WEBVTT - Unlock campaign performance with AI

0:00:09.109 --> 0:00:12.529
<v Speaker 1>Good morning, everyone. Welcome to Executive Insights by Media Corp.

0:00:12.739 --> 0:00:16.500
<v Speaker 1>A lot campaign performance with AI. I'm Tim. I lead

0:00:16.500 --> 0:00:19.739
<v Speaker 1>AI products and solutions team at Media Corp, and I'm

0:00:19.739 --> 0:00:22.899
<v Speaker 1>your host today. So we have brought together an outstanding

0:00:22.899 --> 0:00:27.059
<v Speaker 1>panel of industrial leaders who sits at the intersection of

0:00:27.059 --> 0:00:30.899
<v Speaker 1>the marketing, media and technology. So we have Lynn. She's

0:00:30.899 --> 0:00:34.458
<v Speaker 1>the head of commercial growth at Kentta Consulting. Good morning, Lynn.

0:00:35.020 --> 0:00:36.020
<v Speaker 1>And we have Carrie.

0:00:36.319 --> 0:00:40.900
<v Speaker 1>Uh, he's the client president at WPP Media Singapore. Hi, Carrie.

0:00:41.750 --> 0:00:44.159
<v Speaker 1>And last but not least, we have Justin, and he

0:00:44.159 --> 0:00:47.598
<v Speaker 1>is the director of China Partnership, APEC at Mastercard. So

0:00:47.598 --> 0:00:51.229
<v Speaker 1>each of them brings a unique perspective on how AI

0:00:51.229 --> 0:00:55.430
<v Speaker 1>is reshaping the campaign strategy and execution, not just to

0:00:55.430 --> 0:00:59.639
<v Speaker 1>improve efficiency, but to truly elevate the performances. So get

0:00:59.639 --> 0:01:02.560
<v Speaker 1>ready for an insightful and forward-looking conversation.

0:01:03.369 --> 0:01:07.000
<v Speaker 1>But before we dive deeper, let's zoom out a moment

0:01:07.000 --> 0:01:09.760
<v Speaker 1>to look at the broader trajectory of AI. So, I

0:01:09.760 --> 0:01:12.370
<v Speaker 1>often tell my friend that we are living through one

0:01:12.370 --> 0:01:16.190
<v Speaker 1>of the most transformative moment in the history of AI.

0:01:16.489 --> 0:01:19.330
<v Speaker 1>Think about this. So what begins as the rule-based.

0:01:19.949 --> 0:01:24.010
<v Speaker 1>The system in the early days has evolved into neural networks,

0:01:24.260 --> 0:01:27.699
<v Speaker 1>then deep learning breakthroughs. And today, we have the era

0:01:27.699 --> 0:01:30.629
<v Speaker 1>of the generative AI, a large language model. And we

0:01:30.629 --> 0:01:34.220
<v Speaker 1>have seen that AI has surpassed the human performances in

0:01:34.220 --> 0:01:38.169
<v Speaker 1>many technical tasks today. So what makes, what makes this

0:01:38.169 --> 0:01:42.209
<v Speaker 1>ship so critical? It's, it's not just the technology itself,

0:01:42.500 --> 0:01:43.779
<v Speaker 1>it's how we are using it.

0:01:44.470 --> 0:01:49.750
<v Speaker 1>But move beyond just automating the isolated task. Today, AI

0:01:49.750 --> 0:01:54.510
<v Speaker 1>is starting to orchestrate the entire workflow. From research from

0:01:54.510 --> 0:01:58.300
<v Speaker 1>research and creative to the media planning, execution, and of course,

0:01:58.309 --> 0:01:59.059
<v Speaker 1>the measurement.

0:01:59.900 --> 0:02:03.849
<v Speaker 1>So what really that means for us in the media industry.

0:02:05.139 --> 0:02:10.490
<v Speaker 1>So, it means that now we can create faster, smarter,

0:02:11.020 --> 0:02:15.820
<v Speaker 1>and ultimately deliver the bigger impact. So at Mediacorp, that's

0:02:15.820 --> 0:02:18.630
<v Speaker 1>exactly what we are focusing on, driving the meaningful AI

0:02:18.630 --> 0:02:22.460
<v Speaker 1>adoption across the whole chain. And in today's session, you

0:02:22.460 --> 0:02:26.610
<v Speaker 1>will hear from our panelists about how their organizations approaching it.

0:02:27.660 --> 0:02:29.779
<v Speaker 1>So I'm delighted to pass it over to my, to

0:02:29.779 --> 0:02:33.419
<v Speaker 1>our first speaker, Lin, who will share how Kanta are

0:02:33.419 --> 0:02:37.100
<v Speaker 1>thinking about growth in the evolving AI landscape. Lin, over

0:02:37.100 --> 0:02:37.609
<v Speaker 1>to you.

0:02:38.100 --> 0:02:38.369
<v Speaker 1>Thank you,

0:02:38.429 --> 0:02:41.089
<v Speaker 2>Tim, and thank you so much to Mediacorp for having me.

0:02:41.419 --> 0:02:44.339
<v Speaker 2>Good morning, everyone. My name's Lyn and I represent Canta,

0:02:44.399 --> 0:02:47.179
<v Speaker 2>a firm that you may or may not have heard before. Well,

0:02:47.220 --> 0:02:49.139
<v Speaker 2>if you've seen some of the biggest brands out there

0:02:49.139 --> 0:02:52.258
<v Speaker 2>like Apple, Coca-Cola, and you've ever wondered how do they

0:02:52.258 --> 0:02:53.419
<v Speaker 2>get to where they are?

0:02:53.990 --> 0:02:56.100
<v Speaker 2>Please know that they got to where they are because

0:02:56.100 --> 0:02:58.989
<v Speaker 2>they actually use some of the data and brand insights

0:02:58.990 --> 0:03:02.538
<v Speaker 2>and marketing analytics that was supplied to them from Canta.

0:03:02.949 --> 0:03:05.070
<v Speaker 2>Canta is one of the world's leading brand and marketing

0:03:05.070 --> 0:03:08.538
<v Speaker 2>data insights and consulting companies. We partner with more than

0:03:08.538 --> 0:03:12.100
<v Speaker 2>96 out of the top 100 biggest advertisers around the world,

0:03:12.309 --> 0:03:14.630
<v Speaker 2>and we have presence across 90 markets and a global

0:03:14.630 --> 0:03:16.589
<v Speaker 2>team of around 20,000 people.

0:03:17.029 --> 0:03:21.610
<v Speaker 2>We help organizations understand how people think, feel, and act

0:03:21.610 --> 0:03:24.399
<v Speaker 2>both globally as well as locally here in Singapore.

0:03:25.199 --> 0:03:27.660
<v Speaker 2>So thank you for having us again today and I'm

0:03:27.660 --> 0:03:31.418
<v Speaker 2>here to talk about how AI infuses the brand market

0:03:31.419 --> 0:03:35.179
<v Speaker 2>and marketing data and analytics that Canta provides for our

0:03:35.179 --> 0:03:37.910
<v Speaker 2>clients and I hope that you'll take something useful for

0:03:37.910 --> 0:03:39.240
<v Speaker 2>your organizations today.

0:03:39.860 --> 0:03:42.949
<v Speaker 2>And when we ran a study as Cantor with uh

0:03:42.949 --> 0:03:46.910
<v Speaker 2>global marketers around the world, uh what we found is

0:03:46.910 --> 0:03:51.029
<v Speaker 2>that marketing is one of the most optimistic functions within

0:03:51.029 --> 0:03:54.229
<v Speaker 2>a company when it comes to their embracing of Gen AI.

0:03:54.860 --> 0:03:58.179
<v Speaker 2>On the left hand side, the results of uh optimism

0:03:58.179 --> 0:04:01.779
<v Speaker 2>scores from marketers is almost 9 which far surpasses any

0:04:01.779 --> 0:04:05.139
<v Speaker 2>other functions within a business. And what most marketers are

0:04:05.139 --> 0:04:08.690
<v Speaker 2>telling us is marketing will be transformed by Gen AI

0:04:08.690 --> 0:04:12.410
<v Speaker 2>in the form of boosting human skills, making processes smoother,

0:04:12.580 --> 0:04:15.100
<v Speaker 2>and therefore, it is critical for us as marketers to

0:04:15.100 --> 0:04:17.100
<v Speaker 2>be early adopters of Gen AI.

0:04:17.670 --> 0:04:21.160
<v Speaker 2>But make no um haste in this. There's still a

0:04:21.160 --> 0:04:23.760
<v Speaker 2>lot much work that we need to do to unlock

0:04:23.760 --> 0:04:26.160
<v Speaker 2>true value. When we flip the page over to the

0:04:26.160 --> 0:04:28.950
<v Speaker 2>right hand side, we can see that when it comes

0:04:28.950 --> 0:04:32.178
<v Speaker 2>to how marketers are feeling the current impact of GenAI

0:04:32.178 --> 0:04:32.600
<v Speaker 2>is on market.

0:04:32.734 --> 0:04:37.005
<v Speaker 2>Within their organizations, it really still is early stage. A

0:04:37.005 --> 0:04:40.515
<v Speaker 2>score of about 5.3 was given by marketers on how

0:04:40.515 --> 0:04:43.954
<v Speaker 2>they feel about the potential of uh Gen AI, uh,

0:04:43.964 --> 0:04:48.565
<v Speaker 2>within marketing, but companies still limiting the existing usage. And

0:04:48.565 --> 0:04:50.565
<v Speaker 2>when it comes to internal readiness, this is where it

0:04:50.565 --> 0:04:53.844
<v Speaker 2>gets even more bleak, uh, a rating of 4.9 and

0:04:53.845 --> 0:04:56.433
<v Speaker 2>what marketers are reflecting is there's a lack of training,

0:04:56.644 --> 0:04:58.803
<v Speaker 2>a lack of, uh, tools that are really, uh, being

0:04:58.803 --> 0:05:00.445
<v Speaker 2>given to for them to deploy.

0:05:00.738 --> 0:05:03.809
<v Speaker 2>And therefore, it's imperative that we actually invest much more

0:05:03.809 --> 0:05:06.609
<v Speaker 2>in education and investment. So take this slide and show

0:05:06.609 --> 0:05:09.279
<v Speaker 2>it back to your management on how the state of

0:05:09.279 --> 0:05:13.089
<v Speaker 2>marketers and our optimism is really embracing, uh, our adoption

0:05:13.089 --> 0:05:16.130
<v Speaker 2>of technology. But the limitations are really in the rest

0:05:16.130 --> 0:05:18.519
<v Speaker 2>of the business. But have no fear because that's what

0:05:18.519 --> 0:05:21.369
<v Speaker 2>we're really here to address today. Now, from Canter's point

0:05:21.369 --> 0:05:24.040
<v Speaker 2>of view on the next slide, what we're seeing is

0:05:24.040 --> 0:05:27.368
<v Speaker 2>really the limiting factor now for Gen AI and AI

0:05:27.369 --> 0:05:28.570
<v Speaker 2>in general in marketing.

0:05:28.809 --> 0:05:31.040
<v Speaker 2>Is a lot of the times the use cases have

0:05:31.040 --> 0:05:34.440
<v Speaker 2>been focused so far on replacing task, right? How do

0:05:34.440 --> 0:05:38.000
<v Speaker 2>I get a um a meeting summarized more quickly? How

0:05:38.000 --> 0:05:40.679
<v Speaker 2>do I actually write a copy much more faster?

0:05:41.140 --> 0:05:44.029
<v Speaker 2>But the real opportunity we we believe with AI isn't

0:05:44.029 --> 0:05:46.630
<v Speaker 2>embedding it in a way that transforms your end to

0:05:46.630 --> 0:05:50.510
<v Speaker 2>end processes. Now, what do we mean by processes? Every

0:05:50.510 --> 0:05:53.029
<v Speaker 2>single day we as marketers go through different kinds of

0:05:53.029 --> 0:05:55.859
<v Speaker 2>processes to get to an end outcome which could be

0:05:55.859 --> 0:06:00.549
<v Speaker 2>a campaign or an entire uh year uh uh marketing strategy.

