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 weirdâ¦
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, uhâ¦
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, uhâ¦
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 knowâ¦
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, toâ¦
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 wasâ¦
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: theâ¦
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 rightâ¦
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 thingsâ¦
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, theâ¦
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 knowâ¦
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 youâ¦
And, and they were gonna leave no matter what you did.
Um, so anyway, that was, uh, that was aâ¦
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, umâ¦
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 'causeâ¦
But anyway, my time there was pretty short.
It was a y- a year and a half.
And, um, whatâ¦
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 isâ¦"
Uh, it, it's a lotâ¦
Scott: So,
Tim Berry: I, I wasâ¦
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'dâ¦
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 wasâ¦
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 aâ¦
And again, it's not a big company.
There's about 100 people there.
And, uh, I knew one of the ladies in the, uhâ¦
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 kindaâ¦
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, thatâ¦
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 toâ¦
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 tookâ¦
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 itâ¦
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.
Tim Berry: There was a couple steps in between that, but
yes, that's what happened.
And, um, importantâ¦
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, uhâ¦
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, uhâ¦
When it was less than 10 million.
And then he and I stayed working and leading groups together.
And to Scott's point, weâ¦
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 helpâ¦
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 isâ¦
I, I think you're at Merkle till 2018-ish, uh, uh,
Tim Berry: Yep.
Scott: I think you go toâ¦
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 theâ¦
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 theâ¦
Now Merkle got sold in 2018.
We hadâ¦
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 wereâ¦
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, "Boyâ¦"
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, uhâ¦
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 â¦ 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 findâ¦
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 gotâ¦
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 wasâ¦
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, umâ¦
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'reâ¦
Right now we're about $200 million in revenue.
We have about 1,500 employees.
And, um, um, the, uh, c- theâ¦
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 sixâ¦
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 youâ¦
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 aboutâ¦
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 â¦ 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, umâ¦
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 onâ¦
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'tâ¦
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 toâ¦
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 whatâ¦"
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 soâ¦
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.
It's, it'sâ¦
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, youâ¦
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, getâ¦
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