December 4, 2024 • 31 mins
According to our latest Statista report, only 54% of marketers are embracing AI to optimize their campaigns. The reasons for slow adoption include challenges with implementation, the change management behind new tools, or a misunderstanding of the power that AI can bring to the marketing table. Sadly, the result is that more than half of today’s marketers are not using AI to its fullest potential.
Today we’re demystifying how marketers can accelerate their creative measurement and better use AI and prediction tools.
In this episode we’re speaking to Crystalyn Stuart-Loayza, Chief Digital Officer at Citizen Relations & Mekanism, and Michael Cohen, Plus Company’s Global Chief Data & Analytics Officer. Together they will help us understand how we can harness the power of AI to provide actionable insights, and real-time data to drive successful campaigns.
Subscribe to Partners in Possibility on all podcast platforms and never miss a tip, insight, or new marketing idea!
Today we’re demystifying how marketers can accelerate their creative measurement and better use AI and prediction tools.
In this episode we’re speaking to Crystalyn Stuart-Loayza, Chief Digital Officer at Citizen Relations & Mekanism, and Michael Cohen, Plus Company’s Global Chief Data & Analytics Officer. Together they will help us understand how we can harness the power of AI to provide actionable insights, and real-time data to drive successful campaigns.
Subscribe to Partners in Possibility on all podcast platforms and never miss a tip, insight, or new marketing idea!
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Transcript
Episode Transcript
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Speaker 1 (00:02):
This unlocks the potential for us all to have democratize
access to intelligence to be more creative. You know, we
those of us who can't draw our design now have
the potential to think like a creative.
Speaker 2 (00:20):
In an ever changing world. That's all about stay connected,
building connections and seeing where the next collaboration takes a
marketing campaign from an initial brief to the follow through.
What paths are going to make a campaign success more
than a possibility? Hi, I'm Brett marchand CEO of Plus Company.
This is partners and Possibility. Welcome to another episode of
(00:44):
partners and Possibility. Marketing tools are only as good as
the marketer who uses them. A few months ago, we
sat down with Statista and Citizen Relations to understand the
new wave of AI in marketing and what we as
marketers could expect to see in the future. Today, exploring
the barriers to entry for using AI and how we
can fully adopt the tools in a meaningful and actionable way.
(01:08):
Michael Cohen, Global Chief Data and Analytics Officer at Plus
Company and Krystalline Stuart Louisa, chief Digital Officer at Citizen
Relations and Mechanism are going to explain what exactly these
barriers to entry are how to better adopt these tools
and what the real possibilities are, not just in marketing
but in the world at large. Well, welcome Michael and Crystalline.
(01:36):
We're going to have a discussion about AI and creative measurement.
That might not sound like an exciting topic, but if
you're a marketer it's obviously super important. But before we
dive into that, I actually thought I would reflect a
little bit about, you know, having been a client and
as well as on the agency side. But I remember
as a client, you know, I did a major when
(01:58):
we would do a major launch, like I launched I
Am Canadian as an example at Moulson, and we had
to research everything, you know, and the idea was that
people want to know would it work or not? Would
we have success? And we were changing the label that
you would that change be successful? We were putting a
brand new campaign out there. Would it be successful? We
were you know, we had to spend a lot of
(02:18):
money on media. Would it be successful? We launched a
brand website. Would it be successful? We paid for a
bunch of sponsorships that went with it. Would it be successful?
And we take all this research and you know, either
I or somebody who worked for me would have to
go through these research reports and figure out well and
predict whether you know it was going to work or not,
(02:39):
and what decisions we would made. So I say that
because with AI and the power of AI, I mean
AI can read every research research study ever done. It
could take in proprietary research. It can read everything that's
on the internet, including demographic data, and even use synthetic data.
(03:00):
So I'm sure we'll talk about to fill in the
gaps where we don't have answers and can do it
in a matter of seconds. What you know, I in
a team at Molson, you know, would have taken probably
hundreds of years to come up with the same kind
of insights and predictions. Does that ring true with you guys?
Speaker 1 (03:16):
First of all, yeah, for sure it does. I think
certainly the amount of manpower and brain power that went
into every single decision still only to have sort of
a percentage of those decisions be accurate. In market is
certainly the way the world has been for a lot
of us operating in marketing, and good thing we're moving
towards a period where we have a lot more rigor
(03:38):
and solutions we can provide than where we were.
Speaker 2 (03:42):
So why do you think, Michael, I think our Statistas
study said that only about half or fifty four percent
of marketers have actually implemented AI into their marketing process. Sees.
I mean, given the incredible role it could play, given
how marketing has been done and how much it relies
on and research, what do you think is in the way?
(04:02):
What are the barriers?
Speaker 3 (04:03):
People don't know where to start. There's a lot out there,
and it's pretty general. So we're still having a long
way to go into application layer, and we're about to
move into a period in which there'll be more applications
that are more relatable for these sort of users, more
natural for them to use, rather than just say, oh,
here's a GPT. Figure out how to make be more
(04:25):
productive and do great things with This isn't necessarily an
easy starting place, and of course initially the costs are high.
If you're talking about out fitting an organization, whether it's
bigger small If it's small, the costs are lower, but
the stakes are higher for a small organization to buy
a bunch of seats and licenses.
Speaker 2 (04:47):
And I know Crystal and I mean you both are
in front of clients a lot you, in particular as
a chief digital officer declient see the potential of AI.
Speaker 1 (04:56):
Yeah, I mean, I think overall, we're starting to see
a real move from thinking of AI as sort of
a theoretical way to maybe save costs on production or
on productivity into a lot of our clients really seeing
the fundamental sort of systems level change that AI can
drive for them and their business. I know, you know
some of the statistics that came out of this recent
(05:17):
report talked about a huge part of this is that,
you know, while maybe ninety percent of our clients see
the possibility of leveraging AID to measure let's say creative effectiveness,
such a few percentage of them, less than thirty percent
of them are even able to start to operationalize that
because it isn't just about adding one element to the mix.
It's about changing fundamentally how we're running creative and to end.
(05:39):
And I think that, frankly, is one of the biggest
barriers is really just the human disruption of sort of
workflow and mindset and be able to integrate system solutions
into their ways of running creative. That's why it takes
so long, right.
Speaker 2 (05:53):
Mike, maybe you could talk a little bit about how
AI can do creative measurements, because I mean, again, the
old way was you took your creative concepts. You first
of all, you tested them either in qualitative groups, and
then you did quantitative testing and you asked them to
you know, how distinctive is it? Do you remember the brand?
