Speaker 1: Good morning, everyone. Welcome to Executive Insights by Media Corp.
Speaker 1: A lot campaign performance with AI. I'm Tim. I lead
Speaker 1: AI products and solutions team at Media Corp, and I'm
Speaker 1: your host today. So we have brought together an outstanding
Speaker 1: panel of industrial leaders who sits at the intersection of
Speaker 1: the marketing, media and technology. So we have Lynn. She's
Speaker 1: the head of commercial growth at Kentta Consulting. Good morning, Lynn.
Speaker 1: And we have Carrie.
Speaker 1: Uh, he's the client president at WPP Media Singapore. Hi, Carrie.
Speaker 1: And last but not least, we have Justin, and he
Speaker 1: is the director of China Partnership, APEC at Mastercard. So
Speaker 1: each of them brings a unique perspective on how AI
Speaker 1: is reshaping the campaign strategy and execution, not just to
Speaker 1: improve efficiency, but to truly elevate the performances. So get
Speaker 1: ready for an insightful and forward-looking conversation.
Speaker 1: But before we dive deeper, let's zoom out a moment
Speaker 1: to look at the broader trajectory of AI. So, I
Speaker 1: often tell my friend that we are living through one
Speaker 1: of the most transformative moment in the history of AI.
Speaker 1: Think about this. So what begins as the rule-based.
Speaker 1: The system in the early days has evolved into neural networks,
Speaker 1: then deep learning breakthroughs. And today, we have the era
Speaker 1: of the generative AI, a large language model. And we
Speaker 1: have seen that AI has surpassed the human performances in
Speaker 1: many technical tasks today. So what makes, what makes this
Speaker 1: ship so critical? It's, it's not just the technology itself,
Speaker 1: it's how we are using it.
Speaker 1: But move beyond just automating the isolated task. Today, AI
Speaker 1: is starting to orchestrate the entire workflow. From research from
Speaker 1: research and creative to the media planning, execution, and of course,
Speaker 1: the measurement.
Speaker 1: So what really that means for us in the media industry.
Speaker 1: So, it means that now we can create faster, smarter,
Speaker 1: and ultimately deliver the bigger impact. So at Mediacorp, that's
Speaker 1: exactly what we are focusing on, driving the meaningful AI
Speaker 1: adoption across the whole chain. And in today's session, you
Speaker 1: will hear from our panelists about how their organizations approaching it.
Speaker 1: So I'm delighted to pass it over to my, to
Speaker 1: our first speaker, Lin, who will share how Kanta are
Speaker 1: thinking about growth in the evolving AI landscape. Lin, over
Speaker 1: to you.
Speaker 1: Thank you,
Speaker 2: Tim, and thank you so much to Mediacorp for having me.
Speaker 2: Good morning, everyone. My name's Lyn and I represent Canta,
Speaker 2: a firm that you may or may not have heard before. Well,
Speaker 2: if you've seen some of the biggest brands out there
Speaker 2: like Apple, Coca-Cola, and you've ever wondered how do they
Speaker 2: get to where they are?
Speaker 2: Please know that they got to where they are because
Speaker 2: they actually use some of the data and brand insights
Speaker 2: and marketing analytics that was supplied to them from Canta.
Speaker 2: Canta is one of the world's leading brand and marketing
Speaker 2: data insights and consulting companies. We partner with more than
Speaker 2: 96 out of the top 100 biggest advertisers around the world,
Speaker 2: and we have presence across 90 markets and a global
Speaker 2: team of around 20,000 people.
Speaker 2: We help organizations understand how people think, feel, and act
Speaker 2: both globally as well as locally here in Singapore.
Speaker 2: So thank you for having us again today and I'm
Speaker 2: here to talk about how AI infuses the brand market
Speaker 2: and marketing data and analytics that Canta provides for our
Speaker 2: clients and I hope that you'll take something useful for
Speaker 2: your organizations today.
Speaker 2: And when we ran a study as Cantor with uh
Speaker 2: global marketers around the world, uh what we found is
Speaker 2: that marketing is one of the most optimistic functions within
Speaker 2: a company when it comes to their embracing of Gen AI.
Speaker 2: On the left hand side, the results of uh optimism
Speaker 2: scores from marketers is almost 9 which far surpasses any
Speaker 2: other functions within a business. And what most marketers are
Speaker 2: telling us is marketing will be transformed by Gen AI
Speaker 2: in the form of boosting human skills, making processes smoother,
Speaker 2: and therefore, it is critical for us as marketers to
Speaker 2: be early adopters of Gen AI.
Speaker 2: But make no um haste in this. There's still a
Speaker 2: lot much work that we need to do to unlock
Speaker 2: true value. When we flip the page over to the
Speaker 2: right hand side, we can see that when it comes
Speaker 2: to how marketers are feeling the current impact of GenAI
Speaker 2: is on market.
Speaker 2: Within their organizations, it really still is early stage. A
Speaker 2: score of about 5.3 was given by marketers on how
Speaker 2: they feel about the potential of uh Gen AI, uh,
Speaker 2: within marketing, but companies still limiting the existing usage. And
Speaker 2: when it comes to internal readiness, this is where it
Speaker 2: gets even more bleak, uh, a rating of 4.9 and
Speaker 2: what marketers are reflecting is there's a lack of training,
Speaker 2: a lack of, uh, tools that are really, uh, being
Speaker 2: given to for them to deploy.
Speaker 2: And therefore, it's imperative that we actually invest much more
Speaker 2: in education and investment. So take this slide and show
Speaker 2: it back to your management on how the state of
Speaker 2: marketers and our optimism is really embracing, uh, our adoption
Speaker 2: of technology. But the limitations are really in the rest
Speaker 2: of the business. But have no fear because that's what
Speaker 2: we're really here to address today. Now, from Canter's point
Speaker 2: of view on the next slide, what we're seeing is
Speaker 2: really the limiting factor now for Gen AI and AI
Speaker 2: in general in marketing.
Speaker 2: Is a lot of the times the use cases have
Speaker 2: been focused so far on replacing task, right? How do
Speaker 2: I get a um a meeting summarized more quickly? How
Speaker 2: do I actually write a copy much more faster?
Speaker 2: But the real opportunity we we believe with AI isn't
Speaker 2: embedding it in a way that transforms your end to
Speaker 2: end processes. Now, what do we mean by processes? Every
Speaker 2: single day we as marketers go through different kinds of
Speaker 2: processes to get to an end outcome which could be
Speaker 2: a campaign or an entire uh year uh uh marketing strategy.
