June 23, 2025 • 35 mins
Variety hosts a conversation during the Cannes Lions festival with Michelle McGuire, principle and chief commercial officer of Converge by Deloitte, and Ruba Borno, VP, global specialists and partners for Amazon’s AWS. The two discuss how their companies are harnessing AI tools and data analytics to guide sports marketing expenditures for blue-chip brands. The pair reflect on how their experiences working together underscore that even the biggest corporate players need partners to help drive the AI productivity revolution.
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Speaker 1 (00:07):
Hello, strinkly business listeners. Here is a special bonus episode
for you. I'm Cynthia Lyttleton, co editor in chief of Variety.
I had the good fortune to spend last week in
the gorgeous French riviera at the Canlon Festival of Creativity.
It's a week long event that brings together heavy hitters
in marketing, media, tech and business. It's a whirlwind of
(00:31):
sales pitches, elaborate brand showcases, old fashioned PR stunts, and
deep discussions about how marketing, media, technology and content are evolving.
As you could imagine, There's a lot to take in
and almost all of it involves AI. Variety was fortunate
to host a discussion with two senior executives who are
(00:54):
deep in the trenches of transformation at companies with incredible
scale and global reach. Michelle McGuire is principal and Chief
Commercial Officer of Converge by Deloitte. Ruba Borno is Vice president,
Global Specialists and Partners for AWS, Amazon's busy cloud computing arm.
(01:15):
The two speak with me about how their companies are
harnessing tech, data, analytics, and AI tools. They also reflect
on how their experiences working together underscore that even the
biggest corporate players are going to need partners. It's going
to take companies with highly specialized superpowers to help drive
(01:35):
the AI productivity revolution that is already in progress. McGuire
and Borno give us a lot of practical, real world
examples that help make some of these mind numbing concepts
more understandable. Because we were in can in June. We
recorded this outside on a yacht docked just off the Crosset.
(01:56):
If you listen all the way to the end, we'll
give you a little color on what was going on
around us as we spoke. That's all coming up after
this break.
Speaker 2 (02:14):
Can Lyon is where the advertising and communications industry meet
to celebrate the world's best work. During the Festival of Creativity,
Deloitte shared insights for sport, media and entertainment companies, from
designing end to end data platforms built on leading cloud
technologies to understanding fan data to better personalized digital experiences.
(02:34):
Don't miss the opportunity to meet your fans with the
content and experience that mattered most to them. It's time
to be as obsessed about the data as we are
about the game. Deloitte and AWS can help.
Speaker 1 (02:48):
And we're back with a special Strictly Business Live episode
from can Lion with Deloitte's Michelle McGuire and aws's Ruba Borno.
Speaker 3 (02:58):
Welcome, thank you.
Speaker 4 (03:00):
Thank you for joining us today. This is a first
for Strictly Business varieties weekly podcast featuring conversations with industry
leaders about the business of media and entertainment and sports.
Today we're going to be talking a lot about sports.
We are at the beautiful Canlon Festival of Creativity in
the south of France, the French riviy Era, and it
(03:22):
is gorgeous. We have a terrific pairing here of two
major companies that everybody has heard of. And what's really
cool about this pairing is that both separately and individually,
Deloitte and AWS are doing very cool, very innovative, thinking,
forward looking activities. Together and separately, we have Michelle McGuire,
(03:45):
Principal and Chief Commercial Officer of Converge by Deloitte, and
next to Michelle is Ruba Borno, vice president and Global
Specialist and Partnerships for AWS. Thank you both for making time.
I know it's a schedule here, the place is hoppened,
so we really appreciate you giving us this time.
Speaker 5 (04:04):
Thanks for having us.
Speaker 2 (04:05):
It's so beautiful here at great Weather, So could not
be better positioned to talk about sports and media and entertainment.
Speaker 5 (04:12):
Absolutely sharing space. So thank you Cynthia, and thanks Michelle.
Speaker 3 (04:16):
Again, thanks for making the time.
Speaker 4 (04:18):
So, as I said, you guys are both doing very
cool innovative things separately and doing some work together in partnership.
Let's start there. Let's start with Converge. Michelle, why don't
you give us that elevator pitch on what Converge is?
Speaker 2 (04:32):
Sure? Yeah, So a few years ago, Deloitte decided to
start investing ahead of the curve in all things AI
and sports, and so Converge was born with the kind
of intent of at the intersection of AI and industry.
They were coming together to create differentiated, accelerated solutions for
(04:52):
our clients. And so the firm is invested over a
billion dollars at that intersection and gladly partnered with a
WS to build out industrialized products that are available to
clients to accelerate them to value to outcomes and to
impact Ruba.
Speaker 4 (05:11):
Can you talk about how AWS plugged into that and
why you were the right platform.
Speaker 3 (05:17):
Yeah.
Speaker 5 (05:17):
Our partnership with Deloitte goes a really long time. I
mean Deloitte and AWS have been partners for over a
decade where we have been serving clients together, supporting them
first on their cloud journeys. So initially, at the inception
of cloud services, Deloitte and AWS would go together to
customers and help them migrate out of their data centers
which were energy inefficient and also didn't have the innovation
(05:40):
and the number of services that AWS could offer. Today
we have two hundred and forty plus services. And so
with Deloitte where they have a deep understanding of the industry,
and so you take even sub industry, so within sports,
it could go into different types of sports and different
audiences and sports, and then we've got the technology underlying
technology services that can support them in delivering some of
(06:02):
those outcomes. And so we've been working on business outcomes
and solutions with Deloitte for a really long time. I
think that's where the partnership comes to life, is how
do we deliver an outcome to a customer. And that's
what's really exciting about the converged platform is the outcome
is a differentiated and unique fan experience, and it's in sports,
but it can actually apply more broadly than that.
Speaker 2 (06:23):
Yeah, I think AWS is uniquely positioned given there obviously
the infrastructure associated with cloud, but also with their AI
enabled tools. It allows us to tap into their subject
matter expertise, their engineers, They support us from a product perspective.
Speaker 3 (06:39):
So for us, it's.
Speaker 2 (06:40):
Always easy to go with best in breed and so
partnering with AWS is always a great choice.
Speaker 4 (06:45):
Right, And I think, I mean obviously if you have,
if Deloitte has put a billion dollars into this UC,
massive opportunity on the horizon, I think.
Speaker 5 (06:54):
So like AI is everywhere here and can and.
Speaker 2 (06:57):
You would imagine that a festival of creativity would be
about you know human you know innovation. However, it's about
how you partner with the tech and with the technology
to technology to transform the creative process for a different outcome.
And it's no different with fans in sport, right they
expect a personalized experience and that's what Converge does is
(07:20):
it enables fans to identify, raise their hand and then
for us to basically understand what is their affinity, what
is their propensity to engage to buy a ticket, How
do we you know, understand your fandom score of which
is effectively lifetime value of those fans to both sports
organizations as well as to partners who or brands who
(07:44):
really are about the coming together of the league the team.
So I feel like, you know, there's a bit of
a push towards all things fandom and the ability to
measure it in a way that historically we cannot.
Speaker 5 (07:57):
And just maybe Michelle building on that, because I do
think with Generator AI and personalization, partnerships are the future.
It is the only way to provide that personalized experience.
Speaker 4 (08:08):
Because nobody has all the data and all of the
ability that makes it.
Speaker 5 (08:13):
There's just much more value out of bringing the data
together and the insights from it. So actually a service
that Deloitte uses as aws clean Rooms, and this is
one where you can bring data from multiple parties and
the underlying raw data is still secure and owned by
the entity that brought that data, but you can draw
insights from the collective data, and so that allows us
(08:36):
to get insights. One of our customers is Coca Cola,
for example, and they're able to use it to provide
aggregate insights to their advertising team, their marketing teams and
then be able to provide a personalized experience to their customers.