(06:14):
Does make you want to buy it? You know, stuff
like that, and then obviously you'd put it in market
and you can do some ab testing by showing different
versions to different people. But that's sort of been historically
the you know what marketers did, how does it work
with the.
Speaker 3 (06:27):
I What we've seen with the explosion and computation, the
ability to have large models, use the large language models
and examples people are becoming familiar with it is that
the number of tokens that it's trained on, you know,
the number of parameters in the model is just massive.
That's how it can produce the beauty of the complex
English language in ways that we find acceptable when it
(06:50):
comes out. And if you think about creative there's there's
a lot of information about about career of just in it.
Whether it's an image or a message. You can imagine
tokens that relate to the different language, the image, the
(07:10):
customers that are exposed to it, the music, the inflection
points in the cadence, the accent, like, all of these
things can be broken down into finer components that you
can train into a model such that it can derive
new components, like the same way the human brain does. Right,
these things are architected based on the inspiration of what
(07:32):
nature's done in ourselves. So when you can do that,
then you can imagine different renditions, which is creativity of things.
For one, you can relate it to the individuals that
you aim to influence and the differences in them, like
how they experience that creative and what sort of communication vehicle,
(07:54):
and then what behaviors it elicits in them. When you
have all of those things together, you have a direct
measurement of how that creative is going to you know,
listed a behavior that you care about, either from a
business perspective or a societal perspective, in a directly measurable way.
And once you have that, you can ask, well, how
do I do it differently? What if I want to
(08:15):
try this other thing? What is the world going to
look like under that scenario? And that that's how it works.
I mean, it's breaking it down to very small pieces
and building it up. However, you'd like to tell you
directly what you need to know.
Speaker 2 (08:30):
Christ how do you see clients using AI written now
to test the creative effectiveness?
Speaker 1 (08:35):
I think we're at such an important point where there
is a fundamental shift in the way that clients are
beginning to mount these types of annual plans and brand
level campaigns or big brand acts, and that historically the
way that creative has been developed is sort of like
a waterfall. It's sort of at the top and we're
developing our big platform and then we're launching sort of
(08:57):
a campaign and market. We may have some market signals
or tests in advance, but then it goes over this cliff.
There's an enormous gap, and when it hits the bottom,
then you have all of your day to day business
as usual assets, your CRM, your social content, your DCO,
you're programmatic, all these different assets in market that are
sort of to swirl at the bottom, and we have
lost a chance to frankly, even evaluate all those assets
(09:20):
against a single set of effectiveness pillars. Further, we're not
connecting them back to the bigger brand message or any
of the core platforms. So there's been this big disconnect
even in the way we've siloed our teams, our agencies,
and certainly our measurement models. That has to be fixed
to take advantage of what Michael just called out, this
really great approach to creative effectiveness. But we can do
(09:42):
it now because we have the power of AI to
help us sort of close that gap. And you know,
we talk often about the creative flywheel, the art of
connecting all those elements together in order to use those
real time signals and insights to in real time affect
creative at all scale and level. And this Buyers does
not only use the technology but to change some of
our ways of working as brand marketers and even as agencies.
Speaker 2 (10:06):
And you when you say silo you mean because silos
you mean because at one end, somebody's creating a brand
campaign that's then you know, driving image and or affiliation
with the brand and or consideration. At the other end,
people are like doing lower funnel campaigns that are like
just trying to get you to click and buy.
Speaker 1 (10:23):
Or yeah, and even mid funnel. I mean, this is
an era we're coming into where a lot of even
CpG brands are realizing they've neglected a lot of that
middle funnel, like first point of experience for customers. They're
building content strategies for site without thinking about a landing
experience from an AD unit. They're developing CRM content that's
totally disparate from what they're pushing in their loyalty programs
(10:45):
or inside of their social and so part of this
is also about how we're thinking about organizing our creative
messaging so that we then can measure it and work
in a much more synergistic and effective way, really like
powered by AI, and it gives us the like since
a chance to do all of those things.
Speaker 2 (11:02):
You know. I had Ted Garard on the podcast to
talk about the Versus campaign a sick kids. You know.
One of the things we talked about is not just
that the campaign drew people to the cause of building
a new hospital, but also it drove like date to
day stuff, like people on Facebook were talking more about it,
you know, and then and it drove ten dollars a
month donations and not just you know, big foundational donations,
(11:28):
et cetera. And the secret there was that it was
completely to your point. It was aligned right, you know,
what was said in almost all the channels was very
similar to what was said at a brand standpoint, and
there was an interaction between all of those things that
you know, proved to be super effective. So and I
guess if we would have put the Versus campaign through RAEOS,
(11:48):
we probably would have seen that. Yeah.
Speaker 1 (11:50):
I would also just add I think we're coming into
a really exciting time because of the sort of capabilities
of machines that Michael just illuminated. We have the chance
now for brand and performance to come together in greater ways,
for creative and media to come together in greater ways,
for us to really bring forward a much more synergistic
(12:12):
approach the way we build relationships with customers. And I
think that's such an exciting part of what creative effectiveness
unlocks is bringing all of those elements together but in
service of better brand outcomes, right, probably lower costs in
the long run, but also certainly better customer experience, which
I think is what makes all of this like even
(12:33):
more worthwhile. The impact is going to be shown to
us through customer response.
Speaker 2 (12:38):
Yeah, and the kind of personalization you're going to be
able to do that actually presumably will get us to
a place where you're going to show me stuff I
want to see and that I enjoy and laugh at
or cry at, or whatever the case may be, instead
of showing me stuff that you know I don't like.
Presumably can help do that too, right, Michael.
Speaker 3 (12:55):
For sure. And one thing I want people to realize
is that we've started to see examples of AI where
the outputs are multimodal, as we call them. Right, you
have images and sounds, and it can put together a
video from some images you put in, but the inputs
are actually in some of the newer stuff that will
be coming out our multimodal meaning that it can take
(13:18):
as inputs everything from gene sequences to audio where you're
actually picking up differences and accent, inflection and all those
things along with text, along with images, and then put
those together in a certain way. So for anybody that
works in the creative space and tries to come up
(13:40):
with big ideas that are meant to influence people, it's
that multimodal sort of inputs that they have in their
brain naturally that helps them come up with their big ideas.
And with AI's ability to do that and just do
it at such scale, right, it's just it's not tired.
It can hold everything in its head at once. Basically,
(14:04):
we're on the cusp of a creativity explosion.