Speaker 2: The 3 main processes are respectively planning and strategy, which
Speaker 2: is really around how you're taking market data, consumer data
Speaker 2: and translating that into insights to go into your overall
Speaker 2: planning and strategy.
Speaker 2: The middle part is integrated marketing communications. And that's what
Speaker 2: has happened when uh you take AI to uh create
Speaker 2: your end to end communications, uh, approach, uh, and going
Speaker 2: really from the early big idea to the creative and
Speaker 2: all the way through to how it gets deployed in
Speaker 2: market via media and being measured.
Speaker 2: And finally, on the right hand side, the last process
Speaker 2: is on innovation, which is a core role that marketing
Speaker 2: contributes to as well. How do you actually innovate and
Speaker 2: develop new products off the back of the marketing, consumer
Speaker 2: insights and data that are being generated today. So over
Speaker 2: the next 3 slides, we will deep dive a little
Speaker 2: bit into this, and I'll bring some real examples to
Speaker 2: bring this to life.
Speaker 2: Now let's talk about planning and strategy and what uh
Speaker 2: AI opportunities there are for us to improve the way
Speaker 2: that we plan and strategize today.
Speaker 2: In the middle part of this slide, we see the
Speaker 2: breakdown step by step processes that go into planning and strategy.
Speaker 2: They're starting off with your landscape assessment, which is really
Speaker 2: understanding what the macro, uh, market and customer uh landscape
Speaker 2: looks like all the way through to pinpointing the issues
Speaker 2: and opportunities, setting your objectives, translating that into marketing jobs
Speaker 2: to be done, then planning your activities, and then going
Speaker 2: to uh measuring it. We think that there's several standout
Speaker 2: opportunities over here for marketers to embed AI even more.
Speaker 2: And one of the key areas is in issues and opportunities,
Speaker 2: for example, GAI can help you easily identify gaps by
Speaker 2: detecting patterns in customer needs and competitive weaknesses. And a
Speaker 2: good example of a company that has brought this to
Speaker 2: life is L'Oreal. They actually had a partnership with Nvidia
Speaker 2: uh to actually develop a gen.
Speaker 2: to to augment their employees' ability to support growth opportunities,
Speaker 2: really forming a good partnership between some of the internal
Speaker 2: employees understanding human understanding of the customers with some of
Speaker 2: the uh AI synthesized landscape assessment and issues and opportunities
Speaker 2: that uh acts as a sparring partner to them.
Speaker 2: Under objective setting, for example, you can use Gen AI
Speaker 2: to use and connect historical data and predictive analytics, set
Speaker 2: realistic data-backed objectives, and a good example of how BBVA
Speaker 2: has done this is they really use uh Gen AI
Speaker 2: as a sparring partner for them to create and refine
Speaker 2: some of the strategies going into the markets.
Speaker 2: Well. And finally, on activity planning, this is one of
Speaker 2: the process steps where the use of AI will be
Speaker 2: a quick win because it can help map out activity
Speaker 2: plans from how you select your channels today to your
Speaker 2: messaging strategies to predict the optimal combinations for different kinds
Speaker 2: of segments. And what Heineken has done here is truly astounding.
Speaker 2: They've used Gen AI to enhance their customer segmentation, which
Speaker 2: has led to more targeted marketing and significant marketing, uh,
Speaker 2: media savings, uh, in, in order to drive real commercial
Speaker 2: uh effectiveness.
Speaker 2: So that's planning and strategy. Let's turn the page over
Speaker 2: to the next process, which is really in the space
Speaker 2: of integrated marketing communication. I know many of us in
Speaker 2: the room today are actually responsible for the day to day, uh, uh,
Speaker 2: execution of marketing. So this should be particularly interesting for you. Again, uh,
Speaker 2: in the middle part, we see the end to end
Speaker 2: process from how you develop the brief to the creative
Speaker 2: and testing it, creating the actual production of the creative
Speaker 2: itself through to media planning and measurement and optimization.
Speaker 2: And similarly, we see multiple spaces where actually Jay can
Speaker 2: be deployed, deployed to actually make things more effective. Under
Speaker 2: creative testing, for example, Unilever has embraced digital training technology
Speaker 2: to create multiple copies of their uh uh creative assets
Speaker 2: and then implemented that AI to speed up and scale
Speaker 2: that creative evaluation. So they have actually tested 3 times
Speaker 2: more digital assets and 15% more TV assets as a
Speaker 2: result of the embracing of these technologies.
Speaker 2: And for media planning and campaign activation, a really nice example,
Speaker 2: my personal favorite is from Catree India. They use Gen
Speaker 2: AI to create hyperlocal personalized ads featuring a very famous
Speaker 2: Bollywood actor, Shah Rukh Khan.
Speaker 2: And by analyzing the small business data, they actually use
Speaker 2: uh the uh customized ads to promote local stores with
Speaker 2: Shah Rukh Khan um by name, helping them to gain
Speaker 2: their visibility and recognition and collect, connect with customers during
Speaker 2: the festive season.
Speaker 2: Finally, in measurement and optimization, TSB used AI to allow
Speaker 2: the ingestion of hyper granular daily data across both the
Speaker 2: digital and offline channels at speed.
Speaker 2: They refreshed their MMM model and we'll talk a little
Speaker 2: bit about what an MMM is, uh, later on. But
Speaker 2: they refresh this on a daily basis to enable real-time
Speaker 2: decision making. And I want you to pay particular attention
Speaker 2: to the last line over there. Uh, I know it's
Speaker 2: a little bit small, but it's in the TSB box.
Speaker 2: They actually, uh, managed to increase their sales by 42%
Speaker 2: and reduce their media spend by 70%.
Speaker 2: Now, I think this is, uh, for most marketers in
Speaker 2: the room, and I used to be on the marketing
Speaker 2: side myself, uh, something so powerful that you can say
Speaker 2: to your end stakeholders, whether that's your, uh, executive or
Speaker 2: whether that is your, uh, uh, stakeholders internally, to be
Speaker 2: able to.
Speaker 2: Drive real, uh, commercial impact by improving marketing is something
Speaker 2: that is just, uh, becoming more and more essential in
Speaker 2: today's businesses. And Gen AI, especially in the, uh, topic
Speaker 2: of measurement, is one of those things that can make
Speaker 2: a real differentiator in the way marketing shows up in
Speaker 2: the organizations. Now, finally, on the last one, which is around, uh, innovation.