Another customer I would call out is the weather channel.
They worked with Latima on a travel and hospitality customer
(08:56):
of THEIRS, and they're pulling data from a bunch of
different source and using clean rooms. They care about the insight,
not the underlying data. They care about the answer, which
is this individual a low frequency flyer or a high
frequency flyer? What is their brand affinity? What are the
brands that they are associated with? Do they travel by
air or land or by sea? And then as a
(09:18):
result of that those attributes, they're able to actually get
those insights. I think the number I saw was ninety
eight percent faster, and the entire process and the cost
to run it is seven times more efficient than what
they were doing before. And so that's the power of
partnerships is you get the answer faster, it's lower cost,
but more importantly, you're able to provide that personalized experience
(09:42):
to that end customer. And I think that for consumers,
for customers, for advertisers, this is table stakes today, and
so the more data you have, the better insights you have,
and you can be differentiated.
Speaker 4 (09:54):
Right, you are going light years beyond age, gender general
g To your point, it doesn't matter whether that person
who flies all the time, you know, lives in Cleveland
or lives in Los Angeles.
Speaker 3 (10:08):
I guess some of it matters according to their airport.
Speaker 4 (10:09):
But the larger point is that you're able to just
find those those discrete pockets of potential audience in a
way that is just it's fascinating. It's three dimensional chests
versus when I started, I would get a fax, so
this dates when I started covering up television.
Speaker 3 (10:28):
I would literally get a fax of.
Speaker 4 (10:30):
The overnight ratings, and when I could see the grainy numbers,
sometimes they'd blur together. I could see the grainy numbers
and I'd look market by market. They did fifty you know,
people in television know this, fifty markets or fifty six markets.
Speaker 3 (10:44):
It got to fifty six markets.
Speaker 4 (10:45):
That was seventy percent of the country, and we'd scrutinize
that and look for the patterns. But this is just
incredibly written large Now with Converge, one of the big
big use cases for you is really really connecting sports teams, events,
games and sponsors and just making them forgive me home
runs every time. All the sports analogies I'm coming really
(11:08):
showing some discipline here and what.
Speaker 2 (11:10):
Happened to your baseball hat you had it on earlier.
I think we're going to need that back for this
many sports analogies. It's a great point because if you
are a sponsor of a team or a league, what
you were interested in is that then diagram of the team,
the league, and your own brand plus what Converge then
offers you is a basically a data fabric of two
(11:34):
hundred and sixty million adult Americans. We know who you are,
we know what you care about, and then we use
our propensity modeling orip our understanding of sports and fandom
to hand that off to sponsors so that they're then
able to activate.
Speaker 5 (11:51):
In a more personalized way.
Speaker 2 (11:52):
To your point, right, so it's about what player do
you care about, what merchandise do you buy as a result,
and how do I get you to kind of engage
with the brand across the lifetime of fandom in a
way that makes engagement feel more personal and like it's
going to continue to engage me with both my team
(12:16):
and the brand.
Speaker 4 (12:16):
At that intersection, the metric that everybody's looking for is
that time spent?
Speaker 3 (12:22):
Would you say, is that?
Speaker 2 (12:23):
So we've actually evolved this idea of lifetime value into
something called a fandom score. So it looks at all
of the attributes associated with your engagement. So that might
be in person, that might be online, that might be social,
it might be your sentiment that you're putting out into
the universe, into the social universe. And then it applies
(12:43):
some of your behaviors, whether that's purchase behavior or it's
browsing behavior in a way that is, you know, frankly
a bit evolved than the traditional time spent viewing, which
we both come from a TV background. I spend lots
of years at Turner Broadcasting.
Speaker 5 (12:57):
Like Nielsen used to.
Speaker 2 (12:59):
Be the metric and it was yes or no? Did
your eyeballs watch this content?
Speaker 5 (13:03):
Yes or no?
Speaker 2 (13:04):
So now to think about, we now have a metric
that is twenty four different data attributes associated with how
much you love a brand in your fandom. Is you know,
light years ahead of where we were, you know, just
a few years ago.
Speaker 4 (13:18):
Rubert, were there anything in helping Deloitte put Converge together?
Speaker 3 (13:22):
Were there any was there any R and D any.
Speaker 4 (13:24):
Innovations that AWS did to empower what they wanted to do.
Speaker 5 (13:30):
Yeah, there's a wide variety of services, but maybe a
couple that I'll dig into is Amazon Personalization, which actually,
you know, you can put in the data. Whatever the
customer is puts in the data. It then pull is
data like user their interactions, clicks, time spent somewhere, any
purchasing behavior. So it's not just what you tell me
you want to do, but what you actually do that
(13:51):
we're now measuring. And that's I think what the personalization
is all about. Then taking that and it actually selects
the data to train a model that is based on
the user's actual behavior, and then that model is used
now to predict what the next user is going to
do based on similar attributes. So that's just one example
(14:11):
of what we're using. And then the other piece is
Deloitte's been an amazing partner with us with Generative AI
and using Amazon Bedrock, which is our managed service that
allows the customer and the partner to utilize whatever large
language model is fit for purpose for that use case.
So whether the fit for purpose is analyzing image data,
(14:32):
or taking real time speech and then converting it to text,
or getting a whole bunch of data points and putting
out insights. So one example we were talking about earlier
Cynthia was Formula One. We worked with Formula one on
Track plus and in this solution you've got it's not
like there's one ball in a stadium, not that we
(14:53):
don't like ballsports, but this is a sport with twenty drivers.
They're going up to two hundred and thirty miles per
hour around a racetrack, and there are one million data
points per car per second. Now imagine your former commentator self.
That's like reviewing that and trying to give a fan
some useful information. Oh and by the way, this isn't
(15:14):
the only race they've raced in. There's historical data they've
switched the tires because it's wet or dry, or maybe
they chose not to switch the tires for whatever reason.
All of that information now can be provided in real time,
contextualized historically and giving the why behind it. And now
the experience for fans is much richer. And this is
(15:35):
a very global audience. Five hundred million plus fans and
I think anyone that watches Drive to Survive like that
took the viewership up, so very diverse group. And now
you're able to tailor these insights to that audience.
Speaker 3 (15:49):
That is fascinating.
Speaker 5 (15:51):
Think about it. From a fan experience because some fans
want the stats and the technical details, some want like
the history, and then there are some that really want
the drama. I mean they want like you know, they
bang the steering wheel or they were really upset or
through their helmets or whatever. You know. That was actually
something that we learned working with Bundeslega, which is the
(16:12):
German Football Association professional football sairly rabid fans, yes, yes,
but very diverse, right. There are some who want the metrics,
they want to know every play and how does that
rank versus other players and history, and then there are
some that want the drama and they want to know
about any fights or they want to know about injuries.
And it's you know, now with the Bundesloga app, you
(16:33):
can actually drive up engagement because you are personalizing the
content that's delivered to the user based on what they want.
And that's what's really exciting is it's a personalized fan
experience and they're seeing engagement go up significantly. The time
that the app users are spending there going up because
it's the stuff they are interested in.
Speaker 4 (16:52):
In terms of just pure connectivity, the fact that the
drivers can speak to their pit crews and speak to
each other on the and and you know we at
times we can hear that definitely adds to the drama, Michelle,
no I was.
Speaker 2 (17:03):
Just going to add the tech piece associated with that.
You know, we use something called a non supervised clustering
algorithm to have the machine basically look at the data
in a way that humans don't. So to your fact's
reference where you used to sit and do the analysis
yourself that you know, we're now allowing the machine to
train itself to understand who those audiences are and then
(17:25):
using jen Ai and Betrock and in this case stage
Maker two also an AWS product to basically name those segments.
