Hey there, welcome to Data Democracy. This is
a podcast where we explore ways to make data
and AI more accessible to everyone. I'm super
excited to have our dear friend Matt Layton today
on the podcast. Matt Layton in the house. Matt
is the Senior Vice President of Data Insights
and Intelligence at LegalShield. He also advises
startups consulting on the side. He's also involved
in community service and leadership. He was the
vice mayor and councilman of Ada, Oklahoma. He's
also a rancher who took me ranching one day and
it did not go well. Let's just say I was more
of a hazard than help. Matt was my boss for a
long time and I've just learned so much from
him. Matt's like John McClane of leaders. He's
always got your back. If you mess anything up,
he just absorbs it and takes care of it. He says
you're not learning if you don't make mistakes.
All he asks is don't make the same mistake twice.
Matt's been such a solid support system for me,
for the podcast. I'm just thrilled to have him
here with me. Matt, welcome. Hey man, thanks
for having me. We've been trying to put this
together for a really long time now. I've been
jealous watching all your other episodes and
I haven't been on yet, so finally. I was so excited.
Let's do this. Yeah, let's start with your story.
How did you get where you are? What's your story?
Hey, well, you know what? I think at the end
of the day, my story is, you know, mid... 40s,
and I still don't know what I want to do when
I grow up, right? I'm interested in a whole lot
of different things, love to learn, and probably
spread myself way too thin, but that's a good
way to go through life, right? And like you kind
of mentioned, I'm in data analytics now, never...
never thought or intended that's where I would
end up right I you know went to college and I've
got a political science degree I was going to
go to law school got sick of school between you
know graduating undergrad and finally you know
going to law school and I said the heck with
that went to work full -time at a you know, a
little company called Prepaid Legal Services
at the time, you know, LegalShield, PPLSI now,
and, you know, kind of stayed there ever since.
And, you know, as I learned about that business,
I kind of just moved through the ranks there.
I like to say. I've sat in just about every seat
at that company, right? And I don't know if that's
a positive or a negative, right? Other folks
either wanted me or the current folks wanted
to get rid of me. But anyway, it's helped me
create a knowledge base that I think probably
meshes well with my love of learning and always
looking for new. And that kind of just lands
me right in. analytics business insights you
know i i know the business i love digging through
the through the data i've always said i think
the answer is there i mean if you have enough
data the answer is there right you you just have
to you know have the smarts and the tools to
mine it mine it out and you know You kind of
mentioned politics. Again, I had a political
science undergrad, so I kind of toyed in politics
there, was a local city councilman. Now I like
to say I'm up to here with civic pride, right?
I mean, I've always thought if I'm going to do
that again, the next one's going to come with
a check, because the last one certainly didn't.
And again, I've always loved the outdoors. There's
no cattle ranchers in my family, but I thought,
you know what, I can give this a shot. So, I
mean, I like to say I'm a pretend rancher. I've
been doing that for, well, 15 years now, right?
It's, you know, it's probably a better tax write
-off than it is a profit center. But, you know,
it gets me out of the office. It gets me out
from behind the computer screen. You know, it
gets me out into the real. real, you know, world
at times instead of sitting here looking at Excel
spreadsheets all day. And you know what? I will
say I have had worse ranch hands come out to
feed hay with me. But, you know, there is a proper
way to carry a hay bale and, you know, Mitty's
show just about ever weighs other than the proper
way. But hey, again, you mentioned it early on.
Mistakes are fine, just don't make the same one
twice. I think Mitty taught himself not to care.
So, you know, all good. Yep, I never showed up
on the ranch again. Just the ones. No, yeah,
I think that's a great story, right? So that
feeds right off. And you've been, like you said,
you've been at... every rank at LegalShield and
you've been a veteran of LegalShield. I think
there's so much to say about understanding, gaining
a deep understanding of our company, its business,
especially at LegalShield. It's 50 years old
and we've been digging into data that never ends
and there's just so much. Average tenure of employees
around It's around about three to four years.
You and I are kind of anomalies, right? So you're
in a different league. I just put myself in there
as well. You know what I said? I think I'm going
on again. Me and you both, we do a lot of random
stuff on the side, right? So we do a lot of different
things. But as far as working at LegalShield,
prepaid legal services, I think I'm going on
my 27th year now. There you go. Of course, I
started when I was in elementary school, paying
no attention to the white beard. I always joke,
it's like, I don't know if that's a good thing
or this is just the result of a string of bad
decisions that have caused me to be here 27 years.
But I, you know, love it, love the company, love
what I do. Most of all, I love the teams I've
got to work with and kind of building teams.
go on and do better, better, better and bigger
and better things. And that's probably the coolest,
coolest reason that you would, you know, want
to build and run what I call high performing
teams. Yeah, no, absolutely. I never thought
so either. Like that I would stay at a company
for a really long time and it's been about eight
years for now, but yeah, definitely. 27 is awesome.
But like you said, I mean, we both kind of do
things on the side to keep us out of the bubble
and keep us in the loop with outside stuff that's
going on. Yeah, yeah. So if you were to, and
I know we talked about it, if you were to kind
of make the case for people staying longer at
a firm, what would those be? I think I lost you
there. Oh, can you hear me now? Yeah, I can.
Okay. Now, if we were to make a case for people
staying at a company longer, what would those
merits be? First of all, I think you have to
enjoy the people you're there with. I think I...
...to the people that you have to be with every
single day, right? I mean, if you don't get along
and like the folks you're working with, I don't
think the company itself or the mission or the
vision matters, right? I think that's probably
the number one reason that anyone would want
to stay, right? Because, I mean, of course, you
can say money. I want more money. I'm not getting
paid enough. Or, you know, the job duties are
boring. Or I don't see a path to advancement.
Or, you know, I just don't believe in what the
company is, you know, based on or what the company's
goals are. And all those things are valid, right?
But at the end of the day, if you like the people
you're working with, you're probably more interested
in getting up in the morning and going you know
hanging out with your buddies right i think that's
the kind of the team that we build right you
know mistakes are okay no one no one's getting
in trouble for screwing something up again you
know as long as you learn from it right and don't
screw the same Enjoy the people you're working
with. You look forward to going hanging out with
them every single day. Because again, if you
enjoy the folks you're working with, I think
everything else lines up, right? You're going
to get the pace going to come, right? You're
going to enjoy the projects you're working on.
You're going to find value in the output of the
team. Even if maybe you're not, you know, at
least initially fully engaged in maybe what the
company mission is. Yeah, 100%. I think working
with people you like makes things a lot, you
know, a lot exciting and a lot more fun. Sweet.
I think we need to tell the audience about LegalShield
because we're kind of the insiders. So tell us
what's LegalShield and what do we do? Why don't
many tell us about LegalShield? I think if I
meet somebody and I don't have a lot of time,
I just say LegalShield is, we're like a... We're
the Netflix of legal services. I know I'll get
into trouble for it, but the basic premise is
a subscription company for legal services and
identity theft services. And we're the OGs. We've
been around for 53 years now. This was before
subscription model was cool. So we've been at
it for a long time. Yeah, and I think you're
spot on, right? Whether you say Netflix, whether
you say Uber, whatever it is. So really what
we do is we connect, again, exactly like Netflix,
Uber, any of these other models. I don't even
have to talk about legal or law, right? We connect
a person with a want or a need to someone who
can solve that issue or answer those questions,
right? And we just do that for the legal industry.
A lot of times, who knows, do you even know a
lawyer, right? Or are you afraid of how much
of one costs? Or who do I call? Or my buddy's
a real estate lawyer, but I've got a bankruptcy
issue. I don't even know what to do, right? So
again, we kind of take that education and investigation
out of the equation. And we give you access either
through a mobile app, through a telephone, through
the web to, you know, contact a law firm that
has experts in the legal issues that you're going
to need assistance with. And it's as easy as,
you know, dialing a phone or pushing a button
on your app and we'll immediately connect you
to a lawyer who can answer the questions that
you have, right? Again. I just always explain
it as simple as, you know, we connect a person
with a need to someone who can solve the issue.