Speaker 2 (14:07):
I guess the other thing is time, right, Like being
able to get signals and insights on a real time
basis has got to be helpful for developing effective campaigns
for clients.
Speaker 1 (14:21):
Yeah, I think real time is an important facet of this.
We're trying to take in huge volumes of data and
quickly turn those into actionable motions that are also integrated motions,
not just sort of single point content or a single response,
but really integrated campaign level motions. And that sort of
real time dynamic requires a shift in the way our
(14:43):
talent is working, and we're really starting to recast a
lot of the different roles and functions of different key
players who will play a part of being able to
operationalize like real time optimization and impact that comes from AI,
and that's everything from changing the way our strateg just
are functioning, starting to build connections based experience for a
(15:04):
number of our strategists and even our tacticians, starting to
train our creatives to really better understand how to be
creative analysts and better understand how to take in those
signals and apply them quickly to sort of an on
brand creative message. And it certainly changes the way our
analysts work overall in that instead of kind of spending
a lot of their time distilling information, they're spending more
(15:26):
time actioning it and putting it into an insight driven model.
And this takes time to train our talent and to
get them accustomed to working this way, but we're seeing
huge gains in especially our middle level talent, who are
really embracing this call to work differently and seeing the
benefits in their own day to day productivity pretty quickly, really,
(15:48):
as we introduce them to more of the platform usage
that they have.
Speaker 2 (16:06):
Michael maybe talk a little bit about synthetic data on
how that can help with researching ideas and or figuring
out or predicting whether they're going to be effective or not.
Speaker 3 (16:15):
The idea is that with the information that you have,
you can get the information that you need and want.
So there's lots of signals that you do have access to,
but it's often doesn't come in the form of what
you want to actually answer the question, but there's lots
of information in it, so prediction can actually help translate
(16:40):
the data that you have, whether it's at a more
aggregate level, whether it can be combined with different sources
of information about audience, about the creative and the media
vehicle and the context. All of those sort of things
and the effectiveness of it are all signals about data
(17:00):
that you don't have, and then you can predict it
and the prediction can be very accurate. In fact, you know,
we always find that the prediction looks more business representative
than deterministic data because deterministic data suffers from you know,
how we collect it and when it's available, or that
(17:23):
it's behind the walled gardener, that it's properly protected by privacy. Yeah,
that's always going to be the case. So, like we've
spent the last twenty years pretending that we're going to
be able to create the perfect deterministic data or put
it in a clean room or something like that and
be able to do it and the answers we're never
going to be able to. But prediction can give us
(17:44):
exactly what we want and it looks actually more representative
of business because it can solve that complex. I describe
it as a Sidoku puzzle. Right, there's information you have
and there's information you don't, and it can solve that
very complex to give you the data need. Well, once
you have that data, then you can build all sorts
of other applications and use other ais on it.
Speaker 2 (18:07):
So back to my story at the beginning. So I've
got this new label for my beer, I can actually
ask that label without actually showing it to real humans
and show it to you know, and basically AI can
predict what people how they would react to that new
(18:28):
level and tell me whether it's going to sell more
than the old label or or the other options I have.
Speaker 3 (18:34):
It would be even better because it would be based
on human behavior, not human statement. Right right, people don't
tell you what they actually do, but with you know,
data synthesis, you can use actual behavior to tell you,
so it's more powerful.
Speaker 1 (18:49):
I have noticed that's another behavioral shift that's taking a
little bit of time for brand marketers to adapt to,
especially client side. Is we're so used to asking like
what happened last year? And how does that come this year?
And what can we learn from the past, and how
is our learning agenda rooted in like past progress? And
I think this shift and time orientation towards really looking
(19:09):
ahead and leveraging data to do more predictive activations instead
of always spending all of our time looking backwards. Because
of what we can do through some of these technologies
does require like a bit of a mind shift change
when we look at quarterly business reviews, when we do
annual planning. It's an important kind of shift for us to.
Speaker 2 (19:27):
Me or even just trying to sell your CEO that
this is the right label, even though you've never shown
it to a human you know, I mean, because it
does take a leap of faith to understand the you know,
how the how AI really works right, and so I'm
sure that'll be a you know, a barrier for.
Speaker 1 (19:44):
Some Well, it's not just the big ideas that we're
testing and like using synthetic data on. It's also like
the campaign go to market strategies. I think that this
has some of the most profound effects on you know,
so many corporations have like codified a checklist of a
launch strategy, you know, they have like a canned go
to market approach when they're releasing a new product or
a new skew. And I think what we're trying to
(20:05):
move people towards is understanding we can use a lot
of this predictive technology to help us actually build the
right alchemy for a launch and be able to gather
real time insights and make adjustments to that instead of
always having to use the same playbook, which is exciting.
Speaker 2 (20:21):
Yeah. I remember from IPMG days, fifteen hundred GRPs per
week for a launch that was sort of like the standard.
And as you say, there's probably especially given the product
and the competitive set and the way the world's changed,
and you know, obviously people don't just use GRPs anymore,
but you know, optimizing that using AI will be super interesting.
(20:45):
I'm sure there's like a million better ways to do
it than just you know that traditional.
Speaker 1 (20:51):
Yeah, beyond even just channel mix, like also the type
of content we're putting out into the world, the mix
of assets, you know, instead of your traditional list of
sixties and fifteens and cut downs and graphics, like, we're
actually able to produce a higher volume of assets and
put them in market faster, so we can change our
production strategies also, which is really exciting.
Speaker 2 (21:10):
Yeah. I mean, at leads you mentioned the creative flywheel
or creative effectiveness flywheel earlier. Maybe just talk a little
bit about that and how it works. I'm interested in
both your perspective on it because I think it's a
super interesting concept.
Speaker 1 (21:26):
Yeah, I mean, with all the diagram I'll just have
to visualize it for you. But the idea is to
close down this gap and really begin to connect all
the parts of creative measurement, activation and strategy together. And
what this does is by putting AI sort of at
the center to power this model, we can supercharge each
of the individual parts. We can do way more in
(21:46):
creative in production than ever before. We can measure out
a much more regular capacity, we have the ability to
activate and personalize, and we have the chance to bring
strategy to a whole new level with real time insights
and faster real world data, plus the predictive capabilities. But
what's really exciting is not only is it connecting and
supercharging each of those parts, but when it's all working together,
(22:08):
it starts to create this like very seamless, very proactive
motion where the individuals are working in concert in service
of overall impact. And so you almost see no divide
between those functions. You almost see no divide in terms
of the time it takes to produce and adapt our
creative and you most certainly see higher impact on every
single move you make because of how fast and rapid
(22:30):
this motion is enabling effectiveness. And we're really beginning to
move this into this center of our strategy for a
lot of our core customers, aligning ourselves and our technology
and their internal operations to support this flywheel effect.