So something that used to take marketers, you know, in
some cases weeks to do the analysis to name the
actual segment.
Speaker 5 (17:41):
We're now letting the machine do it for us.
Speaker 2 (17:44):
And then it's the When it names the segment, marketers
are then able to act on it and push that
data downstream to do the outreach, whether it be via
mobile or social or app, whatever it might be. The
machine is now doing that for you, and it's closing
the loop via mL operations to continue to train it
on what those fans want.
Speaker 4 (18:03):
The loop or you know where the line ends between
the machine learning, the training and the generative AI. That
is going to be a science going forward for any
number of applications. And I think this is a really
interesting one.
Speaker 1 (18:17):
Don't go anywhere. We'll be right back with more from
Strictly Business Live from can Lion.
Speaker 2 (18:25):
While brand alone used to build fan engagement and loyalty,
today's fan expectations have shifted and organizations can be challenged
to deliver interconnected fan experiences. What if you could give
fans the experience they want by seamlessly integrating touch points
like ticketing, athlete interactions, game streaming, and more within a
single intuitive platform. Imagine a fan data platform that takes
(18:49):
your customer data and puts it to work, creating advantage
for your team and stakeholders. Converged by Deloitte for Sports
empowers organizations to design personalized digital experiences at scale to
delight your fan base with holistic experiences.
Speaker 1 (19:08):
And we're back with Strictly Business Live from Canline.
Speaker 3 (19:12):
Michelle.
Speaker 4 (19:12):
Are there anything so if I understand right, Converge has
been it's been in the works for a while, it's
been active in the marketplace for about a year.
Speaker 3 (19:19):
Anything you know, any top.
Speaker 4 (19:21):
Line kind of something that surprised you about you know
the behavior of sports fans or what you know, what
motivated sponsorship or you know, things that maybe things that
weren't you know, obviously intuitive.
Speaker 2 (19:35):
Well, so first converge consumer was the first converge. So
we took that same data fabric that we talked about
around retail and consumer product data, and we learned from their.
Speaker 5 (19:47):
General behavior and share of wallet.
Speaker 2 (19:49):
Right, So we took all our learnings from retail, and
then we applied sports specific data around ticket purchase as
well as t affinity based on primary research or digital research,
and then brought the two together to inform the fandom
score that I mentioned. And so the thing that I
(20:11):
find most interesting about fandom is that your fandom is
not unique. Right, It crosses it crosses music, it crosses
your retail and buying habits, it crosses all the areas
of your life. So you know you think about that,
that's intuitive. Yeah, Like sports fans are not you know,
they're not a monolith. They are they are rabbit about teams,
(20:33):
leagues in different sports. So how do you look at
them holistically as a fan and then act upon that
unique fandom? In that audience of one.
Speaker 5 (20:43):
Just to add to that, Cynthia, because it's it goes
beyond a personalized experience, it's actually a personalized monetization experience.
Speaker 3 (20:52):
So you can think it doesn't like it.
Speaker 5 (20:56):
Right, You've got you know, subscribers, maybe businesses that have
subscription service, or businesses that are selling certain content, or
an ADS based business, and you can now with the
fandom score, be able to apply that score or any
of those insights provided by Generative AI to determine, Hey,
is this consumer do they have Maybe they have a
(21:17):
low propensity to subscribe, but they have a high propensity
for or they have a high AD score, and we'd
be able to make we as a vendor, whomever the
vendor is, could make money off of advertising. So it's okay,
if they don't subscribe, maybe we'll let them pass through
the paywall to get the content because the AD is
going to generate more revenue for us than the subscription
(21:40):
even though they're a low propensity to subscribe. So thinking
through how to use that type of information in the
monetization model and to do that in real time based
on the person I think is really valuable.
Speaker 4 (21:52):
I'm curious about the score is Is it a numerical score,
is it a like certain traits about a person? How
do you you say the fandom score? How do you
exactly calculate it? And how do you express it in
a way that marketers can interpret it? Does it expresses.
Speaker 2 (22:06):
Itself as a number one, two, one hundred, And it's
always in relationship to the brand you're talking about and
the sports league you're talking about, right, So, but the
good news is that you can adjust it. It's obviously
a weighted metric. You can adjust it based on the
behaviors that your sponsor might be interested in. Right, So,
(22:30):
if a sponsor is interested in selling more live event tickets,
then you can wait it based on discretionary income available
to purchase tickets. And obviously an F one ticket is
a much more pricier spend than a Dodger's game. So
you can look at that in a way that is,
you know, that downstream enablement and enactment of you know,
(22:53):
the score as the you know, kind of numerical representation.
Speaker 4 (22:57):
And I would imagine although with you know, some three
hundred million people in certainly in the US, monitoring those
scores would be would be challenging but I would bet
that the concept of that that people are would are
very eager to look at.
Speaker 3 (23:12):
And see what those scores are for folks.
Speaker 4 (23:14):
And I would imagine that if you're really high in
the fan engagement, you're going to have other attributes. You
might be an early adopter of technology. I would imagine
that you're able to draw those kind of parallels.
Speaker 3 (23:24):
Absolutely.
Speaker 2 (23:25):
The algorithm basically surfaces the attributes that are associated with
certain behaviors, right, So this attribute drives live event propensity, right,
So it tells you and we also use the gen
AI to say, this is a high and tending population
who care about, to your point, early technology adoption. So
(23:48):
give them an immersive experience that looks different than you know,
somebody who might be cost conscious who does not want
a technology component.
Speaker 3 (23:56):
Right.
Speaker 2 (23:56):
So the ability to give those i'll say in person
activations in a way that the sponsor cares about is
important to CMOS everywhere.
Speaker 5 (24:08):
I'll add one more thing that's with the emergence of
agentic AI, where it's in some cases it's not eyeballs,
it's it's actually machines. You know, how do you know
whether it's a machine or a person. And then what
data if it's a machine scraping to get an insight
to give to someone, versus it's a person that's actually
(24:28):
engaging and wants to giving. Yeah, yeah, so how do
you you know? We're using AI to do that. And
actually I was on a panel yesterday with a couple
of our partners, and Adobe had mentioned that they've been
measuring the number of machines that are interacting with their
their platform and in the last six months it went
(24:49):
up something like three thousand, five hundred percent. Maybe don't
quote me exactly on the number. It was over three
thousand of machines versus people. The machines are doing something useful, right,
They're getting information and sending it somewhere to someone who's
going to use it. But how you present content for
that is very different. How you monetize that it is
very different.
Speaker 4 (25:08):
You're using AI to ferret out the machines that are
confounding the AAI that is that that is very.
Speaker 3 (25:15):
Indicative of our times.
Speaker 2 (25:16):
Yeah, it's the future, right, agents acting on your behalf
is the evolution of AI gen AI that are you know,
doing schedule optimization and always looking at the scores, making recommendations,
running in the background, Those algorithms and agents are are
the future of how fans are going to engage with Historically,
maybe it was statistics, or it was you know, any
(25:38):
sort of live event. Even right how you're engaging with
the device at all times is going to radically change
in the next year.
Speaker 5 (25:48):
What's cool about agents, especially for in this space, is
it they can allow you to run multiple campaigns at once,
do A B testing way faster than you could before.
I mean, they can parallelize the entire higher workload. And
so I mean, I think kind of traditional A B
testing maybe you can do two tests per week, you know,
per analyst, and now with agents, I mean that number
(26:09):
can grow exponentially, and then you can, you know, you
can figure out which of these campaigns or which of
these experiences is actually driving better results because the agent
was actually able to modify the campaign that you were
going to put out there, test it, get the data,
give you the results, and then you keep pushing and.