Yeah, absolutely. And again, I mean, founded
in 1973, I think many kind of hit on it. OGs
of the subscription model business. I mean, that's
fully in vogue today. Everyone wants to, you
know, to be in the subscription model business.
You know, we've been doing it and perfecting
it for, you know 53 54 years now yeah it's it's
it's amazing and especially the the cost we're
able to bring the cost uh down so much and you
you've worked with the provider services uh division
so much How are we able to get the cost down
to like $30 to $60 a month instead of $300 for
the hour? Sure. Why do we say it's like less
than a cup of coffee a day is kind of the tagline.
But then on the other side is like, okay, if
some of these best lawyers are out there charging
$300, $400, $500, $600 an hour, the question
comes back to us. well, hell, what does a $30
a month lawyer look like? And is that who I want
solving my issues? But you hit exactly on it,
right? It's the efficiency and the model that
we've built. And I really say it's our competitive
advantage. It's the moat around our business
is our network of provider law firms that we've
built throughout North America, the United States
and Canada. almost 40 years, average 10 year
with us. Again, these are full service law firms
throughout the United States and Canada. And
the model that we pay them on is really interesting,
right? They don't get paid based upon the services
they provide. It's a fully capitated model. You
have an area of responsibility. Every single
month, we're going to pay you per active member
or per active customer within your area. Regardless,
I mean, Mitty could be a member. He could never
use the service, but we're going to pay his provider
law firm every single month that Mitty remains
active. And that's kind of the power in numbers
that we've created. And really, it not only aligns
our company. but also the provider law firms
because retention matters, right? Mitty, you've
been deep into retention. In a subscription model
business, it's, you know, cost per acquisition,
lifetime value, right? How much do I have to
pay to, you know, create a new customer and maintain
that customer? How much is that customer going
to pay us over the life of the membership? directly
aligned with the providers who provide our services
and the company in order to maintain high customer
satisfaction rates. We know when we meet or exceed
customers' expectations, they stay with us longer,
right? And then honestly, the longer they stay
with us, the less they use the service. And that's
really when our provider offers begin making
a lot of money. Might have to put a little more
effort in up front to like, all right, Middy,
you made a good choice. We're probably going
to go above and beyond the first one or two times
you call and you need any assistance. But at
that point, you're sold, right? I mean, we do
a lot of net promoter scoring. I mean, Middy's
a promoter at that point. If we meet or exceed
his expectations on the first or the second time
he uses the service, he's a member for life.
He's most likely not going to cancel. And then
again, that's where the snowball of the number
of active members that we can maintain because
we're meeting our exceeding customer satisfaction
rates. You know, we all make a whole lot of money
there. And again, we've been doing this for 50
plus years. So I think we're getting pretty,
pretty good at it. Yeah, I think the company
has nailed two things, right? Like you said,
economy of scale, the number of people, and also
aligning incentives with everybody's incentives
together. So I think that those are key things
that the company has nailed. Can you hear me?
I think you kind of froze up on me, but hey,
and again, it's really that capitated model and
that... I couldn't tell if I lost you again.
I don't know what's going on. I don't either.
My internet seems to be fine. Is Zoom weird today?
I don't know. Let's keep going and we'll cut
it out. So, again, this is why I say our provider
law firms and our capitated model is that, you
know, competitive moat, right, that we've built
around the business. Who can start this from
scratch today, right? What startup can build
a network of provider law firms across the country,
you know, pay? based on active subscribers and
run the business, right? I mean, you can't start
that today, right? So again, we don't want to
sit here and rest on our laurels, right? And
just let a new startup with AI or something sneak
up and take over the world. But again, I think
we're going to see someone coming from a long
ways off just because of the... network of provider
law firms that we've built over 50 plus years
now. Yeah, the model only works once you have
massive traction and that's hard to reach. Yeah,
and hey, you're a data geek, data genius. Tell
me, what do you think, do we think people are
going to, either today or quickly in the future?
Are they going to get to the point where they're
going to trust and believe in AI giving them
legal advice, right? At what point does that
tip over, right? I would say today that probably
some people are going to ask some basic questions,
but I don't know how important of a decision
someone is going to make today if they talk to
an AI lawyer, right? But there is a... There
is a point into the future, right? Especially
as, you know, AIs gets, right? I saw something
the other day. The AIs are taking IQ tests and
they're, you know, getting up close to genius
level. So at some point it probably works, but
I don't know when that tipping point or if there
will be, right? If I'm making, you know, really
important life decisions that are based on. you
know, legal opinions from lawyers, am I ever
going to be comfortable talking to a machine
as opposed to, you know, sitting across from
a real person? That's the one challenge I see
potentially in the future of the business that,
you know, we're kind of employed with today.
When do we employ AI more deeper? And if we don't,
Is that where someone's going to sneak up on
us and take over? Yeah, that's the billion -dollar
question for companies and legal services. I
feel some of the lower -hanging or low -effort
things will be automated and people will start
trusting it if it's standard enough. So I'm trying
to start a company now. There, it's all the same
thing. It's the same forms, same this thing.
I just don't know what it is. So for that, I
could probably rely on AI to just tell me what
these mean. So if it's like a very standard traffic
ticket, I need to just do it. Those could be
potentially automated, but legal issues are so
complicated most of the times that... Even lawyers
have to be up to speed on new laws and stuff
like that. Even further, it's up to interpretation.
Especially a lot of these trials, it's what lawyer
is arguing the best. So it's like, are you going
to have two AI machines in the room arguing with
each other? I 100 % agree with your point, right?
If I'm filling out forms, maybe the AI can ask
me a set of questions that pull out the answers,
help me fill out the form. But I think at the
end of the day, if I've got a multimillion dollar
estate and I'm doing a will and a trust, I don't
know what AI has got to prove to me before I
would be comfortable just... totally agreeing
with what it said, hit print and go, right? Yeah.
And large language models need to have seen the
situation enough to be able to answer, but every
situation is so different that it's hard. And
you never know when it's making stuff up and
when it's not. I mean, I'm in the industry. I
work on AI, but... There are limitations to it
and we need to address those. It's really cool,
the technology, but there are things that I don't
think we can use AI for everything at this point.
We always hear the guys talk about AI is not
going to take your job. The guy using AI is going
to take your job. I think at some point, All
of this is going to be solved, right? So at what
point in the future, and again, you've created
lots of different AI models that answer a lot
of different questions. At what point in the
future do you think that we're going to solve
that hallucination issue, right? Because again,
you can ask the AI something today and it's confident
and it's going to tell you X, Y, Z, and it could
be talking. You could ask it about how to start
a fire and it's going to tell you how to make
an ice cube, right? But it's going to be confident
and lay it all out, right? But I think at some
point, again, as we keep investing more and more,
right? And again, I think this is probably even
a bigger question of who's going to eventually
rule the economies and maybe even the world,
right? China and the US, probably the two main
players, they're going to keep investing in this.
They're going to keep perfecting it. And whoever
figures it out the fastest first is probably
going to win, right? So I think there's a point
in time in the future that this probably gets
solved. I just don't know how far we are away
from that. Yeah, yeah. I think the trick is to...
build smaller, more niche models for specific
use cases. Because right now, the foundational
models from OpenAI or Anthropic, anybody, any
of these foundational models that are famous
are basically trying to do everything all at
once. It's the whole internet, it's an aggregation
of the entire human intellect. I think as we
build smaller models for specific use cases,
if I'm able to train a large language model just
to fill out traffic tickets, I can get that to
be like 95, 98 % accurate versus asking ChatGPT
today. Retrieval augmented generation, RAG, is
a technique that you upload a lot of text or
files or resources and say, just look at this
and nothing else. Look at this and answer from
it. And that'll give us a lot of accuracy, but
not flexibility. So it's a balance between flexibility
and accuracy. So I think that's a good way to
approach it for smaller companies that are wanting
to build niche. tools that are solving real problems.
I honestly think that's where we can actually
make real impact from AI. AI models, solving
real problems. This is really cool. This is like
the foundational models are like the internet.
