Psychological Targeting for Small Business

Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Algorithms and data collection is on everybody's mind right now,
from the algorithms being used by doge to the algorithms
that we know Facebook and other social media are using.
How do we actually discover how to use them in

(00:20):
our own businesses, how do they actually drive behavior? And
what tools are available to us as as a smaller
businesses and entrepreneurs to drive that behavior. And we have
with us today an expert in this field, Sandra Matts
from Columbia University, and she is the author of mind Masters,

(00:43):
The Data Driven Science of Predicting and Changing Human Behavior.
That's a mouthful, but that is that is It is
a fascinating area. We've all we all experience it every day.
We know we're getting news feeds that are specific to us,
We're getting we're getting advertisements that are specific to us.

(01:03):
And what we all want to know is, Okay, how
do they work and what are they and how do
we and how would we actually use them? Because if
other people are using them again with our customers, we
need to be using them with our customers or our
prospective customers. And Sandra, I know you've got a ton
of experience in this area. Can you give us a

(01:24):
little of your background and what drove you to this
particular study.

Speaker 2 (01:29):
Yeah, no, absolutely, Well, first of all, thank you for
having me on the show, Tom. So my background is
actually in psychology and computer science, So in a way,
I work in these two worlds that almost seem a
little bit antagonistic, right, So, the messy, complex world of
human desires, feelings, behaviors, preferences, and then the other one,

(01:52):
which is like the somewhat cold and structured world of math,
algorithms and data. And for me, the interesting part is
actually aying that you so the idea that we can
now understand human psychology, human preferences, human behavior, not just understand,
but actually predict and ultimately change by using tools from

(02:13):
computer science. So instead of sitting you down as psychologists
traditionally did, to give you a questionnaire and ask you
for your self perception, or invite you to the laboratory
and have you press a couple of buttons on a
computer screen, we can now study daily behaviors, routines, habits,
preferences just by observing the data that you generate as

(02:34):
you interact with technology. And that's pretty broadly construed anywhere
from what you post on social media to your credit cards,
wipes your browsing history, the data gets gets captured by
your smartphone, so really using this data as a window
into your psychology.

Speaker 1 (02:51):
All right, so let's talk about the background here, which
we're talking about psychological targeting, right. How do you target
to somebody's I'm going to say, targeting their psychosis, right,
which is kind of what we're targeting, right, So because
we're going, Okay, what's going to influence this person? And

(03:11):
how are they going to get influenced and what does
influence them? So what is it? I mean, as at
its most fundamental base? What is it? Sandra?

Speaker 2 (03:21):
Yeah, So I wouldn't I wouldn't go as far as, say,
like focusing on psychosis because it's really, for me, it's
about trying to understand people's motivations, desires, preferences, and needs
based on data. So for me, psychological targeting has these
two steps. Number one is how can what can we
learn about the people who are on the other side

(03:42):
by observing their behavior? Right? And in a way, that's
something that we all do as humans. We all kind
of intuitively and automatically and instinctively whenever we have a
conversation with someone else, we try and figure out, yeah,
who's that person, like, what might they be interested in
when it comes to topics we could talk about, when
it comes to the way that they like to be
talked to. So as humans we kind of do this

(04:05):
very naturally, and algorithms can now also do this with
these kind of more predictive frameworks where we take someone's
data and we train a machine to turn it into
something that makes sense on a psychological level. Then the
second part of psychological targeting, which is really the if
you think about it, like the applied one with the
intervention part, the one that we're trying to not just

(04:27):
understand who someone is, but potentially change it. So, once
I know, for example, that you are somewhat neurotic, impulsive,
open minded, can I use these insights into your again
motivations and needs to pretentially shift your behavior or the
way that you think, the way that you feel about
a certain topic in the direction that either I want

(04:48):
you to go in, which then sometimes shifts a little
bit too like manipulation. Right if you don't want to
go in that direction anyway, that's me trying to push
you in a certain way. Or but I think is
incredibly helpful for companies to think through. It's like, how
can I help consumers move in the direction that they
want to go in? Anyway? But can I help them

(05:10):
find some of the stuff that is relevant to them
that they otherwise wouldn't find? Can I help them see
how certain products that we might have to offer actually
help them meet some of these psychological needs that they have. So,
if you're extroverted, can I help them make the most
of this social life? If you're neurotic, can I help
you reduce some of that anxiety of that stress. So

(05:31):
for me, psychological targeting has these two parts, all.