0:06:01.140 --> 0:06:04.950
<v Speaker 2>The 3 main processes are respectively planning and strategy, which

0:06:04.950 --> 0:06:08.279
<v Speaker 2>is really around how you're taking market data, consumer data

0:06:08.390 --> 0:06:11.850
<v Speaker 2>and translating that into insights to go into your overall

0:06:11.850 --> 0:06:12.989
<v Speaker 2>planning and strategy.

0:06:13.459 --> 0:06:17.339
<v Speaker 2>The middle part is integrated marketing communications. And that's what

0:06:17.339 --> 0:06:21.260
<v Speaker 2>has happened when uh you take AI to uh create

0:06:21.260 --> 0:06:24.779
<v Speaker 2>your end to end communications, uh, approach, uh, and going

0:06:24.779 --> 0:06:27.500
<v Speaker 2>really from the early big idea to the creative and

0:06:27.500 --> 0:06:29.779
<v Speaker 2>all the way through to how it gets deployed in

0:06:29.779 --> 0:06:31.899
<v Speaker 2>market via media and being measured.

0:06:32.269 --> 0:06:34.670
<v Speaker 2>And finally, on the right hand side, the last process

0:06:34.670 --> 0:06:37.519
<v Speaker 2>is on innovation, which is a core role that marketing

0:06:37.519 --> 0:06:40.799
<v Speaker 2>contributes to as well. How do you actually innovate and

0:06:40.799 --> 0:06:44.750
<v Speaker 2>develop new products off the back of the marketing, consumer

0:06:44.750 --> 0:06:48.079
<v Speaker 2>insights and data that are being generated today. So over

0:06:48.079 --> 0:06:50.279
<v Speaker 2>the next 3 slides, we will deep dive a little

0:06:50.279 --> 0:06:52.760
<v Speaker 2>bit into this, and I'll bring some real examples to

0:06:52.760 --> 0:06:53.359
<v Speaker 2>bring this to life.

0:06:54.160 --> 0:06:57.279
<v Speaker 2>Now let's talk about planning and strategy and what uh

0:06:57.279 --> 0:07:00.479
<v Speaker 2>AI opportunities there are for us to improve the way

0:07:00.480 --> 0:07:02.399
<v Speaker 2>that we plan and strategize today.

0:07:02.869 --> 0:07:05.140
<v Speaker 2>In the middle part of this slide, we see the

0:07:05.140 --> 0:07:08.529
<v Speaker 2>breakdown step by step processes that go into planning and strategy.

0:07:08.700 --> 0:07:11.369
<v Speaker 2>They're starting off with your landscape assessment, which is really

0:07:11.369 --> 0:07:14.890
<v Speaker 2>understanding what the macro, uh, market and customer uh landscape

0:07:14.890 --> 0:07:17.500
<v Speaker 2>looks like all the way through to pinpointing the issues

0:07:17.500 --> 0:07:21.260
<v Speaker 2>and opportunities, setting your objectives, translating that into marketing jobs

0:07:21.260 --> 0:07:24.190
<v Speaker 2>to be done, then planning your activities, and then going

0:07:24.190 --> 0:07:27.769
<v Speaker 2>to uh measuring it. We think that there's several standout

0:07:27.769 --> 0:07:32.190
<v Speaker 2>opportunities over here for marketers to embed AI even more.

0:07:32.570 --> 0:07:35.540
<v Speaker 2>And one of the key areas is in issues and opportunities,

0:07:35.589 --> 0:07:39.470
<v Speaker 2>for example, GAI can help you easily identify gaps by

0:07:39.470 --> 0:07:43.829
<v Speaker 2>detecting patterns in customer needs and competitive weaknesses. And a

0:07:43.829 --> 0:07:46.190
<v Speaker 2>good example of a company that has brought this to

0:07:46.190 --> 0:07:50.339
<v Speaker 2>life is L'Oreal. They actually had a partnership with Nvidia

0:07:50.339 --> 0:07:51.829
<v Speaker 2>uh to actually develop a gen.

0:07:52.070 --> 0:07:56.429
<v Speaker 2>to to augment their employees' ability to support growth opportunities,

0:07:56.720 --> 0:08:00.109
<v Speaker 2>really forming a good partnership between some of the internal

0:08:00.109 --> 0:08:04.350
<v Speaker 2>employees understanding human understanding of the customers with some of

0:08:04.350 --> 0:08:08.750
<v Speaker 2>the uh AI synthesized landscape assessment and issues and opportunities

0:08:08.959 --> 0:08:11.440
<v Speaker 2>that uh acts as a sparring partner to them.

0:08:12.059 --> 0:08:15.079
<v Speaker 2>Under objective setting, for example, you can use Gen AI

0:08:15.079 --> 0:08:18.809
<v Speaker 2>to use and connect historical data and predictive analytics, set

0:08:18.809 --> 0:08:22.820
<v Speaker 2>realistic data-backed objectives, and a good example of how BBVA

0:08:22.820 --> 0:08:25.910
<v Speaker 2>has done this is they really use uh Gen AI

0:08:25.910 --> 0:08:28.929
<v Speaker 2>as a sparring partner for them to create and refine

0:08:28.929 --> 0:08:30.929
<v Speaker 2>some of the strategies going into the markets.

0:08:31.045 --> 0:08:34.474
<v Speaker 2>Well. And finally, on activity planning, this is one of

0:08:34.474 --> 0:08:36.315
<v Speaker 2>the process steps where the use of AI will be

0:08:36.315 --> 0:08:38.875
<v Speaker 2>a quick win because it can help map out activity

0:08:38.875 --> 0:08:41.954
<v Speaker 2>plans from how you select your channels today to your

0:08:41.955 --> 0:08:46.114
<v Speaker 2>messaging strategies to predict the optimal combinations for different kinds

0:08:46.114 --> 0:08:49.914
<v Speaker 2>of segments. And what Heineken has done here is truly astounding.

0:08:50.260 --> 0:08:53.739
<v Speaker 2>They've used Gen AI to enhance their customer segmentation, which

0:08:53.739 --> 0:08:56.929
<v Speaker 2>has led to more targeted marketing and significant marketing, uh,

0:08:56.940 --> 0:09:00.409
<v Speaker 2>media savings, uh, in, in order to drive real commercial

0:09:00.409 --> 0:09:01.809
<v Speaker 2>uh effectiveness.

0:09:02.479 --> 0:09:05.549
<v Speaker 2>So that's planning and strategy. Let's turn the page over

0:09:05.549 --> 0:09:08.150
<v Speaker 2>to the next process, which is really in the space

0:09:08.150 --> 0:09:11.429
<v Speaker 2>of integrated marketing communication. I know many of us in

0:09:11.429 --> 0:09:15.059
<v Speaker 2>the room today are actually responsible for the day to day, uh, uh,

0:09:15.109 --> 0:09:19.450
<v Speaker 2>execution of marketing. So this should be particularly interesting for you. Again, uh,

0:09:19.510 --> 0:09:21.229
<v Speaker 2>in the middle part, we see the end to end

0:09:21.229 --> 0:09:23.780
<v Speaker 2>process from how you develop the brief to the creative

0:09:23.780 --> 0:09:27.270
<v Speaker 2>and testing it, creating the actual production of the creative

0:09:27.270 --> 0:09:30.429
<v Speaker 2>itself through to media planning and measurement and optimization.

0:09:30.780 --> 0:09:34.669
<v Speaker 2>And similarly, we see multiple spaces where actually Jay can

0:09:34.669 --> 0:09:38.549
<v Speaker 2>be deployed, deployed to actually make things more effective. Under

0:09:38.549 --> 0:09:42.900
<v Speaker 2>creative testing, for example, Unilever has embraced digital training technology

0:09:42.900 --> 0:09:46.900
<v Speaker 2>to create multiple copies of their uh uh creative assets

0:09:46.900 --> 0:09:49.949
<v Speaker 2>and then implemented that AI to speed up and scale

0:09:49.950 --> 0:09:53.669
<v Speaker 2>that creative evaluation. So they have actually tested 3 times

0:09:53.669 --> 0:09:57.130
<v Speaker 2>more digital assets and 15% more TV assets as a

0:09:57.130 --> 0:09:59.229
<v Speaker 2>result of the embracing of these technologies.

0:09:59.929 --> 0:10:03.590
<v Speaker 2>And for media planning and campaign activation, a really nice example,

0:10:03.640 --> 0:10:07.069
<v Speaker 2>my personal favorite is from Catree India. They use Gen

0:10:07.070 --> 0:10:11.440
<v Speaker 2>AI to create hyperlocal personalized ads featuring a very famous

0:10:11.440 --> 0:10:12.909
<v Speaker 2>Bollywood actor, Shah Rukh Khan.

0:10:13.369 --> 0:10:17.099
<v Speaker 2>And by analyzing the small business data, they actually use

0:10:17.099 --> 0:10:21.820
<v Speaker 2>uh the uh customized ads to promote local stores with

0:10:21.820 --> 0:10:24.659
<v Speaker 2>Shah Rukh Khan um by name, helping them to gain

0:10:24.659 --> 0:10:28.770
<v Speaker 2>their visibility and recognition and collect, connect with customers during

0:10:28.770 --> 0:10:29.819
<v Speaker 2>the festive season.

0:10:30.690 --> 0:10:34.689
<v Speaker 2>Finally, in measurement and optimization, TSB used AI to allow

0:10:34.690 --> 0:10:38.049
<v Speaker 2>the ingestion of hyper granular daily data across both the

0:10:38.049 --> 0:10:40.090
<v Speaker 2>digital and offline channels at speed.

0:10:40.489 --> 0:10:42.890
<v Speaker 2>They refreshed their MMM model and we'll talk a little

0:10:42.890 --> 0:10:45.848
<v Speaker 2>bit about what an MMM is, uh, later on. But

0:10:45.849 --> 0:10:48.929
<v Speaker 2>they refresh this on a daily basis to enable real-time

0:10:48.929 --> 0:10:51.679
<v Speaker 2>decision making. And I want you to pay particular attention

0:10:51.679 --> 0:10:54.010
<v Speaker 2>to the last line over there. Uh, I know it's

0:10:54.010 --> 0:10:56.419
<v Speaker 2>a little bit small, but it's in the TSB box.

0:10:56.650 --> 0:11:00.479
<v Speaker 2>They actually, uh, managed to increase their sales by 42%

0:11:00.679 --> 0:11:03.520
<v Speaker 2>and reduce their media spend by 70%.

0:11:03.960 --> 0:11:06.919
<v Speaker 2>Now, I think this is, uh, for most marketers in

0:11:06.919 --> 0:11:08.880
<v Speaker 2>the room, and I used to be on the marketing

0:11:08.880 --> 0:11:12.479
<v Speaker 2>side myself, uh, something so powerful that you can say

0:11:12.479 --> 0:11:15.880
<v Speaker 2>to your end stakeholders, whether that's your, uh, executive or

0:11:15.880 --> 0:11:20.159
<v Speaker 2>whether that is your, uh, uh, stakeholders internally, to be

0:11:20.159 --> 0:11:20.439
<v Speaker 2>able to.

0:11:20.530 --> 0:11:24.669
<v Speaker 2>Drive real, uh, commercial impact by improving marketing is something

0:11:24.669 --> 0:11:27.400
<v Speaker 2>that is just, uh, becoming more and more essential in

0:11:27.400 --> 0:11:31.760
<v Speaker 2>today's businesses. And Gen AI, especially in the, uh, topic

0:11:31.760 --> 0:11:34.039
<v Speaker 2>of measurement, is one of those things that can make

0:11:34.039 --> 0:11:36.429
<v Speaker 2>a real differentiator in the way marketing shows up in

0:11:36.429 --> 0:11:40.880
<v Speaker 2>the organizations. Now, finally, on the last one, which is around, uh, innovation.