Speaker 2: We see again a few opportunities over here. I'll just
Speaker 2: spotlight one in the interest of time. Our spotlight opportunity identification,
Speaker 2: which is really where marketing shines, uh, because of our
Speaker 2: closeness to the consumer. Underportunity identification, a case study from
Speaker 2: Hellion was how they developed their Gen AI tool called
Speaker 2: Ask Halion, which is used as again, aspiring partner for
Speaker 2: innovation through the stages of articulating consumer insights through the
Speaker 2: way to interrogating the signs inside the products as a
Speaker 2: consumer health firm.
Speaker 2: And this really helps them come up with superior concepts
Speaker 2: that they can activate and test with consumers.
Speaker 2: So those were the top 3 processes that we went through. Now,
Speaker 2: let's turn the page over to understanding a little bit
Speaker 2: more about how AI can be really deployed in extra
Speaker 2: solutions itself. First thing I'll say is this is not
Speaker 2: new to us at Canta. We've been around for a
Speaker 2: long time and we've been building expertise in AI for
Speaker 2: a similarly long time. Now, what many of you, uh,
Speaker 2: might share in our journey is we've gone from in
Speaker 2: the early 1980s from machine learning, which is really around
Speaker 2: how do you do things in a much, uh, faster way.
Speaker 2: Uh, to all the way, uh, in the middle of
Speaker 2: the part, uh, of the screen, deep learning, which is, uh,
Speaker 2: embedding AI to really help us understand and harness the
Speaker 2: data even better, to where we are now in generative AI,
Speaker 2: which is helping clients to take all of the, uh,
Speaker 2: legacy data that they have with us in order for
Speaker 2: them to understand the use cases and applications to improve
Speaker 2: into the future as well. And that's what we're really
Speaker 2: excited for.
Speaker 2: So on the next page, you'll see that the transformation
Speaker 2: journey that Kanta has been on is that we've then
Speaker 2: started to infuse AI into every single solution that we have.
Speaker 2: Whether that is AI for innovation, as you heard me
Speaker 2: talk about earlier, to uh make uh concept evaluation much
Speaker 2: more faster and much more effective through to AI for
Speaker 2: creative and being able to test at a lot higher
Speaker 2: scale as well as use predictive analytics to try to
Speaker 2: understand how creatives would perform even before they uh hit
Speaker 2: the markets, uh, as well as AI for media, which
Speaker 2: is uh optimizing your media allocation.
Speaker 2: Uh, and finally, for brand as well on the right-hand side,
Speaker 2: in order to predict your brand KPIs, whether that's uh
Speaker 2: top of mind awareness all the way through to consideration
Speaker 2: and purchase intent. This is so powerful because all of
Speaker 2: our data and all of our AI is running off
Speaker 2: the back of a model that has a massive database
Speaker 2: over here.
Speaker 2: Uh, we use a model, uh, that's powered by something
Speaker 2: called Link, which is the world's largest normative advertising database.
Speaker 2: It consists of 250,000+ tests, 307 million human interactions that
Speaker 2: have been recorded in our particular database, and therefore, we're
Speaker 2: able to predict how real human behavior will change as
Speaker 2: a result of seeing ads.
Speaker 2: The last set I'll leave you with is our clients
Speaker 2: typically see a 30% increase in ROI when they improve
Speaker 2: and add creative quality of the back of this insights
Speaker 2: from average to best. 30%, that's phenomenal.
Speaker 2: So I'm turning to the last pages in my presentation.
Speaker 2: I just wanted to spend a bit of time just, uh, uh,
Speaker 2: bringing to life three core, uh, solutions that might be
Speaker 2: relevant and interesting for folks on this call. Uh, the
Speaker 2: first is, uh, what we call Lift ROI, which is
Speaker 2: essentially marketing mix modeling. It helps you understand, uh, of
Speaker 2: all of the media spend or all of the marketing
Speaker 2: spend that you're putting into, uh, the market, how much
Speaker 2: of it is actually generating true business results.
Speaker 2: I.e. sales or uh if your financial services, assets under management,
Speaker 2: or any business metric that really resonates with you. Not
Speaker 2: only that, it can also be linked to brand equity
Speaker 2: metrics and be able to predict what is the lift
Speaker 2: in awareness, uh, consideration and purchase intent when you tweak
Speaker 2: your marketing mix or increase your marketing spend. In the middle,
Speaker 2: Link AI is our solution for really understanding how creatives
Speaker 2: would be performing in market.
Speaker 2: And be able to predict how people would be uh
Speaker 2: responding to ads and on the right hand side, uh
Speaker 2: I evaluating concepts for innovation uh will uh require something
Speaker 2: that's called uh concept evaluate as well.
Speaker 2: You ingest some marketing data and non-marketing data, like your
Speaker 2: macro factors into a model. Uh, through ingesting of this
Speaker 2: particular data, it can actually, uh, simulate what the short-term
Speaker 2: sales and brand equity would look like in order for
Speaker 2: you to understand what is the exact tweaks that you
Speaker 2: should be making in general. And if you're not using
Speaker 2: MMM in your organizations today, you should be really thinking
Speaker 2: about how you can actually deploy something like that in
Speaker 2: order to make your marketing much more measurable.
Speaker 2: On the next pitch
Speaker 2: Uh, I just spoke a little bit about how, uh,
Speaker 2: marketing comms can be improved. Uh, here's a list of
Speaker 2: some of the metrics that can be actually optimized off
Speaker 2: the back of this. Uh, what's important to note is
Speaker 2: it's not just on the left-hand side, the brand and
Speaker 2: creative metrics, but you can also optimize your behavioral metrics
Speaker 2: off the back of this.
Speaker 2: Now, finally, on the last two slides.
Speaker 2: I'd like to uh just show you a prediction of
Speaker 2: where Cantar is going with, uh, the AI uh future.
Speaker 2: We believe that today, whilst the majority of use cases
Speaker 2: is in, uh, the effectiveness, uh, area, which is really
Speaker 2: just summarization, automated reporting in the future under the Toor
Speaker 2: bar chart, it will actually change to become a lot more, uh, pivoted.