Speaker 2 (22:46):
So is the way I should understand this is that
you go from sort of the old batch process, right,
come up with a strategy, then do the do the creative,
test the creative then decide you know how you're going
to put it in market then or how it did
in market, then take that measurement and go back and
change the strategy, etc. It goes really from a batch
(23:07):
process too. It sounds like a real time process where
each thing is being impacted and changed on the fly,
as you call it, with a flywheel, based on what
the signals are coming from the market.
Speaker 3 (23:19):
Yeah, you've talked about system change often on this podcast
bro Various Gas, and this is the system change, going
from sequential to going multi threaded at the same time.
So you're rearranging the process and then how you put
people into it and it performs better.
Speaker 2 (23:40):
It's like a.
Speaker 3 (23:42):
Big shift a seismic shift upward. It is a result
of being able to go from that sequential to multi
threaded and fast construct.
Speaker 2 (23:52):
It also give direction on who to target, where to target,
what to sell, where to put your empat is, what
price like, yeah, how big could this be? How expensive?
Speaker 1 (24:05):
All of those things? And I think initially we're applying
the flywheel as like an alternative to your point, to
the traditional sort of waterfall approach the way we develop
creative But what we're finding is as we begin to implement,
we are not only finding a capability to make much
much more progress around the rapid adaptations of this, but
the types of ways that we're adjusting the work are
(24:27):
very widespread. So we're able to implement bigger learning agendas
than ever before without having to have enormous tests and
learn budgets, And we can actively work to answer much
more difficult and burning questions that our customers have, not
just about ROI or row ass effectiveness, which is traditional
learning agendas are a lot often focused.
Speaker 2 (24:48):
On is being returned on an advertising.
Speaker 1 (24:50):
Spend precisely, I think you know, we can answer those
questions certainly, but we also can start to answer bigger
business level questions like, which of my audience cohorts are
most like to have a collapsed journey and move faster
to conversion? How might I define influence in the way
that this target audience is moving in its relationship with
my business? And we're really up leveling our learning agendas
(25:13):
and our integrated measurement models because we can now effectively
and affordably run those in real time.
Speaker 2 (25:20):
Super interesting, And I mean you could see this working
not just in marketing of products and services, but also
presumably for charities, for politicians, for I mean, this is
you know, how broad do you think this could be
as far as the kinds of industries and organizations that impacts.
Speaker 3 (25:39):
Yeah, I mean we're just giving one very clear case
of where this idea applies in our industry, but just
every reason to believe that it can work in anybody's
into I mean, we see it working in different industries
as it relates to communications industry that we're in, anywhere
where that matters everything for education to I mean, education
(26:03):
is just a big area. Whether it's about policy and
communication of government policy and the effectiveness of our media system,
or the way that we educate children, all of these
things are open to impact from this sort of responsive
(26:26):
system that can very quickly monitor what's going on in
that interaction with the human and respond to it in
a way that affects the outcome with them, whether it
be a learning goal or whether we're trying to influence
somebody to vote for our favorite political candidate.
Speaker 2 (26:44):
And Chrislin like, do you I mean it will to
be a need for agencies or even marketers in the future,
given what day I can do.
Speaker 1 (26:51):
Yeah, I mean I love the proposition of the agency
of the future. I think it's so exciting because I
have always worked as more of a generalist type prectic titioner,
and I think this unlocks the potential for us all
to have democratized access to intelligence, to be more creative.
You know, we those of us who can't draw our
design now have the potential to think like a creative,
(27:13):
to be an analyst without having to work inside of
a dashboard or manage sort of critical data feeds on
a daily basis. It democratizes access to so much of
our discipline. I still think that you know, everybody says this,
you know, human ideation, human strategy, our ability to think
ahead and use the technology as a superpower is probably
where we're going to be for the next you know,
(27:35):
several years. But the idea that maybe we can do
more with less and maybe have more time with our
families and still be as productive as we are today
actually seems like an awesome proposition to be in, one
that our young talent seems to be getting behind for sure.
Speaker 2 (27:49):
Yeah. I had to have a Goldfarb who I know
you both know, on a podcast, and he had this
interesting analogy around what happened with websites, you know, when
people had to code them themselves. You know, there were
lots of websites being built, and then along game software
that made that much easier, whether it was no code
(28:12):
or low code or whatever, and suddenly the number of
websites and apps, etc. Exploded, Right, So you just had
more people being able to do them and do them
much more efficiently. You know, I agree with you. I
think that's what's going to happen for marketers and agency people.
They're just going to be able to do a lot more.
Speaker 1 (28:29):
Yeah, engineers of everything they need in front of them,
which is a brilliant role for smart people to play.
Speaker 2 (28:35):
Last word to you, Michael, so where do you see
this going? Like it take us a year or two out.
And I know we talked about system change, but what
do you think the role of AI is going to
be in organizations? You know, get over this barrier of
adopting it.
Speaker 3 (28:51):
It relates to what we're just talking about. And you know,
I'm sort of stealing this from something I heard from
Jensen Hlong recently. He was on the podcasts with Bill
Gurley and Brad gersonal Meg two podcast and he said, listen,
we're heading towards a world where everybody's going to be
their own CEO. Where we didn't talk much about agentic architecture,
(29:13):
but it's the idea that you very quickly can spin
up your own staff of ais with different abilities and
talents and be the CEO of them. And that's what
the world is likely to look. I believe in that,
and we believe in agentic AI as a path for
scaling among other paths for scaling AI, and it gives
(29:38):
more individuals control. It's like this, we're talking about human
in the loop of AI, Like how much easier can
it be than allowing humans to be more in control
of around destiny versus having to work in organizations and
communicate with each other. That can't be as efficient as
we'd like to think it is. I think we'll see
more of that, whether it's moving. We're talking about the
(29:59):
creative flywheel in particular, it will allow individuals to have
greater access to the things that need to be put
together to do great things, whether it's great campaigns or
bring great ideas to market.