It's everything for everybody. But really, you
need to build apps on top of it to solve specific
problems. And that's where I think there's going
to be a lot of startups come up that will solve
specific problems. Do you think the foundational
models at some point in the future will kind
of... attach on to the idea you're talking about
and instead of just trying to answer a question
over the entire soup of the online intellect
right they're going to start grabbing up all
these niche models that are built very specifically
but are very accurate and very smart and then
stitch those together in order to go back and
just do what they attempted to in the beginning,
you know, one ring to serve them all, right?
But it's made up of all these niche models that
have proven themselves individually. And then
maybe once, you know, those can all be put together,
it wins, right? Maybe that's how we solve the
hallucination issue. And again. Hey, man, I am
way outside my area of expertise. I'm a political
scientist here, so I'm just pretending to know
what I'm talking about. But I think you're right.
It's what's the models best tuned for, right?
And are the computers smart enough, strong enough,
have enough power, right? Because we've seen,
I think, even OpenAI talked about whenever they...
They pushed out this new image generator and
everyone was making the cartoons and all this
stuff. I think Altman came out and said their
processors were literally melting in the machines
and they had to turn it all off, right? So, you
know, maybe the functionality that we're building
into some of these models is... far ahead currently
of a lot of the hardware that we've even got
that we're trying to run it over, right? So it
seems like it's a race on both, right? The hardware
that we're building to run it and kind of the
software that we're developing that's getting
smarter and smarter. Yeah, it's definitely both
sides. You mentioned about, hey, R is, say, for
example, OpenAI going to build all of these little
niche models as well. I was thinking about it,
and I think we have an analogy to this in the
mobile era, right? Apple and Google could have
built all the apps. They knew how to do it, but
it wasn't in their... I think it wasn't their
expertise to... build all these apps, Uber and
even LegalShield app. So we are the experts in
LegalShield's use case. So we build an app for
it versus Apple doing a half good job at it.
So I think that's where I think there will be
a lot of startups that come in and build AI models
on top of the foundational models. And then OpenAI
can... Take a 30 % cut like Apple is doing today,
and then that's how they win, and that's how
the others win. Or is that how someone beats
OpenAI or Grok or Gemini or something, right?
Somebody with really deep pockets comes in and
just starts buying up all these niche models,
puts them together, and now all of a sudden I've
got the super AI, right? Yeah. yeah there are
super apps now wechat and china and they kind
of do everything all all at once so uh it is
possible so again it's uh and i wonder did wechat
build it all themselves or did they just start
buying smaller companies and you know piecing
them together that'd be interesting yeah i don't
know so it's a good good question to ask i think
yeah tencent may own i don't know Don't they
own TikTok? Yeah. That's the same company, I
think. It's super interesting. We don't know
what's going to happen, but it's fun. It is fun.
Again, it's kind of back to what we were talking
about earlier. Again, LegalShield, the company
that we're working for, we've got 53 years of
data. right and you know not only until about
10 or 15 years ago did we ever even get serious
about mining that information what's that data
telling us How do we run the business from a,
you know, data direction instead of just a feeling
or a thought, right? Because again, and I'm not
knocking either method over the other because,
you know, up until 2010 -ish, right, we were
founder -led, founder -driven, right? The guy
that started the company in 1973, he was the
CEO, he was the leader. charismatic leader. He
was driving the direction of the company. And
again, when you're in something from the very,
very, very beginning, you might miss some nuances
of the data, but you've built something. It works.
You've been successful. So I think intuitively,
you kind of know. what's happening in the business,
how to move, how to make decisions. And it was
really only after he stepped down did we really
begin diving into all the data that we had. Again,
you know, 50 plus years of information. And again,
and we had all that data, right? Because again,
the software and the systems that we used to
run the company were all, you know, in -house
built. Right. So nothing was outsourced. All
the data maintained on site. And at that point,
we kept everything right. Regardless of whether
we used it or thought we might use it or if it
was good data or if it was bad data. I mean,
we kept it all. So it was all there. So we really
had an interesting playground. And probably,
you know, a little bit before MIDI, you started,
we started going down this direction. But really,
when you. When you joined the team, I mean, we
kind of exploded in the intelligence, right?
And I like to say we create actionable intelligence,
right? We're not an analytics team that just
creates spreadsheets and just blasts out data
to the entire company, right? One, who cares?
And two, we ain't got time to read spreadsheets,
but what's actionable? Do some analysis, find
your data, have the data to back up what you're
saying. But the power, right, or the value is
in the analysis. And how can we apply this to
the goals of the company, right, or the direction
that we're going in? And, you know, not to pat
myself and you on the back, but, you know, I
think we got really, really good at that. And
we learned a lot of things. specifically around
the subscription model business and, you know,
retention of those subscriptions. Because, you
know, at the end of the day, if you're paying,
you know, $400 to acquire a new member and they're
paying a $30 monthly subscription, you know,
do the math, right? They have to stay around
quite a while before you begin to make, you know,
any profit or any real revenue on that person.
Membership retention is extremely important.
And you've been, I think, one of the pioneers
on, you know, how we've gotten as good as we've
gotten on, you know, retaining members, lowering
churn, increasing member satisfaction. And again,
all of that's come from data. And as we've gotten
smarter, you've gotten extremely versed and really
good at building these AI models, these chat.
chat models that are pulling out a lot of these
insights that, you know, I never would have gotten
to or it would have taken me three months of
just only working on that data to even find half
of what, you know, some of the new insights that
we've been churning out. So, you know, kind of
taking it from the very, very top, you know.
artificial general intelligence, open AI, just
down to what we're doing every single day. You
know, we're head and shoulders above where we
would have been if I still just had a team of
analysts, you know, chiseling data out of an
Excel spreadsheet. Yeah, appreciate that, Ryan.
Yeah, definitely. It's a really strong team,
right? Like shout out to Kevin Hardcastle and
Sam Harris, OGs of retention and yeah. Vanessa
Madden -Glass, all the good people. It's really,
like you said, I think it comes down to data
and technology in service of business to provide
actionable intelligence versus just, there's
a lot of teams that get bogged down in building
the best. technology for the use case the the
technology is so complicated and so fascinating
that people lose sight of the actual use case
for it yeah and hey i think you're hitting on
an extremely important point that i don't hear
talked about enough and i'd talk i try to talk
about it every chance i get right and it's exactly
what you're talking about that marriage between
technology and the business right when i'm out
looking for a new analyst or a team member or
i've got an opening on the team you know my inbox
is flooded with you know data engineers statisticians
you know i like the code i'm all about technology
Or I'll get a whole list of folks that, you know,
I've got an MBA. I love the business, business
administration. You know, I know front and back
the textbook on, you know, how to run a business,
how to do meetings, how to project manage all
this stuff. But really, I call them the unicorns
of, you know, analysts is how can we find someone
that's, you know, probably not. the top or the
best at both, but can do both, understand both
and, you know, like both, right? Because again,
for us to do our job well, we've got to be versed
in the technology. We've got to understand the
data. We've got to know what it's telling us.
And we also have to understand the business objectives,
what it is that we're trying to accomplish. You
know, what are the sales targets? What are the
churn targets? What are their cancellation targets?
And how that data speaks to us in a way that
we can implement what we learn into the business
to meet the objectives. That's kind of the person
that I don't see that's being trained out there,
right? I mean, someone can go to business school
and then maybe, you know, get a computer science
degree. But, you know, there's not a good way,
at least today, that I see in, you know, education.
that's creating that person that can do both.
And again, I think that's what a lot of companies
have trouble with or fail because then we end
up siloed ourselves, right? We've got the tech
geeks and nerds over here and we've got the business
MBAs with their ties over here. And these suckers
can't even talk to each other, right? Or they're
talking past each other. There's like, oh, they're...
I can't work with them. They don't understand
the technology, right? And then the business
guys go, they don't even know what it is that
we're in business for. They're talking about,
you know, pipelines and moving data and blah,
blah, blah, blah, right? But again, you've got
to find, if you can't find enough of those people
to build out the team, then you need to find
a handful of really good, right, to help bridge
that divide. And I think that's where. That's
where you create a really high -performing, powerful
team is when you have a core group of folks that
understand technology, data, and how the business
is run. And those people are hard to find, right?