Speaker 2: Towards edge cases of AI. And what we mean by
Speaker 2: edge cases are things like contextual prediction to forecast not
Speaker 2: just the effectiveness of, um, advertising based on images or
Speaker 2: in music, but also things like the characters dialogue and
Speaker 2: the contextual factors as well. So these are applications that
Speaker 2: are not common today, and we expect AI to be
Speaker 2: able to accelerate this into the future as well.
Speaker 2: So with that, um, I've come to the end of
Speaker 2: my particular segment. I know I kind of breezed through it.
Speaker 2: Please feel free to use the chat to ask me
Speaker 2: any questions as I see some of you have already done.
Speaker 2: I'm gonna hand the time back to Tim. I'm also
Speaker 2: contactable via my email that you see on screen right now. Thanks,
Speaker 1: Tim.
Speaker 1: All right. Thank you, Lin. I think that's really resonate.
Speaker 1: I think what you brought up a very, uh, a
Speaker 1: critical point, right? AI isn't just about technology, but it's
Speaker 1: about updating intelligence into the entire marketing life cycle. And
Speaker 1: I think that's something that we are very aligned with
Speaker 1: that at Mediacorp. And, and of course, and transformation and
Speaker 1: scale also need like different.
Speaker 1: Integration. I think uh that probably will bring us to
Speaker 1: the next speaker. So let's hear from Kry who will
Speaker 1: share how WPP is leveraging uh its intelligent marketing operating system.
Speaker 1: WPP open to drive the performance, automate workflows, and reimagine
Speaker 1: how media get planned and activated. And over to you, Kari.
Speaker 1: Yeah, thank you, um, Tim. And so, good morning, everyone.
Speaker 1: Honored to be here. I'm Carrie and I'm the client
Speaker 1: president for WPP Media Singapore. We are the media arm
Speaker 1: of the communications group, WPP. Um, as, uh, the speakers, uh, as,
Speaker 1: as we mentioned earlier, AI is evolving. It is also
Speaker 1: evolving very fast, very quickly, literally changes almost every day. Uh,
Speaker 1: I think one important point Lin touched on.
Speaker 1: And she gave the lay of the land is that
Speaker 1: more work needs to be done. There's a lot more
Speaker 1: value to be unlocked with the, with the potential of AI. Uh,
Speaker 1: I will speak from the angle of a user or
Speaker 1: of an organization using AI and weaving AI into our processes.
Speaker 1: Uh, as we deliver for our clients. Uh, so I'll
Speaker 1: share how we have geared up and how AI is
Speaker 1: starting to play a big part for our organization and
Speaker 1: how we work with clients. Just a bit about WPP Media.
Speaker 1: We are a media agency, as I said, and we
Speaker 1: manage over 50 billion.
Speaker 1: Media budgets for our clients and our clients, uh, some
Speaker 1: of the logos, I've listed below, we expands uh global clients,
Speaker 1: local clients, both public and private sector. So the aim
Speaker 1: is for us as an agency, as a business, is
Speaker 1: continue to be a trusted advisor to our clients. Uh,
Speaker 1: as an organization, uh, just like yourselves, we are upscaling
Speaker 1: our entire workforce. AI is, uh, as I said, it's
Speaker 1: evolving on a daily basis, so there's a lot of
Speaker 1: work to be, uh.
Speaker 1: here. Uh, our advice to all companies and all the
Speaker 1: listeners out here is that we have to stay nimble
Speaker 1: and age out in order to thrive in this dynamic environment.
Speaker 1: If you go on to the next slide, um, I
Speaker 1: will touch a bit about how AI is being used
Speaker 1: today in our organization, um.
Speaker 1: The first one is a personal productivity tool and um
Speaker 1: I'm sure many of you have many examples of how
Speaker 1: you are using AI today from a personal level. I mean,
Speaker 1: you're doing searches apart from your Google search and your
Speaker 1: Bing search or using LM search.
Speaker 1: And so on. You're using this to summarize documents to
Speaker 1: generate ideas and so on. So, the productivity to angle,
Speaker 1: I think it's um it's easy to visualize. Uh, as
Speaker 1: a media planning agency in the context uh of media
Speaker 1: planning and activation and measurement, uh, we have leaned into
Speaker 1: AI in a couple of ways, uh, to discover new audiences.
Speaker 1: What this means is, uh, looking for new sources of
Speaker 1: growth or for client growth.
Speaker 1: Uh, for clients, businesses, uh, planning media campaigns, uh, that
Speaker 1: are turbocharged in terms of getting outputs quicker, so we
Speaker 1: become more efficient and, uh,
Speaker 1: Easily what can be done in 2 days when it
Speaker 1: used to be 2 weeks. This doesn't mean that planners
Speaker 1: become redundant, but AI has allowed us to have richer conversations,
Speaker 1: more conversations and discussions with clients throughout the process. So yes,
Speaker 1: there's efficiency, but I think the important and salient point
Speaker 1: here is that we get to better outputs as well. Uh, from,
Speaker 1: from a media reporting standpoint, it's also improving by leaps
Speaker 1: and bounds. We don't spend a lot of time.
Speaker 1: Now just providing observations, it's not just about compiling data,
Speaker 1: it's a lot more time spent on understanding uh the
Speaker 1: insights and I think importantly, what are the actions, um.
Speaker 1: Uh, that will come out of, of all these uh insights, right?
Speaker 1: So how do you optimize the campaigns and so on.
Speaker 1: What are some of our clients telling us? So, as,
Speaker 1: as a media agency, our clients are equally curious, they
Speaker 1: are consumers themselves. They are, they're asking questions as basic as,
Speaker 1: you know, what, what is this, uh, what is the
Speaker 1: AI landscape about? They're asking about what the AI capabilities
Speaker 1: are for AI agency, meaning us, right? So as a
Speaker 1: media agency providing our media investment services to clients, what
Speaker 1: is it?
Speaker 1: How are we using AI and how are we staying
Speaker 1: relevant for their business. So we're being asked these questions
Speaker 1: uh quite a lot. Uh, some clients are asking us
Speaker 1: to connect how we do, uh, how we use AI
Speaker 1: to how they're getting tangible business outcomes, especially for clients
Speaker 1: that do a lot of business on .com. Uh, so
Speaker 1: those are very common questions and we continue to get
Speaker 1: these questions, uh, on, on a very regular basis.
Speaker 1: We're turbocharging ourselves through this, we call it an operating system,
Speaker 1: and this operating system we have named WPT Open. Uh,
Speaker 1: we have committed to investing over 300 million to building
Speaker 1: cutting edge capabilities within this OS.