0:11:41.440 --> 0:11:44.789
<v Speaker 2>We see again a few opportunities over here. I'll just

0:11:44.789 --> 0:11:48.960
<v Speaker 2>spotlight one in the interest of time. Our spotlight opportunity identification,

0:11:49.070 --> 0:11:52.039
<v Speaker 2>which is really where marketing shines, uh, because of our

0:11:52.039 --> 0:11:56.469
<v Speaker 2>closeness to the consumer. Underportunity identification, a case study from

0:11:56.469 --> 0:11:59.400
<v Speaker 2>Hellion was how they developed their Gen AI tool called

0:11:59.400 --> 0:12:02.299
<v Speaker 2>Ask Halion, which is used as again, aspiring partner for

0:12:02.299 --> 0:12:06.239
<v Speaker 2>innovation through the stages of articulating consumer insights through the

0:12:06.239 --> 0:12:09.190
<v Speaker 2>way to interrogating the signs inside the products as a

0:12:09.190 --> 0:12:10.239
<v Speaker 2>consumer health firm.

0:12:10.630 --> 0:12:12.770
<v Speaker 2>And this really helps them come up with superior concepts

0:12:12.770 --> 0:12:14.890
<v Speaker 2>that they can activate and test with consumers.

0:12:15.919 --> 0:12:18.890
<v Speaker 2>So those were the top 3 processes that we went through. Now,

0:12:18.929 --> 0:12:22.159
<v Speaker 2>let's turn the page over to understanding a little bit

0:12:22.159 --> 0:12:25.449
<v Speaker 2>more about how AI can be really deployed in extra

0:12:25.450 --> 0:12:27.890
<v Speaker 2>solutions itself. First thing I'll say is this is not

0:12:27.890 --> 0:12:29.848
<v Speaker 2>new to us at Canta. We've been around for a

0:12:29.849 --> 0:12:32.369
<v Speaker 2>long time and we've been building expertise in AI for

0:12:32.369 --> 0:12:35.520
<v Speaker 2>a similarly long time. Now, what many of you, uh,

0:12:35.530 --> 0:12:38.250
<v Speaker 2>might share in our journey is we've gone from in

0:12:38.250 --> 0:12:42.179
<v Speaker 2>the early 1980s from machine learning, which is really around

0:12:42.179 --> 0:12:44.849
<v Speaker 2>how do you do things in a much, uh, faster way.

0:12:45.429 --> 0:12:47.150
<v Speaker 2>Uh, to all the way, uh, in the middle of

0:12:47.150 --> 0:12:50.179
<v Speaker 2>the part, uh, of the screen, deep learning, which is, uh,

0:12:50.190 --> 0:12:53.309
<v Speaker 2>embedding AI to really help us understand and harness the

0:12:53.309 --> 0:12:57.030
<v Speaker 2>data even better, to where we are now in generative AI,

0:12:57.039 --> 0:13:00.140
<v Speaker 2>which is helping clients to take all of the, uh,

0:13:00.150 --> 0:13:03.309
<v Speaker 2>legacy data that they have with us in order for

0:13:03.309 --> 0:13:06.469
<v Speaker 2>them to understand the use cases and applications to improve

0:13:06.469 --> 0:13:08.630
<v Speaker 2>into the future as well. And that's what we're really

0:13:08.630 --> 0:13:09.340
<v Speaker 2>excited for.

0:13:09.729 --> 0:13:12.780
<v Speaker 2>So on the next page, you'll see that the transformation

0:13:12.780 --> 0:13:15.659
<v Speaker 2>journey that Kanta has been on is that we've then

0:13:15.659 --> 0:13:19.609
<v Speaker 2>started to infuse AI into every single solution that we have.

0:13:22.419 --> 0:13:25.140
<v Speaker 2>Whether that is AI for innovation, as you heard me

0:13:25.140 --> 0:13:28.989
<v Speaker 2>talk about earlier, to uh make uh concept evaluation much

0:13:28.989 --> 0:13:32.250
<v Speaker 2>more faster and much more effective through to AI for

0:13:32.250 --> 0:13:34.859
<v Speaker 2>creative and being able to test at a lot higher

0:13:34.859 --> 0:13:38.599
<v Speaker 2>scale as well as use predictive analytics to try to

0:13:38.599 --> 0:13:42.488
<v Speaker 2>understand how creatives would perform even before they uh hit

0:13:42.489 --> 0:13:45.099
<v Speaker 2>the markets, uh, as well as AI for media, which

0:13:45.099 --> 0:13:47.659
<v Speaker 2>is uh optimizing your media allocation.

0:13:47.929 --> 0:13:50.989
<v Speaker 2>Uh, and finally, for brand as well on the right-hand side,

0:13:51.159 --> 0:13:54.098
<v Speaker 2>in order to predict your brand KPIs, whether that's uh

0:13:54.099 --> 0:13:56.650
<v Speaker 2>top of mind awareness all the way through to consideration

0:13:56.650 --> 0:14:00.380
<v Speaker 2>and purchase intent. This is so powerful because all of

0:14:00.380 --> 0:14:03.909
<v Speaker 2>our data and all of our AI is running off

0:14:03.909 --> 0:14:06.419
<v Speaker 2>the back of a model that has a massive database

0:14:06.419 --> 0:14:06.989
<v Speaker 2>over here.

0:14:07.330 --> 0:14:10.450
<v Speaker 2>Uh, we use a model, uh, that's powered by something

0:14:10.450 --> 0:14:13.799
<v Speaker 2>called Link, which is the world's largest normative advertising database.

0:14:14.010 --> 0:14:21.049
<v Speaker 2>It consists of 250,000+ tests, 307 million human interactions that

0:14:21.049 --> 0:14:24.059
<v Speaker 2>have been recorded in our particular database, and therefore, we're

0:14:24.059 --> 0:14:27.729
<v Speaker 2>able to predict how real human behavior will change as

0:14:27.729 --> 0:14:28.969
<v Speaker 2>a result of seeing ads.

0:14:29.489 --> 0:14:31.940
<v Speaker 2>The last set I'll leave you with is our clients

0:14:31.940 --> 0:14:36.059
<v Speaker 2>typically see a 30% increase in ROI when they improve

0:14:36.059 --> 0:14:39.780
<v Speaker 2>and add creative quality of the back of this insights

0:14:39.780 --> 0:14:42.940
<v Speaker 2>from average to best. 30%, that's phenomenal.

0:14:44.270 --> 0:14:47.119
<v Speaker 2>So I'm turning to the last pages in my presentation.

0:14:47.239 --> 0:14:49.679
<v Speaker 2>I just wanted to spend a bit of time just, uh, uh,

0:14:49.760 --> 0:14:52.950
<v Speaker 2>bringing to life three core, uh, solutions that might be

0:14:52.950 --> 0:14:56.239
<v Speaker 2>relevant and interesting for folks on this call. Uh, the

0:14:56.239 --> 0:14:58.799
<v Speaker 2>first is, uh, what we call Lift ROI, which is

0:14:58.799 --> 0:15:02.840
<v Speaker 2>essentially marketing mix modeling. It helps you understand, uh, of

0:15:02.840 --> 0:15:05.119
<v Speaker 2>all of the media spend or all of the marketing

0:15:05.119 --> 0:15:08.280
<v Speaker 2>spend that you're putting into, uh, the market, how much

0:15:08.280 --> 0:15:10.880
<v Speaker 2>of it is actually generating true business results.

0:15:11.239 --> 0:15:16.539
<v Speaker 2>I.e. sales or uh if your financial services, assets under management,

0:15:16.750 --> 0:15:20.179
<v Speaker 2>or any business metric that really resonates with you. Not

0:15:20.179 --> 0:15:23.030
<v Speaker 2>only that, it can also be linked to brand equity

0:15:23.030 --> 0:15:25.909
<v Speaker 2>metrics and be able to predict what is the lift

0:15:26.630 --> 0:15:30.469
<v Speaker 2>in awareness, uh, consideration and purchase intent when you tweak

0:15:30.469 --> 0:15:34.070
<v Speaker 2>your marketing mix or increase your marketing spend. In the middle,

0:15:34.299 --> 0:15:37.869
<v Speaker 2>Link AI is our solution for really understanding how creatives

0:15:37.869 --> 0:15:39.429
<v Speaker 2>would be performing in market.

0:15:39.679 --> 0:15:42.690
<v Speaker 2>And be able to predict how people would be uh

0:15:42.690 --> 0:15:45.090
<v Speaker 2>responding to ads and on the right hand side, uh

0:15:45.090 --> 0:15:50.130
<v Speaker 2>I evaluating concepts for innovation uh will uh require something

0:15:50.130 --> 0:15:52.239
<v Speaker 2>that's called uh concept evaluate as well.

0:15:53.169 --> 0:15:56.609
<v Speaker 2>You ingest some marketing data and non-marketing data, like your

0:15:56.609 --> 0:16:00.849
<v Speaker 2>macro factors into a model. Uh, through ingesting of this

0:16:00.849 --> 0:16:04.010
<v Speaker 2>particular data, it can actually, uh, simulate what the short-term

0:16:04.010 --> 0:16:06.609
<v Speaker 2>sales and brand equity would look like in order for

0:16:06.609 --> 0:16:09.289
<v Speaker 2>you to understand what is the exact tweaks that you

0:16:09.289 --> 0:16:12.159
<v Speaker 2>should be making in general. And if you're not using

0:16:12.159 --> 0:16:15.130
<v Speaker 2>MMM in your organizations today, you should be really thinking

0:16:15.130 --> 0:16:17.489
<v Speaker 2>about how you can actually deploy something like that in

0:16:17.489 --> 0:16:19.609
<v Speaker 2>order to make your marketing much more measurable.

0:16:20.500 --> 0:16:21.530
<v Speaker 2>On the next pitch

0:16:22.669 --> 0:16:25.469
<v Speaker 2>Uh, I just spoke a little bit about how, uh,

0:16:25.479 --> 0:16:28.250
<v Speaker 2>marketing comms can be improved. Uh, here's a list of

0:16:28.250 --> 0:16:30.750
<v Speaker 2>some of the metrics that can be actually optimized off

0:16:30.750 --> 0:16:34.719
<v Speaker 2>the back of this. Uh, what's important to note is

0:16:34.719 --> 0:16:36.599
<v Speaker 2>it's not just on the left-hand side, the brand and

0:16:36.599 --> 0:16:39.799
<v Speaker 2>creative metrics, but you can also optimize your behavioral metrics

0:16:39.799 --> 0:16:40.760
<v Speaker 2>off the back of this.

0:16:41.630 --> 0:16:45.109
<v Speaker 2>Now, finally, on the last two slides.

0:16:46.400 --> 0:16:49.140
<v Speaker 2>I'd like to uh just show you a prediction of

0:16:49.140 --> 0:16:52.989
<v Speaker 2>where Cantar is going with, uh, the AI uh future.

0:16:53.340 --> 0:16:56.340
<v Speaker 2>We believe that today, whilst the majority of use cases

0:16:56.340 --> 0:16:59.539
<v Speaker 2>is in, uh, the effectiveness, uh, area, which is really

0:16:59.539 --> 0:17:04.180
<v Speaker 2>just summarization, automated reporting in the future under the Toor

0:17:04.180 --> 0:17:08.540
<v Speaker 2>bar chart, it will actually change to become a lot more, uh, pivoted.