Speaker 1 (05:34):
Right, So it's always unsettling right to those because we're
getting it all the time, and we see that our
news feeds, for example, are very specifically targeted at us,
and if we're not careful, we only get one narrow
target from those news feeds. We actually have to go
out and search something else in order to make sure

(05:54):
that we're not being hurted. I would say, into a
particular political view, social view, buying view, whatever. But what
I like here, what you're saying, what I'm hearing you say,
is that look, we can actually instead of manipulating people,
what we can which we do see that going.

Speaker 2 (06:15):
On all the time.

Speaker 1 (06:16):
But instead of doing that, we can actually do what
we should be doing as a business, is we can
actually help them buy and we can actually it sounds
to me like we can do two things. One is
find out who it is that needs what we want
and then target those people in a way that they

(06:37):
will understand that we have what they want. Right. And
on the other side, I presume that it could help
us actually decide are we actually delivering what people want
in the first place? Right, But let's start with the
first person, which is right. Let's say we have a
we have a product, or we have a service that

(06:59):
we think is valuable to the world, else we wouldn't
be doing it. We're not doing it just for money.
Entrepreneurs don't do that most of the time. They're really
doing it because they think they've got something that will
change have a positive impact on people. And then it's
a matter of finding those people that want that, and

(07:21):
then it's a matter of communicating in such a way
that they understand and that you do kind of lead them,
you know, to lead the horse to the water. Right, Yeah,
so kind of walk us through that process. How does
that work?

Speaker 2 (07:37):
Yeah, and I think there's actually that there's another one
that I'm going to add to this one, because sometimes
what you said is you have a certain product and
you're just trying to find the best audience. So that
is one, and I'm going to walk you through in
a second. Another one that I would argue that has
traditionally just been a lot harder to do is to say,
how can I make the product that I have relevant

(07:57):
to different audiences? Right, So there's many many products that
don't have like a very specific affinity a priority. Like
if you think about beauty products kind of appeal to
like anyone, there's like there's not a certain kind of
group of women that buys makeup and beauty products. But
you can just have to figure out, Okay, what makes
it interesting to someone who's more extroverted, what makes it
interesting to someone who's more introverted. Similar thought process, but

(08:20):
it oftentimes allows you to expand on your audience rather
than just saying, like, we're going to try to find
the one that matches our products. So generally speaking, if
I kind of really kind of walk you through step
by step, hort, this could look like in practical terms,
I should say that it's become a lot easier with
these new tools like the transformer models, large language models,

(08:44):
generative AI, because they allow anyone to immediately and pretty
much like an extremely low cost close to nothing and
create an understanding of here's a potential audience, here's my
I think if it's like ideal customer profile, which kind
of used to take a long time to do it manually. Right,

(09:04):
if you have a certain product, you can have someone
in house who kind of figures out, Okay, we're going
to do surveys and we're going to do focus groups,
and we're going to try and figure out what's the
ideal customer profile. Who are the people that we're targeting,
what are their pain points? How is my product speaking
to these pain points? And sometimes that's based on something
like psychology. Sometimes that can also be like very kind

(09:25):
of simple in terms of demographics, in terms of behavioral preferences,
but it used to require a lot of effort to
come up with stuff. What we know is that large
language models, they've never like take a chat GBT Clode
kind of Gemini, really something that is off the shelf.
You don't need to even tweak it. What we know
is that extremely good at simulating human behavior, so they're

(09:48):
really good at kind of simulating different types of personas
and thinking through a problem from the perspective of the
person that you kind of prompt it with. So what
you can do is you can essentially the simplest version
would be say, here's a product that we have. Here's
maybe like our company website. Here's a deck that explains
what it does. Maybe we have some proprietary data on