Speaker 4 (26:27):
All that we're talking about, which is again so personalized,
so individualized. It does underscore the need for massive, massive
amounts of cloud storage. It really does, because somebody has
to maintain that, and I know, you know, there's costs
to maintaining that. There's you have to keep it. I
understand you have to. It's something that you have to
(26:49):
keep it fresh. It can't get too stale, otherwise you
compromises some of the efficacy of the.
Speaker 5 (26:54):
The data challenge is probably you know, step number one
is you have to have a data foundation. If you
look at and I don't want to say legacy media
companies because it actually plagues every single industry frankly, is
the data is not all in one place. So if
you're thinking about personalization by aggregating data from the ads team,
from the marketing team, from the content team, today many
(27:16):
of them still sit in silos. The functions are in silos,
their data is in silos, and the tech is also siloed.
And so being able to bring that together is step
number one is getting your data in order, put it
all in one place, being able to use these tools
that can give you the insights it is really key.
And so that's why actually I love the name Converge.
(27:38):
I'm not a marketing professional or brand person, but I
think you guys did a great job with that name,
because that is what it's all about, is bringing that
data together to converge and insight, and.
Speaker 4 (27:46):
With large organizations, we've all been there in meetings, the
possibility of a small communications lapse or somebody didn't talk
to somebody and you're looking for something and then a
week later, somebody's in a meeting saying, oh, I have
that right here, but you couldn't put your hands on
it because it was all in different spots.
Speaker 5 (28:04):
That's the number one bottleneck. And a B testing is
the analyt doing the work doesn't have access to the
data and it you know, the person that needs to
provide them the data hasn't checked their email and all
of that. And so the idea of getting your data
in one place and having a comprehensive data strategy, which
we do constantly with Deloitte when we support our customers
is really step number one.
Speaker 2 (28:24):
Yeah, and we've gotten to the point where, you know,
prompt engineering it's a misnomer for sure, It's just how
you interact with the model and ask the questions. Has
made it so that you know, when you can't find
that piece of data or research, it now spans your
organization scours it for you and then surfaces it with
an insight or an action. You now need to take
(28:45):
as opposed to having to crunch the number and read the.
Speaker 3 (28:48):
Facts really understanding.
Speaker 4 (28:49):
Just as people that understand code now have a skill,
the coding of the future is going to be prompts.
Speaker 5 (28:55):
There is the prompt side of it, which is a
reactive engagement mode. So like I, I put in a
prompt and you know the the LM is reacting and
giving me an answer. But the agentic kind of wave
that is upon us is just proactive. It's just doing
things on our passing it, and that's you an end goal.
Speaker 4 (29:15):
I'm still wrapping my mind around. You know, there will
come a time everything is becoming Hollywood and Hollywood everybody
has an agent. Soon everybody will have an agent, and
somebody will have to store all that information or maybe
maybe the state of that art will be finding more
efficient ways to store it in a way that you
can put it over here, but grab it when the person,
either the person needs it or the person putting in
(29:36):
the prompt.
Speaker 5 (29:37):
The cool thing about agents is they're kind of optimized
for a task or a part of a workflow. And
so if you think about someone in a marketing function,
you know they've got to think about multiple workflows and processes,
and you can have agents that support a sub segment
of that and so having them all work together on
behalf of that individual to figure out, hey, what is
(29:58):
the most optimal If you're optimizing for cost or if
you're optimizing for reach, or whatever the outcome you're trying
to achieve, multiple agents can come together. We have multiple
partners in our AWS partner network, and they're all contributing
agents to a broader network because you can't do it
with one company. I think it does require partnerships and
(30:22):
experts in certain verticals or in certain processes or in
certain workflows, and bringing all those agents together to interoperate
and deliver that optimized process. I think that is going
to be the future. And it is all proactive, like
they're doing it on their own, and the business leader's
job is now to ask the right question and what
the key outcome is that they're trying to achieve.
Speaker 4 (30:43):
Michelle, is there any next frontier for convergence?
Speaker 2 (30:46):
So the next frontier is media, of course, so converge
media not too far off, but applying the same concepts,
right of measurement, engagement. What does it mean for media
organizations who care about all things fandom for potential subscribers
and who is next as a subscriber and who doesn't
(31:07):
subscribe today? And how do I talk to them in
a tone of voice that gets them down the funnel
to convert faster. I think your fandom in characters, titles,
worlds all influences your purchasing behavior when you think about
streaming and subscription, and so for US, that's the next
(31:27):
frontier to take. All that we've learned and done with
AWS is to apply to yet another industry for US.
Speaker 4 (31:34):
And I wanted to ask you, is this largely US
centric at this point? Are you largely focusing on the
US consumer today?
Speaker 2 (31:40):
Our data fabric is focused on the US consumer. We
definitely have ambitions on global more global markets, but as
you know, data around individuals is variable by country, so
our ability to stitch it together in a fabric is
going to take a little work. But yeah, we're definitely
working on global and other industries as well.
Speaker 3 (32:00):
Well. Ruba.
Speaker 4 (32:00):
Anything on the horizon for AWS that you'd like to
talk about.
Speaker 5 (32:05):
Yeah, I think it's all about, you know, bringing together
the different capabilities and then providing new experiences. And actually,
you know, Cynthia was speaking with you and Michelle earlier
about something I'm really excited about that we're showingcasing here
in the Amazon port and that's taking multiple different large
language models. So Amazon Nova's family of large language models
(32:25):
that are optimized for you know, speech to text or
text to analytics, or creating an image or creating a video,
and you can go in and have an experience where
you tell this machine what inspires you, what are the
fragrances that you like, or what are notes that you like,
and then it'll create an ad campaign for your personalized
(32:47):
perfume with a name, with an image, with a video
and then actually there's a perfumer there that will make
it based on the notes there. And so think about
how generative AI is providing personalization across all aspects of
our lives and taking something that isn't just this it's
all in the cloud. We don't know what that is.
It's you know, big data or data analytics, machine learning agents.
(33:09):
It makes it something tangible that you can touch. And
that's what I love about the possibilities years. It's giving
you things that you can only imagine, and actually you
didn't imagine it. The machine imagined it and made it
real for you, I would you.
Speaker 4 (33:20):
Know the idea of creating your own perfume, and to
the point of even having an ad, the ability to
train it to exactly your agents or your large language
model is to exactly what you need. I think that
that is we're just just starting to scratch the surface
of that, and certainly in media and entertainment, but just
in personal living, our lives.
Speaker 3 (33:41):
So conversations like this express.
Speaker 4 (33:43):
That there is so much there is so much opportunity
to unleash with new technology. It is not something to fear,
it's something to harness in a way that you've given
us some great use cases and listeners. Since you can't
see it, I just want to say that we have
been talking here a lot of about clouds. There is
not a cloud in the blue sky here in Can.
(34:04):
But only in Can would we have this deeply technical
conversation on a yacht where the yacht next door they're
setting up for a party with disco balls and discombobulated heads.
Speaker 3 (34:16):
That's Can for you.
Speaker 4 (34:17):
Thank you all so much, Really appreciate your time, really
appreciate your thoughts.
Speaker 5 (34:21):
Thanks for having us, Thank you, thanks for listening.
Speaker 6 (34:28):
Be sure to leave us a review at Apple podcasts
or Amazon Music. We love to hear from listeners. Please
go to Variety dot com and sign up for the
free weekly Strictly Business newsletter, and don't forget to tune
in next week for another episode of Strictly Business.
Speaker 2 (34:53):
In an era when fan engagement has direct implications on
revenue and brand loyalty, understanding and quantifying fandom becomes crucial
for businesses across industries.
Speaker 3 (35:02):
When teams, leagues.