Speaker 2 (30:12):
Yeah, well listen. As the CEO of Plus Company, I
welcome all of you to be CEOs as well. It's
a great it's a great vocation, so listen. Thank you
for this discussion. Super fascinating. We could probably talk about
this for hours, or at least I could, but I'm
really looking forward to what the future is like. And
thank you for all the work you guys are doing
(30:33):
in order to make AI come to life in our company.
Speaker 3 (30:37):
Thank you for having us to talk about it here.
Speaker 2 (30:39):
Great, Thank you, thanks for joining us for another episode
of partners and Possibility. I really have to thank both
Michael Cohen, Global Chief Data and Analytics Officer at Plus
Company and Krystalin Stuart Louisa, chief Digital Officer at Citizen
Relations and Mechanism for providing their thoughts on how we
(30:59):
can utilize AI and how AI can predict and measure
the future success of marketing campaigns.
This unlocks the potential for us all to have democratize
access to intelligence to be more creative. You know, we
those of us who can't draw our design now have
the potential to think like a creative.
Speaker 2 (00:20):
In an ever changing world. That's all about stay connected,
building connections and seeing where the next collaboration takes a
marketing campaign from an initial brief to the follow through.
What paths are going to make a campaign success more
than a possibility? Hi, I'm Brett marchand CEO of Plus Company.
This is partners and Possibility. Welcome to another episode of
(00:44):
partners and Possibility. Marketing tools are only as good as
the marketer who uses them. A few months ago, we
sat down with Statista and Citizen Relations to understand the
new wave of AI in marketing and what we as
marketers could expect to see in the future. Today, exploring
the barriers to entry for using AI and how we
can fully adopt the tools in a meaningful and actionable way.
(01:08):
Michael Cohen, Global Chief Data and Analytics Officer at Plus
Company and Krystalline Stuart Louisa, chief Digital Officer at Citizen
Relations and Mechanism are going to explain what exactly these
barriers to entry are how to better adopt these tools
and what the real possibilities are, not just in marketing
but in the world at large. Well, welcome Michael and Crystalline.
(01:36):
We're going to have a discussion about AI and creative measurement.
That might not sound like an exciting topic, but if
you're a marketer it's obviously super important. But before we
dive into that, I actually thought I would reflect a
little bit about, you know, having been a client and
as well as on the agency side. But I remember
as a client, you know, I did a major when
(01:58):
we would do a major launch, like I launched I
Am Canadian as an example at Moulson, and we had
to research everything, you know, and the idea was that
people want to know would it work or not? Would
we have success? And we were changing the label that
you would that change be successful? We were putting a
brand new campaign out there. Would it be successful? We
were you know, we had to spend a lot of
(02:18):
money on media. Would it be successful? We launched a
brand website. Would it be successful? We paid for a
bunch of sponsorships that went with it. Would it be successful?
And we take all this research and you know, either
I or somebody who worked for me would have to
go through these research reports and figure out well and
predict whether you know it was going to work or not,
(02:39):
and what decisions we would made. So I say that
because with AI and the power of AI, I mean
AI can read every research research study ever done. It
could take in proprietary research. It can read everything that's
on the internet, including demographic data, and even use synthetic data.
(03:00):
So I'm sure we'll talk about to fill in the
gaps where we don't have answers and can do it
in a matter of seconds. What you know, I in
a team at Molson, you know, would have taken probably
hundreds of years to come up with the same kind
of insights and predictions. Does that ring true with you guys?
Speaker 1 (03:16):
First of all, yeah, for sure it does. I think
certainly the amount of manpower and brain power that went
into every single decision still only to have sort of
a percentage of those decisions be accurate. In market is
certainly the way the world has been for a lot
of us operating in marketing, and good thing we're moving
towards a period where we have a lot more rigor
(03:38):
and solutions we can provide than where we were.
Speaker 2 (03:42):
So why do you think, Michael, I think our Statistas
study said that only about half or fifty four percent
of marketers have actually implemented AI into their marketing process. Sees.
I mean, given the incredible role it could play, given
how marketing has been done and how much it relies
on and research, what do you think is in the way?
(04:02):
What are the barriers?
Speaker 3 (04:03):
People don't know where to start. There's a lot out there,
and it's pretty general. So we're still having a long
way to go into application layer, and we're about to
move into a period in which there'll be more applications
that are more relatable for these sort of users, more
natural for them to use, rather than just say, oh,
here's a GPT. Figure out how to make be more
(04:25):
productive and do great things with This isn't necessarily an
easy starting place, and of course initially the costs are high.
If you're talking about out fitting an organization, whether it's
bigger small If it's small, the costs are lower, but
the stakes are higher for a small organization to buy
a bunch of seats and licenses.
Speaker 2 (04:47):
And I know Crystal and I mean you both are
in front of clients a lot you, in particular as
a chief digital officer declient see the potential of AI.
Speaker 1 (04:56):
Yeah, I mean, I think overall, we're starting to see
a real move from thinking of AI as sort of
a theoretical way to maybe save costs on production or
on productivity into a lot of our clients really seeing
the fundamental sort of systems level change that AI can
drive for them and their business. I know, you know
some of the statistics that came out of this recent
(05:17):
report talked about a huge part of this is that,
you know, while maybe ninety percent of our clients see
the possibility of leveraging AID to measure let's say creative effectiveness,
such a few percentage of them, less than thirty percent
of them are even able to start to operationalize that
because it isn't just about adding one element to the mix.
It's about changing fundamentally how we're running creative and to end.
(05:39):
And I think that, frankly, is one of the biggest
barriers is really just the human disruption of sort of
workflow and mindset and be able to integrate system solutions
into their ways of running creative. That's why it takes
so long, right.
Speaker 2 (05:53):
Mike, maybe you could talk a little bit about how
AI can do creative measurements, because I mean, again, the
old way was you took your creative concepts. You first
of all, you tested them either in qualitative groups, and
then you did quantitative testing and you asked them to
you know, how distinctive is it? Do you remember the brand?
(06:14):
Does make you want to buy it? You know, stuff
like that, and then obviously you'd put it in market
and you can do some ab testing by showing different
versions to different people. But that's sort of been historically
the you know what marketers did, how does it work
with the.
Speaker 3 (06:27):
I What we've seen with the explosion and computation, the
ability to have large models, use the large language models
and examples people are becoming familiar with it is that
the number of tokens that it's trained on, you know,
the number of parameters in the model is just massive.
That's how it can produce the beauty of the complex
English language in ways that we find acceptable when it
(06:50):
comes out. And if you think about creative there's there's
a lot of information about about career of just in it.