And a lot of times, I think we kind of err on
building or creating that person, right? So,
I mean, I think when you're interviewing or talking
to a person, you can kind of see. Are they leaning
tech data? Are they, you know, a business acumen
type person? And I found it's probably, I mean,
as long as the person's willing, I think it's
easy to like teach the opposite side that they're
not good at if they're willing to do it, right?
Not everyone's willing to do it, right? I've
seen data engineers that I... I don't think they
care nor want to know anything about the business,
right? And the same way with business people.
I don't care about the data or any of the technology.
I just got to meet my sales quota, right? So
again, it's a special person that can do both
of those things and turn the data into value
for the business. Yeah, 100%. I think you got
to get it all together. Honestly, I think the
technology piece is being solved by technology
a lot of times. I could chat with ChatGPT and
understand and figure out technical stuff that
I'm not super good at. But I think as long as
the person is willing to go find out the answer,
I think we can get there. But yeah, it definitely
has to be a balance. So that just brings up another
point. And I've got a question for you. And this
is probably heresy for me to even mention this,
right? But one of the things that I kind of run
into is, you know, technology is great. All these
softwares and services and systems that, you
know, generate dashboards and data and reports.
So, you know, we use Tableau a lot. Great tool.
Wonderful. Saves my guys a whole lot of time.
We're not building Excel spreadsheets and dashboards
in Excel. It's automated. We've built the logic.
Updates daily. Beautiful. The answers are always
there. But then at some point, do you lose the
knowledge and the detailed understanding of what...
even that data is, right? And where is that data
coming from that's generating that dashboard?
And if something's a little bit off, how long
do you leave it kind of monitoring this dashboard
before you decide there is a problem to where
if you were like intimately in the data on a
daily basis, you would know like immediately
that something was off. So I struggle with this
problem, right? Because Our output is so much
better. The data is almost immediate. Everyone
can see it. People can self -serve. My guys can
probably be working on bigger and better problems
as opposed to creating some of these reports.
But then I also worry, and I see it, myself included,
I'm losing a lot of the fundamental. intelligence
and education of what some of that data is. So
I guess it's a trade -off, but at what point
are we so far removed from the base data that
no one can explain the business anymore? I do
worry about that. Yeah, no, 100%. It's definitely
a trade -off and it's like, oh, nobody on the
team knows. If something breaks, so we've kind
of built on top of foundation. If nobody takes
care of the foundation, maybe it's rotting and
nobody is looking at it and it's becoming a,
you know, building. Or, and it's not even, maybe
no one is looking at it, right? Three or four
people ago have built it. So I don't even understand
enough to know that. It is rotting, right? Or
there is a hole. I don't even understand the
logic that was used to build this because I don't
understand the raw data because the guy that
was in my seat six iterations ago built it. I'm
just making sure that I get a successful run
every morning. I don't know what's running anymore.
That does worry me. It's the same thing with...
computers and software, right? So you have the
assembly language, which is what really computers
understand. And then you have C, which is a higher
level language, which kind of makes it easier
for humans to understand, but it translates it
to assembly language. And then you have all these
other languages, Python, Java, all those things.
Now we have AI writing code, people are vibe
coding. So they just tell AI what to do, but
The thing is, it is still going down into assembly
language. It's getting converted into ones and
zeros, and that's how really computer understands
it. But something fundamentally breaks, nobody,
10 levels above, nobody knows how to fix it.
I think, I feel like... Companies need to be
aware of it and probably have one or two people
who are experts in that, value them and not just
go after the shiny things. And like you said,
the new things that we can build on top of the
foundational things, that's cool and important
to be more efficient, but really don't underestimate
the value of the people who actually the one
or two people who actually are experts in the
ground level. Yeah, no, I call that your institutional
knowledge base, right? You've really got to protect
that core. And again, I always lean back to folks
instead of the technology, right? It's great
that the tech stack's working and it's built
all these layers and it's wonderful. But again,
I think you need to protect. a core group of
folks that fully understand that. Maybe they've
been here since day one and they built the first
line of code on which everything else was built.
I think those people are invaluable to an organization
because they're the only people that's got that
chain of custody all the way back to the beginning
and fully understand what it is that the machines
are doing. They're the Hercules that are holding
everything together. I mean, the other side of...
Because again, just like we talked about earlier,
if and when we turn all this over to AI, the
AI is confident, right? Whether it's right or
wrong, it's going to tell you exactly what it
thinks and believes. We're going to read it.
We're going to start repeating it and telling
everyone else. And then... I mean, it gets down
to a point that it's like truth is relative,
right? I mean, whatever most of the people believe,
that's now the truth, right? Right or wrong,
that's the way it is. And that's, it's dangerous.
It is dangerous. And it's, yeah. I think that
the other side of the argument is, hey, have
really good documentation for everything. is
the other side, but it's hard to document it.
Have you worked or been in a company or seen
any documentation that you considered really
good documentation? I've seen good documentation.
I don't know that I've been in a situation where
I'm like, oh my goodness, this documents all
the way down to day zero. This is wonderful.
I completely agree. Documentation is good. It's
hard to do that correctly. Yeah, no, 100%. It's
hard to scale that culture of documentation.
And even then, like a person at level 10, it's
really hard for them to really, even if the documentation
is present, it's hard for them to really understand,
read the document and get to the core of the
problems. Sure, right? So it's like... the documentation's
written all encompassing and to know it all,
but then how do you write it in a way that a
brand new person can understand, right? I have
this issue daily again, because I've been with
this company for a long time and I catch myself
speaking to brand new to where it immediately
clicks in my brain is like, we're not even speaking
the same language. I think, I mean, I think I
know exactly what I'm talking about. And, you
know, to an extent I do. They think they know
exactly what they're talking about. And to a
different extent, they know what they're talking
about. But we're not communicating because the
language is so different. I know things that
they don't. They're bringing in new things that
I've probably never, never heard of. So, again,
even even if you've got the best documentation.
Who? who is the audience and you know is you
have to have different level you have to have
different trends trend translation layers depending
on who it is that's even look looking at it right
yeah i mean i think the i think you mentioned
it like the core of the uh solution if you will
is to protect the folks that are they they're
from day zero and understand everything it's
it's hard to everybody is looking at the new
cool things that those uh core people might feel
undermined but yeah it's as leaders it's our
job to make sure that they're protected and taken
care of hey but you know what The brand new person
may come in and know the best way to change everything,
right? We may have been doing it wrong all along,
and this new person's got the greatest and best
idea, right? But until or unless they understand
everything that came before them, I don't think
it's possible for them to improve upon a system
that they don't even understand, right? Theoretically,
high level, you may have them. the best new programming
language and i i can build this app and lay it
on top and it's just going to be beautiful but
i don't i do not think it can work unless there's
humility on both sides right the old person and
the new the old person's got to explain what's
went on and understand that there probably are
better ways to do it and the new person's got
to understand that hey this This may not be the
best way, but it's been working for a really
long time. And the more I understand, the better
I can improve it, right? And a lot of times you
just have an ego clash and nothing gets done
at all, right? Or things get done and they just
start breaking because no one took the time to
explain to the old and the new guard. There was
no meeting other minds in order to create something
better. Yeah, it's always the... I know the solution
mindset versus know -it -all mindset versus learn
-it -all mindset, right? It's hard. Yeah, I mean,
we can talk about this for forever, right? So
let's go back to kind of... Data at LegalShield
and you've pioneered the system of closed loop
infrastructure and data that leads into cool
stuff like Consumer Stress Index. And let's dig
into that. Sure. So kind of talk about the Consumer
Stress Legal Index or the CSLI, right? So on
any given month. We service 150 ,000 requests
for legal service, right? Again, that ebbs and
flows due to the month. But on average, we get
150 ,000 requests for service a month. And we've
been doing that again. We keep talking about
it a long time, 50 plus years. So then we got
to thinking, again, there's got to be some truth
that lives in this data, right? I mean, back
to January 2002, we've got 35 million individual
requests for legal service. So it's like, what
can that tell us? First, we started looking at
it in terms of our business, right? What can
we learn from this data to improve our service,
improve sales rates, improve membership retention?