Speaker 2 (35:04):
And brands understand where their fans and potential partners align,
it creates a unique opportunity. With a deeper understanding of
their fans, organizations can determine which fans to engage at
the right time with the right content, and how to
transform that engagement into value for stakeholders. For example, younger
consumers are more likely to say their fandoms are important
(35:26):
to their identity. According to Deloitte's Digital Trends Report, fandom
can fuel revenue, but only if you know how to
harness it.
Hello, strinkly business listeners. Here is a special bonus episode
for you. I'm Cynthia Lyttleton, co editor in chief of Variety.
I had the good fortune to spend last week in
the gorgeous French riviera at the Canlon Festival of Creativity.
It's a week long event that brings together heavy hitters
in marketing, media, tech and business. It's a whirlwind of
(00:31):
sales pitches, elaborate brand showcases, old fashioned PR stunts, and
deep discussions about how marketing, media, technology and content are evolving.
As you could imagine, There's a lot to take in
and almost all of it involves AI. Variety was fortunate
to host a discussion with two senior executives who are
(00:54):
deep in the trenches of transformation at companies with incredible
scale and global reach. Michelle McGuire is principal and Chief
Commercial Officer of Converge by Deloitte. Ruba Borno is Vice president,
Global Specialists and Partners for AWS, Amazon's busy cloud computing arm.
(01:15):
The two speak with me about how their companies are
harnessing tech, data, analytics, and AI tools. They also reflect
on how their experiences working together underscore that even the
biggest corporate players are going to need partners. It's going
to take companies with highly specialized superpowers to help drive
(01:35):
the AI productivity revolution that is already in progress. McGuire
and Borno give us a lot of practical, real world
examples that help make some of these mind numbing concepts
more understandable. Because we were in can in June. We
recorded this outside on a yacht docked just off the Crosset.
(01:56):
If you listen all the way to the end, we'll
give you a little color on what was going on
around us as we spoke. That's all coming up after
this break.
Speaker 2 (02:14):
Can Lyon is where the advertising and communications industry meet
to celebrate the world's best work. During the Festival of Creativity,
Deloitte shared insights for sport, media and entertainment companies, from
designing end to end data platforms built on leading cloud
technologies to understanding fan data to better personalized digital experiences.
(02:34):
Don't miss the opportunity to meet your fans with the
content and experience that mattered most to them. It's time
to be as obsessed about the data as we are
about the game. Deloitte and AWS can help.
Speaker 1 (02:48):
And we're back with a special Strictly Business Live episode
from can Lion with Deloitte's Michelle McGuire and aws's Ruba Borno.
Speaker 3 (02:58):
Welcome, thank you.
Speaker 4 (03:00):
Thank you for joining us today. This is a first
for Strictly Business varieties weekly podcast featuring conversations with industry
leaders about the business of media and entertainment and sports.
Today we're going to be talking a lot about sports.
We are at the beautiful Canlon Festival of Creativity in
the south of France, the French riviy Era, and it
(03:22):
is gorgeous. We have a terrific pairing here of two
major companies that everybody has heard of. And what's really
cool about this pairing is that both separately and individually,
Deloitte and AWS are doing very cool, very innovative, thinking,
forward looking activities. Together and separately, we have Michelle McGuire,
(03:45):
Principal and Chief Commercial Officer of Converge by Deloitte, and
next to Michelle is Ruba Borno, vice president and Global
Specialist and Partnerships for AWS. Thank you both for making time.
I know it's a schedule here, the place is hoppened,
so we really appreciate you giving us this time.
Speaker 5 (04:04):
Thanks for having us.
Speaker 2 (04:05):
It's so beautiful here at great Weather, So could not
be better positioned to talk about sports and media and entertainment.
Speaker 5 (04:12):
Absolutely sharing space. So thank you Cynthia, and thanks Michelle.
Speaker 3 (04:16):
Again, thanks for making the time.
Speaker 4 (04:18):
So, as I said, you guys are both doing very
cool innovative things separately and doing some work together in partnership.
Let's start there. Let's start with Converge. Michelle, why don't
you give us that elevator pitch on what Converge is?
Speaker 2 (04:32):
Sure? Yeah, So a few years ago, Deloitte decided to
start investing ahead of the curve in all things AI
and sports, and so Converge was born with the kind
of intent of at the intersection of AI and industry.
They were coming together to create differentiated, accelerated solutions for
(04:52):
our clients. And so the firm is invested over a
billion dollars at that intersection and gladly partnered with a
WS to build out industrialized products that are available to
clients to accelerate them to value to outcomes and to
impact Ruba.
Speaker 4 (05:11):
Can you talk about how AWS plugged into that and
why you were the right platform.
Speaker 3 (05:17):
Yeah.
Speaker 5 (05:17):
Our partnership with Deloitte goes a really long time. I
mean Deloitte and AWS have been partners for over a
decade where we have been serving clients together, supporting them
first on their cloud journeys. So initially, at the inception
of cloud services, Deloitte and AWS would go together to
customers and help them migrate out of their data centers
which were energy inefficient and also didn't have the innovation
(05:40):
and the number of services that AWS could offer. Today
we have two hundred and forty plus services. And so
with Deloitte where they have a deep understanding of the industry,
and so you take even sub industry, so within sports,
it could go into different types of sports and different
audiences and sports, and then we've got the technology underlying
technology services that can support them in delivering some of
(06:02):
those outcomes. And so we've been working on business outcomes
and solutions with Deloitte for a really long time. I
think that's where the partnership comes to life, is how
do we deliver an outcome to a customer. And that's
what's really exciting about the converged platform is the outcome
is a differentiated and unique fan experience, and it's in sports,
but it can actually apply more broadly than that.
Speaker 2 (06:23):
Yeah, I think AWS is uniquely positioned given there obviously
the infrastructure associated with cloud, but also with their AI
enabled tools. It allows us to tap into their subject
matter expertise, their engineers, They support us from a product perspective.
Speaker 3 (06:39):
So for us, it's.
Speaker 2 (06:40):
Always easy to go with best in breed and so
partnering with AWS is always a great choice.
Speaker 4 (06:45):
Right, And I think, I mean obviously if you have,
if Deloitte has put a billion dollars into this UC,
massive opportunity on the horizon, I think.
Speaker 5 (06:54):
So like AI is everywhere here and can and.
Speaker 2 (06:57):
You would imagine that a festival of creativity would be
about you know human you know innovation. However, it's about
how you partner with the tech and with the technology
to technology to transform the creative process for a different outcome.
And it's no different with fans in sport, right they
expect a personalized experience and that's what Converge does is
(07:20):
it enables fans to identify, raise their hand and then
for us to basically understand what is their affinity, what
is their propensity to engage to buy a ticket, How
do we you know, understand your fandom score of which
is effectively lifetime value of those fans to both sports
organizations as well as to partners who or brands who
(07:44):
really are about the coming together of the league the team.
So I feel like, you know, there's a bit of
a push towards all things fandom and the ability to
measure it in a way that historically we cannot.
Speaker 5 (07:57):
And just maybe Michelle building on that, because I do
think with Generator AI and personalization, partnerships are the future.
It is the only way to provide that personalized experience.
Speaker 4 (08:08):
Because nobody has all the data and all of the
ability that makes it.
Speaker 5 (08:13):
There's just much more value out of bringing the data
together and the insights from it. So actually a service
that Deloitte uses as aws clean Rooms, and this is
one where you can bring data from multiple parties and
the underlying raw data is still secure and owned by
the entity that brought that data, but you can draw
insights from the collective data, and so that allows us
(08:36):
to get insights. One of our customers is Coca Cola,
for example, and they're able to use it to provide
aggregate insights to their advertising team, their marketing teams and
then be able to provide a personalized experience to their customers.
Another customer I would call out is the weather channel.