(10:10):
customers that we already have that are high value. Now
take the current gold standard in ideal customer profiling, right,
so there's websites that kind of walk you through the
steps how you want to lay it out, how do
you find your customer? Take that, and you could literally
just pop in a website or say, follow this framework,

(10:30):
and now, based on everything that you know about my company,
who do you think would be an ideal customer given
some constraint in terms of location, in terms of target size,
in terms of like whatever it is that you need,
and it does remind and give me some specific examples, right,
kind of either create me if it's a B two C,
create me very specific personas, including things that I can

(10:52):
use to target using existing targeting platforms like Google or
Facebook or whatever it is. If it's a B to
B kind of come up with companies who kind of
might have these certain pain points that we address with
our products, and even kind of go a step further,
tell me why this specific company would be a great customer,
tell me who the person in that company is that

(11:14):
I might want to reach out to. So even in
terms of understanding who you might want to target with
your product, MS do such a phenomenal job, and sometimes
that includes something like personality, but oftentimes it's just going
to really like it kind of goes a step further,
almost and says very concretely with specific companies, specific names,

(11:35):
specific pain points, and here's what you what you could
do in terms of ideal customer and targeting.

Speaker 1 (11:43):
So so you're thinking, I mean, it could even create
an entire potential prospect list for you exactly. That's here's
my so so, here's my ideal persona. Here's here's the
company looking for search base. You're telling it search the
Internet and give me the list of the top five

(12:07):
thousand companies, you know, companies that I should be talking to,
and who in that company is going to be most
likely to be responsive to this, and then what are
they going to be most responsive to presumably is the
next step, right.

Speaker 2 (12:21):
Yeah, that yeah, exactly, So you can like, I think
that the step is like try and identify either consumers
like a consumer persona or specific companies who are struggling
with some of the problems that you're solving. Right, So
there's if you think about how do you want to
how do you want to engage in this outreach? It's
spam if they don't need it, and it's just absolutely frustrating.

(12:44):
That's true for consumers, it's also true for companies, and
there's also like this window of opportunity where you want
to time it right. So the one thing that we know,
for example, like once you've identified this general here's your
ideal customer, here's a list of the top fifty companies
that we I think based on these pain points. So
it kind of explains why you might want to reach
out and how you could reach out that we've identified

(13:07):
that you might want to target. And then you can
set up essentially like almost like a continuous monitoring So
it's very easy to set up with pretty much no
code tools now where you just say here's what I
want set me up like it's scraping Google News, it's
scraping maybe classed, or it's scraping the company's websites. Do

(13:28):
we see anything change in how they talk about their
pain points? But if you're the one, the space that
I'm currently kind of thinking about a lot is employee benefits.
If I have an employee benefits solution, I want to
know what are the which are the companies struggling with that?
But why might they be struggling with it? Because maybe
they're kind of unionizing, maybe there's been like some kind

(13:50):
of cuts, and tell me if something changes. So under
you now understand my product because I've given you enough
information to deal with this, but you also understand the
audience that I might want to reach based on what
I offer. Now, tell me if there's a window of opportunity,
something changes in the news. Maybe it's a change in leadership.
Maybe again it's like the workforce unionizing, and now just

(14:13):
alert me to when this happened, so I now might
be a good time to reach out. So it's like
a it's really this very dynamic and very tangible using
these llms to go through the process that was just
incredibly labor intentse and manual beforehand, and because it knows
so much, right as someone recently told me that they

(14:33):
compare it to the most capable intern that you've ever had,
has unlimited resources, unlimited time, but need some oversight, Right,
So you need to kind of say, here's what we're
looking for, here's what we offer, here's some of the
things that I want you to walk through. And the
more you can for like a specific prompting of an LM,
the more you can create these personas. Right, so you

(14:56):
could say you're a specialist, like you're an HR specialist
us on coming up with ideal customer profiles based on
these resources. Please come up with a workflow. In the
second step, if you were to say how do I
create this content? Like how do I create a message
for the either consumers on the other side or the companies,
you could say, well, you're a McKinsey consultant who's been

(15:19):
tasked with figuring out how to solve their problems. Which
parts of our portfolio actually speak to this? So you
can tell the l l M simulate these different layers
of expertise, and now just give it back to me
and I can then make sense of it.