And we got a lot of really good information out
of that, right? What age are the people? What
legal services are they calling about? What areas
of law can we then train our attorneys and lawyers
to talk to a customer where they're at, right?
You probably talk to a 20 -year -old about a
traffic ticket different than you talk to a 75
-year -old about updating their wheel, right?
And again, We like to say they don't teach customer
service in law school. And it's true. And again
and again and again, that's what we hear from
our customers is the lawyer was rude. They didn't
listen. You know, they were short with me. They
didn't take my issues seriously. Rarely do we
get, you know, complaints from our customers
that. The lawyer didn't know the law. They were
a terrible lawyer. It's all of these bedside
manner, customer service type issues. So we've
really began focusing on how do we improve those?
And we talked about it a little bit earlier.
We implemented net promoter scoring, NPS scores.
So every time any of our customers access legal
services, They get a survey and we generate a
net promoter score for that person. This is kind
of that closed loop feedback system you were
talking about. And again, as far as I know, it's
unique in the legal industry and the kind of
the legal services industry to have this. So
our customer access is legal services. They get
their issue resolved or not. They talk to a lawyer.
We surveyed that member. That survey then generates
a net promoter score. They can leave comments
if they like. We then grade that net promoter
score based upon the strengths and weaknesses
at a law firm level and even an individual lawyer
level. We grade individually, right? So every
lawyer that's working in a month, we... We identify
what their strengths and weaknesses are, right?
You know, we applaud them for their strengths.
Yeah, let's go. And we have a training system
for those specific weaknesses. Again, not identified
by the law firm, not identified by their partner,
not identified by LegalShield where we're just
picking on people. We're telling them what their
weaknesses are. as identified by the customers
or the clients that they've consulted with right
we're not making any determinations here the
actual people are saying what they felt and thought
about their interactions with a with a lawyer
and we're rolling that back to that to that lawyer
through you know we're reporting and telling
them and then we even have a lms system to where
we create specific trainings right we're not
buying trainings off the shelf right again based
on 50 plus years of data we we know the types
of training content you know length delivery
method we know those trainings that you know
create the best outcome when lawyers view them
right and then again we measure before before
and after you had all these nps score surveys
you were you were low in you know listening attentively
well here's a couple trainings we're going to
push out to you that's you know designed to design
for lawyers just like just just like you who've
taken these before whose scores in those areas
have have improved we drip out one or two trainings
to you you take them We're measuring your NPS
scores after, you know, and, you know, overall,
right, again, you got some bad eggs out there
that's probably never going to improve. But again,
the average overall, we see improvements from
before the training to after the training. These
scores go up in these areas. NPS scores go up.
We know that, you know, the NPS score is a leading
indicator of future membership retention rates.
As retention rates goes up, right, the active
customer base goes up, the law firm gets paid
more money because it's based upon a capitated
model. Again, everyone is aligned on, you know,
where are the weaknesses? What can we do to improve?
Improve, and we all make more money, right? That's
really the power, I think, of the closed -loop
feedback system that we've built. um strictly
focused on continuous improvement and again at
the end of the day it's all aligned we all make
more money in this in this company when everyone
is looking at the data you know what are the
actual insights how do we apply it to the business
and how do we improve yeah i think i think that
that's the key part right so uh what the what
the company and you and the teams have nailed
is the alignments of incentives. I think it's
an easy proposition. Hey, we've churned through
the numbers and look what we could do to improve
your bottom line. Or it's easy just to pick on
a lawyer and say, you suck. All your members
hate you. You're terrible. But again, we're kind
of looking at it from the carrot. version instead
of the stick right i mean okay you've got you
know lower lower scores here we have a system
that's been proven to improve that score right
watch some of these trainings think about what's
going on and improve right so we're fully focused
on increasing that net promoter score as opposed
to just firing people you know giving them bad
scores and saying you're terrible improve right
that's not what we've done we've created the
improvement. We're helping them improve. And
again, I'll just say, like overall, you look
at Clio, I think the average NPS for the legal
industry is, you know, depending on, you know,
when you look, it's anywhere from, you know,
27 to 30, 32, depending on the report. You know,
on average, every single month, our NPS score
is between, you know, 54 and 58, right? Double,
again, double. the NPS score of the legal industry.
And again, I think it's because we're focusing
on these customer service related aspects, right?
Meeting or exceeding the members' expectations
instead of just like, we're just spitting law
out at you, move on, churn to the next one, you
know, billable hours, right? Again, our model's
not based upon. average billable hours right
we're going to bonus people because you know
oh my goodness you you build 48 hours today there's
only 20 we're we're not even in that model and
i think it shows again because you know our customers
our members have double the net promoter score
of the legal industry as a as a whole and i think
that's telling due to the way that you know I
think we've revolutionized the practice of law.
I love it. I love it. Yeah, I mean, I think it's
the, like you said, carrots versus stick approach.
And it's not a... It's a culture of learning
and improvement versus culture of fear. Oh, I'm
going to get fired by legal shield if I don't
improve. I think that's never going to happen.
Lawyers are, like you said earlier, our lawyers
are really solid law firms. We don't doubt their
ability to understand and implement the law,
but it's just the bedside manners and some stuff
that... Customers are telling us that we're just
relaying the message over to the lawyers. And
you know what? Hey, maybe the way we provide
service isn't for all lawyers, right? Again,
it's that customer service aspect, it's that
bedside manner that we've shown, not only that
we think, that we've proven time and time again,
increases the entire... you know, interaction
between a lawyer and a member. If I'm your lawyer
and you think I'm listening and, you know, and
I'm consoling and I'm really understanding what
you're saying, I'm letting you have your piece,
I'm, you know, we're feeling it, right? We're
jiving. Even if I, you know, lose your case or
I don't have good information for you or I can't
think of a way to help you solve it the way you
You want it solved? You still feel better at
the end of that interaction than if I cut you
off, if I was short. I just said, no, no, you
don't have any defense. You're screwed. Move
on, right? So we see great scores and comments
from members who lost their freaking case, right?
But they had a good interaction with the lawyer.
The lawyer cared. The lawyer listened. The customer
thought the lawyer did everything they could.
It was just, you know, bad, bad set of facts.
Right. I lost, I lost, I lost this one, but you
know, by God, they were on my side. Yeah. I mean,
this may be a pro tip to lawyers everywhere,
right? Because the large language models can
nail the customer service aspect really well.
Is that how people, AI wins over bad lawyers
with bad bedside manners? Because the AI systems
will make people feel heard and listened to and
all that. So I think it's really key. And it's
the case everywhere, right? So even as data people
or lawyers or any person, even for doctors, I
think... Picking up on soft skills is going to
be key going forward in the age of AI and as
technology improves. Yeah, it is for sure. But
hey, I think I kind of went off on a tangent
there. We kind of started talking about the stress
index, and then you mentioned closed -loop systems,
so I kind of thought I needed to explain kind
of that service model first, but kind of back
to the... consumer stress legal index again 30
35 plus million requests since the year 2000
150 000 requests per month um we got to thinking
there's got to be value in that data right it
has to tell us something this is a large enough
data set right so maybe it not only tells us
something about our business but about the the
world at large, right, or the United States,
the North American economy. And that's really
when we kind of stepped out and we began thinking,
you know, are there any indicators in our data
that can talk about any of the financial economic
conditions of the, you know, North American household?
And we did a lot of tests in the beginning. And
what's interesting, we kind of landed on the
University of Michigan does a consumer sentiment
index and the conference board does consumer
confidence index. And both of those are kind
of held up as like the high most important reading
in kind of what the consumer is feeling out in
the market. And then how that talks to and impacts
the overall economy. And what was really interesting
when we, again, we did a study of our data. all
the way back to 2002, right? So that's what,
23 years now? We saw an extremely high correlation
with the conference board's consumer confidence
index, even up to a, we're a 45 to 60 day leading
indicator of what that consumer confidence index
is going to read, right? Extremely powerful.