Speaker 1: It combines some of this development, uh, our own development,
Speaker 1: but it also um uh combines this with the AI
Speaker 1: capabilities of uh our key partners including Google, Amazon, Microsoft, Meta, and,
Speaker 1: and even TikTok. I hope you enjoyed that and you
Speaker 1: got the gist of what WPP Open is allowing our
Speaker 1: teams uh to do across WPP and WPP Media.
Speaker 1: The what is really some of the platforms, some of
Speaker 1: the some of the uh interfaces that you see in
Speaker 1: the video, um, it's a unifying um area where we
Speaker 1: start doing our work from. It's the first thing that
Speaker 1: we open when we get to work instead of uh
Speaker 1: emails or apart from emails. The how is the nuts
Speaker 1: and bolts of it, but
Speaker 1: I, I think the how is also how the organization
Speaker 1: is bringing along all the employees and the workforce and
Speaker 1: how we're enabling them to do the work. So, uh,
Speaker 1: an important factor to consider here um is that change management, right?
Speaker 1: So AI is not going to be a silver bullet
Speaker 1: for your business just because you implement something. You have
Speaker 1: to think of um some of the processes. I think
Speaker 1: Lin mentioned that a lot of um
Speaker 1: It is not just about productivity and automating some repetitive tasks.
Speaker 1: A lot of the, um, a lot of thinking is
Speaker 1: to go into how to bring everyone along this journey.
Speaker 1: And this is especially tough because it is evolving so fast. The,
Speaker 1: the why, I don't think I need to kind of
Speaker 1: labor the point. It is, it is, it is a necessity,
Speaker 1: like it is going to be the differentiator, if not
Speaker 1: the silver bullet for many organizations.
Speaker 1: Uh, so for the BPP open on the next slide, uh,
Speaker 1: I'm not going to label the point here again. There
Speaker 1: are different, um,
Speaker 1: Uh, what we call command center. This is the command
Speaker 1: center of marketing operations that we call it studios and
Speaker 1: uh different parts of our business use the different studios
Speaker 1: in different combinations. In the next slide, you'll see how
Speaker 1: WPB Media, as the media agency, the studio that we
Speaker 1: use the most often is Open Media Studio. Uh, you
Speaker 1: can move on to the next slide. Yeah. So this
Speaker 1: is the studio that we use most often.
Speaker 1: Uh, alongside the Creative Studio. And um we approach as
Speaker 1: a media arm of the DPP, we approachedri clients and
Speaker 1: media problems through a process called DPAM and in each
Speaker 1: of the phases of Discover plan activate and measure, we
Speaker 1: have different tools that are sit under these processes. Uh,
Speaker 1: let's look at some of the examples.
Speaker 1: I think many of you are using uh AI in
Speaker 1: a personal productivity, um.
Speaker 1: Uh, capacity for discovery and so on. Uh, one of
Speaker 1: my favorite use cases, so I thought I want to
Speaker 1: share this, is, um, the ability to turn long PDF
Speaker 1: documents into podcasts that you can then consume as you're
Speaker 1: on the go, when you're on when you're commuting. I
Speaker 1: think this is a really good personal productivity tool and
Speaker 1: uh we have this weaved into um WPT Open. So
Speaker 1: it's something that a lot of colleagues use um at
Speaker 1: a personal level.
Speaker 1: In the next, uh, example, this sits in the planning
Speaker 1: phase within DPAM and um
Speaker 1: This, what takes what took us a week um to
Speaker 1: do can be achieved in a matter of hours, but
Speaker 1: let's call it a day because I don't want to
Speaker 1: give the impression that the AI spits out work and
Speaker 1: then this is what we pass on to the clients, right? Um,
Speaker 1: at the bottom left of the illustration.
Speaker 1: Uh, what happens here is, this is an example of
Speaker 1: um a school, an education center giving us a brief
Speaker 1: on driving awareness for new postgraduate courses in the next
Speaker 1: school year. So in the past, this would have taken
Speaker 1: quite a while to compile across different data sets, um,
Speaker 1: different research tools that we subscribe to. Uh, right now,
Speaker 1: WPP Open and AI allows us to spit this out
Speaker 1: in literally minutes. The outputs um
Speaker 1: would be a draft of the touch points uh for
Speaker 1: this um particular prompt, right? The prompt is how, how
Speaker 1: do we, uh, what, what should we, what are the
Speaker 1: consumer touch points that we should consider uh as we
Speaker 1: roll out and drive awareness for this course, right? So
Speaker 1: that
Speaker 1: The draft is given to us in terms of what
Speaker 1: are the digital channels you can possibly use, what are
Speaker 1: the physical channels you can, you can use. It even
Speaker 1: breaks it out into different stages from, from a teaser
Speaker 1: phase or awareness phase to consideration and then to a
Speaker 1: sign-up phase. Um, this note, this is not um taking
Speaker 1: from open internet, we're pulling from a syndicated third party data. Um,
Speaker 1: the next speaker, Justin, will speak about this a bit.
Speaker 1: Um, but it is also um pulling from proprietary data
Speaker 1: from WPP itself. So it's important to note again that,
Speaker 1: you know, this doesn't mean that we just, you know,
Speaker 1: throw this across the the fences to the clients. Uh,
Speaker 1: what we are achieving here is, um,
Speaker 1: Spending more time, validating, checking. More importantly, we are idating
Speaker 1: how to bring to life the campaign. We're working with
Speaker 1: creative agencies and clients uh in terms of messaging, better messaging,
Speaker 1: more accurate, more personalized messaging, better formats, uh, formats that
Speaker 1: will achieve better results and eventually better business. Uh, so Netnet,
Speaker 1: we are getting to uh better outputs faster, right?
Speaker 1: Um, the use case that you have, um, here as
Speaker 1: well is the part of planning as well, and it's
Speaker 1: part of, uh, it's where we go deeper now to
Speaker 1: consider channel efficiencies, uh, different types of buying um um
Speaker 1: mechanisms to achieve the best outputs possible. So we are
Speaker 1: doing all this and getting campaign benchmarks from the category,
Speaker 1: from specific campaign data a lot easier. So I mean,
Speaker 1: what is the big deal in all of this? I guess, um.