Speaker 1 (15:33):
Myself interesting, and then how do you how do you
replicate that? I mean, are there tools out there to
actually push that out so that you're actually are out
there then targeting in that way.

Speaker 2 (15:47):
So a lot of so there are always in which
you which you could automate that. My recommendation was would
be that I would always have a human in the loop.
So I think once you get the like, what lms
are really good at is summarizing content giving stuff back
to you. But I think there should get any given step,
like once you have the ideal customer profile, it's helpful

(16:08):
to have someone in the loop and says, okay, so
now that we have identified these companies, what makes sense
from these things here? What do we actually want to
focus on in terms of our strategic priorities? Right, because
you can't do everything. So maybe there's companies that would
be a good target based on your specifications, but you
know that it doesn't really fit the overall strategic mission.

(16:29):
Maybe it suggests kind of putting some thought leadership content
out there that you know it's not exactly what you
want to engage in. So oftentimes just having like the
step in the middle and say here's the target audience
that it recommends, and it kind of tells me exactly
how to set this up on Google or Facebook. Now
I want to kind of quickly check then before I
automatically send out something, right, or it recommends me to

(16:51):
reach out to this person working for company X on LinkedIn.
That's probably something that you want to do yourself. It
can help you craft the message and the content. But
I think at the end of the day, if you
really think about this as relationship building with either the
B to B context or with your consumers, it's still
helpful to have a human in the loop at that point.
So I wouldn't just kind of fully automize this entire process.

Speaker 1 (17:14):
Got it? And how does your book walk through that?
I'm very interested and how the book does that? I
haven't read the book yet, but how does the book
watch through this?

Speaker 2 (17:25):
It doesn't, so what the It's the interesting part is
that the book is essentially focused on understanding some of
the basics, right, So I think, like then when I
give talks, for example, what I mostly want to take
the audience away from my talks is saying, here's what
llms are like, not just lllms, but like algorithms in general,

(17:48):
here's what they're capable of, here's how amazing they are,
and understanding your psychology, here's what they can do, and
then swaying behavior for better or worse. And the book
places quite a lot of emphasis in the end on
how should companies be doing that? Right, So the mere
fact that you can do it doesn't always mean that
you should be doing it. So there's like many ways

(18:09):
in which you can, for example, implement more privacy preserving
technologies to kind of create intelligence from data that's not
really kind of becoming so intrusive that you run all
of these reputational rest when consumers find out. So the
book is really kind of focused on the basic levels
of intelligence that we can get from data and machines,

(18:31):
implications for how does it work in changing behavior and
thinking about how do we collectively as a society and
navigate the space, as well as companies, how can they
do that? Everything that I've mentioned so far, I think
it is like one specific application of all of this, Right,
It's like, Okay, once you understand the power of large

(18:53):
language models of predictive algorithms, it's oftentimes very easy for
or much easier for executives to say Okay, here's actually
here's how we could implement them. Because if you give
and this is again it's like a slightly different approach
between some of the typical business books and I think
the ones that are more kind of foundational science. It
might understand because I mean, I teach a lot of

(19:16):
executive education class and executive MBAs, and what I want
to do is essentially empower them because they come through
the class and I can now give them a playbook
here's how you do X tomorrow. The technology is different,
the problem that they're trying to solve is different, and
then my playbook X is not actually going to be
very helpful. But if they understand the foundations, right, if

(19:37):
they understand if you use personas, they can put themselves
in the shoes of someone who comes up with an
ideal customer profile. If you give them a certain day,
can come up with a kind of consulting company and
playbook on how you kind of should develop your strategy
around this. Then they can be become a lot more
flexible in terms of implementing that. So for me, the

(19:57):
book is really kind of this foundation. It's also a
cocktiparty cocktail. I think it was like a cocktail party wisdom. Right,
It kind of gives you a lot of examples of
here's how this could be playing out, both in terms
of the association of psychology and data and applications, and
then you can take it and translate it to your

(20:19):
business problem specifically.

