1 00:00:07,200 --> 00:00:11,040 Speaker 1: Hello, strinkly business listeners. Here is a special bonus episode 2 00:00:11,119 --> 00:00:15,080 Speaker 1: for you. I'm Cynthia Lyttleton, co editor in chief of Variety. 3 00:00:15,480 --> 00:00:17,959 Speaker 1: I had the good fortune to spend last week in 4 00:00:18,040 --> 00:00:22,480 Speaker 1: the gorgeous French riviera at the Canlon Festival of Creativity. 5 00:00:23,280 --> 00:00:26,239 Speaker 1: It's a week long event that brings together heavy hitters 6 00:00:26,239 --> 00:00:30,880 Speaker 1: in marketing, media, tech and business. It's a whirlwind of 7 00:00:31,000 --> 00:00:37,000 Speaker 1: sales pitches, elaborate brand showcases, old fashioned PR stunts, and 8 00:00:37,120 --> 00:00:42,720 Speaker 1: deep discussions about how marketing, media, technology and content are evolving. 9 00:00:43,640 --> 00:00:46,080 Speaker 1: As you could imagine, There's a lot to take in 10 00:00:46,600 --> 00:00:50,720 Speaker 1: and almost all of it involves AI. Variety was fortunate 11 00:00:50,880 --> 00:00:54,040 Speaker 1: to host a discussion with two senior executives who are 12 00:00:54,080 --> 00:00:57,920 Speaker 1: deep in the trenches of transformation at companies with incredible 13 00:00:57,920 --> 00:01:02,440 Speaker 1: scale and global reach. Michelle McGuire is principal and Chief 14 00:01:02,440 --> 00:01:08,240 Speaker 1: Commercial Officer of Converge by Deloitte. Ruba Borno is Vice president, 15 00:01:08,520 --> 00:01:14,800 Speaker 1: Global Specialists and Partners for AWS, Amazon's busy cloud computing arm. 16 00:01:15,319 --> 00:01:17,880 Speaker 1: The two speak with me about how their companies are 17 00:01:17,880 --> 00:01:22,680 Speaker 1: harnessing tech, data, analytics, and AI tools. They also reflect 18 00:01:22,720 --> 00:01:26,960 Speaker 1: on how their experiences working together underscore that even the 19 00:01:27,000 --> 00:01:31,000 Speaker 1: biggest corporate players are going to need partners. It's going 20 00:01:31,040 --> 00:01:35,440 Speaker 1: to take companies with highly specialized superpowers to help drive 21 00:01:35,560 --> 00:01:40,640 Speaker 1: the AI productivity revolution that is already in progress. McGuire 22 00:01:40,680 --> 00:01:43,480 Speaker 1: and Borno give us a lot of practical, real world 23 00:01:43,520 --> 00:01:46,800 Speaker 1: examples that help make some of these mind numbing concepts 24 00:01:46,920 --> 00:01:51,160 Speaker 1: more understandable. Because we were in can in June. We 25 00:01:51,200 --> 00:01:55,840 Speaker 1: recorded this outside on a yacht docked just off the Crosset. 26 00:01:56,520 --> 00:01:58,600 Speaker 1: If you listen all the way to the end, we'll 27 00:01:58,600 --> 00:02:00,640 Speaker 1: give you a little color on what was going on 28 00:02:00,760 --> 00:02:04,520 Speaker 1: around us as we spoke. That's all coming up after 29 00:02:04,560 --> 00:02:05,160 Speaker 1: this break. 30 00:02:14,400 --> 00:02:17,480 Speaker 2: Can Lyon is where the advertising and communications industry meet 31 00:02:17,600 --> 00:02:21,520 Speaker 2: to celebrate the world's best work. During the Festival of Creativity, 32 00:02:21,560 --> 00:02:26,080 Speaker 2: Deloitte shared insights for sport, media and entertainment companies, from 33 00:02:26,080 --> 00:02:29,320 Speaker 2: designing end to end data platforms built on leading cloud 34 00:02:29,360 --> 00:02:34,239 Speaker 2: technologies to understanding fan data to better personalized digital experiences. 35 00:02:34,639 --> 00:02:36,960 Speaker 2: Don't miss the opportunity to meet your fans with the 36 00:02:37,000 --> 00:02:40,560 Speaker 2: content and experience that mattered most to them. It's time 37 00:02:40,600 --> 00:02:42,680 Speaker 2: to be as obsessed about the data as we are 38 00:02:42,720 --> 00:02:45,840 Speaker 2: about the game. Deloitte and AWS can help. 39 00:02:48,480 --> 00:02:52,040 Speaker 1: And we're back with a special Strictly Business Live episode 40 00:02:52,080 --> 00:02:58,120 Speaker 1: from can Lion with Deloitte's Michelle McGuire and aws's Ruba Borno. 41 00:02:58,800 --> 00:02:59,920 Speaker 3: Welcome, thank you. 42 00:03:00,000 --> 00:03:02,160 Speaker 4: Thank you for joining us today. This is a first 43 00:03:02,200 --> 00:03:07,480 Speaker 4: for Strictly Business varieties weekly podcast featuring conversations with industry 44 00:03:07,560 --> 00:03:11,480 Speaker 4: leaders about the business of media and entertainment and sports. 