They worked with Latima on a travel and hospitality customer
(08:56):
of THEIRS, and they're pulling data from a bunch of
different source and using clean rooms. They care about the insight,
not the underlying data. They care about the answer, which
is this individual a low frequency flyer or a high
frequency flyer? What is their brand affinity? What are the
brands that they are associated with? Do they travel by
air or land or by sea? And then as a
(09:18):
result of that those attributes, they're able to actually get
those insights. I think the number I saw was ninety
eight percent faster, and the entire process and the cost
to run it is seven times more efficient than what
they were doing before. And so that's the power of
partnerships is you get the answer faster, it's lower cost,
but more importantly, you're able to provide that personalized experience
(09:42):
to that end customer. And I think that for consumers,
for customers, for advertisers, this is table stakes today, and
so the more data you have, the better insights you have,
and you can be differentiated.
Speaker 4 (09:54):
Right, you are going light years beyond age, gender general
g To your point, it doesn't matter whether that person
who flies all the time, you know, lives in Cleveland
or lives in Los Angeles.
Speaker 3 (10:08):
I guess some of it matters according to their airport.
Speaker 4 (10:09):
But the larger point is that you're able to just
find those those discrete pockets of potential audience in a
way that is just it's fascinating. It's three dimensional chests
versus when I started, I would get a fax, so
this dates when I started covering up television.
Speaker 3 (10:28):
I would literally get a fax of.
Speaker 4 (10:30):
The overnight ratings, and when I could see the grainy numbers,
sometimes they'd blur together. I could see the grainy numbers
and I'd look market by market. They did fifty you know,
people in television know this, fifty markets or fifty six markets.
Speaker 3 (10:44):
It got to fifty six markets.
Speaker 4 (10:45):
That was seventy percent of the country, and we'd scrutinize
that and look for the patterns. But this is just
incredibly written large Now with Converge, one of the big
big use cases for you is really really connecting sports teams, events,
games and sponsors and just making them forgive me home
runs every time. All the sports analogies I'm coming really
(11:08):
showing some discipline here and what.
Speaker 2 (11:10):
Happened to your baseball hat you had it on earlier.
I think we're going to need that back for this
many sports analogies. It's a great point because if you
are a sponsor of a team or a league, what
you were interested in is that then diagram of the team,
the league, and your own brand plus what Converge then
offers you is a basically a data fabric of two
(11:34):
hundred and sixty million adult Americans. We know who you are,
we know what you care about, and then we use
our propensity modeling orip our understanding of sports and fandom
to hand that off to sponsors so that they're then
able to activate.
Speaker 5 (11:51):
In a more personalized way.
Speaker 2 (11:52):
To your point, right, so it's about what player do
you care about, what merchandise do you buy as a result,
and how do I get you to kind of engage
with the brand across the lifetime of fandom in a
way that makes engagement feel more personal and like it's
going to continue to engage me with both my team
(12:16):
and the brand.
Speaker 4 (12:16):
At that intersection, the metric that everybody's looking for is
that time spent?
Speaker 3 (12:22):
Would you say, is that?
Speaker 2 (12:23):
So we've actually evolved this idea of lifetime value into
something called a fandom score. So it looks at all
of the attributes associated with your engagement. So that might
be in person, that might be online, that might be social,
it might be your sentiment that you're putting out into
the universe, into the social universe. And then it applies
(12:43):
some of your behaviors, whether that's purchase behavior or it's
browsing behavior in a way that is, you know, frankly
a bit evolved than the traditional time spent viewing, which
we both come from a TV background. I spend lots
of years at Turner Broadcasting.
Speaker 5 (12:57):
Like Nielsen used to.
Speaker 2 (12:59):
Be the metric and it was yes or no? Did
your eyeballs watch this content?
Speaker 5 (13:03):
Yes or no?
Speaker 2 (13:04):
So now to think about, we now have a metric
that is twenty four different data attributes associated with how
much you love a brand in your fandom. Is you know,
light years ahead of where we were, you know, just
a few years ago.
Speaker 4 (13:18):
Rubert, were there anything in helping Deloitte put Converge together?
Speaker 3 (13:22):
Were there any was there any R and D any.
Speaker 4 (13:24):
Innovations that AWS did to empower what they wanted to do.
Speaker 5 (13:30):
Yeah, there's a wide variety of services, but maybe a
couple that I'll dig into is Amazon Personalization, which actually,
you know, you can put in the data. Whatever the
customer is puts in the data. It then pull is
data like user their interactions, clicks, time spent somewhere, any
purchasing behavior. So it's not just what you tell me
you want to do, but what you actually do that
(13:51):
we're now measuring. And that's I think what the personalization
is all about. Then taking that and it actually selects
the data to train a model that is based on
the user's actual behavior, and then that model is used
now to predict what the next user is going to
do based on similar attributes. So that's just one example
(14:11):
of what we're using. And then the other piece is
Deloitte's been an amazing partner with us with Generative AI
and using Amazon Bedrock, which is our managed service that
allows the customer and the partner to utilize whatever large
language model is fit for purpose for that use case.
So whether the fit for purpose is analyzing image data,
(14:32):
or taking real time speech and then converting it to text,
or getting a whole bunch of data points and putting
out insights. So one example we were talking about earlier
Cynthia was Formula One. We worked with Formula one on
Track plus and in this solution you've got it's not
like there's one ball in a stadium, not that we
(14:53):
don't like ballsports, but this is a sport with twenty drivers.
They're going up to two hundred and thirty miles per
hour around a racetrack, and there are one million data
points per car per second. Now imagine your former commentator self.
That's like reviewing that and trying to give a fan
some useful information. Oh and by the way, this isn't
(15:14):
the only race they've raced in. There's historical data they've
switched the tires because it's wet or dry, or maybe
they chose not to switch the tires for whatever reason.
All of that information now can be provided in real time,
contextualized historically and giving the why behind it. And now
the experience for fans is much richer. And this is
(15:35):
a very global audience. Five hundred million plus fans and
I think anyone that watches Drive to Survive like that
took the viewership up, so very diverse group. And now
you're able to tailor these insights to that audience.
Speaker 3 (15:49):
That is fascinating.
Speaker 5 (15:51):
Think about it. From a fan experience because some fans
want the stats and the technical details, some want like
the history, and then there are some that really want
the drama. I mean they want like you know, they
bang the steering wheel or they were really upset or
through their helmets or whatever. You know. That was actually
something that we learned working with Bundeslega, which is the
(16:12):
German Football Association professional football sairly rabid fans, yes, yes,
but very diverse, right. There are some who want the metrics,
they want to know every play and how does that
rank versus other players and history, and then there are
some that want the drama and they want to know
about any fights or they want to know about injuries.
And it's you know, now with the Bundesloga app, you
(16:33):
can actually drive up engagement because you are personalizing the
content that's delivered to the user based on what they want.
And that's what's really exciting is it's a personalized fan
experience and they're seeing engagement go up significantly. The time
that the app users are spending there going up because
it's the stuff they are interested in.
Speaker 4 (16:52):
In terms of just pure connectivity, the fact that the
drivers can speak to their pit crews and speak to
each other on the and and you know we at
times we can hear that definitely adds to the drama, Michelle,
no I was.
Speaker 2 (17:03):
Just going to add the tech piece associated with that.
You know, we use something called a non supervised clustering
algorithm to have the machine basically look at the data
in a way that humans don't. So to your fact's
reference where you used to sit and do the analysis
yourself that you know, we're now allowing the machine to
train itself to understand who those audiences are and then
(17:25):
using jen Ai and Betrock and in this case stage
Maker two also an AWS product to basically name those segments.
So something that used to take marketers, you know, in
some cases weeks to do the analysis to name the
actual segment.
Speaker 5 (17:41):
We're now letting the machine do it for us.