Speaker 1 (20:21):
So let's take one thing you've talked about, which is
personas and actually coming up with that because I've long
thought that the clearer you can get a business can
get on their ideal client, the more successful they're going
to be. Because you want you don't want to target everybody.
You want to target those people who really make sense

(20:42):
for you. And every business is unique, because every business
owner is unique. Right, So there are people who want us,
they just don't know.

Speaker 2 (20:49):
They want us.

Speaker 1 (20:50):
Right, So how do you what are some of the
questions you would ask a large language model, What are
some questions you'd ask in order to help you get
narrower on that focus.

Speaker 2 (21:04):
Yeah, it's a great and it's so funny because again
it's something that's so fundamentally human, right, So every kid understands. Yeah,
it is like maybe maybe I'm going to go to
mom to ask for the candy because she's more likely
to respond positively to this. But they also understand that
if you have an audience, you talk to them differently.
So maybe I ask Mom in a certain way, but

(21:25):
then I also ask Dad in a certain way. So
we do this so naturally that it's essentially just translating
this to the business world and then scaling it. That's
how I think about it a little bit in terms
of personas. So there's sometimes there's different approaches. So one
is I think an expertise driven one that is really
driven by people working in the company and having a

(21:46):
sense of here's the people that we want to target
in a certain way right here's the product at least
the way that we envision it, and I think it's
always good to double check if like other people think
about it the same way. And then there's the approach
that is like much more driven by here's what we
have to offer right now. So you can imagine if
again I'm going back to this employee benefits and company,

(22:09):
we have a certain product, we have a slide that
that's a pitch deck, we have a website, there's communication
around what the product can do and how we see
this play out. Now you can essentially just take all
of the data that you have. Maybe you even have
like some customer data from people who have already shown interests,
and maybe you have like some kind of data from
if it's like a B to B from the companies

(22:30):
that have shown interest. You can pop all of this
in and say, Okay, if you're an icy, like an
ideal customer profile specialist, come up with a PERSONA. Come
up with ten different personas, right, you can you can
be very specific and say, come up with ten different
personas where you outline what part of the product is

(22:51):
going to be the most relevant, why are they interested
in the product. But if you think about an employee benefits,
one is it that they're kind of the current employer
are not using the benefits that they have, so that's
their pain point and that's what we're trying to change.
So that could be one of the personas. And the
people that you're most likely going to talk to are
HR people, and they oftentimes very risk averse because it's

(23:14):
kind of takes a lot of time implementing these things
and you really don't want to make a mistake. So
as you go and talk through them to them, here's
some of the things that you want to highlight in
the way that you're going to protect data. There's like
essentially you kind of be very specific and you can
also drill down. So once you have this general prompt
and say, come up with these ten different personas, tell
me exactly how my product speaks to their pain points,

(23:38):
what their pain points are, how I might be communicating
with the people that I expect to find on the
other side when I reach out, and then kind of
go deeper. Right, if there's one that stands out, you
can kind of try and say, okay, now just tell
me everything that's happened in the history of these companies.
Can you give me a sense of which products they
might currently be using, So you kind of once you start,

(23:58):
you can be very very specific with the prompts that
you asked the same way that again, if you think
about it as an intern, intern typically comes back to
you off the week with the research that they've done,
and then you kind of go and send them to
do a new task. Led m's just come back with
you in seconds. That's the that's the only difference.

Speaker 1 (24:14):
So, well, well, the l ms, will they actually capture
what people how they've searched, So do they actually pull
from like the Google searches and so so will they
pull that information for you basically so that you know
what questions the perspective customers asked.