Whether it's an image or a message. You can imagine
tokens that relate to the different language, the image, the
(07:10):
customers that are exposed to it, the music, the inflection
points in the cadence, the accent, like, all of these
things can be broken down into finer components that you
can train into a model such that it can derive
new components, like the same way the human brain does. Right,
these things are architected based on the inspiration of what
(07:32):
nature's done in ourselves. So when you can do that,
then you can imagine different renditions, which is creativity of things.
For one, you can relate it to the individuals that
you aim to influence and the differences in them, like
how they experience that creative and what sort of communication vehicle,
(07:54):
and then what behaviors it elicits in them. When you
have all of those things together, you have a direct
measurement of how that creative is going to you know,
listed a behavior that you care about, either from a
business perspective or a societal perspective, in a directly measurable way.
And once you have that, you can ask, well, how
do I do it differently? What if I want to
(08:15):
try this other thing? What is the world going to
look like under that scenario? And that that's how it works.
I mean, it's breaking it down to very small pieces
and building it up. However, you'd like to tell you
directly what you need to know.
Speaker 2 (08:30):
Christ how do you see clients using AI written now
to test the creative effectiveness?
Speaker 1 (08:35):
I think we're at such an important point where there
is a fundamental shift in the way that clients are
beginning to mount these types of annual plans and brand
level campaigns or big brand acts, and that historically the
way that creative has been developed is sort of like
a waterfall. It's sort of at the top and we're
developing our big platform and then we're launching sort of
(08:57):
a campaign and market. We may have some market signals
or tests in advance, but then it goes over this cliff.
There's an enormous gap, and when it hits the bottom,
then you have all of your day to day business
as usual assets, your CRM, your social content, your DCO,
you're programmatic, all these different assets in market that are
sort of to swirl at the bottom, and we have
lost a chance to frankly, even evaluate all those assets
(09:20):
against a single set of effectiveness pillars. Further, we're not
connecting them back to the bigger brand message or any
of the core platforms. So there's been this big disconnect
even in the way we've siloed our teams, our agencies,
and certainly our measurement models. That has to be fixed
to take advantage of what Michael just called out, this
really great approach to creative effectiveness. But we can do
(09:42):
it now because we have the power of AI to
help us sort of close that gap. And you know,
we talk often about the creative flywheel, the art of
connecting all those elements together in order to use those
real time signals and insights to in real time affect
creative at all scale and level. And this Buyers does
not only use the technology but to change some of
our ways of working as brand marketers and even as agencies.
Speaker 2 (10:06):
And you when you say silo you mean because silos
you mean because at one end, somebody's creating a brand
campaign that's then you know, driving image and or affiliation
with the brand and or consideration. At the other end,
people are like doing lower funnel campaigns that are like
just trying to get you to click and buy.
Speaker 1 (10:23):
Or yeah, and even mid funnel. I mean, this is
an era we're coming into where a lot of even
CpG brands are realizing they've neglected a lot of that
middle funnel, like first point of experience for customers. They're
building content strategies for site without thinking about a landing
experience from an AD unit. They're developing CRM content that's
totally disparate from what they're pushing in their loyalty programs
(10:45):
or inside of their social and so part of this
is also about how we're thinking about organizing our creative
messaging so that we then can measure it and work
in a much more synergistic and effective way, really like
powered by AI, and it gives us the like since
a chance to do all of those things.
Speaker 2 (11:02):
You know. I had Ted Garard on the podcast to
talk about the Versus campaign a sick kids. You know.
One of the things we talked about is not just
that the campaign drew people to the cause of building
a new hospital, but also it drove like date to
day stuff, like people on Facebook were talking more about it,
you know, and then and it drove ten dollars a
month donations and not just you know, big foundational donations,
(11:28):
et cetera. And the secret there was that it was
completely to your point. It was aligned right, you know,
what was said in almost all the channels was very
similar to what was said at a brand standpoint, and
there was an interaction between all of those things that
you know, proved to be super effective. So and I
guess if we would have put the Versus campaign through RAEOS,
(11:48):
we probably would have seen that. Yeah.
Speaker 1 (11:50):
I would also just add I think we're coming into
a really exciting time because of the sort of capabilities
of machines that Michael just illuminated. We have the chance
now for brand and performance to come together in greater ways,
for creative and media to come together in greater ways,
for us to really bring forward a much more synergistic
(12:12):
approach the way we build relationships with customers. And I
think that's such an exciting part of what creative effectiveness
unlocks is bringing all of those elements together but in
service of better brand outcomes, right, probably lower costs in
the long run, but also certainly better customer experience, which
I think is what makes all of this like even
(12:33):
more worthwhile. The impact is going to be shown to
us through customer response.
Speaker 2 (12:38):
Yeah, and the kind of personalization you're going to be
able to do that actually presumably will get us to
a place where you're going to show me stuff I
want to see and that I enjoy and laugh at
or cry at, or whatever the case may be, instead
of showing me stuff that you know I don't like.
Presumably can help do that too, right, Michael.
Speaker 3 (12:55):
For sure. And one thing I want people to realize
is that we've started to see examples of AI where
the outputs are multimodal, as we call them. Right, you
have images and sounds, and it can put together a
video from some images you put in, but the inputs
are actually in some of the newer stuff that will
be coming out our multimodal meaning that it can take
(13:18):
as inputs everything from gene sequences to audio where you're
actually picking up differences and accent, inflection and all those
things along with text, along with images, and then put
those together in a certain way. So for anybody that
works in the creative space and tries to come up
(13:40):
with big ideas that are meant to influence people, it's
that multimodal sort of inputs that they have in their
brain naturally that helps them come up with their big ideas.
And with AI's ability to do that and just do
it at such scale, right, it's just it's not tired.
It can hold everything in its head at once. Basically,
(14:04):
we're on the cusp of a creativity explosion.
Speaker 2 (14:07):
I guess the other thing is time, right, Like being
able to get signals and insights on a real time
basis has got to be helpful for developing effective campaigns
for clients.
Speaker 1 (14:21):
Yeah, I think real time is an important facet of this.
We're trying to take in huge volumes of data and
quickly turn those into actionable motions that are also integrated motions,
not just sort of single point content or a single response,
but really integrated campaign level motions. And that sort of
real time dynamic requires a shift in the way our
(14:43):
talent is working, and we're really starting to recast a
lot of the different roles and functions of different key
players who will play a part of being able to
operationalize like real time optimization and impact that comes from AI,
and that's everything from changing the way our strateg just
are functioning, starting to build connections based experience for a
(15:04):
number of our strategists and even our tacticians, starting to
train our creatives to really better understand how to be
creative analysts and better understand how to take in those
signals and apply them quickly to sort of an on
brand creative message. And it certainly changes the way our
analysts work overall in that instead of kind of spending
a lot of their time distilling information, they're spending more
(15:26):
time actioning it and putting it into an insight driven model.