Speaker 2 (17:44):
And then it's the When it names the segment, marketers
are then able to act on it and push that
data downstream to do the outreach, whether it be via
mobile or social or app, whatever it might be. The
machine is now doing that for you, and it's closing
the loop via mL operations to continue to train it
on what those fans want.
Speaker 4 (18:03):
The loop or you know where the line ends between
the machine learning, the training and the generative AI. That
is going to be a science going forward for any
number of applications. And I think this is a really
interesting one.
Speaker 1 (18:17):
Don't go anywhere. We'll be right back with more from
Strictly Business Live from can Lion.
Speaker 2 (18:25):
While brand alone used to build fan engagement and loyalty,
today's fan expectations have shifted and organizations can be challenged
to deliver interconnected fan experiences. What if you could give
fans the experience they want by seamlessly integrating touch points
like ticketing, athlete interactions, game streaming, and more within a
single intuitive platform. Imagine a fan data platform that takes
(18:49):
your customer data and puts it to work, creating advantage
for your team and stakeholders. Converged by Deloitte for Sports
empowers organizations to design personalized digital experiences at scale to
delight your fan base with holistic experiences.
Speaker 1 (19:08):
And we're back with Strictly Business Live from Canline.
Speaker 3 (19:12):
Michelle.
Speaker 4 (19:12):
Are there anything so if I understand right, Converge has
been it's been in the works for a while, it's
been active in the marketplace for about a year.
Speaker 3 (19:19):
Anything you know, any top.
Speaker 4 (19:21):
Line kind of something that surprised you about you know
the behavior of sports fans or what you know, what
motivated sponsorship or you know, things that maybe things that
weren't you know, obviously intuitive.
Speaker 2 (19:35):
Well, so first converge consumer was the first converge. So
we took that same data fabric that we talked about
around retail and consumer product data, and we learned from their.
Speaker 5 (19:47):
General behavior and share of wallet.
Speaker 2 (19:49):
Right, So we took all our learnings from retail, and
then we applied sports specific data around ticket purchase as
well as t affinity based on primary research or digital research,
and then brought the two together to inform the fandom
score that I mentioned. And so the thing that I
(20:11):
find most interesting about fandom is that your fandom is
not unique. Right, It crosses it crosses music, it crosses
your retail and buying habits, it crosses all the areas
of your life. So you know you think about that,
that's intuitive. Yeah, Like sports fans are not you know,
they're not a monolith. They are they are rabbit about teams,
(20:33):
leagues in different sports. So how do you look at
them holistically as a fan and then act upon that
unique fandom? In that audience of one.
Speaker 5 (20:43):
Just to add to that, Cynthia, because it's it goes
beyond a personalized experience, it's actually a personalized monetization experience.
Speaker 3 (20:52):
So you can think it doesn't like it.
Speaker 5 (20:56):
Right, You've got you know, subscribers, maybe businesses that have
subscription service, or businesses that are selling certain content, or
an ADS based business, and you can now with the
fandom score, be able to apply that score or any
of those insights provided by Generative AI to determine, Hey,
is this consumer do they have Maybe they have a
(21:17):
low propensity to subscribe, but they have a high propensity
for or they have a high AD score, and we'd
be able to make we as a vendor, whomever the
vendor is, could make money off of advertising. So it's okay,
if they don't subscribe, maybe we'll let them pass through
the paywall to get the content because the AD is
going to generate more revenue for us than the subscription
(21:40):
even though they're a low propensity to subscribe. So thinking
through how to use that type of information in the
monetization model and to do that in real time based
on the person I think is really valuable.
Speaker 4 (21:52):
I'm curious about the score is Is it a numerical score,
is it a like certain traits about a person? How
do you you say the fandom score? How do you
exactly calculate it? And how do you express it in
a way that marketers can interpret it? Does it expresses.
Speaker 2 (22:06):
Itself as a number one, two, one hundred, And it's
always in relationship to the brand you're talking about and
the sports league you're talking about, right, So, but the
good news is that you can adjust it. It's obviously
a weighted metric. You can adjust it based on the
behaviors that your sponsor might be interested in. Right, So,
(22:30):
if a sponsor is interested in selling more live event tickets,
then you can wait it based on discretionary income available
to purchase tickets. And obviously an F one ticket is
a much more pricier spend than a Dodger's game. So
you can look at that in a way that is,
you know, that downstream enablement and enactment of you know,
(22:53):
the score as the you know, kind of numerical representation.
Speaker 4 (22:57):
And I would imagine although with you know, some three
hundred million people in certainly in the US, monitoring those
scores would be would be challenging but I would bet
that the concept of that that people are would are
very eager to look at.
Speaker 3 (23:12):
And see what those scores are for folks.
Speaker 4 (23:14):
And I would imagine that if you're really high in
the fan engagement, you're going to have other attributes. You
might be an early adopter of technology. I would imagine
that you're able to draw those kind of parallels.
Speaker 3 (23:24):
Absolutely.
Speaker 2 (23:25):
The algorithm basically surfaces the attributes that are associated with
certain behaviors, right, So this attribute drives live event propensity, right,
So it tells you and we also use the gen
AI to say, this is a high and tending population
who care about, to your point, early technology adoption. So
(23:48):
give them an immersive experience that looks different than you know,
somebody who might be cost conscious who does not want
a technology component.
Speaker 3 (23:56):
Right.
Speaker 2 (23:56):
So the ability to give those i'll say in person
activations in a way that the sponsor cares about is
important to CMOS everywhere.
Speaker 5 (24:08):
I'll add one more thing that's with the emergence of
agentic AI, where it's in some cases it's not eyeballs,
it's it's actually machines. You know, how do you know
whether it's a machine or a person. And then what
data if it's a machine scraping to get an insight
to give to someone, versus it's a person that's actually
(24:28):
engaging and wants to giving. Yeah, yeah, so how do
you you know? We're using AI to do that. And
actually I was on a panel yesterday with a couple
of our partners, and Adobe had mentioned that they've been
measuring the number of machines that are interacting with their
their platform and in the last six months it went
(24:49):
up something like three thousand, five hundred percent. Maybe don't
quote me exactly on the number. It was over three
thousand of machines versus people. The machines are doing something useful, right,
They're getting information and sending it somewhere to someone who's
going to use it. But how you present content for
that is very different. How you monetize that it is
very different.
Speaker 4 (25:08):
You're using AI to ferret out the machines that are
confounding the AAI that is that that is very.
Speaker 3 (25:15):
Indicative of our times.
Speaker 2 (25:16):
Yeah, it's the future, right, agents acting on your behalf
is the evolution of AI gen AI that are you know,
doing schedule optimization and always looking at the scores, making recommendations,
running in the background, Those algorithms and agents are are
the future of how fans are going to engage with Historically,
maybe it was statistics, or it was you know, any
(25:38):
sort of live event. Even right how you're engaging with
the device at all times is going to radically change
in the next year.
Speaker 5 (25:48):
What's cool about agents, especially for in this space, is
it they can allow you to run multiple campaigns at once,
do A B testing way faster than you could before.
I mean, they can parallelize the entire higher workload. And
so I mean, I think kind of traditional A B
testing maybe you can do two tests per week, you know,
per analyst, and now with agents, I mean that number
(26:09):
can grow exponentially, and then you can, you know, you
can figure out which of these campaigns or which of
these experiences is actually driving better results because the agent
was actually able to modify the campaign that you were
going to put out there, test it, get the data,
give you the results, and then you keep pushing and.
Speaker 4 (26:27):
All that we're talking about, which is again so personalized,
so individualized. It does underscore the need for massive, massive
amounts of cloud storage. It really does, because somebody has
to maintain that, and I know, you know, there's costs
to maintaining that. There's you have to keep it. I
understand you have to. It's something that you have to
(26:49):
keep it fresh. It can't get too stale, otherwise you
compromises some of the efficacy of the.