Speaker 2 (24:37):
So they don't. They don't pull from the data itself.
So if you have and it is that that depends
a little bit on the if it's a B to
B because with B to B you oftentimes have information
out there about the companies, right. Either that's investor calls,
that's some of the strategic communication from websites, that's news
about companies that you might be interested in. That could

(24:57):
be glassdoor data where you see kind of coploies talking
about some of the things that are not going well.
So like for companies, the data that is out there
is oftentimes somewhat accessible. For individual consumers, it's oftentimes a
little bit different. So there's different ways in which you
can think about it. Right, if I'm a B two
C and I'm let's say i'm a again like a

(25:20):
beauty retailer, and what you could say is you could say, Okay,
come up with these different personas for women who might
be interested in buying my beauty products. Come up with
messaging around this, and the beauty retailer I picked because
we actually did a project almost ten years ago now
with a beauty retailer where we did this all manually.

(25:40):
So we went in and said, we want to target
women who are extroverted and women who were introverted. We
had to have a creative team come up with ads
that were kind of responding, corresponding to extroverts and introverts
to forever and L ANDM can do this like beautifully.
If you tell an NLM kind of create me ads
for beauty retailer that speak to someone who's open minded

(26:01):
and extroverted, what you get it looks incredible. And you
can also say, now, help me find a way of
getting to these people. So if it's new customers and
its acquisition, it might tell you, well, Google offers you
a way to target people based on their interests, and
we know that certain interests are related to certain types

(26:21):
of personality traits, So why don't you try this list
of interests? And if you have proprietary first tend data, right,
So like a lot of companies do that, and you
can depending on what the data is. Sometimes it's social
media profiles when you have a Facebook login for example.
Sometimes it's purchase histories. Sometimes it's location data that you

(26:41):
can map against activities and locations categories out there. Ellen
M's are so good at just saying I'm going to
give you everything I know about this consumer. About this customer, now,
try and tell me, based on this framework of personas
that you've come up with before, which one are they
most likely to fall into? Or just give me. If
you're operating with a personality framework, for example, which is

(27:04):
my home turf? Just given that data, what do you
think is this big is that person's Big five personality profile?
And again it's it's never perfect, And I think that's
something that is important for companies to realize. It's gonna
make mistakes the same way that we make mistakes when
we judge other people. And so for me, the big
question is how consequential are these mistakes? Right? If if

(27:28):
I make a mistake and now I'm selling my beauty
product in an extroverted way to an introvert, yeah, not
the end of the world. If you're rejecting someone for
a loan or a job, then it's probably you probably
want to do a slightly better job. But that's essentially
like the way that you can use these off the
shelf at elamps is, you can create these personas. For companies,

(27:49):
you can scrape it off the internet. Just try and
find me anything that you can can about the company
speaking to the point pain points that you've identified. And
for consumers, it's either asking it to identify find off
the shelf way ways of targeting people online that don't
require any first party data, or translating your first party
data into these personas and mapping these personas onto the

(28:12):
customers that you already have.

Speaker 1 (28:14):
Yeah. I actually think you make a really good point
there is that there's a lot of there's a lot
of benefit in data mining from our own customers, from
our existing customers. What you know, what is the persona
of our existing customers? What caused them to buy? What
things were they looking at? Because there's they can probably
do all. I mean, we have everybody has hundreds thousands

(28:37):
of customers, right or they have had over the years.
What causes you know, what? What was it that? What's
in common with these people who know what you're asking for?

Speaker 2 (28:47):
Right? Yeah? And so in that case, so and you're
absolutely right. So these these models are amazing at picking
up on patterns in data. And there's many where you
can actually upload data sets for the for the model
to make sense of. Now, the one thing that I
should note on this front is that if you do that,
if you upload proprietary data, make sure that the settings

(29:09):
of the language models are set so that it doesn't
get shared, because you don't want all of your intellectual
property to be out there now that they build the
next iteration of your product and you just ask, hey,
can you just tell me a little bit about the
customers of company X and how they kind of think
about their strategic persona building. So if you do that,

(29:30):
and I again I think it's an incredible tool and
incredible opportunity, but make sure that your IP is protected
so that you and it's just like a setting where
you can say, don't share my data or don't use
it to build your next iteration of the large language model.

Speaker 1 (29:45):
I love it. So if you were a smaller sized
business and you were kind of saying, okay, I'm going
to take step one, step two, step three, what would
those steps be?