So, and then really what that says is, We like
to say we've got hard data, right? Folks calling
in, requesting services for specific issues that
we've shown there's a high correlation to economic
stress and economic confidence out in the market.
We've got hard data versus what I call soft data,
which is, you know, survey responses, sentiment
data. So we're not going out and asking people
what they think and how they feel about things.
We're measuring, you know, specific actions and
then extrapolating that onto the economy and
saying, we're seeing consumer financial stress,
you know, go up. It's getting worse or it's going
down. Things are getting better. Yeah, I think
this is how legal shields, proprietary data can
be tied to what's happening in the world, right?
Sure, because I like to say folks don't call
lawyers for a good time. They call because they've
got a problem, right? And when we're seeing issues
like calls for bankruptcy increase, calls for
foreclosure increase, consumer finance issues,
billing disputes, collections, over the last
three quarters, we've seen a slow but consistent
increase. And the Consumer Stress Index, which
tells us that, you know, stress is increasing,
right? Folks are continuing to, you know, feel
squeezed by increasing prices, higher interest
rates. You know, we've had high inflation for,
you know, multiple years now. That compounded
upon itself is really squeezing the household
budgets. And we're seeing that again daily. and
the requests for legal assistance that we're
receiving for a lot of those, you know, kitchen
table, consumer finance issues. Yeah. I think
it's, it's, it's really interesting. And also
I think, I think we need to clarify, maybe make
sure we know we inherently understand, but when
we're talking about churning all these data,
looking at what, what customers are talking about,
we really, We really have high -level data and
insights around it, but not anything related
to client attorney privilege where we don't,
as LegalShield folks, we don't have access to
it or we don't look at it. Sure, right. We only
look in aggregate at the highest level. The law
firms themselves attach what we call an area
of law. to a specific request. So we don't know
any of the details or the substance of any of
these requests. We only know the category or
the area of law that the law firm assigned to
this specific request. You've been really humble
not to bring these up, but you've been killing
it with doing a media spree with Forbes and Wall
Street Journal. CNBC or a host of other things.
I think you were brought in as an expert to talk
about how, like what you said, how our Legal
Shields data relates to what's happening in the
world. What are some of the insights that we're
seeing from the data? Sure. And it's just kind
of what we've been talking about. You know, you
kind of mentioned, oh, we're getting some media
attention. Yeah, I like to say this is about
a 10 -year overnight success, right? We've been
doing this index for a long time. You know, it
almost feels like no one was paying attention
to it. You know, maybe they were, maybe they
weren't. But, you know, certainly recently, kind
of beginning with the, you know, election in
November through current. We've been lucky enough
to probably over the last 10 plus years kind
of prove that our data is legitimate. It does
have these leading indications and it is valuable.
It's up to the moment of the economic of the
American household. And again, what we're seeing.
The Consumer Stress Legal Index is kind of the
high overarching index, and it's composed of
several sub -indices, the Bankruptcy Index, Foreclosure
Index, Consumer Finance Index. Those three roll
up to the Consumer Stress Legal Index. And we're
seeing, you know, increased bankruptcies right
now. So when the pandemic hit, we had a lot of
government in. intervention that kind of stopped
payments on some bills, pushed out a lot of money
to a whole lot of folks. So we saw bankruptcy
filings, as reported by the U .S. government,
go down significantly. And so did our index.
But what we're seeing now is that bankruptcy
index is back up to and moving above those pre
-pandemic bankruptcy levels. And what's really
key here is that our index is a two -quarter
up to a six -month leading indicator of U .S.
bankruptcy filings, with almost a 0 .96 positive
correlation, which you just don't find that out
in the real world with data, right? But when
we look at the U .S. reported bankruptcy filings
and line that up with our index, again, we're
a two -quarter leading indicator, which leads
us to believe through summer into the fall, we're
going to continue seeing increased bankruptcy
filings, right? And again, we talk about bankruptcy.
I mean, folks just don't wake up one morning
and start thinking about bankruptcy, that this
is a process, right? I've had prolonged what
we're going to call consumer financial stress
over a long period of time. And that's, it has
been showing up for probably a year now. Again,
billing disputes. collections issues, consumer
finance increases in requests for service. As
those have been increasing, we can go all the
way back to when we began increasing the interest
rates that have been so low for so long, we can
see consumer financial stress begin to creep
up. Again, it's because I think we saw reports
that The delinquency rate on paying the mortgages
are going up, right? Revolving credit card debt
is as high or higher than it's ever been before.
And as interest rates remain high, those monthly
payments are taking a bigger and a bigger and
a bigger bite out of whatever salary comes in,
right? We like to say, when we talk to our lawyers,
they tell us, folks are concerned. They don't
have any money left at the end of the month.
Right. But I still have a stack of bills. So,
you know, which bills should I pay? Which ones
can I push to the side? What's going to happen
if I don't pay? Right. We've all of these questions
kind of lead up to now we see the bankruptcy
index increasing. But we've been reporting this
all along. It's like, hey, stress, stress is
rising. Folks are having having issues. But,
you know, once we see that bankruptcy number
tick up again. higher than what we saw pre -pandemic
before all the government intervention that it's
telling yeah it's yeah it's hard and it's kind
of depressing at times that you know we we know
some of these we look at this data and okay this
is going to lead to something bad or people are
hurting and we know that out of any of these
means extreme uh indexes can can report on those
are all usually uh lagging indicators well and
then we like to i mean again i may be pontificating
a little bit here but i think we we try to stay
out of narratives and politics and positions
because again depending on where you turn tune
in to get your to get your news oh the economy's
going great oh or oh my goodness It's blowing
up. The tariffs are going to increase prices
through the roof. No one can afford anything.
And then the other side is like, oh, this is
the greatest thing we can do. Bring back. I mean,
I'm not even commenting on any of that stuff.
Right. Again, all that we're commenting on is
every single day we, you know, field requests
for help from people with issues. And then we're
categorizing those requests. And again, over
20 plus years now, based upon the request we
see, we know where that leads, right? And we
can foresee what those mean, right? So again,
that doesn't care what color, red, blue, what
your affiliations are, has nothing to do with
this. We've got 25 plus years of data that says
when we see X happen, we know that Y is extremely
likely. And right now, we're saying stress has
been high for a while now. Our bankruptcy index
is a two -quarter leading indicator of filings.
We expect bankruptcy filings to continue to increase
through summer into the fall. Yeah, yeah. I think,
yeah, to your point, I think we, regardless of
what caused these things, we're just looking
at the data and saying, okay, this is happening,
so this is going to lead to... X, Y, Z. Yep.
Makes a lot of sense. And then just real quick,
they don't get quite as much play, but other
than the Consumer Stress Legal Index and kind
of those sub -indices, bankruptcy, foreclosure,
consumer finance, we also have a Housing Sales
Index and a Housing Construction Index. And again,
they track extremely closely and they're a 30
to a 45 -day leading indicator of... housing
starts and existing home sales, right? And again,
so we're seeing kind of flat to maybe even a
little bit lower on both of those indices. We
can kind of go back to the increasing consumer
stress and kind of lay those over, right, folks?
A lot of people just aren't looking to, you know,
upgrade their house, add to their monthly obligations
in terms of bills, right? And then on the housing
construction side, As these tariffs begin to
pile up, especially when we're talking about
Canada and Mexico, where we get a lot of lumber
and a lot of steel and a lot of sheet rock, as
those tariffs are layered onto those construction
materials, builders are having difficulties.
Should I pay this much more to build a house
that may or may not be sold because... Consumer
financial stress is up and, you know, delinquency
rates on mortgages are already high. You know,
can I afford to invest this much more into building
a house that may or may not be purchased, right?
So again, it's kind of a trickle down, right?
