at Merkle about should

we do this, should we do that?

About should we be embracing AI?

Should we be selling AI?

Are we an agentic AI creation company?

What are … I mean, is that
process in Blend of that

Tim Berry: Yeah.

Yeah.

One is the marketplace and, and, and
I use that term a little bit loosely.

Is it the AI, AI marketplace or not?

But the, the world we live
into is changing every day.

It's fast-paced.

And one of the lessons we learned at, uh,
Merkle was to make sure that we stay--

we continue to change, and we continue
to adopt to what people need, what the

trends are, where this business is going.

And AI is a huge word,
huge word these days.

And, and, uh, we have
transformed ourselves into

much more of an AI-native shop.

But with that said, there's still
fundamental roots in the data

science and data engineering.

And most of our work we do in the
AI world is around agentic AI.

It's developing agents for our clients
to help them drive more productive,

more efficient business results.

And so I, I heard one of the-

Scott: what's an example of that
without confidential information

Tim Berry: Yeah.

Yes.

Scott: build?

Tim Berry: A lot of it is around,
um, kinda right now it's, it's around

back office workflows type stuff.

So for example, we do work with a, a,
a large financial advising firm, and

they advise their clients on where
the, the investments they should make,

how do they deal with their portfolio.

And we're building agentic AI agents
to replace what some of the back

office, um, researchers would do
around the data associated with this

to make better recommendations to the
financial advisor themselves that then

they can go to their clients with.

So just replacing the whole
process around, um, doing that.

It's more efficient, it's more
effective, um, and it, and it requires

less, although there's still some
of it, less change management.

Because one of the things you'll
hear in the industry, and I'm sure

a lot of you guys have heard it, is
how agents are gonna replace people.

And, and we're seeing some of
that, um, but not nearly the

extent that the noise is going on.

And, um, and what's, what's having--
There's, there's-- I could, I could

probably have multiple podcasts
on this and that line, but one

of the big things that's hurting
that is change management, right?

Which is, okay, I got this
agent that's more efficient

and, and cheaper than a person.

How do I just replace that?

It's very hard, and people aren't used
to that, and that's gonna take some time.

And, um, and again, we're gonna see
evolutions of all kinds of different

things here and, and, uh, um…

But a lot of, lot of cool things
going on around agentic right now.

But it's not moving, I guess a lot--
I've said it, I'll say it again.

It's not moving at the pace
that the press is talking about

Scott: Uh, but forecast a little bit.

What, what is your view on…

I, and I know 10 years is kind of
too, too challenging, but you're

predicting baseball games in the future.

You're predicting if people
are gonna jump from AT&T.

What, what do you think AI's
gonna look like, though?

Is it gonna have the huge impact
that, that the press is talking

about, we're gonna have layoffs, we're
gonna replace people in three years?

Tim Berry: Yeah.

I, um, um, I-- one thing I'm pretty
positive of, and you call me a bit old

school, is I c- you know, I've s- there's
a, there's a big paper out there, um,

I, I wish I remember the, the name of
the paper, but it got a lot of press,

especially in the markets, around, um, the
unemployment rates going to 30 40, 50%.

A, um, AI coming in and replacing
jobs and I just don't…

I-- it's that-- I can't
see that happening.

I cannot see a world where-- that we allow
that as human beings, that to happen.

We just need to be productive.

We need to…

So that, that whole Armageddon side of
AI, uh, of AI, can't see happening, right?

Now, then I, then I say to
myself, "Well, I wonder what…"

And, and unfortunately, how old
I am, I lived through some of

this, the whole tech revolution.

Remember back in the '80s and '90s,
and I talked about it took us two and a

half weeks to run a logistic regression
on a mainframe computer, right?

That's a, that's a whole revolution
that went on, and technology made things

so much easier to do, and people were
worried about losing their job then.

And, uh, and in many ways, this is just
another phase of that, but on steroids.

I think this is on steroids compared
to that, and the whole on steroids

is gonna be, uh, interesting what
that really means and how fast it is.

Um, and so I think AI is gonna be great.

I think it's gonna save lives.

I, I, I love this concept of AI for good.

It's gonna do some bad things as well,
just like technology does some bad things.

Um, and so…

But hopefully, um, it'll, it'll just be
much more of an accelerator for us and

make us easier, make our lives easier

Scott: So, so we at Barry are trying
to use it to save lives, uh, in

Tim Berry: Yes

Scott: drug development the whole time.

Uh, I, I'm interested in maybe even
somewhat, uh, uh, um, uh, thinking

of your, your nephew Cooper.

He's a, he's going to be a
senior next year in college.

He's a math major.

He's a quantitative kid.

People like that, I, I don't know
scared is the right word, they're,

they're uncertain as to what this is.

Advice to somebody like that?

Should they become AI engineers?

Is this a skill that
everybody's gonna have?

Uh, within it you gotta be
so- I mean, what's advice to,

to, to your nephew Cooper?

Tim Berry: Yeah.

Yeah.

Yeah.

It's, it's…

I, I get that question a lot.

One is I just wouldn't over-engineer it.

Don't, you know, like, like I can't
tell you the people, some of the

smartest people I've meet in the
corporate world that didn't plan

to do what they're doing, right?

And so you, you…

No matter what you do in school and
as you're looking into the workplace,

your background's important, but I
wouldn't over-engineer that 'cause

you don't have this skill or that
skill, that you can't get places.

So I, my, my advice to someone
like Cooper would be be aware.

Absolutely.

The more you can be aware of these
technologies, understand them,

be, be, um, uh, knowledgeable in
them, can only help you, right?

I'm a big believer i- in, um, learning
even after school, even I, I, I can't

tell you how many business books I've
read, 'cause that became a passion for me.

But get in, get…

Just get in a spot where you could
start to have an impact somewhere.

Take a job, right?

Start to learn.

Get your head up around
what do I need to do?

Where's the, where's the, the, the,
the, the, you know, the business going?

Where's the opportunity?

Where's the gaps, um, in there?

And so I think people tend to worry
too much about I don't have this

background, I don't have that.

You, you've got the opportunity to
get that background in a business.

Just get in a spot where you can do it

Scott: Fantastic.

I, a-and I'll go back to you started
this, you left the University

of Minnesota saying, you know,
"I, I've learned a bunch, I'm

Tim Berry: Yeah

Scott: work," and you probably
learned a heck of a lot more working,

uh, and, and continue to learn, uh,

Tim Berry: Yep.

Scott: Yeah.

Tim Berry: Yep.

Yep.

Scott: All

Tim Berry: sure

Scott: fantastic.

Well, appreciate you joining
us here In the Interim.

Tim Berry: Yes, hopefully my story
could be of some help to, to people.

And, um, you know, I know some of you
are connected to Scott, probably all

of you, and I'm always happy to have an
additional conversation if you have a

certain situation and would like some,
you know, advice or how I dealt with it.

Uh, open invitation there

Scott: Fantastic.

Well, thank you very
much, Tim, and, uh, thank,

Tim Berry: for that, Miss Scott

Scott: thanks everybody for
joining us, and until next time,

we'll be here, uh, in the interim