Speaker 1: If you think about it, the, what in the past,
Speaker 1: what we have, I mean, I, in fact, some of
Speaker 1: you are probably still doing this, you know, opening up
Speaker 1: different folders in your computer, finding different Excel, different pivot tables,
Speaker 1: putting it onto a a big screen and then kind
Speaker 1: of combining and getting.
Speaker 1: The core relations, so I'm, I'm sure a lot of
Speaker 1: us are still doing this today. Uh, from, from the
Speaker 1: point of view of a media planner, what we are
Speaker 1: doing now with AI is simplifying and, and solving uh
Speaker 1: for time, right? It is quite a big deal. Um, the,
Speaker 1: the analysis.
Speaker 1: I'll give to you is that instead of using a
Speaker 1: mobile phone, try going, put a coin into a pay
Speaker 1: phone and, and, and make phone calls that you will
Speaker 1: never go back to those days, right? Your mobile phone
Speaker 1: is with you all the time. So this is the
Speaker 1: reality today. In the next couple of years, new media
Speaker 1: planners joining the industry will not know that this wasn't
Speaker 1: the norm before. And the last um
Speaker 1: Use case I'll share is this one called uh agents.
Speaker 1: uh Lin touched on uh generative AI. There's another branch
Speaker 1: of AI called Agentic AI, you might have heard before.
Speaker 1: This is used quite a lot in within WPP Open
Speaker 1: where we are creating agents, um, and agents are like
Speaker 1: the approximate of a real person. So we, we use
Speaker 1: this to create potential um audience with um.
Speaker 1: Target audience, potential um uh experts who can weigh in on, uh,
Speaker 1: say your creatives or uh being part of a focus group.
Speaker 1: In this case, we have created um
Speaker 1: The approximate of an intern, right? So we call this
Speaker 1: um the intern agent and we have built many interns
Speaker 1: for different clients. In this particular use case, um, this
Speaker 1: agent uh or this intern, uh, we have fed it
Speaker 1: a lot of the campaign data of um the uh
Speaker 1: a specific client. So all the.
Speaker 1: campaign reports and all that, we have trained this agent
Speaker 1: up such that we, we can ask this uh agent
Speaker 1: a number of questions. In the past, we would have
Speaker 1: to kind of again look into different folders for uh
Speaker 1: data and all that. Right now, it's just a prompt
Speaker 1: like send me all the post campaign data um from
Speaker 1: this period.
Speaker 1: And the agent will respond, actually, this data, you know,
Speaker 1: it doesn't sit within this period. Would you like to
Speaker 1: look at it from another period? So it becomes a
Speaker 1: very natural way of finding data and I did not
Speaker 1: share the full um screenshot because of uh client confidentiality,
Speaker 1: but um what this conversation continued to was bringing up
Speaker 1: different data and then uh different prompts to bring
Speaker 1: bring us closer to what exactly uh clients are looking for.
Speaker 1: So this is an example of uh agentic AI and
Speaker 1: it's getting a lot more sophisticated. There are a lot
Speaker 1: more interesting use cases. If you can share the next video.
Speaker 1: Where AI innovation meets the art of storytelling, creating a
Speaker 1: new way to tell stories through our nation's languages. When
Speaker 1: we first embarked on the project, we wanted to really
Speaker 1: find an opportunity to explore the possibilities of AI because art, music,
Speaker 1: storytelling is such a big part of our culture. First,
Speaker 1: we went through a lot of folk tales to finally
Speaker 1: settle on.
Speaker 1: One that was appropriate, kids friendly, and we also settled
Speaker 1: on the story of Pulau Ubin. We invited eager parents
Speaker 1: and grandparents to try out our tech while assuring them
Speaker 1: that the data would be stored and handled securely. Making
Speaker 1: sure that they feel comfortable with using our AI tools
Speaker 1: was a big success to me that they actually trust
Speaker 1: us in handling their data. With their full consent, they
Speaker 1: were ready to experience narrative.
Speaker 1: Over 40 AI tools were experimented with before we shortlisted
Speaker 1: 6 for our AI experience. We captured facial.
Speaker 1: Thank you, Kerry. I think that's was really a powerful
Speaker 1: look into like what's possible when AI is, isn't just
Speaker 1: a tool, but it's actually becomes the operating system. And
Speaker 1: I think AI may be, you know, the engine, but
Speaker 1: we still definitely need the fuel, right? In this case,
Speaker 1: I think data will be the field. So that's, you know, uh,
Speaker 1: let's welcome our next speaker, Justin, uh, who will share
Speaker 1: how Mastercard is using their data and AI to unlock
Speaker 1: a deeper audience inside, enabling personalize the scale and power
Speaker 1: more intelligent marketing outcomes. Justin, over to you.
Speaker 1: Thanks and um good morning. I hope you can hear
Speaker 1: me clearly. I'm just starting from Melbourne today and very
Speaker 1: happy to be here. I just a quick introduction about myself.
Speaker 1: I lead channel partnerships and alliances for Mastercard in the
Speaker 1: APEC region, and we work very closely with our ecosystem
Speaker 1: partners like WPP Media as well as Media Corp. and
Speaker 1: just prior to Mastercard, I spent close to 14 years
Speaker 1: across the different big tech platforms from Google to Mata
Speaker 1: to TikTok.
Speaker 1: So really have had the opportunity to have a front
Speaker 1: row seat on the development of AI over the last
Speaker 1: decade and a half. So we've heard a lot about
Speaker 1: the exciting possibilities of AI from Kerry and Lin. Um,
Speaker 1: one thing is certain in the age of AI.
Speaker 1: Data is more important than ever, right? As I think
Speaker 1: one of the panelists answered this, it is one of
Speaker 1: the challenges today is access to data, but more importantly,
Speaker 1: access to high quality data and as they always say
Speaker 1: garbage in garbage out. Um, so hopefully today we can
Speaker 1: share a little bit more about what Mastercard is doing
Speaker 1: in this space to unlock value with our transaction insights
Speaker 1: and how we play a role in supporting media campaigns
Speaker 1: and delivering value across the customer journey.
Speaker 1: So I'm sure all of us, or most of us
Speaker 1: in this room, you have heard of Mastercard. You know
Speaker 1: Mastercard is a payments and credit card company, but not
Speaker 1: many of us might know that over the last 50 years,
Speaker 1: we have evolved to become a data and technology leader globally. Uh,
Speaker 1: as you can see on this slide, our superpower lies
Speaker 1: in the depth and the breadth of data that we
Speaker 1: see across the Mastercard payment network. And on an annual basis,
Speaker 1: we see more than 200 billion purchase transaction points sourced
Speaker 1: from more than 3 billion cards in circulation today.