When the average American consumer household
is feeling stressed over a long period of time,
all of these other, you know, the housing industry
certainly takes the hit on that too. Yeah, no,
I think it's all, it's really cool that you're
able to kind of build these indexes that are
kind of related to each other. I mean, in the
economy, everything is connected to each other,
right? So it's really cool to see that interplay
as a data person. But I mean, the results are
kind of bleak sometimes. The data is cool in
and of itself and building an index that correlates
with something out in the real world and then
moves. And then you can say, oh my goodness,
this is a leading indicator. That's really super
cool, right? But then when you can use that data,
again, every single day we talk about actionable
intelligence. The data and index is cool, but
so what? What the hell? Who cares, right? Until
or unless you can apply it. to something in the
real world. And I think that's really why a lot
of the kind of the major financial news outlets
and medias are finally starting to see us and
want to talk to us because we have created valuable
data and we are talking about how it applies
and how it's affecting the real world. Yeah,
100%. I think, yeah, if we don't impact either
the business or outside world, we're just...
playing with toys in our garage it may be really
cool toys but if it's not that that's not what
we're getting paid for to build cool toys right
yeah it's fun and it's cool but who cares at
the end of the day right you have to apply it
to something right right 100 percent um cool
um i think we can probably talk about your um
i think your career as a councilman. We've got
a really cool story about the election campaign
and how you did that. Please tell us about that.
Sure. Again, I'm kind of an analyst on the side,
always interested in politics. Can I run for
a local election? How do I use data to do this?
Heck, I don't even know what the year was anymore.
04, 05, 2003, maybe. I'm not sure what it was.
So I decided I was going to run for city council
of the local small city we lived in, Ada, Oklahoma.
Ran for the first election. Most folks didn't
know who I was. Ran against an incumbent, got
my butt beat. I was upset. Was just going to
throw it all in. But the lucky thing was the
elections lined up to where you had to run for
a specific ward within where you lived. And there
was an at -large seat, which anyone in the entire
city could run for this at -large seat. So it
just lined up to the fact that the... At -large
seat was up for election, and that's what I ran
for and lost. The very next election cycle, the
next year, my ward was up for election. So luckily,
I stopped feeling sorry for myself shortly thereafter
I lost, and I just rolled the campaign over.
I was like, this is what I want to do. I can't
let one loss beat me. So I immediately began
running for the seat for the ward that I lived
in. just rolled the campaign over. And that's
when I really got serious about, I don't see
a whole lot of people out campaigning other than,
you know, they're putting yard signs up and then
they may go to the rotary meeting, you know,
in the morning or whatever, but that's it. So
I got really interested in, and at this time,
I don't know that. Instagram was that big. There
wasn't a whole lot of TikTok stuff. It was mainly
Facebook was the main medium of social media.
So I got really interested in and began researching
Facebook ads, right? And again, no one else,
at least at that point in time, I haven't looked
at it recently, but I can geofence my Facebook
ad to the city that I was in, right? So Ada,
Oklahoma. And at this time, no... I think there
was one other guy that had like an oil business
that was running some Facebook ads in the area.
So I got on Facebook, you know, built up a couple
of small, of small ads. I had no competition.
So I was paying like half a cent per impression.
I mean, basically free for the, for the volume
of people. I mean, I think eight is probably
30 ,000 people on a good day. I'm not sure what
the population is today, but I was paying per
impression. I had zero competition. I finally
called the other guy that was running ads, and
we kind of talked about how we could adjust our
metrics to where we weren't competing or bidding
against each other. So again, basically free
ads. I did the yard signs, you know, I walked
door to door. But I think the biggest key was
no one else was running those Facebook ads targeted
to my area. So I was just basically just blanketed
everyone that logged into the social media app
with my city council ads. So again, use data.
I went to the... You know, the state election
board, they they will give you the full download
of like the entire voter history, voter registration.
When's the last time someone someone voted? When
did they register? So, again, because, you know,
I didn't feel like I could walk and talk to every
single person or I didn't want to spend so much
money to mail every single person on this on
this list, especially those who weren't going
to vote. You know, then I began sorting the list,
right? Who's most likely to vote? And then I
created a walking and a mailing list on those
people that, you know, I knew had an extremely
high likelihood to vote in that election. Also
kind of, you know, tweak some of the targeting
on the Facebook ads to go after these specific
people. So, hey, long, long story short. The
person I was running against, I think they'd
been on the council for 30 -something years.
I'd talked to a lot of, you know, elders in the
city. They're like, hey, man, we're happy you're
running, but you don't have a shot. No one's
ever beaten her. Like the president of the bank
had just run against this individual and lost.
So everyone's like, hey, you know, good luck,
but you don't have a chance. So, hey, long story
short. To this day, again, off -cycle election.
There was no presidential election. There was
no other races on the ballot except me. There
was two city council elections. To this day,
largest turnout on an off -cycle Ada City election.
I think I probably got the largest amount of
votes for a single candidate, you know, more
than doubled the votes of my opponent, who supposedly
could not be beat, right? And again, I don't
think it had anything to do with I was going
to be a better councilman than this person. I
don't think probably, you know, that many more
people liked me than liked them. But again, it's
the power of using data. Having a goal in mind,
collecting the information, analyzing it, creating
actionable intelligence, applying it towards
a goal, and just going through the process. I
mean, really fun, and it probably cemented in
my mind the power of data and what someone can
do with it if they can harness it, right? If
you can figure it out, and if you can harness
what you have. Anything is possible at that point.
Yeah, you're kind of the OG of data and politics,
especially social media. Yeah, I wouldn't say
that. Maybe social media, political campaigning,
right? And you know what? Probably, you know,
it was really fun, loved every second I was on
the council, but I probably realized that, you
know, I like the campaigning and the strategy
better than the... governing, right? Again, wouldn't
trade it for a second, but I was super pumped
when I won and it was exciting. And then a day
or two later, I'm like, what have I done? What
have I gotten myself into? It's two entirely
different job descriptions. It is, right? And
honestly, politicians get a bad... bad rap and
a bad name and you know a lot of times deservedly
so but kind of that campaigning politician that's
a total you have to have a totally different
mindset from campaigning and selling yourself
to actually governing right because this big
ego person over here that says i'm the best i'm
better vote for me that's not always the best
way to govern and you know lead yeah how do you
switch The big ego, big charisma to a servant
leadership mindset. Absolutely. And again, I
think that's why a lot of politicians get a bad
rap is they never make that transition, right?
They stay in this, I'm the best, big ego, look
at me. And they don't move over to, I'm here
for you, right? I'm serving you. Yeah, not everybody
can get that. But I think your story is, Maybe
a lesson for me and everybody in the data space
is, like you said, how do you... I think you
bring it all together, right? So you went in
and deeply understood what the... issues were
you looked at the data gathered the data understood
the data and then implemented created actionable
insights from it and then implemented it so it's
it's all of those aspects have to come together
to make a business subjective business goal achievable
or you know in in politics or business or any
anywhere And any one of those on themselves would
fail, right? I mean, if I'm just really good
and love data, great. I mean, I make myself happy,
but what do you improve, right? So you've got
to take it one step at a time. What can the data
do, right? How can I use the data to do X or
Y, right? That's the question we should all be
asking ourselves is, what can I learn from the
data? How can I implement it? results cannot
create. Yeah, yeah. That's really cool. And then
I mean, fast forward from like, like you were
saying 2004, five to now, a lot of people have
our data savvy, a lot of political campaigns
are have figured it out. But we have a new era
of AI is have you? Do you have any opinions on
what? What? kind of role AI would play? I know
we've heard about AI being used to doctor videos
and stuff like that, but is there a productive
way AI can play a role in politics? Sure, I'll
just say supplement, right? None of what we just
talked about is replaced by AI, right? AI can
only make it better or make you better at it.
So at the end of the day, It would be interesting,
I guess, if AI put themselves in to run for office
and they could actually win, which they may be
able to, right? But again, I think it's all about
supplementation, not replacement. And again,
you're working on a whole lot of projects. It's
kind of on just that specific idea. in and of
itself right how can we use ai to improve a process
to improve learning not to replace you know education
and teachers same way with same way with politics
if i don't have a map built in my head that says
i want to win this office right so then i build
the plan back to i've got this data What can
I learn from the data? Who can I target? Who
do I talk to? How do I get out the vote in order
to win? And what does that data tell me about
each of those things that I can implement to
make those things happen? AI is going to be worthless,
right? I mean, I guess I could go to chat GBT
and say, I want to win a local election. Give
me the steps. Right. And you know what? It'll
probably build you a high level process. But
again, you still got to understand. not only
the area that you're running in, right? Who are
the people? Who are the audience? How do I talk
to those people? How do I talk to different people,
right? I've got a singular message, but, you
know, I'm probably going to talk to a businessman,
tech PhD middie. I'm probably going to talk to
you different than I talk to the, you know, garbage
man, right? Or the guy that comes to replace
the shingles on my house after. you know, the
two tornadoes go through the local community.