Speaker 2 (29:58):
Yeah, And I think you've actually walked us through pretty beautifully.
I think it's like understand, like always think about it.
What would you do in a face to face context,
how can you replicate it right? If I'm a car salesperson.
Step number one is collect as much intelligence about the
personal on the other side as possible. Try and understand
who are you most likely to reach out to, who's
your audience, and how do you best speak to them,

(30:20):
what are some of their pain points and how does
your product respond to that. Step number two is what
is the message right that you want to craft and
how do you reach out to people? Is that through
an ad? Is that what's the value that you're adding?
Is it that it's like a long term relationship that
you're building. So maybe consumers will have to be a

(30:40):
lot more involved, but maybe they're actually providing you some
of the data and because you can show them the
value and it's like much more of a back and forth.
Or is it really just that you're trying to expand
your audience and you're kind of trying to acquire new customers.
That's a different approach, So kind of here's who you
want to get, how do you get them in with
what message? And then also like follow up like how

(31:03):
do you retain them? But how do you make sure
that they're not going to leave for the competition? The
next day. And for me, the last part really comes
down to real value add is like what are you
using the data? What are you adding to their experience?
And do you kind of communicate that in the way
that shows what the value add is, maybe even making

(31:25):
it so transparent that they can interact with some of
the predictions and kind of have this back and forth
the same way that we have this in an offline context.
So it's really kind of intelligence. What is the message,
like the fit in terms of your product? How do
you follow up and make sure that they're going to
stay with you and not just jump to the competition
the next day.

Speaker 1 (31:45):
That actually reminds me of a few years ago. I
had the opportunity to set across from Richard Branson and
I asked him, I said, so, you've changed all these industries, right,
recording industry, airline industry, the hospitality is you keep change
in the industry. What did you do? He goes, well, we
we just sat and listed everything we didn't like about
the industry and then we did the opposite, right, So

(32:09):
we we actually fixed that. So but it sounds like
what we can do now is we actually have the
uh lll ms to be able to tell us what
is it people find is missing in this industry. You know,
if for example, I'm in the CPA industry tax advisory,
what's missing? What are people missing? What are they looking

(32:31):
for that they're not finding? It sounds to me like
you probably get a pretty good, pretty pretty good feedback
on that.

Speaker 2 (32:38):
Absolutely totally. And it's funny because like one of the
use cases that you can use these lllms for is
essentially create your own advisory board of people that you admire. Right,
so you said, like I said across from Ridget Branson,
it's probably not going to join your board, but you
can just create an LLM that says you're Rigebrandson kind
of here's like something that I want you to get

(32:59):
feedback on. Here's bort materials. Kind of write me a
report and it does it and it just like a
remarkable job. So it's good ways in which we can
leverage some of these simulated personas of AI to tap
into the brains of people that we otherwise might not
have access to.

Speaker 1 (33:15):
Well, this has been awesome. We could go on forever, Sentra,
There's just this this has been so interesting. I had
no idea what we were starting with with with data
and data driven science. And we spend the whole time
talking about AX. That's all it is. It's client So
thank you so much. So the book is Mind Masters,

(33:36):
and I've got to say, after this conversation, not only
will I be reading, I think this is a book
that everybody should be understanding because we need to understand
what what is it? You know, what is psychological targeting?
How does it work? Because then it's really sounds to
me it's not that hard to use, and there's lots
of tools that are like twenty bucks a month. I mean,

(33:59):
it's not like it's expensive to use it, and we
can do that, Sandra. If we wanted more information about
what you're doing and your research, where would we go?

Speaker 2 (34:10):
So we do. Actually with my husband and I set
up it's called a Center for Advanced Technology and Human Performance.
You can find it at Humanminus Performance dot AI. I
think that's the best place, that is the most up
to date.

Speaker 1 (34:25):
Awesome, Thank you so much, Thank you, Sandra. And remember
when when we start using these tools, I mean, and
we really understand you know, predicting and changing human behavior
and we have all these tools to do it, then
we're going to end up making way more money and
pay way less tax. We'll see all next time on
the Weltability Show.

Speaker 2 (34:45):
Thanks, thank you. This podcast is a presentation of rich
Dad Media Network.

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