Speaker 1: Across more than 150 merchants, million merchants, but importantly across
Speaker 1: a few 100 industry categories. So again, just to emphasize
Speaker 1: the depth and the breadth of the data that we have,
Speaker 1: and we leverage all of this aggregated and anonymized transaction
Speaker 1: insights and expertise to serve more than 4000 customers across
Speaker 1: 120 countries today.
Speaker 1: So very big numbers that I'm throwing out there, but
Speaker 1: I think as you can imagine, this really speaks to
Speaker 1: the trust and the quality of the data uh that
Speaker 1: Mastercard is holding today.
Speaker 1: Net Mastercard data is not just an asset, it is
Speaker 1: the core of our network. Every transaction, every interaction, every
Speaker 1: insight is powered by the data flowing through our systems.
Speaker 1: And as I mentioned earlier on in the age of AI,
Speaker 1: quality of data is most important, and AI can process
Speaker 1: information and unprecedented skill and speed, but it's only as
Speaker 1: good as the data that it is set.
Speaker 1: So if your data is flawed, if your data is incomplete,
Speaker 1: or if your data is biased, then your AI outputs
Speaker 1: will be also. So as I mentioned, garbage in garbage out.
Speaker 1: And that's why Mastercard has invested so heavily in building
Speaker 1: this trusted, anonymized and high-quality data ecosystem that spends 200
Speaker 1: billion purchase transactions. This is because the depth and the
Speaker 1: breadth of data allows us to drive innovation at scale,
Speaker 1: helping our partners, advertisers and brands like you.
Speaker 1: Unlock performance across marketing, payments, as well as customer engagement.
Speaker 1: So whether it's powering hyper personalized campaigns or enabling real-time
Speaker 1: decisioning as what Carrie had mentioned across the life cycle
Speaker 1: of media, our AI capabilities are built on the foundation
Speaker 1: of clean, structured, and actionable data.
Speaker 1: So as you think about your own AI journey, just
Speaker 1: remember that data integrity isn't just a technical concern, it
Speaker 1: is a strategic imperative.
Speaker 1: In today's fragmented media landscape, one fun fact I learned
Speaker 1: today is.
Speaker 1: On average, all of us are exposed to more than
Speaker 1: 20 plus different media channels. So that's a lot of
Speaker 1: media channels that we're all being exposed to. The marketers
Speaker 1: face a very growing challenge as to how do you
Speaker 1: reach the right audience with the right message at the
Speaker 1: right time.
Speaker 1: And also proof that it works in driving business outcomes.
Speaker 1: And this is where transaction data becomes a game changer
Speaker 1: because unlike previously proxy signals like clicks or views or
Speaker 1: interest behavior, transaction data reflects real world consumer behavior because
Speaker 1: if you are a top spender in men's apparel, you
Speaker 1: are likely going to be in the market to buy
Speaker 1: men's apparel. So what people actually buy.
Speaker 1: When, where, and how these are where transaction insights really
Speaker 1: come in.
Speaker 1: So by leveraging into anonymized and aggregated transaction data, advertisers
Speaker 1: can make media more intelligent.
Speaker 1: Right, where you can use real world market and audience
Speaker 1: intelligence to guide media planning as well as targeting.
Speaker 1: You can make media more personal by delivering hyper personalized
Speaker 1: experiences based on actual spending patterns and preferences of the individual.
Speaker 1: You can make me more measurable, where you can link
Speaker 1: campaigns to tangible outcomes like incremental sales, and no longer
Speaker 1: just engagement metrics or vanity metrics like impressions of views.
Speaker 1: So for example, instead of targeting urban millennials interested in travel,
Speaker 1: We can identify micro geographies where there are high frequency
Speaker 1: standards in travel categories where they are actually transacting and
Speaker 1: actively transacting, and you can then tailor those messages accordingly.
Speaker 1: So this level of precision helps advertisers move beyond assumptions
Speaker 1: and generalizations, moving to data back decisions that can drive performance.
Speaker 1: And we look at how this translates into real world
Speaker 1: media and advertising workflows with data.
Speaker 1: So in a world where consumers are increasingly engaging across
Speaker 1: multiple channels, online, in-store, mobile, social, we all need a
Speaker 1: way to connect the dots across the full customer journey,
Speaker 1: and this is where transaction data becomes a powerful enabler,
Speaker 1: because it provides you with a direct lens into what
Speaker 1: people are actually buying, not just what they're browsing or
Speaker 1: clicking on. So if you look at number one here.
Speaker 1: Mastercard's data can help advertisers and brands like you leverage
Speaker 1: real world market and consumer insights to a more effective
Speaker 1: media planning.
Speaker 1: So, for example, we have transaction data across a few
Speaker 1: 100 industry categories.
Speaker 1: Across the time series. So as a brand, if you
Speaker 1: want to understand when does retail stand in Kits apparel
Speaker 1: pick up leading into mega sale day, such as 99
Speaker 1: or Christmas in Singapore.
Speaker 1: This data will help you to understand that and help
Speaker 1: you plan when you should start your media plan or
Speaker 1: the media cycle. But we can also look at benchmarking
Speaker 1: of year on year growth in the kits apparel industry
Speaker 1: so that you can better understand how is your business
Speaker 1: performing against your competition. We can also help you understand
Speaker 1: online versus offline spending, so that helps you understand media
Speaker 1: budget allocation to offline and online media channels.
Speaker 1: And more importantly, we can even go down to a
Speaker 1: brand level to help you understand market share, like pre
Speaker 1: and post a media campaign. Are you actually gaining or
Speaker 1: decreasing in market share against your competitors? As you can imagine,
Speaker 1: all of this data becomes very powerful in the world
Speaker 1: of agentic AI as Carrie mentioned, because as you query
Speaker 1: an agent, all of these insights will really power that model.
Speaker 1: 2nd, #2 here is we can help to enhance audience
Speaker 1: targeting by reaching high value and high-end customers, right, where
Speaker 1: you can identify where all of the top spenders or
Speaker 1: frequent spenders on hotel bookings or travel in Singapore, and
Speaker 1: you can then serve them very targeted and personalized creative messaging.