And this takes time to train our talent and to
get them accustomed to working this way, but we're seeing
huge gains in especially our middle level talent, who are
really embracing this call to work differently and seeing the
benefits in their own day to day productivity pretty quickly, really,
(15:48):
as we introduce them to more of the platform usage
that they have.
Speaker 2 (16:06):
Michael maybe talk a little bit about synthetic data on
how that can help with researching ideas and or figuring
out or predicting whether they're going to be effective or not.
Speaker 3 (16:15):
The idea is that with the information that you have,
you can get the information that you need and want.
So there's lots of signals that you do have access to,
but it's often doesn't come in the form of what
you want to actually answer the question, but there's lots
of information in it, so prediction can actually help translate
(16:40):
the data that you have, whether it's at a more
aggregate level, whether it can be combined with different sources
of information about audience, about the creative and the media
vehicle and the context. All of those sort of things
and the effectiveness of it are all signals about data
(17:00):
that you don't have, and then you can predict it
and the prediction can be very accurate. In fact, you know,
we always find that the prediction looks more business representative
than deterministic data because deterministic data suffers from you know,
how we collect it and when it's available, or that
(17:23):
it's behind the walled gardener, that it's properly protected by privacy. Yeah,
that's always going to be the case. So, like we've
spent the last twenty years pretending that we're going to
be able to create the perfect deterministic data or put
it in a clean room or something like that and
be able to do it and the answers we're never
going to be able to. But prediction can give us
(17:44):
exactly what we want and it looks actually more representative
of business because it can solve that complex. I describe
it as a Sidoku puzzle. Right, there's information you have
and there's information you don't, and it can solve that
very complex to give you the data need. Well, once
you have that data, then you can build all sorts
of other applications and use other ais on it.
Speaker 2 (18:07):
So back to my story at the beginning. So I've
got this new label for my beer, I can actually
ask that label without actually showing it to real humans
and show it to you know, and basically AI can
predict what people how they would react to that new
(18:28):
level and tell me whether it's going to sell more
than the old label or or the other options I have.
Speaker 3 (18:34):
It would be even better because it would be based
on human behavior, not human statement. Right right, people don't
tell you what they actually do, but with you know,
data synthesis, you can use actual behavior to tell you,
so it's more powerful.
Speaker 1 (18:49):
I have noticed that's another behavioral shift that's taking a
little bit of time for brand marketers to adapt to,
especially client side. Is we're so used to asking like
what happened last year? And how does that come this year?
And what can we learn from the past, and how
is our learning agenda rooted in like past progress? And
I think this shift and time orientation towards really looking
(19:09):
ahead and leveraging data to do more predictive activations instead
of always spending all of our time looking backwards. Because
of what we can do through some of these technologies
does require like a bit of a mind shift change
when we look at quarterly business reviews, when we do
annual planning. It's an important kind of shift for us to.
Speaker 2 (19:27):
Me or even just trying to sell your CEO that
this is the right label, even though you've never shown
it to a human you know, I mean, because it
does take a leap of faith to understand the you know,
how the how AI really works right, and so I'm
sure that'll be a you know, a barrier for.
Speaker 1 (19:44):
Some Well, it's not just the big ideas that we're
testing and like using synthetic data on. It's also like
the campaign go to market strategies. I think that this
has some of the most profound effects on you know,
so many corporations have like codified a checklist of a
launch strategy, you know, they have like a canned go
to market approach when they're releasing a new product or
a new skew. And I think what we're trying to
(20:05):
move people towards is understanding we can use a lot
of this predictive technology to help us actually build the
right alchemy for a launch and be able to gather
real time insights and make adjustments to that instead of
always having to use the same playbook, which is exciting.
Speaker 2 (20:21):
Yeah. I remember from IPMG days, fifteen hundred GRPs per
week for a launch that was sort of like the standard.
And as you say, there's probably especially given the product
and the competitive set and the way the world's changed,
and you know, obviously people don't just use GRPs anymore,
but you know, optimizing that using AI will be super interesting.
(20:45):
I'm sure there's like a million better ways to do
it than just you know that traditional.
Speaker 1 (20:51):
Yeah, beyond even just channel mix, like also the type
of content we're putting out into the world, the mix
of assets, you know, instead of your traditional list of
sixties and fifteens and cut downs and graphics, like, we're
actually able to produce a higher volume of assets and
put them in market faster, so we can change our
production strategies also, which is really exciting.
Speaker 2 (21:10):
Yeah. I mean, at leads you mentioned the creative flywheel
or creative effectiveness flywheel earlier. Maybe just talk a little
bit about that and how it works. I'm interested in
both your perspective on it because I think it's a
super interesting concept.
Speaker 1 (21:26):
Yeah, I mean, with all the diagram I'll just have
to visualize it for you. But the idea is to
close down this gap and really begin to connect all
the parts of creative measurement, activation and strategy together. And
what this does is by putting AI sort of at
the center to power this model, we can supercharge each
of the individual parts. We can do way more in
(21:46):
creative in production than ever before. We can measure out
a much more regular capacity, we have the ability to
activate and personalize, and we have the chance to bring
strategy to a whole new level with real time insights
and faster real world data, plus the predictive capabilities. But
what's really exciting is not only is it connecting and
supercharging each of those parts, but when it's all working together,
(22:08):
it starts to create this like very seamless, very proactive
motion where the individuals are working in concert in service
of overall impact. And so you almost see no divide
between those functions. You almost see no divide in terms
of the time it takes to produce and adapt our
creative and you most certainly see higher impact on every
single move you make because of how fast and rapid
(22:30):
this motion is enabling effectiveness. And we're really beginning to
move this into this center of our strategy for a
lot of our core customers, aligning ourselves and our technology
and their internal operations to support this flywheel effect.
Speaker 2 (22:46):
So is the way I should understand this is that
you go from sort of the old batch process, right,
come up with a strategy, then do the do the creative,
test the creative then decide you know how you're going
to put it in market then or how it did
in market, then take that measurement and go back and
change the strategy, etc. It goes really from a batch
(23:07):
process too. It sounds like a real time process where
each thing is being impacted and changed on the fly,
as you call it, with a flywheel, based on what
the signals are coming from the market.