0:17:08.645 --> 0:17:12.114
<v Speaker 2>Towards edge cases of AI. And what we mean by

0:17:12.114 --> 0:17:15.795
<v Speaker 2>edge cases are things like contextual prediction to forecast not

0:17:15.795 --> 0:17:19.784
<v Speaker 2>just the effectiveness of, um, advertising based on images or

0:17:19.785 --> 0:17:22.635
<v Speaker 2>in music, but also things like the characters dialogue and

0:17:22.635 --> 0:17:25.194
<v Speaker 2>the contextual factors as well. So these are applications that

0:17:25.194 --> 0:17:27.954
<v Speaker 2>are not common today, and we expect AI to be

0:17:27.954 --> 0:17:30.714
<v Speaker 2>able to accelerate this into the future as well.

0:17:31.650 --> 0:17:33.910
<v Speaker 2>So with that, um, I've come to the end of

0:17:33.910 --> 0:17:36.180
<v Speaker 2>my particular segment. I know I kind of breezed through it.

0:17:36.229 --> 0:17:37.910
<v Speaker 2>Please feel free to use the chat to ask me

0:17:37.910 --> 0:17:40.180
<v Speaker 2>any questions as I see some of you have already done.

0:17:40.390 --> 0:17:42.739
<v Speaker 2>I'm gonna hand the time back to Tim. I'm also

0:17:42.739 --> 0:17:45.469
<v Speaker 2>contactable via my email that you see on screen right now. Thanks,

0:17:45.540 --> 0:17:45.750
<v Speaker 1>Tim.

0:17:46.359 --> 0:17:48.790
<v Speaker 1>All right. Thank you, Lin. I think that's really resonate.

0:17:49.069 --> 0:17:51.270
<v Speaker 1>I think what you brought up a very, uh, a

0:17:51.270 --> 0:17:55.030
<v Speaker 1>critical point, right? AI isn't just about technology, but it's

0:17:55.030 --> 0:17:58.468
<v Speaker 1>about updating intelligence into the entire marketing life cycle. And

0:17:58.469 --> 0:18:01.000
<v Speaker 1>I think that's something that we are very aligned with

0:18:01.000 --> 0:18:04.030
<v Speaker 1>that at Mediacorp. And, and of course, and transformation and

0:18:04.030 --> 0:18:05.229
<v Speaker 1>scale also need like different.

0:18:05.324 --> 0:18:07.625
<v Speaker 1>Integration. I think uh that probably will bring us to

0:18:07.625 --> 0:18:10.834
<v Speaker 1>the next speaker. So let's hear from Kry who will

0:18:10.834 --> 0:18:15.944
<v Speaker 1>share how WPP is leveraging uh its intelligent marketing operating system.

0:18:16.194 --> 0:18:20.714
<v Speaker 1>WPP open to drive the performance, automate workflows, and reimagine

0:18:20.714 --> 0:18:23.814
<v Speaker 1>how media get planned and activated. And over to you, Kari.

0:18:24.489 --> 0:18:28.160
<v Speaker 1>Yeah, thank you, um, Tim. And so, good morning, everyone.

0:18:28.359 --> 0:18:30.639
<v Speaker 1>Honored to be here. I'm Carrie and I'm the client

0:18:30.640 --> 0:18:35.119
<v Speaker 1>president for WPP Media Singapore. We are the media arm

0:18:35.119 --> 0:18:41.079
<v Speaker 1>of the communications group, WPP. Um, as, uh, the speakers, uh, as,

0:18:41.160 --> 0:18:43.959
<v Speaker 1>as we mentioned earlier, AI is evolving. It is also

0:18:43.959 --> 0:18:49.629
<v Speaker 1>evolving very fast, very quickly, literally changes almost every day. Uh,

0:18:49.680 --> 0:18:52.079
<v Speaker 1>I think one important point Lin touched on.

0:18:52.540 --> 0:18:54.649
<v Speaker 1>And she gave the lay of the land is that

0:18:54.920 --> 0:18:56.800
<v Speaker 1>more work needs to be done. There's a lot more

0:18:56.800 --> 0:19:01.438
<v Speaker 1>value to be unlocked with the, with the potential of AI. Uh,

0:19:01.520 --> 0:19:04.310
<v Speaker 1>I will speak from the angle of a user or

0:19:04.310 --> 0:19:09.429
<v Speaker 1>of an organization using AI and weaving AI into our processes.

0:19:09.675 --> 0:19:13.055
<v Speaker 1>Uh, as we deliver for our clients. Uh, so I'll

0:19:13.055 --> 0:19:15.054
<v Speaker 1>share how we have geared up and how AI is

0:19:15.055 --> 0:19:17.375
<v Speaker 1>starting to play a big part for our organization and

0:19:17.375 --> 0:19:20.525
<v Speaker 1>how we work with clients. Just a bit about WPP Media.

0:19:20.734 --> 0:19:23.295
<v Speaker 1>We are a media agency, as I said, and we

0:19:23.295 --> 0:19:25.844
<v Speaker 1>manage over 50 billion.

0:19:27.750 --> 0:19:31.109
<v Speaker 1>Media budgets for our clients and our clients, uh, some

0:19:31.109 --> 0:19:35.149
<v Speaker 1>of the logos, I've listed below, we expands uh global clients,

0:19:35.270 --> 0:19:39.239
<v Speaker 1>local clients, both public and private sector. So the aim

0:19:39.239 --> 0:19:42.079
<v Speaker 1>is for us as an agency, as a business, is

0:19:42.079 --> 0:19:45.790
<v Speaker 1>continue to be a trusted advisor to our clients. Uh,

0:19:45.829 --> 0:19:49.349
<v Speaker 1>as an organization, uh, just like yourselves, we are upscaling

0:19:49.349 --> 0:19:52.920
<v Speaker 1>our entire workforce. AI is, uh, as I said, it's

0:19:52.920 --> 0:19:54.989
<v Speaker 1>evolving on a daily basis, so there's a lot of

0:19:54.989 --> 0:19:56.189
<v Speaker 1>work to be, uh.

0:19:56.545 --> 0:20:00.064
<v Speaker 1>here. Uh, our advice to all companies and all the

0:20:00.064 --> 0:20:03.084
<v Speaker 1>listeners out here is that we have to stay nimble

0:20:03.704 --> 0:20:07.135
<v Speaker 1>and age out in order to thrive in this dynamic environment.

0:20:07.464 --> 0:20:09.305
<v Speaker 1>If you go on to the next slide, um, I

0:20:09.305 --> 0:20:12.145
<v Speaker 1>will touch a bit about how AI is being used

0:20:12.145 --> 0:20:13.864
<v Speaker 1>today in our organization, um.

0:20:14.469 --> 0:20:18.959
<v Speaker 1>The first one is a personal productivity tool and um

0:20:18.959 --> 0:20:20.959
<v Speaker 1>I'm sure many of you have many examples of how

0:20:20.959 --> 0:20:24.119
<v Speaker 1>you are using AI today from a personal level. I mean,

0:20:24.160 --> 0:20:28.629
<v Speaker 1>you're doing searches apart from your Google search and your

0:20:29.180 --> 0:20:30.839
<v Speaker 1>Bing search or using LM search.

0:20:31.005 --> 0:20:33.994
<v Speaker 1>And so on. You're using this to summarize documents to

0:20:33.994 --> 0:20:37.314
<v Speaker 1>generate ideas and so on. So, the productivity to angle,

0:20:37.354 --> 0:20:41.635
<v Speaker 1>I think it's um it's easy to visualize. Uh, as

0:20:41.635 --> 0:20:45.234
<v Speaker 1>a media planning agency in the context uh of media

0:20:45.234 --> 0:20:49.395
<v Speaker 1>planning and activation and measurement, uh, we have leaned into

0:20:49.395 --> 0:20:53.064
<v Speaker 1>AI in a couple of ways, uh, to discover new audiences.

0:20:53.194 --> 0:20:56.635
<v Speaker 1>What this means is, uh, looking for new sources of

0:20:56.635 --> 0:20:58.594
<v Speaker 1>growth or for client growth.

0:20:59.119 --> 0:21:03.089
<v Speaker 1>Uh, for clients, businesses, uh, planning media campaigns, uh, that

0:21:03.089 --> 0:21:06.109
<v Speaker 1>are turbocharged in terms of getting outputs quicker, so we

0:21:06.109 --> 0:21:08.479
<v Speaker 1>become more efficient and, uh,

0:21:09.319 --> 0:21:11.790
<v Speaker 1>Easily what can be done in 2 days when it

0:21:11.790 --> 0:21:15.119
<v Speaker 1>used to be 2 weeks. This doesn't mean that planners

0:21:15.119 --> 0:21:18.718
<v Speaker 1>become redundant, but AI has allowed us to have richer conversations,

0:21:18.920 --> 0:21:23.250
<v Speaker 1>more conversations and discussions with clients throughout the process. So yes,

0:21:23.280 --> 0:21:26.199
<v Speaker 1>there's efficiency, but I think the important and salient point

0:21:26.199 --> 0:21:30.188
<v Speaker 1>here is that we get to better outputs as well. Uh, from,

0:21:30.319 --> 0:21:33.760
<v Speaker 1>from a media reporting standpoint, it's also improving by leaps

0:21:33.760 --> 0:21:35.979
<v Speaker 1>and bounds. We don't spend a lot of time.

0:21:36.439 --> 0:21:40.630
<v Speaker 1>Now just providing observations, it's not just about compiling data,

0:21:40.760 --> 0:21:45.239
<v Speaker 1>it's a lot more time spent on understanding uh the

0:21:45.239 --> 0:21:48.709
<v Speaker 1>insights and I think importantly, what are the actions, um.

0:21:49.510 --> 0:21:52.300
<v Speaker 1>Uh, that will come out of, of all these uh insights, right?

0:21:52.380 --> 0:21:54.579
<v Speaker 1>So how do you optimize the campaigns and so on.

0:21:55.540 --> 0:21:57.560
<v Speaker 1>What are some of our clients telling us? So, as,

0:21:57.660 --> 0:22:02.020
<v Speaker 1>as a media agency, our clients are equally curious, they

0:22:02.020 --> 0:22:07.060
<v Speaker 1>are consumers themselves. They are, they're asking questions as basic as,

0:22:07.140 --> 0:22:09.219
<v Speaker 1>you know, what, what is this, uh, what is the

0:22:09.219 --> 0:22:15.020
<v Speaker 1>AI landscape about? They're asking about what the AI capabilities

0:22:15.020 --> 0:22:17.500
<v Speaker 1>are for AI agency, meaning us, right? So as a

0:22:17.500 --> 0:22:21.900
<v Speaker 1>media agency providing our media investment services to clients, what

0:22:21.900 --> 0:22:22.459
<v Speaker 1>is it?

0:22:22.555 --> 0:22:24.584
<v Speaker 1>How are we using AI and how are we staying

0:22:24.584 --> 0:22:27.444
<v Speaker 1>relevant for their business. So we're being asked these questions

0:22:27.704 --> 0:22:30.584
<v Speaker 1>uh quite a lot. Uh, some clients are asking us

0:22:30.584 --> 0:22:33.694
<v Speaker 1>to connect how we do, uh, how we use AI

0:22:34.064 --> 0:22:37.954
<v Speaker 1>to how they're getting tangible business outcomes, especially for clients

0:22:37.954 --> 0:22:41.305
<v Speaker 1>that do a lot of business on .com. Uh, so

0:22:41.305 --> 0:22:44.104
<v Speaker 1>those are very common questions and we continue to get

0:22:44.104 --> 0:22:47.025
<v Speaker 1>these questions, uh, on, on a very regular basis.

0:22:47.800 --> 0:22:51.589
<v Speaker 1>We're turbocharging ourselves through this, we call it an operating system,

0:22:51.640 --> 0:22:55.270
<v Speaker 1>and this operating system we have named WPT Open. Uh,

0:22:55.319 --> 0:22:58.479
<v Speaker 1>we have committed to investing over 300 million to building

0:22:58.479 --> 0:23:01.050
<v Speaker 1>cutting edge capabilities within this OS.