Speaker 5 (26:54):
The data challenge is probably you know, step number one
is you have to have a data foundation. If you
look at and I don't want to say legacy media
companies because it actually plagues every single industry frankly, is
the data is not all in one place. So if
you're thinking about personalization by aggregating data from the ads team,
from the marketing team, from the content team, today many
(27:16):
of them still sit in silos. The functions are in silos,
their data is in silos, and the tech is also siloed.
And so being able to bring that together is step
number one is getting your data in order, put it
all in one place, being able to use these tools
that can give you the insights it is really key.
And so that's why actually I love the name Converge.
(27:38):
I'm not a marketing professional or brand person, but I
think you guys did a great job with that name,
because that is what it's all about, is bringing that
data together to converge and insight, and.
Speaker 4 (27:46):
With large organizations, we've all been there in meetings, the
possibility of a small communications lapse or somebody didn't talk
to somebody and you're looking for something and then a
week later, somebody's in a meeting saying, oh, I have
that right here, but you couldn't put your hands on
it because it was all in different spots.
Speaker 5 (28:04):
That's the number one bottleneck. And a B testing is
the analyt doing the work doesn't have access to the
data and it you know, the person that needs to
provide them the data hasn't checked their email and all
of that. And so the idea of getting your data
in one place and having a comprehensive data strategy, which
we do constantly with Deloitte when we support our customers
is really step number one.
Speaker 2 (28:24):
Yeah, and we've gotten to the point where, you know,
prompt engineering it's a misnomer for sure, It's just how
you interact with the model and ask the questions. Has
made it so that you know, when you can't find
that piece of data or research, it now spans your
organization scours it for you and then surfaces it with
an insight or an action. You now need to take
(28:45):
as opposed to having to crunch the number and read the.
Speaker 3 (28:48):
Facts really understanding.
Speaker 4 (28:49):
Just as people that understand code now have a skill,
the coding of the future is going to be prompts.
Speaker 5 (28:55):
There is the prompt side of it, which is a
reactive engagement mode. So like I, I put in a
prompt and you know the the LM is reacting and
giving me an answer. But the agentic kind of wave
that is upon us is just proactive. It's just doing
things on our passing it, and that's you an end goal.
Speaker 4 (29:15):
I'm still wrapping my mind around. You know, there will
come a time everything is becoming Hollywood and Hollywood everybody
has an agent. Soon everybody will have an agent, and
somebody will have to store all that information or maybe
maybe the state of that art will be finding more
efficient ways to store it in a way that you
can put it over here, but grab it when the person,
either the person needs it or the person putting in
(29:36):
the prompt.
Speaker 5 (29:37):
The cool thing about agents is they're kind of optimized
for a task or a part of a workflow. And
so if you think about someone in a marketing function,
you know they've got to think about multiple workflows and processes,
and you can have agents that support a sub segment
of that and so having them all work together on
behalf of that individual to figure out, hey, what is
(29:58):
the most optimal If you're optimizing for cost or if
you're optimizing for reach, or whatever the outcome you're trying
to achieve, multiple agents can come together. We have multiple
partners in our AWS partner network, and they're all contributing
agents to a broader network because you can't do it
with one company. I think it does require partnerships and
(30:22):
experts in certain verticals or in certain processes or in
certain workflows, and bringing all those agents together to interoperate
and deliver that optimized process. I think that is going
to be the future. And it is all proactive, like
they're doing it on their own, and the business leader's
job is now to ask the right question and what
the key outcome is that they're trying to achieve.
Speaker 4 (30:43):
Michelle, is there any next frontier for convergence?
Speaker 2 (30:46):
So the next frontier is media, of course, so converge
media not too far off, but applying the same concepts,
right of measurement, engagement. What does it mean for media
organizations who care about all things fandom for potential subscribers
and who is next as a subscriber and who doesn't
(31:07):
subscribe today? And how do I talk to them in
a tone of voice that gets them down the funnel
to convert faster. I think your fandom in characters, titles,
worlds all influences your purchasing behavior when you think about
streaming and subscription, and so for US, that's the next
(31:27):
frontier to take. All that we've learned and done with
AWS is to apply to yet another industry for US.
Speaker 4 (31:34):
And I wanted to ask you, is this largely US
centric at this point? Are you largely focusing on the
US consumer today?
Speaker 2 (31:40):
Our data fabric is focused on the US consumer. We
definitely have ambitions on global more global markets, but as
you know, data around individuals is variable by country, so
our ability to stitch it together in a fabric is
going to take a little work. But yeah, we're definitely
working on global and other industries as well.
Speaker 3 (32:00):
Well. Ruba.
Speaker 4 (32:00):
Anything on the horizon for AWS that you'd like to
talk about.
Speaker 5 (32:05):
Yeah, I think it's all about, you know, bringing together
the different capabilities and then providing new experiences. And actually,
you know, Cynthia was speaking with you and Michelle earlier
about something I'm really excited about that we're showingcasing here
in the Amazon port and that's taking multiple different large
language models. So Amazon Nova's family of large language models
(32:25):
that are optimized for you know, speech to text or
text to analytics, or creating an image or creating a video,
and you can go in and have an experience where
you tell this machine what inspires you, what are the
fragrances that you like, or what are notes that you like,
and then it'll create an ad campaign for your personalized
(32:47):
perfume with a name, with an image, with a video
and then actually there's a perfumer there that will make
it based on the notes there. And so think about
how generative AI is providing personalization across all aspects of
our lives and taking something that isn't just this it's
all in the cloud. We don't know what that is.
It's you know, big data or data analytics, machine learning agents.
(33:09):
It makes it something tangible that you can touch. And
that's what I love about the possibilities years. It's giving
you things that you can only imagine, and actually you
didn't imagine it. The machine imagined it and made it
real for you, I would you.
Speaker 4 (33:20):
Know the idea of creating your own perfume, and to
the point of even having an ad, the ability to
train it to exactly your agents or your large language
model is to exactly what you need. I think that
that is we're just just starting to scratch the surface
of that, and certainly in media and entertainment, but just
in personal living, our lives.
Speaker 3 (33:41):
So conversations like this express.
Speaker 4 (33:43):
That there is so much there is so much opportunity
to unleash with new technology. It is not something to fear,
it's something to harness in a way that you've given
us some great use cases and listeners. Since you can't
see it, I just want to say that we have
been talking here a lot of about clouds. There is
not a cloud in the blue sky here in Can.
(34:04):
But only in Can would we have this deeply technical
conversation on a yacht where the yacht next door they're
setting up for a party with disco balls and discombobulated heads.
Speaker 3 (34:16):
That's Can for you.
Speaker 4 (34:17):
Thank you all so much, Really appreciate your time, really
appreciate your thoughts.
Speaker 5 (34:21):
Thanks for having us, Thank you, thanks for listening.
Speaker 6 (34:28):
Be sure to leave us a review at Apple podcasts
or Amazon Music. We love to hear from listeners. Please
go to Variety dot com and sign up for the
free weekly Strictly Business newsletter, and don't forget to tune
in next week for another episode of Strictly Business.
Speaker 2 (34:53):
In an era when fan engagement has direct implications on
revenue and brand loyalty, understanding and quantifying fandom becomes crucial
for businesses across industries.
Speaker 3 (35:02):
When teams, leagues.
Speaker 2 (35:04):
And brands understand where their fans and potential partners align,
it creates a unique opportunity. With a deeper understanding of
their fans, organizations can determine which fans to engage at
the right time with the right content, and how to
transform that engagement into value for stakeholders. For example, younger
consumers are more likely to say their fandoms are important
(35:26):
to their identity. According to Deloitte's Digital Trends Report, fandom
can fuel revenue, but only if you know how to
harness it.
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