I mean, you've got to understand your audience
and talk to them differently. And I don't think
AI is there yet, right? So again, AI supplements
what we do. It doesn't replace any of the hard
work to actually, you know, win an election and
then, you know, govern appropriately. Yeah, no,
100%. And I really feel that it... It will, and
it is making each of those steps a little more
easier, a little more efficient, but you cannot
skip over those steps and say, oh, let AI take
care of it. That's not happening or will happen
in the real world for a long time. And I think
you hit it right. What it probably does is it
shortens the learning curve, right? Because a
brand new person who's not studied, who may not
know it all, may not know all the right steps
to get from, you know, A to B, right? And I don't
even know what they are. So it's going to be
hard to get where I'm going without a map, right?
To me, and I use AI a lot for this, is like,
what is the map, right? I'm here today. This
is my goal. I want to do this. Kind of what are
the steps in between, right? I may know half
of them. I may know three quarter of them, but
there's probably one that I'm forgetting about
that might just make it all possible, right?
So again, I think using AI is all about supplementation.
Yeah. I mean, I feel like to add to your point,
AI is like a supercharged. So you've got your
specialists and generalists. AI is kind of a
really good generalist that can get you to. 80
in any step but in order to get those 20 additional
20 you have to be uh you have to have people
who can design and we were talking like you you
mentioned about us discussing about um supplementing
uh human experiences with ai designing ai for
augmented human experiences versus just replacing
it i think that's where i think a lot of uh a
lot of money is put into, hey, let's just replace
all developers. Let's just replace all lawyers.
Let's just replace all teachers. I think that's
the, in my opinion, it's the wrong way of doing
it because I wrote an article the other day.
It's like, we've got, even before AI, we've got
all these automated tools with, say, in customer
care, we're closing down on more tickets than
ever. customer care tickets but everybody is
frustrated we're we're closing uh health in healthcare
people automated systems of people have automated
a bunch of processes but patients are frustrated
they don't feel like they're they're it's a human
experience anymore so if we really have to I
mean, I hope there are some investors and VCs
and people who are thinking about investing in
technology that will not just replace, but even
with EVs, we saw that, yes, there are early adopters
that went all in on EVs. Over the last couple
of years or a year or two, we were seeing that
everybody that wants to buy an EV, which is 7%,
8%, have got an EV. And then the market's kind
of saturated now because people want that middle
ground. A lot of times hybrids are taken off
a lot more than EVs. So people need to be thinking
of those transitional times where... Maybe it's
not even a transition time. It's how it's always
going to be that AI is going to augment human
experience. Sure. And I kind of think of it,
and I just kind of had this thought as you were
talking, I kind of think of AI in terms of a
mentor, right? Kind of back to the political
discussion, right? Maybe I don't know anyone
that's ran for office before. Maybe I don't know
any current office holders who... could impart
all this wisdom on me right or could counsel
me or could talk to me or help me learn if i
don't have that person i think ai is a you know
a decent replacement right i don't i don't i
don't think they can replace the discussion we're
having to like make new thoughts shoot off in
my head but it could certainly you know prove
and be used as a as a mentor to help me learn
kind of hey go down this path right it'll tell
me all these things i was like oh i hadn't thought
about that i need to go research and think about
that one yeah it's a it's a great it's unbelievably
great research partner right i don't have to
be an expert in anything to start learning and
getting to 70 60 70 there It's immediately, so
I've got that in the palm of my hands. I don't
even have to go read up on what others have written
on it. It just gives me the answer. So that's
really powerful. So I think AI will generate
a lot more generalists. Somebody was saying there
will be billion -dollar businesses with just
one person running it. It's entirely possible
because they can streamline each part of those
steps. Like you were saying, each part of those
process can be made super efficient, but you
cannot replace all of it. So instead of maybe
having to have like three, four people that can
have one person do it, but that one person have
to really be dedicated to use AI and have to
know. how to leverage AI for all of those different
aspects. It's interesting times. I don't know.
Are there any AI tools that are your favorites?
are your go -tos for every day? I was an early
chat GPT guy and I've just stuck with it, right?
I mean, occasionally I'll play with a lot of
the other tools, but it's almost like I've invested
a lot of time into kind of learning how to talk
to the open AI model. And, you know, now it kind
of knows what I'm asking and I know how to phrase
things to. get the output that I'm looking for.
So I just kind of stick to it. Yeah, I like it.
I think we're living in a... Have you seen the
movie Her? Yes. You can also talk to it now.
And it remembers everything. So it's eerie, but
also cool. It's eerie, but helpful. I go back
and forth on whether I should have it remember
all the... things that i've talked to it about
but i also get frustrated when it's like i've
asked this before and you know we've had this
discussion and now you're back to not knowing
anything right so i go back and flip that memory
back on yeah yeah i mean i've turned it off i'm
i'm a little uh scared of it remembering everything
also i think i feel another point is if i'm okay
saying the same things again and again because
it'll start hallucinating if I have a big like
a thousand chats going on and it needs to sift
through all of it. It'll start hallucinating
and the results are kind of less accurate at
that point. I agree. I agree with that. What
I found is I think there's a new feature on there
where you can break your discussions up into
different projects. Yeah. And it can like and
it seems to do better keeping into the train
of thought and understanding everything instead
of like it having to sift through every single
thing you've ever talked about. It seems to focus
in on the output of that specific project. And
I think that's I think that's helped it compartmentalize
and know what the response is ought to be. Yeah,
100%. Yeah, I think I've been using that as well.
Create those specific projects that, hey, I want
you to remember these things and nothing else.
So that's really helpful that it knows about
the specific things I wanted to know. Yeah, that's
really cool. Cool. So we do have this question
that we ask everybody. If money and resources
were not constraints, what kind of... uh data
or ai models would you wish to have um access
to or you want to build if there's if you had
a magic wand and you had access to everything
goodness what kind of answers do you do you get
on that one right hey i think i would say just
probably we've talked about all the things right
but just a better model at the things that we've
talked about here, right? So, I mean, that may
get us, I mean, we may be talking about, you
know, AGI, right? How do we create a model that,
you know, with all appropriate security mechanisms
in place, right? I mean, you kind of... It gets
difficult for a model to know everything it needs
to know about me that can converse with me and
understand and like answer my questions and really
be a sidekick, assistant type thing, but then
also not share that information out to others,
right? So that's probably if I could get a self
-contained AGI model. but I was fully confident
that it could know everything and there was no
security issues about that data leaking out,
basically create an AGI super assistant. That
would be it, right? Because again, I'm multiple
times more productive today based upon the AI
models that are out there now. If that was like
10x or 100x, and I didn't have to worry about
any security concerns or leaking my data, that's
probably exactly where I would go first. Yeah,
you would store all the information about your
own life there, and so it can be just accessed
immediately. All of it, right? So maybe it's
like... I forget what it's called in cosmology,
but they're like, where are all the aliens at,
right? And it's like, well, we're not going to
find them because they've already uploaded themselves.
So maybe it even gets to the point to where,
you know, if I could download my consciousness
every night into this AGI model, I wouldn't have
to tell it anything. It would know all that I
know. And it's not going to forget and it's not
going to procrastinate. And then I can just,
we can both have our list of, you know, tasks
for the day. We may a thousand X our productivity.
Yeah. Yeah. There's no, there's no stopping.
That's awesome. Yeah. Thank you so much, Matt.
Appreciate it. It was always, it's always a good
hang. No, thanks for having me on. We need to
do this again soon. Absolutely.