0:23:01.890 --> 0:23:05.569
<v Speaker 1>It combines some of this development, uh, our own development,

0:23:05.650 --> 0:23:10.010
<v Speaker 1>but it also um uh combines this with the AI

0:23:10.010 --> 0:23:16.199
<v Speaker 1>capabilities of uh our key partners including Google, Amazon, Microsoft, Meta, and,

0:23:16.209 --> 0:23:18.849
<v Speaker 1>and even TikTok. I hope you enjoyed that and you

0:23:18.849 --> 0:23:22.609
<v Speaker 1>got the gist of what WPP Open is allowing our

0:23:22.609 --> 0:23:26.160
<v Speaker 1>teams uh to do across WPP and WPP Media.

0:23:26.709 --> 0:23:30.389
<v Speaker 1>The what is really some of the platforms, some of

0:23:30.390 --> 0:23:32.948
<v Speaker 1>the some of the uh interfaces that you see in

0:23:32.949 --> 0:23:37.430
<v Speaker 1>the video, um, it's a unifying um area where we

0:23:37.430 --> 0:23:39.069
<v Speaker 1>start doing our work from. It's the first thing that

0:23:39.069 --> 0:23:41.699
<v Speaker 1>we open when we get to work instead of uh

0:23:41.699 --> 0:23:45.310
<v Speaker 1>emails or apart from emails. The how is the nuts

0:23:45.310 --> 0:23:46.310
<v Speaker 1>and bolts of it, but

0:23:46.589 --> 0:23:49.510
<v Speaker 1>I, I think the how is also how the organization

0:23:49.510 --> 0:23:53.959
<v Speaker 1>is bringing along all the employees and the workforce and

0:23:53.959 --> 0:23:56.430
<v Speaker 1>how we're enabling them to do the work. So, uh,

0:23:56.439 --> 0:24:00.839
<v Speaker 1>an important factor to consider here um is that change management, right?

0:24:00.959 --> 0:24:03.829
<v Speaker 1>So AI is not going to be a silver bullet

0:24:03.829 --> 0:24:06.760
<v Speaker 1>for your business just because you implement something. You have

0:24:06.760 --> 0:24:10.119
<v Speaker 1>to think of um some of the processes. I think

0:24:10.119 --> 0:24:12.010
<v Speaker 1>Lin mentioned that a lot of um

0:24:12.219 --> 0:24:16.399
<v Speaker 1>It is not just about productivity and automating some repetitive tasks.

0:24:16.479 --> 0:24:19.989
<v Speaker 1>A lot of the, um, a lot of thinking is

0:24:19.989 --> 0:24:22.510
<v Speaker 1>to go into how to bring everyone along this journey.

0:24:22.760 --> 0:24:26.750
<v Speaker 1>And this is especially tough because it is evolving so fast. The,

0:24:26.920 --> 0:24:28.739
<v Speaker 1>the why, I don't think I need to kind of

0:24:28.739 --> 0:24:31.989
<v Speaker 1>labor the point. It is, it is, it is a necessity,

0:24:32.000 --> 0:24:35.040
<v Speaker 1>like it is going to be the differentiator, if not

0:24:35.040 --> 0:24:37.599
<v Speaker 1>the silver bullet for many organizations.

0:24:38.560 --> 0:24:40.879
<v Speaker 1>Uh, so for the BPP open on the next slide, uh,

0:24:41.020 --> 0:24:43.139
<v Speaker 1>I'm not going to label the point here again. There

0:24:43.140 --> 0:24:44.609
<v Speaker 1>are different, um,

0:24:45.140 --> 0:24:47.319
<v Speaker 1>Uh, what we call command center. This is the command

0:24:47.319 --> 0:24:50.810
<v Speaker 1>center of marketing operations that we call it studios and

0:24:50.810 --> 0:24:54.399
<v Speaker 1>uh different parts of our business use the different studios

0:24:54.400 --> 0:24:57.560
<v Speaker 1>in different combinations. In the next slide, you'll see how

0:24:57.560 --> 0:25:00.719
<v Speaker 1>WPB Media, as the media agency, the studio that we

0:25:00.719 --> 0:25:04.839
<v Speaker 1>use the most often is Open Media Studio. Uh, you

0:25:04.839 --> 0:25:06.560
<v Speaker 1>can move on to the next slide. Yeah. So this

0:25:06.560 --> 0:25:09.000
<v Speaker 1>is the studio that we use most often.

0:25:09.589 --> 0:25:14.989
<v Speaker 1>Uh, alongside the Creative Studio. And um we approach as

0:25:14.989 --> 0:25:20.390
<v Speaker 1>a media arm of the DPP, we approachedri clients and

0:25:20.390 --> 0:25:24.619
<v Speaker 1>media problems through a process called DPAM and in each

0:25:24.619 --> 0:25:28.429
<v Speaker 1>of the phases of Discover plan activate and measure, we

0:25:28.430 --> 0:25:32.180
<v Speaker 1>have different tools that are sit under these processes. Uh,

0:25:32.189 --> 0:25:34.180
<v Speaker 1>let's look at some of the examples.

0:25:34.650 --> 0:25:37.989
<v Speaker 1>I think many of you are using uh AI in

0:25:37.989 --> 0:25:40.310
<v Speaker 1>a personal productivity, um.

0:25:40.880 --> 0:25:44.660
<v Speaker 1>Uh, capacity for discovery and so on. Uh, one of

0:25:44.660 --> 0:25:46.300
<v Speaker 1>my favorite use cases, so I thought I want to

0:25:46.300 --> 0:25:50.458
<v Speaker 1>share this, is, um, the ability to turn long PDF

0:25:50.459 --> 0:25:53.979
<v Speaker 1>documents into podcasts that you can then consume as you're

0:25:53.979 --> 0:25:56.359
<v Speaker 1>on the go, when you're on when you're commuting. I

0:25:56.359 --> 0:25:59.069
<v Speaker 1>think this is a really good personal productivity tool and

0:25:59.459 --> 0:26:03.179
<v Speaker 1>uh we have this weaved into um WPT Open. So

0:26:03.180 --> 0:26:06.659
<v Speaker 1>it's something that a lot of colleagues use um at

0:26:06.660 --> 0:26:07.419
<v Speaker 1>a personal level.

0:26:08.579 --> 0:26:13.010
<v Speaker 1>In the next, uh, example, this sits in the planning

0:26:13.229 --> 0:26:15.959
<v Speaker 1>phase within DPAM and um

0:26:17.109 --> 0:26:20.489
<v Speaker 1>This, what takes what took us a week um to

0:26:20.489 --> 0:26:24.569
<v Speaker 1>do can be achieved in a matter of hours, but

0:26:24.569 --> 0:26:26.530
<v Speaker 1>let's call it a day because I don't want to

0:26:26.530 --> 0:26:28.968
<v Speaker 1>give the impression that the AI spits out work and

0:26:28.969 --> 0:26:32.280
<v Speaker 1>then this is what we pass on to the clients, right? Um,

0:26:32.290 --> 0:26:35.129
<v Speaker 1>at the bottom left of the illustration.

0:26:35.599 --> 0:26:38.640
<v Speaker 1>Uh, what happens here is, this is an example of

0:26:39.180 --> 0:26:42.819
<v Speaker 1>um a school, an education center giving us a brief

0:26:42.819 --> 0:26:46.300
<v Speaker 1>on driving awareness for new postgraduate courses in the next

0:26:46.300 --> 0:26:48.659
<v Speaker 1>school year. So in the past, this would have taken

0:26:48.660 --> 0:26:52.359
<v Speaker 1>quite a while to compile across different data sets, um,

0:26:52.500 --> 0:26:56.060
<v Speaker 1>different research tools that we subscribe to. Uh, right now,

0:26:56.260 --> 0:26:59.180
<v Speaker 1>WPP Open and AI allows us to spit this out

0:26:59.180 --> 0:27:02.589
<v Speaker 1>in literally minutes. The outputs um

0:27:03.469 --> 0:27:06.189
<v Speaker 1>would be a draft of the touch points uh for

0:27:06.189 --> 0:27:09.949
<v Speaker 1>this um particular prompt, right? The prompt is how, how

0:27:09.949 --> 0:27:11.910
<v Speaker 1>do we, uh, what, what should we, what are the

0:27:11.910 --> 0:27:14.988
<v Speaker 1>consumer touch points that we should consider uh as we

0:27:14.989 --> 0:27:17.708
<v Speaker 1>roll out and drive awareness for this course, right? So

0:27:17.709 --> 0:27:18.160
<v Speaker 1>that

0:27:18.410 --> 0:27:20.869
<v Speaker 1>The draft is given to us in terms of what

0:27:20.869 --> 0:27:22.909
<v Speaker 1>are the digital channels you can possibly use, what are

0:27:22.910 --> 0:27:25.550
<v Speaker 1>the physical channels you can, you can use. It even

0:27:25.550 --> 0:27:29.060
<v Speaker 1>breaks it out into different stages from, from a teaser

0:27:29.060 --> 0:27:31.708
<v Speaker 1>phase or awareness phase to consideration and then to a

0:27:31.709 --> 0:27:36.708
<v Speaker 1>sign-up phase. Um, this note, this is not um taking

0:27:36.709 --> 0:27:41.420
<v Speaker 1>from open internet, we're pulling from a syndicated third party data. Um,

0:27:41.550 --> 0:27:43.790
<v Speaker 1>the next speaker, Justin, will speak about this a bit.

0:27:44.219 --> 0:27:48.399
<v Speaker 1>Um, but it is also um pulling from proprietary data

0:27:48.400 --> 0:27:51.969
<v Speaker 1>from WPP itself. So it's important to note again that,

0:27:52.079 --> 0:27:54.698
<v Speaker 1>you know, this doesn't mean that we just, you know,

0:27:54.959 --> 0:27:57.780
<v Speaker 1>throw this across the the fences to the clients. Uh,

0:27:57.880 --> 0:28:01.030
<v Speaker 1>what we are achieving here is, um,

0:28:01.739 --> 0:28:05.619
<v Speaker 1>Spending more time, validating, checking. More importantly, we are idating

0:28:05.619 --> 0:28:07.930
<v Speaker 1>how to bring to life the campaign. We're working with

0:28:08.219 --> 0:28:13.020
<v Speaker 1>creative agencies and clients uh in terms of messaging, better messaging,

0:28:13.140 --> 0:28:18.260
<v Speaker 1>more accurate, more personalized messaging, better formats, uh, formats that

0:28:18.260 --> 0:28:23.099
<v Speaker 1>will achieve better results and eventually better business. Uh, so Netnet,

0:28:23.140 --> 0:28:27.260
<v Speaker 1>we are getting to uh better outputs faster, right?

0:28:27.515 --> 0:28:30.814
<v Speaker 1>Um, the use case that you have, um, here as

0:28:30.814 --> 0:28:33.854
<v Speaker 1>well is the part of planning as well, and it's

0:28:33.854 --> 0:28:36.415
<v Speaker 1>part of, uh, it's where we go deeper now to

0:28:36.415 --> 0:28:41.094
<v Speaker 1>consider channel efficiencies, uh, different types of buying um um

0:28:41.094 --> 0:28:44.694
<v Speaker 1>mechanisms to achieve the best outputs possible. So we are

0:28:44.694 --> 0:28:48.564
<v Speaker 1>doing all this and getting campaign benchmarks from the category,

0:28:48.655 --> 0:28:51.494
<v Speaker 1>from specific campaign data a lot easier. So I mean,

0:28:51.614 --> 0:28:55.104
<v Speaker 1>what is the big deal in all of this? I guess, um.

0:28:56.209 --> 0:28:58.780
<v Speaker 1>If you think about it, the, what in the past,

0:28:58.910 --> 0:29:00.869
<v Speaker 1>what we have, I mean, I, in fact, some of

0:29:00.869 --> 0:29:03.270
<v Speaker 1>you are probably still doing this, you know, opening up

0:29:03.270 --> 0:29:07.060
<v Speaker 1>different folders in your computer, finding different Excel, different pivot tables,

0:29:07.310 --> 0:29:09.988
<v Speaker 1>putting it onto a a big screen and then kind

0:29:09.989 --> 0:29:11.439
<v Speaker 1>of combining and getting.