Speaker 3 (23:19):
Yeah, you've talked about system change often on this podcast
bro Various Gas, and this is the system change, going
from sequential to going multi threaded at the same time.
So you're rearranging the process and then how you put
people into it and it performs better.
Speaker 2 (23:40):
It's like a.
Speaker 3 (23:42):
Big shift a seismic shift upward. It is a result
of being able to go from that sequential to multi
threaded and fast construct.
Speaker 2 (23:52):
It also give direction on who to target, where to target,
what to sell, where to put your empat is, what
price like, yeah, how big could this be? How expensive?
Speaker 1 (24:05):
All of those things? And I think initially we're applying
the flywheel as like an alternative to your point, to
the traditional sort of waterfall approach the way we develop
creative But what we're finding is as we begin to implement,
we are not only finding a capability to make much
much more progress around the rapid adaptations of this, but
the types of ways that we're adjusting the work are
(24:27):
very widespread. So we're able to implement bigger learning agendas
than ever before without having to have enormous tests and
learn budgets, And we can actively work to answer much
more difficult and burning questions that our customers have, not
just about ROI or row ass effectiveness, which is traditional
learning agendas are a lot often focused.
Speaker 2 (24:48):
On is being returned on an advertising.
Speaker 1 (24:50):
Spend precisely, I think you know, we can answer those
questions certainly, but we also can start to answer bigger
business level questions like, which of my audience cohorts are
most like to have a collapsed journey and move faster
to conversion? How might I define influence in the way
that this target audience is moving in its relationship with
my business? And we're really up leveling our learning agendas
(25:13):
and our integrated measurement models because we can now effectively
and affordably run those in real time.
Speaker 2 (25:20):
Super interesting, And I mean you could see this working
not just in marketing of products and services, but also
presumably for charities, for politicians, for I mean, this is
you know, how broad do you think this could be
as far as the kinds of industries and organizations that impacts.
Speaker 3 (25:39):
Yeah, I mean we're just giving one very clear case
of where this idea applies in our industry, but just
every reason to believe that it can work in anybody's
into I mean, we see it working in different industries
as it relates to communications industry that we're in, anywhere
where that matters everything for education to I mean, education
(26:03):
is just a big area. Whether it's about policy and
communication of government policy and the effectiveness of our media system,
or the way that we educate children, all of these
things are open to impact from this sort of responsive
(26:26):
system that can very quickly monitor what's going on in
that interaction with the human and respond to it in
a way that affects the outcome with them, whether it
be a learning goal or whether we're trying to influence
somebody to vote for our favorite political candidate.
Speaker 2 (26:44):
And Chrislin like, do you I mean it will to
be a need for agencies or even marketers in the future,
given what day I can do.
Speaker 1 (26:51):
Yeah, I mean I love the proposition of the agency
of the future. I think it's so exciting because I
have always worked as more of a generalist type prectic titioner,
and I think this unlocks the potential for us all
to have democratized access to intelligence, to be more creative.
You know, we those of us who can't draw our
design now have the potential to think like a creative,
(27:13):
to be an analyst without having to work inside of
a dashboard or manage sort of critical data feeds on
a daily basis. It democratizes access to so much of
our discipline. I still think that you know, everybody says this,
you know, human ideation, human strategy, our ability to think
ahead and use the technology as a superpower is probably
where we're going to be for the next you know,
(27:35):
several years. But the idea that maybe we can do
more with less and maybe have more time with our
families and still be as productive as we are today
actually seems like an awesome proposition to be in, one
that our young talent seems to be getting behind for sure.
Speaker 2 (27:49):
Yeah. I had to have a Goldfarb who I know
you both know, on a podcast, and he had this
interesting analogy around what happened with websites, you know, when
people had to code them themselves. You know, there were
lots of websites being built, and then along game software
that made that much easier, whether it was no code
(28:12):
or low code or whatever, and suddenly the number of
websites and apps, etc. Exploded, Right, So you just had
more people being able to do them and do them
much more efficiently. You know, I agree with you. I
think that's what's going to happen for marketers and agency people.
They're just going to be able to do a lot more.
Speaker 1 (28:29):
Yeah, engineers of everything they need in front of them,
which is a brilliant role for smart people to play.
Speaker 2 (28:35):
Last word to you, Michael, so where do you see
this going? Like it take us a year or two out.
And I know we talked about system change, but what
do you think the role of AI is going to
be in organizations? You know, get over this barrier of
adopting it.
Speaker 3 (28:51):
It relates to what we're just talking about. And you know,
I'm sort of stealing this from something I heard from
Jensen Hlong recently. He was on the podcasts with Bill
Gurley and Brad gersonal Meg two podcast and he said, listen,
we're heading towards a world where everybody's going to be
their own CEO. Where we didn't talk much about agentic architecture,
(29:13):
but it's the idea that you very quickly can spin
up your own staff of ais with different abilities and
talents and be the CEO of them. And that's what
the world is likely to look. I believe in that,
and we believe in agentic AI as a path for
scaling among other paths for scaling AI, and it gives
(29:38):
more individuals control. It's like this, we're talking about human
in the loop of AI, Like how much easier can
it be than allowing humans to be more in control
of around destiny versus having to work in organizations and
communicate with each other. That can't be as efficient as
we'd like to think it is. I think we'll see
more of that, whether it's moving. We're talking about the
(29:59):
creative flywheel in particular, it will allow individuals to have
greater access to the things that need to be put
together to do great things, whether it's great campaigns or
bring great ideas to market.
Speaker 2 (30:12):
Yeah, well listen. As the CEO of Plus Company, I
welcome all of you to be CEOs as well. It's
a great it's a great vocation, so listen. Thank you
for this discussion. Super fascinating. We could probably talk about
this for hours, or at least I could, but I'm
really looking forward to what the future is like. And
thank you for all the work you guys are doing
(30:33):
in order to make AI come to life in our company.
Speaker 3 (30:37):
Thank you for having us to talk about it here.
Speaker 2 (30:39):
Great, Thank you, thanks for joining us for another episode
of partners and Possibility. I really have to thank both
Michael Cohen, Global Chief Data and Analytics Officer at Plus
Company and Krystalin Stuart Louisa, chief Digital Officer at Citizen
Relations and Mechanism for providing their thoughts on how we
(30:59):
can utilize AI and how AI can predict and measure
the future success of marketing campaigns.
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