Speaker 1: Yeah. And the last piece of number 5 is where
Speaker 1: we can then look at how do we measure close
Speaker 1: loop attribution. So MasterCard owns a technology called Tess and
Speaker 1: learn for media measurement, where we have the ability to
Speaker 1: understand what was the incremental sales slip that was driven
Speaker 1: off an omni Media channel campaign and also understand those
Speaker 1: business drivers.
Speaker 1: And all of this becomes very valuable data that we
Speaker 1: can inject into AI models across the entire media planning
Speaker 1: flywheel to help automate and democratize research, audience planning, as
Speaker 1: well as attribution to make media a lot more actionable
Speaker 1: and dependable. So hopefully this gives you a very quick
Speaker 1: sense of where our data comes in.
Speaker 1: Uh, and then on the next slide, I just want
Speaker 1: to walk through a very simple use case of how
Speaker 1: we have partnered with a leading airline in this region
Speaker 1: to apply AI in a very practical and high impact way.
Speaker 1: So the problem statement that was brought to us at
Speaker 1: Mastercard was, how can we help this airline partner optimize
Speaker 1: promotional mechanics and customer engagement strategies using data and AI.
Speaker 1: So working together with them, we co-created past specific AI
Speaker 1: agencies what Kerry had mentioned, these are like your interns,
Speaker 1: your internal agents that can leverage MasterCard's data insights, platform capabilities,
Speaker 1: and our deep analytic expertise. And so these AI agents
Speaker 1: help to power and scale in two areas, knowledge management,
Speaker 1: as well as insights generation for the airlines in-house teams,
Speaker 1: and it helps them answer key questions like
Speaker 1: What are the key insights from previous initiatives to help
Speaker 1: me guide customer engagement strategy?
Speaker 1: What discount levels did I offer previously that were able
Speaker 1: to drive the highest revenue uplift for my business or
Speaker 1: which SKU or which flight route or brands overperformed during
Speaker 1: the past campaigns, right? And lastly, what promotion periods you
Speaker 1: did the best results.
Speaker 1: In addition, we also supported the area of customer segmentation
Speaker 1: and personalized messaging to identify high-value segments based on historical
Speaker 1: behavior and the agents, in this case, the AI agents
Speaker 1: that help to tailor email messages, in-app messages by dynamically
Speaker 1: using customer level metrics like redemption rates or booking patterns.
Speaker 1: So the result is a more intelligent and responsive marketing engine,
Speaker 1: one that is grounded in real transaction data and is
Speaker 1: capable of adapting to customer behavior in real time.
Speaker 1: And this really goes beyond just traditional AI, but how
Speaker 1: AI is in action today with Gen AI as well
Speaker 1: as predictive AI.
Speaker 1: So I put on to the next slide.
Speaker 1: And this is just another tangible example of how Mastercard
Speaker 1: is helping our clients bring AI use cases to life. First,
Speaker 1: we provide our data and insights in multi-formats that are
Speaker 1: optimized for rack. In this case it's retrieval augmented generation
Speaker 1: to support large language models as well as AI agents. Second,
Speaker 1: we share instruction templates and best practices, including prom libraries,
Speaker 1: sample Q&A, and tested use cases. And third, we have
Speaker 1: our in-house digital labs team that helps provide consulting.
Speaker 1: To design a prototype and build these custom AI solutions.
Speaker 1: So the goal is to help our clients like you
Speaker 1: move beyond experimentation, operationalization, and building AI into your workflow
Speaker 1: in a way that's measurable, secure, and future.
Speaker 1: So these are just the 5 e takeaways.
Speaker 1: In summary, generative AI is really redefining consumer engagement today.
Speaker 1: We believe that transaction data is a key strategic asset
Speaker 1: and it is a competitive differentiator to brands and marketers.
Speaker 1: With the use of AI today, personalization is now truly
Speaker 1: scalable and AI powered is also a lot more accountable
Speaker 1: to you as well as the decision makers and leaders
Speaker 1: in the company.
Speaker 1: So hopefully this gives you a a comprehensive or even
Speaker 1: a concise overview of where data plays as a key
Speaker 1: enabler across the entire world of AI.
Speaker 1: And with that, I will take a pause and pass
Speaker 1: the time back to Tim and Chai. All right, thank
Speaker 1: you so much, uh, Dustin. I think that's a great sharing.
Speaker 1: So I think for the interest of time, um, let's
Speaker 1: just move to the next section, which is the Q&A.
Speaker 1: I think some of the questions has been answered by
Speaker 1: Lin and Carrie in the uh in the, in the
Speaker 1: chat uh section.
Speaker 1: But I think I'll just pick up one of the
Speaker 1: common questions being asked like what are the tools that
Speaker 1: are being, uh, what are the generator tools that are
Speaker 1: being used and also what's the differentiator if one is,
Speaker 1: you know, use the same kind of tools, right? So
Speaker 1: I think.
Speaker 1: Uh, actually, the, uh, Justin summarized it in his takeaways.
Speaker 1: I think one of the points that he made is actually,
Speaker 1: of course, data will be one of the key differentiators.
Speaker 1: I think if you see from all the three, panelists
Speaker 1: who was sharing, so what is the common is they
Speaker 1: started to use the tool, they started to actually building
Speaker 1: their own workflows. You can tell from, you know, uh
Speaker 1: Carris DPAM and also uh uh Justin also share their
Speaker 1: own master class, uh, you know, the over, uh, the,
Speaker 1: the frameworks, right? I think.
Speaker 1: That's definitely something that common, but I think what's difference
Speaker 1: is how you are integrating the data and your own data, right?
Speaker 1: And then also basically fit into your own workflow because
Speaker 1: that will make a bigger differences. And many of those
Speaker 1: tools are model uh uh large language model, um, agnostic, meaning,
Speaker 1: whether you're using JGBT or use a different, you know, tool, actually,
Speaker 1: you know, it.
Speaker 1: Differences, but when you are connecting that with your own data,
Speaker 1: with your own workflow, I think that's how you basically
Speaker 1: differentiate from other players, right? I think with that, I
Speaker 1: would like to thank, uh, you know, three of our
Speaker 1: distinguished guests, panelists, for giving uh the great sharing, and
Speaker 1: I think we learned a lot. And I also want
Speaker 1: to thank everyone, all the participants, all the audiences joining
Speaker 1: us today in the morning. Thank you so much. And hopefully,
Speaker 1: we're looking uh forward to see you in the next session.
Speaker 1: Thank you, everyone.