0:29:12.010 --> 0:29:14.420
<v Speaker 1>The core relations, so I'm, I'm sure a lot of

0:29:14.420 --> 0:29:17.619
<v Speaker 1>us are still doing this today. Uh, from, from the

0:29:17.619 --> 0:29:19.859
<v Speaker 1>point of view of a media planner, what we are

0:29:19.859 --> 0:29:24.900
<v Speaker 1>doing now with AI is simplifying and, and solving uh

0:29:24.900 --> 0:29:28.060
<v Speaker 1>for time, right? It is quite a big deal. Um, the,

0:29:28.099 --> 0:29:28.500
<v Speaker 1>the analysis.

0:29:28.744 --> 0:29:30.744
<v Speaker 1>I'll give to you is that instead of using a

0:29:30.744 --> 0:29:34.385
<v Speaker 1>mobile phone, try going, put a coin into a pay

0:29:34.385 --> 0:29:36.864
<v Speaker 1>phone and, and, and make phone calls that you will

0:29:36.864 --> 0:29:39.625
<v Speaker 1>never go back to those days, right? Your mobile phone

0:29:39.625 --> 0:29:43.224
<v Speaker 1>is with you all the time. So this is the

0:29:43.224 --> 0:29:47.464
<v Speaker 1>reality today. In the next couple of years, new media

0:29:47.464 --> 0:29:50.785
<v Speaker 1>planners joining the industry will not know that this wasn't

0:29:50.785 --> 0:29:53.824
<v Speaker 1>the norm before. And the last um

0:29:54.589 --> 0:29:57.839
<v Speaker 1>Use case I'll share is this one called uh agents.

0:29:58.739 --> 0:30:03.130
<v Speaker 1>uh Lin touched on uh generative AI. There's another branch

0:30:03.130 --> 0:30:06.410
<v Speaker 1>of AI called Agentic AI, you might have heard before.

0:30:06.489 --> 0:30:09.609
<v Speaker 1>This is used quite a lot in within WPP Open

0:30:09.609 --> 0:30:15.650
<v Speaker 1>where we are creating agents, um, and agents are like

0:30:15.650 --> 0:30:19.329
<v Speaker 1>the approximate of a real person. So we, we use

0:30:19.329 --> 0:30:23.930
<v Speaker 1>this to create potential um audience with um.

0:30:24.290 --> 0:30:30.469
<v Speaker 1>Target audience, potential um uh experts who can weigh in on, uh,

0:30:30.479 --> 0:30:33.949
<v Speaker 1>say your creatives or uh being part of a focus group.

0:30:34.119 --> 0:30:36.150
<v Speaker 1>In this case, we have created um

0:30:37.060 --> 0:30:40.339
<v Speaker 1>The approximate of an intern, right? So we call this

0:30:40.339 --> 0:30:44.060
<v Speaker 1>um the intern agent and we have built many interns

0:30:44.060 --> 0:30:47.189
<v Speaker 1>for different clients. In this particular use case, um, this

0:30:47.189 --> 0:30:50.579
<v Speaker 1>agent uh or this intern, uh, we have fed it

0:30:50.579 --> 0:30:53.739
<v Speaker 1>a lot of the campaign data of um the uh

0:30:53.739 --> 0:30:55.619
<v Speaker 1>a specific client. So all the.

0:30:56.040 --> 0:30:58.369
<v Speaker 1>campaign reports and all that, we have trained this agent

0:30:58.369 --> 0:31:02.760
<v Speaker 1>up such that we, we can ask this uh agent

0:31:02.760 --> 0:31:04.959
<v Speaker 1>a number of questions. In the past, we would have

0:31:04.959 --> 0:31:08.050
<v Speaker 1>to kind of again look into different folders for uh

0:31:08.050 --> 0:31:10.369
<v Speaker 1>data and all that. Right now, it's just a prompt

0:31:10.369 --> 0:31:13.489
<v Speaker 1>like send me all the post campaign data um from

0:31:13.489 --> 0:31:14.209
<v Speaker 1>this period.

0:31:14.589 --> 0:31:18.630
<v Speaker 1>And the agent will respond, actually, this data, you know,

0:31:18.869 --> 0:31:20.910
<v Speaker 1>it doesn't sit within this period. Would you like to

0:31:20.910 --> 0:31:24.469
<v Speaker 1>look at it from another period? So it becomes a

0:31:24.469 --> 0:31:27.390
<v Speaker 1>very natural way of finding data and I did not

0:31:27.390 --> 0:31:33.099
<v Speaker 1>share the full um screenshot because of uh client confidentiality,

0:31:33.390 --> 0:31:37.670
<v Speaker 1>but um what this conversation continued to was bringing up

0:31:37.670 --> 0:31:41.339
<v Speaker 1>different data and then uh different prompts to bring

0:31:41.709 --> 0:31:44.780
<v Speaker 1>bring us closer to what exactly uh clients are looking for.

0:31:44.989 --> 0:31:47.869
<v Speaker 1>So this is an example of uh agentic AI and

0:31:47.869 --> 0:31:51.319
<v Speaker 1>it's getting a lot more sophisticated. There are a lot

0:31:51.319 --> 0:31:55.109
<v Speaker 1>more interesting use cases. If you can share the next video.

0:31:56.109 --> 0:32:00.390
<v Speaker 1>Where AI innovation meets the art of storytelling, creating a

0:32:00.390 --> 0:32:03.630
<v Speaker 1>new way to tell stories through our nation's languages. When

0:32:03.630 --> 0:32:06.790
<v Speaker 1>we first embarked on the project, we wanted to really

0:32:06.790 --> 0:32:13.189
<v Speaker 1>find an opportunity to explore the possibilities of AI because art, music,

0:32:13.430 --> 0:32:17.500
<v Speaker 1>storytelling is such a big part of our culture. First,

0:32:17.560 --> 0:32:20.910
<v Speaker 1>we went through a lot of folk tales to finally

0:32:20.910 --> 0:32:21.349
<v Speaker 1>settle on.

0:32:21.464 --> 0:32:24.535
<v Speaker 1>One that was appropriate, kids friendly, and we also settled

0:32:24.535 --> 0:32:27.775
<v Speaker 1>on the story of Pulau Ubin. We invited eager parents

0:32:27.775 --> 0:32:30.895
<v Speaker 1>and grandparents to try out our tech while assuring them

0:32:30.895 --> 0:32:34.214
<v Speaker 1>that the data would be stored and handled securely. Making

0:32:34.214 --> 0:32:37.814
<v Speaker 1>sure that they feel comfortable with using our AI tools

0:32:37.814 --> 0:32:40.535
<v Speaker 1>was a big success to me that they actually trust

0:32:40.535 --> 0:32:43.974
<v Speaker 1>us in handling their data. With their full consent, they

0:32:43.974 --> 0:32:46.805
<v Speaker 1>were ready to experience narrative.

0:32:48.020 --> 0:32:52.339
<v Speaker 1>Over 40 AI tools were experimented with before we shortlisted

0:32:52.339 --> 0:32:56.500
<v Speaker 1>6 for our AI experience. We captured facial.

0:32:57.459 --> 0:32:59.780
<v Speaker 1>Thank you, Kerry. I think that's was really a powerful

0:32:59.780 --> 0:33:03.209
<v Speaker 1>look into like what's possible when AI is, isn't just

0:33:03.209 --> 0:33:06.459
<v Speaker 1>a tool, but it's actually becomes the operating system. And

0:33:06.459 --> 0:33:08.739
<v Speaker 1>I think AI may be, you know, the engine, but

0:33:08.739 --> 0:33:10.790
<v Speaker 1>we still definitely need the fuel, right? In this case,

0:33:10.859 --> 0:33:13.780
<v Speaker 1>I think data will be the field. So that's, you know, uh,

0:33:13.900 --> 0:33:17.290
<v Speaker 1>let's welcome our next speaker, Justin, uh, who will share

0:33:17.290 --> 0:33:20.180
<v Speaker 1>how Mastercard is using their data and AI to unlock

0:33:20.180 --> 0:33:24.130
<v Speaker 1>a deeper audience inside, enabling personalize the scale and power

0:33:24.130 --> 0:33:26.660
<v Speaker 1>more intelligent marketing outcomes. Justin, over to you.

0:33:27.469 --> 0:33:30.709
<v Speaker 1>Thanks and um good morning. I hope you can hear

0:33:30.709 --> 0:33:34.349
<v Speaker 1>me clearly. I'm just starting from Melbourne today and very

0:33:34.349 --> 0:33:36.550
<v Speaker 1>happy to be here. I just a quick introduction about myself.

0:33:36.630 --> 0:33:39.069
<v Speaker 1>I lead channel partnerships and alliances for Mastercard in the

0:33:39.069 --> 0:33:41.709
<v Speaker 1>APEC region, and we work very closely with our ecosystem

0:33:41.709 --> 0:33:44.949
<v Speaker 1>partners like WPP Media as well as Media Corp. and

0:33:44.949 --> 0:33:47.630
<v Speaker 1>just prior to Mastercard, I spent close to 14 years

0:33:47.630 --> 0:33:50.510
<v Speaker 1>across the different big tech platforms from Google to Mata

0:33:50.510 --> 0:33:51.229
<v Speaker 1>to TikTok.

0:33:51.719 --> 0:33:54.189
<v Speaker 1>So really have had the opportunity to have a front

0:33:54.189 --> 0:33:56.589
<v Speaker 1>row seat on the development of AI over the last

0:33:56.589 --> 0:33:59.459
<v Speaker 1>decade and a half. So we've heard a lot about

0:33:59.709 --> 0:34:02.949
<v Speaker 1>the exciting possibilities of AI from Kerry and Lin. Um,

0:34:03.150 --> 0:34:05.270
<v Speaker 1>one thing is certain in the age of AI.

0:34:06.489 --> 0:34:09.959
<v Speaker 1>Data is more important than ever, right? As I think

0:34:09.959 --> 0:34:12.469
<v Speaker 1>one of the panelists answered this, it is one of

0:34:12.469 --> 0:34:15.969
<v Speaker 1>the challenges today is access to data, but more importantly,

0:34:16.239 --> 0:34:18.810
<v Speaker 1>access to high quality data and as they always say

0:34:18.810 --> 0:34:22.339
<v Speaker 1>garbage in garbage out. Um, so hopefully today we can

0:34:22.600 --> 0:34:24.520
<v Speaker 1>share a little bit more about what Mastercard is doing

0:34:24.520 --> 0:34:27.469
<v Speaker 1>in this space to unlock value with our transaction insights

0:34:27.800 --> 0:34:30.120
<v Speaker 1>and how we play a role in supporting media campaigns

0:34:30.120 --> 0:34:32.159
<v Speaker 1>and delivering value across the customer journey.

0:34:32.879 --> 0:34:34.560
<v Speaker 1>So I'm sure all of us, or most of us

0:34:34.560 --> 0:34:37.149
<v Speaker 1>in this room, you have heard of Mastercard. You know

0:34:37.149 --> 0:34:40.359
<v Speaker 1>Mastercard is a payments and credit card company, but not

0:34:40.360 --> 0:34:42.500
<v Speaker 1>many of us might know that over the last 50 years,

0:34:42.669 --> 0:34:46.760
<v Speaker 1>we have evolved to become a data and technology leader globally. Uh,

0:34:46.790 --> 0:34:49.750
<v Speaker 1>as you can see on this slide, our superpower lies

0:34:49.750 --> 0:34:51.589
<v Speaker 1>in the depth and the breadth of data that we

0:34:51.590 --> 0:34:55.389
<v Speaker 1>see across the Mastercard payment network. And on an annual basis,

0:34:55.459 --> 0:34:58.919
<v Speaker 1>we see more than 200 billion purchase transaction points sourced

0:34:58.919 --> 0:35:01.370
<v Speaker 1>from more than 3 billion cards in circulation today.

0:35:01.810 --> 0:35:06.189
<v Speaker 1>Across more than 150 merchants, million merchants, but importantly across

0:35:06.189 --> 0:35:10.199
<v Speaker 1>a few 100 industry categories. So again, just to emphasize

0:35:10.199 --> 0:35:12.509
<v Speaker 1>the depth and the breadth of the data that we have,

0:35:13.000 --> 0:35:17.159
<v Speaker 1>and we leverage all of this aggregated and anonymized transaction

0:35:17.159 --> 0:35:21.080
<v Speaker 1>insights and expertise to serve more than 4000 customers across

0:35:21.080 --> 0:35:22.439
<v Speaker 1>120 countries today.

0:35:22.879 --> 0:35:25.259
<v Speaker 1>So very big numbers that I'm throwing out there, but

0:35:25.260 --> 0:35:28.000
<v Speaker 1>I think as you can imagine, this really speaks to

0:35:28.000 --> 0:35:30.719
<v Speaker 1>the trust and the quality of the data uh that

0:35:30.719 --> 0:35:32.000
<v Speaker 1>Mastercard is holding today.

0:35:32.850 --> 0:35:36.330
<v Speaker 1>Net Mastercard data is not just an asset, it is

0:35:36.330 --> 0:35:40.689
<v Speaker 1>the core of our network. Every transaction, every interaction, every

0:35:40.689 --> 0:35:44.000
<v Speaker 1>insight is powered by the data flowing through our systems.

0:35:44.370 --> 0:35:46.290
<v Speaker 1>And as I mentioned earlier on in the age of AI,

0:35:46.530 --> 0:35:50.209
<v Speaker 1>quality of data is most important, and AI can process

0:35:50.209 --> 0:35:53.888
<v Speaker 1>information and unprecedented skill and speed, but it's only as

0:35:53.889 --> 0:35:55.679
<v Speaker 1>good as the data that it is set.

0:35:56.560 --> 0:35:59.560
<v Speaker 1>So if your data is flawed, if your data is incomplete,

0:35:59.570 --> 0:36:02.889
<v Speaker 1>or if your data is biased, then your AI outputs

0:36:02.889 --> 0:36:06.479
<v Speaker 1>will be also. So as I mentioned, garbage in garbage out.

0:36:06.850 --> 0:36:09.840
<v Speaker 1>And that's why Mastercard has invested so heavily in building

0:36:09.840 --> 0:36:15.209
<v Speaker 1>this trusted, anonymized and high-quality data ecosystem that spends 200

0:36:15.209 --> 0:36:18.529
<v Speaker 1>billion purchase transactions. This is because the depth and the

0:36:18.530 --> 0:36:21.729
<v Speaker 1>breadth of data allows us to drive innovation at scale,

0:36:22.090 --> 0:36:25.080
<v Speaker 1>helping our partners, advertisers and brands like you.

0:36:25.489 --> 0:36:30.070
<v Speaker 1>Unlock performance across marketing, payments, as well as customer engagement.

0:36:30.570 --> 0:36:35.250
<v Speaker 1>So whether it's powering hyper personalized campaigns or enabling real-time

0:36:35.250 --> 0:36:39.310
<v Speaker 1>decisioning as what Carrie had mentioned across the life cycle

0:36:39.310 --> 0:36:42.929
<v Speaker 1>of media, our AI capabilities are built on the foundation

0:36:42.929 --> 0:36:46.560
<v Speaker 1>of clean, structured, and actionable data.

0:36:47.290 --> 0:36:49.489
<v Speaker 1>So as you think about your own AI journey, just

0:36:49.489 --> 0:36:53.810
<v Speaker 1>remember that data integrity isn't just a technical concern, it

0:36:53.810 --> 0:36:55.800
<v Speaker 1>is a strategic imperative.

0:36:56.500 --> 0:37:00.379
<v Speaker 1>In today's fragmented media landscape, one fun fact I learned

0:37:00.379 --> 0:37:00.889
<v Speaker 1>today is.

0:37:01.790 --> 0:37:04.100
<v Speaker 1>On average, all of us are exposed to more than

0:37:04.100 --> 0:37:07.379
<v Speaker 1>20 plus different media channels. So that's a lot of

0:37:07.379 --> 0:37:10.779
<v Speaker 1>media channels that we're all being exposed to. The marketers

0:37:10.780 --> 0:37:13.300
<v Speaker 1>face a very growing challenge as to how do you

0:37:13.300 --> 0:37:16.259
<v Speaker 1>reach the right audience with the right message at the

0:37:16.260 --> 0:37:17.090
<v Speaker 1>right time.

0:37:17.780 --> 0:37:21.319
<v Speaker 1>And also proof that it works in driving business outcomes.

0:37:21.889 --> 0:37:25.509
<v Speaker 1>And this is where transaction data becomes a game changer

0:37:26.090 --> 0:37:30.709
<v Speaker 1>because unlike previously proxy signals like clicks or views or

0:37:30.709 --> 0:37:36.719
<v Speaker 1>interest behavior, transaction data reflects real world consumer behavior because

0:37:37.250 --> 0:37:40.929
<v Speaker 1>if you are a top spender in men's apparel, you

0:37:40.929 --> 0:37:43.020
<v Speaker 1>are likely going to be in the market to buy

0:37:43.020 --> 0:37:45.449
<v Speaker 1>men's apparel. So what people actually buy.

0:37:45.830 --> 0:37:48.709
<v Speaker 1>When, where, and how these are where transaction insights really

0:37:48.709 --> 0:37:49.029
<v Speaker 1>come in.

0:37:49.850 --> 0:37:53.959
<v Speaker 1>So by leveraging into anonymized and aggregated transaction data, advertisers

0:37:53.959 --> 0:37:55.479
<v Speaker 1>can make media more intelligent.

0:37:56.530 --> 0:37:58.799
<v Speaker 1>Right, where you can use real world market and audience

0:37:58.800 --> 0:38:02.489
<v Speaker 1>intelligence to guide media planning as well as targeting.

0:38:03.229 --> 0:38:07.620
<v Speaker 1>You can make media more personal by delivering hyper personalized

0:38:07.620 --> 0:38:13.010
<v Speaker 1>experiences based on actual spending patterns and preferences of the individual.

0:38:13.310 --> 0:38:16.689
<v Speaker 1>You can make me more measurable, where you can link

0:38:16.689 --> 0:38:21.139
<v Speaker 1>campaigns to tangible outcomes like incremental sales, and no longer

0:38:21.139 --> 0:38:25.129
<v Speaker 1>just engagement metrics or vanity metrics like impressions of views.

0:38:25.659 --> 0:38:30.489
<v Speaker 1>So for example, instead of targeting urban millennials interested in travel,

0:38:31.169 --> 0:38:36.069
<v Speaker 1>We can identify micro geographies where there are high frequency

0:38:36.070 --> 0:38:40.469
<v Speaker 1>standards in travel categories where they are actually transacting and

0:38:40.469 --> 0:38:43.909
<v Speaker 1>actively transacting, and you can then tailor those messages accordingly.

0:38:44.590 --> 0:38:48.260
<v Speaker 1>So this level of precision helps advertisers move beyond assumptions

0:38:48.260 --> 0:38:53.129
<v Speaker 1>and generalizations, moving to data back decisions that can drive performance.

0:38:54.159 --> 0:38:56.469
<v Speaker 1>And we look at how this translates into real world

0:38:56.469 --> 0:38:58.379
<v Speaker 1>media and advertising workflows with data.

0:38:59.669 --> 0:39:02.580
<v Speaker 1>So in a world where consumers are increasingly engaging across

0:39:02.580 --> 0:39:07.379
<v Speaker 1>multiple channels, online, in-store, mobile, social, we all need a

0:39:07.379 --> 0:39:10.060
<v Speaker 1>way to connect the dots across the full customer journey,

0:39:10.070 --> 0:39:13.779
<v Speaker 1>and this is where transaction data becomes a powerful enabler,

0:39:14.219 --> 0:39:16.580
<v Speaker 1>because it provides you with a direct lens into what

0:39:16.580 --> 0:39:18.979
<v Speaker 1>people are actually buying, not just what they're browsing or

0:39:18.979 --> 0:39:21.719
<v Speaker 1>clicking on. So if you look at number one here.

0:39:22.639 --> 0:39:27.629
<v Speaker 1>Mastercard's data can help advertisers and brands like you leverage

0:39:27.629 --> 0:39:31.520
<v Speaker 1>real world market and consumer insights to a more effective

0:39:31.520 --> 0:39:32.239
<v Speaker 1>media planning.

0:39:33.050 --> 0:39:37.029
<v Speaker 1>So, for example, we have transaction data across a few

0:39:37.030 --> 0:39:38.580
<v Speaker 1>100 industry categories.

0:39:39.399 --> 0:39:42.070
<v Speaker 1>Across the time series. So as a brand, if you

0:39:42.070 --> 0:39:45.469
<v Speaker 1>want to understand when does retail stand in Kits apparel

0:39:45.469 --> 0:39:49.270
<v Speaker 1>pick up leading into mega sale day, such as 99

0:39:49.270 --> 0:39:50.699
<v Speaker 1>or Christmas in Singapore.

0:39:51.679 --> 0:39:53.790
<v Speaker 1>This data will help you to understand that and help

0:39:53.790 --> 0:39:56.589
<v Speaker 1>you plan when you should start your media plan or

0:39:56.590 --> 0:39:59.899
<v Speaker 1>the media cycle. But we can also look at benchmarking

0:39:59.899 --> 0:40:02.699
<v Speaker 1>of year on year growth in the kits apparel industry

0:40:02.989 --> 0:40:05.270
<v Speaker 1>so that you can better understand how is your business

0:40:05.270 --> 0:40:09.509
<v Speaker 1>performing against your competition. We can also help you understand

0:40:10.050 --> 0:40:13.549
<v Speaker 1>online versus offline spending, so that helps you understand media

0:40:13.550 --> 0:40:16.310
<v Speaker 1>budget allocation to offline and online media channels.

0:40:17.040 --> 0:40:19.189
<v Speaker 1>And more importantly, we can even go down to a

0:40:19.189 --> 0:40:22.509
<v Speaker 1>brand level to help you understand market share, like pre

0:40:22.510 --> 0:40:26.070
<v Speaker 1>and post a media campaign. Are you actually gaining or

0:40:26.070 --> 0:40:30.270
<v Speaker 1>decreasing in market share against your competitors? As you can imagine,

0:40:30.370 --> 0:40:33.069
<v Speaker 1>all of this data becomes very powerful in the world

0:40:33.070 --> 0:40:36.279
<v Speaker 1>of agentic AI as Carrie mentioned, because as you query

0:40:36.280 --> 0:40:39.830
<v Speaker 1>an agent, all of these insights will really power that model.

0:40:40.820 --> 0:40:44.659
<v Speaker 1>2nd, #2 here is we can help to enhance audience

0:40:44.659 --> 0:40:48.419
<v Speaker 1>targeting by reaching high value and high-end customers, right, where

0:40:48.419 --> 0:40:52.138
<v Speaker 1>you can identify where all of the top spenders or

0:40:52.139 --> 0:40:57.179
<v Speaker 1>frequent spenders on hotel bookings or travel in Singapore, and

0:40:57.179 --> 0:41:01.179
<v Speaker 1>you can then serve them very targeted and personalized creative messaging.

0:41:01.860 --> 0:41:05.969
<v Speaker 1>Yeah. And the last piece of number 5 is where

0:41:05.969 --> 0:41:09.330
<v Speaker 1>we can then look at how do we measure close

0:41:09.330 --> 0:41:13.350
<v Speaker 1>loop attribution. So MasterCard owns a technology called Tess and

0:41:13.350 --> 0:41:16.449
<v Speaker 1>learn for media measurement, where we have the ability to

0:41:16.449 --> 0:41:19.770
<v Speaker 1>understand what was the incremental sales slip that was driven

0:41:19.770 --> 0:41:23.770
<v Speaker 1>off an omni Media channel campaign and also understand those

0:41:23.770 --> 0:41:24.850
<v Speaker 1>business drivers.

0:41:25.489 --> 0:41:28.540
<v Speaker 1>And all of this becomes very valuable data that we

0:41:28.540 --> 0:41:31.830
<v Speaker 1>can inject into AI models across the entire media planning

0:41:31.830 --> 0:41:38.330
<v Speaker 1>flywheel to help automate and democratize research, audience planning, as

0:41:38.330 --> 0:41:41.780
<v Speaker 1>well as attribution to make media a lot more actionable

0:41:42.350 --> 0:41:45.580
<v Speaker 1>and dependable. So hopefully this gives you a very quick

0:41:45.580 --> 0:41:47.860
<v Speaker 1>sense of where our data comes in.

0:41:48.620 --> 0:41:50.840
<v Speaker 1>Uh, and then on the next slide, I just want

0:41:50.840 --> 0:41:54.909
<v Speaker 1>to walk through a very simple use case of how

0:41:55.479 --> 0:41:58.310
<v Speaker 1>we have partnered with a leading airline in this region

0:41:58.310 --> 0:42:02.009
<v Speaker 1>to apply AI in a very practical and high impact way.

0:42:02.439 --> 0:42:04.560
<v Speaker 1>So the problem statement that was brought to us at

0:42:04.560 --> 0:42:08.439
<v Speaker 1>Mastercard was, how can we help this airline partner optimize

0:42:08.439 --> 0:42:13.399
<v Speaker 1>promotional mechanics and customer engagement strategies using data and AI.

0:42:13.949 --> 0:42:17.939
<v Speaker 1>So working together with them, we co-created past specific AI

0:42:18.050 --> 0:42:20.580
<v Speaker 1>agencies what Kerry had mentioned, these are like your interns,

0:42:20.659 --> 0:42:26.419
<v Speaker 1>your internal agents that can leverage MasterCard's data insights, platform capabilities,

0:42:26.489 --> 0:42:30.138
<v Speaker 1>and our deep analytic expertise. And so these AI agents

0:42:30.139 --> 0:42:34.330
<v Speaker 1>help to power and scale in two areas, knowledge management,

0:42:34.729 --> 0:42:37.979
<v Speaker 1>as well as insights generation for the airlines in-house teams,

0:42:38.020 --> 0:42:40.139
<v Speaker 1>and it helps them answer key questions like

0:42:40.760 --> 0:42:43.479
<v Speaker 1>What are the key insights from previous initiatives to help

0:42:43.479 --> 0:42:45.620
<v Speaker 1>me guide customer engagement strategy?

0:42:46.570 --> 0:42:50.290
<v Speaker 1>What discount levels did I offer previously that were able

0:42:50.290 --> 0:42:54.049
<v Speaker 1>to drive the highest revenue uplift for my business or

0:42:54.050 --> 0:42:59.050
<v Speaker 1>which SKU or which flight route or brands overperformed during

0:42:59.050 --> 0:43:03.790
<v Speaker 1>the past campaigns, right? And lastly, what promotion periods you

0:43:03.790 --> 0:43:05.129
<v Speaker 1>did the best results.

0:43:06.199 --> 0:43:09.629
<v Speaker 1>In addition, we also supported the area of customer segmentation

0:43:09.629 --> 0:43:14.259
<v Speaker 1>and personalized messaging to identify high-value segments based on historical

0:43:14.260 --> 0:43:17.389
<v Speaker 1>behavior and the agents, in this case, the AI agents

0:43:17.389 --> 0:43:21.709
<v Speaker 1>that help to tailor email messages, in-app messages by dynamically

0:43:21.709 --> 0:43:26.659
<v Speaker 1>using customer level metrics like redemption rates or booking patterns.

0:43:27.110 --> 0:43:30.620
<v Speaker 1>So the result is a more intelligent and responsive marketing engine,

0:43:31.110 --> 0:43:33.629
<v Speaker 1>one that is grounded in real transaction data and is

0:43:33.629 --> 0:43:35.908
<v Speaker 1>capable of adapting to customer behavior in real time.

0:43:36.320 --> 0:43:40.120
<v Speaker 1>And this really goes beyond just traditional AI, but how

0:43:40.120 --> 0:43:43.279
<v Speaker 1>AI is in action today with Gen AI as well

0:43:43.280 --> 0:43:44.199
<v Speaker 1>as predictive AI.

0:43:45.590 --> 0:43:47.129
<v Speaker 1>So I put on to the next slide.

0:43:48.040 --> 0:43:50.699
<v Speaker 1>And this is just another tangible example of how Mastercard

0:43:50.699 --> 0:43:53.659
<v Speaker 1>is helping our clients bring AI use cases to life. First,

0:43:53.669 --> 0:43:57.219
<v Speaker 1>we provide our data and insights in multi-formats that are

0:43:57.219 --> 0:44:00.770
<v Speaker 1>optimized for rack. In this case it's retrieval augmented generation

0:44:00.770 --> 0:44:04.270
<v Speaker 1>to support large language models as well as AI agents. Second,

0:44:04.280 --> 0:44:08.109
<v Speaker 1>we share instruction templates and best practices, including prom libraries,

0:44:08.189 --> 0:44:11.310
<v Speaker 1>sample Q&amp;A, and tested use cases. And third, we have

0:44:11.310 --> 0:44:14.510
<v Speaker 1>our in-house digital labs team that helps provide consulting.

0:44:14.949 --> 0:44:18.189
<v Speaker 1>To design a prototype and build these custom AI solutions.

0:44:18.760 --> 0:44:21.149
<v Speaker 1>So the goal is to help our clients like you

0:44:21.149 --> 0:44:26.379
<v Speaker 1>move beyond experimentation, operationalization, and building AI into your workflow

0:44:26.379 --> 0:44:28.469
<v Speaker 1>in a way that's measurable, secure, and future.

0:44:29.500 --> 0:44:31.989
<v Speaker 1>So these are just the 5 e takeaways.

0:44:32.850 --> 0:44:37.259
<v Speaker 1>In summary, generative AI is really redefining consumer engagement today.

0:44:37.800 --> 0:44:41.429
<v Speaker 1>We believe that transaction data is a key strategic asset

0:44:41.429 --> 0:44:45.830
<v Speaker 1>and it is a competitive differentiator to brands and marketers.

0:44:46.939 --> 0:44:50.739
<v Speaker 1>With the use of AI today, personalization is now truly

0:44:50.739 --> 0:44:55.010
<v Speaker 1>scalable and AI powered is also a lot more accountable

0:44:55.610 --> 0:44:58.209
<v Speaker 1>to you as well as the decision makers and leaders

0:44:58.209 --> 0:44:58.899
<v Speaker 1>in the company.

0:44:59.780 --> 0:45:03.569
<v Speaker 1>So hopefully this gives you a a comprehensive or even

0:45:03.570 --> 0:45:06.850
<v Speaker 1>a concise overview of where data plays as a key

0:45:06.850 --> 0:45:09.469
<v Speaker 1>enabler across the entire world of AI.

0:45:10.290 --> 0:45:12.699
<v Speaker 1>And with that, I will take a pause and pass

0:45:12.699 --> 0:45:16.340
<v Speaker 1>the time back to Tim and Chai. All right, thank

0:45:16.340 --> 0:45:18.810
<v Speaker 1>you so much, uh, Dustin. I think that's a great sharing.

0:45:19.000 --> 0:45:21.979
<v Speaker 1>So I think for the interest of time, um, let's

0:45:21.979 --> 0:45:23.929
<v Speaker 1>just move to the next section, which is the Q&amp;A.

0:45:23.939 --> 0:45:26.060
<v Speaker 1>I think some of the questions has been answered by

0:45:26.060 --> 0:45:28.580
<v Speaker 1>Lin and Carrie in the uh in the, in the

0:45:28.580 --> 0:45:29.979
<v Speaker 1>chat uh section.

0:45:30.429 --> 0:45:32.158
<v Speaker 1>But I think I'll just pick up one of the

0:45:32.159 --> 0:45:34.760
<v Speaker 1>common questions being asked like what are the tools that

0:45:34.760 --> 0:45:36.389
<v Speaker 1>are being, uh, what are the generator tools that are

0:45:36.389 --> 0:45:39.759
<v Speaker 1>being used and also what's the differentiator if one is,

0:45:39.840 --> 0:45:41.959
<v Speaker 1>you know, use the same kind of tools, right? So

0:45:41.959 --> 0:45:42.270
<v Speaker 1>I think.

0:45:42.530 --> 0:45:46.239
<v Speaker 1>Uh, actually, the, uh, Justin summarized it in his takeaways.

0:45:46.320 --> 0:45:48.589
<v Speaker 1>I think one of the points that he made is actually,

0:45:48.760 --> 0:45:51.040
<v Speaker 1>of course, data will be one of the key differentiators.

0:45:51.120 --> 0:45:54.129
<v Speaker 1>I think if you see from all the three, panelists

0:45:54.129 --> 0:45:56.560
<v Speaker 1>who was sharing, so what is the common is they

0:45:56.560 --> 0:45:58.770
<v Speaker 1>started to use the tool, they started to actually building

0:45:58.770 --> 0:46:01.718
<v Speaker 1>their own workflows. You can tell from, you know, uh

0:46:01.719 --> 0:46:05.270
<v Speaker 1>Carris DPAM and also uh uh Justin also share their

0:46:05.270 --> 0:46:07.949
<v Speaker 1>own master class, uh, you know, the over, uh, the,

0:46:07.959 --> 0:46:09.040
<v Speaker 1>the frameworks, right? I think.

0:46:09.500 --> 0:46:12.219
<v Speaker 1>That's definitely something that common, but I think what's difference

0:46:12.219 --> 0:46:16.979
<v Speaker 1>is how you are integrating the data and your own data, right?

0:46:17.040 --> 0:46:20.819
<v Speaker 1>And then also basically fit into your own workflow because

0:46:20.820 --> 0:46:23.259
<v Speaker 1>that will make a bigger differences. And many of those

0:46:23.260 --> 0:46:28.339
<v Speaker 1>tools are model uh uh large language model, um, agnostic, meaning,

0:46:28.739 --> 0:46:32.409
<v Speaker 1>whether you're using JGBT or use a different, you know, tool, actually,

0:46:32.620 --> 0:46:33.929
<v Speaker 1>you know, it.

0:46:35.020 --> 0:46:38.250
<v Speaker 1>Differences, but when you are connecting that with your own data,

0:46:38.459 --> 0:46:40.979
<v Speaker 1>with your own workflow, I think that's how you basically

0:46:40.979 --> 0:46:44.699
<v Speaker 1>differentiate from other players, right? I think with that, I

0:46:44.699 --> 0:46:47.060
<v Speaker 1>would like to thank, uh, you know, three of our

0:46:47.060 --> 0:46:51.020
<v Speaker 1>distinguished guests, panelists, for giving uh the great sharing, and

0:46:51.020 --> 0:46:52.860
<v Speaker 1>I think we learned a lot. And I also want

0:46:52.860 --> 0:46:55.739
<v Speaker 1>to thank everyone, all the participants, all the audiences joining

0:46:55.739 --> 0:46:59.580
<v Speaker 1>us today in the morning. Thank you so much. And hopefully,

0:46:59.620 --> 0:47:01.979
<v Speaker 1>we're looking uh forward to see you in the next session.

0:47:02.500 --> 0:47:03.300
<v Speaker 1>Thank you, everyone.