45 00:03:11,600 --> 00:03:13,639 Speaker 4: Today we're going to be talking a lot about sports. 46 00:03:13,919 --> 00:03:19,760 Speaker 4: We are at the beautiful Canlon Festival of Creativity in 47 00:03:19,800 --> 00:03:22,320 Speaker 4: the south of France, the French riviy Era, and it 48 00:03:22,400 --> 00:03:27,200 Speaker 4: is gorgeous. We have a terrific pairing here of two 49 00:03:27,560 --> 00:03:30,560 Speaker 4: major companies that everybody has heard of. And what's really 50 00:03:30,560 --> 00:03:33,559 Speaker 4: cool about this pairing is that both separately and individually, 51 00:03:34,040 --> 00:03:38,880 Speaker 4: Deloitte and AWS are doing very cool, very innovative, thinking, 52 00:03:38,960 --> 00:03:44,760 Speaker 4: forward looking activities. Together and separately, we have Michelle McGuire, 53 00:03:45,360 --> 00:03:50,680 Speaker 4: Principal and Chief Commercial Officer of Converge by Deloitte, and 54 00:03:50,760 --> 00:03:54,640 Speaker 4: next to Michelle is Ruba Borno, vice president and Global 55 00:03:54,680 --> 00:03:59,040 Speaker 4: Specialist and Partnerships for AWS. Thank you both for making time. 56 00:03:59,120 --> 00:04:02,280 Speaker 4: I know it's a schedule here, the place is hoppened, 57 00:04:02,320 --> 00:04:04,400 Speaker 4: so we really appreciate you giving us this time. 58 00:04:04,560 --> 00:04:05,480 Speaker 5: Thanks for having us. 59 00:04:05,560 --> 00:04:08,760 Speaker 2: It's so beautiful here at great Weather, So could not 60 00:04:08,880 --> 00:04:13,040 Speaker 2: be better positioned to talk about sports and media and entertainment. 61 00:04:12,640 --> 00:04:16,200 Speaker 5: Absolutely sharing space. So thank you Cynthia, and thanks Michelle. 62 00:04:16,160 --> 00:04:17,520 Speaker 3: Again, thanks for making the time. 63 00:04:18,080 --> 00:04:21,120 Speaker 4: So, as I said, you guys are both doing very 64 00:04:21,120 --> 00:04:25,440 Speaker 4: cool innovative things separately and doing some work together in partnership. 65 00:04:26,080 --> 00:04:29,200 Speaker 4: Let's start there. Let's start with Converge. Michelle, why don't 66 00:04:29,200 --> 00:04:32,039 Speaker 4: you give us that elevator pitch on what Converge is? 67 00:04:32,640 --> 00:04:35,520 Speaker 2: Sure? Yeah, So a few years ago, Deloitte decided to 68 00:04:35,600 --> 00:04:39,080 Speaker 2: start investing ahead of the curve in all things AI 69 00:04:39,400 --> 00:04:43,640 Speaker 2: and sports, and so Converge was born with the kind 70 00:04:43,680 --> 00:04:47,920 Speaker 2: of intent of at the intersection of AI and industry. 71 00:04:48,160 --> 00:04:52,800 Speaker 2: They were coming together to create differentiated, accelerated solutions for 72 00:04:52,880 --> 00:04:55,839 Speaker 2: our clients. And so the firm is invested over a 73 00:04:55,839 --> 00:04:59,840 Speaker 2: billion dollars at that intersection and gladly partnered with a 74 00:05:00,160 --> 00:05:04,800 Speaker 2: WS to build out industrialized products that are available to 75 00:05:05,040 --> 00:05:09,359 Speaker 2: clients to accelerate them to value to outcomes and to 76 00:05:09,400 --> 00:05:11,240 Speaker 2: impact Ruba. 77 00:05:11,320 --> 00:05:14,359 Speaker 4: Can you talk about how AWS plugged into that and 78 00:05:14,480 --> 00:05:16,920 Speaker 4: why you were the right platform. 79 00:05:17,240 --> 00:05:17,440 Speaker 3: Yeah. 80 00:05:17,440 --> 00:05:19,760 Speaker 5: Our partnership with Deloitte goes a really long time. I 81 00:05:19,760 --> 00:05:22,839 Speaker 5: mean Deloitte and AWS have been partners for over a 82 00:05:22,920 --> 00:05:26,520 Speaker 5: decade where we have been serving clients together, supporting them 83 00:05:26,680 --> 00:05:30,039 Speaker 5: first on their cloud journeys. So initially, at the inception 84 00:05:30,200 --> 00:05:33,680 Speaker 5: of cloud services, Deloitte and AWS would go together to 85 00:05:33,839 --> 00:05:36,719 Speaker 5: customers and help them migrate out of their data centers 86 00:05:36,760 --> 00:05:40,159 Speaker 5: which were energy inefficient and also didn't have the innovation 87 00:05:40,600 --> 00:05:43,240 Speaker 5: and the number of services that AWS could offer. Today 88 00:05:43,279 --> 00:05:46,440 Speaker 5: we have two hundred and forty plus services. And so 89 00:05:46,520 --> 00:05:49,680 Speaker 5: with Deloitte where they have a deep understanding of the industry, 90 00:05:49,760 --> 00:05:52,839 Speaker 5: and so you take even sub industry, so within sports, 91 00:05:52,880 --> 00:05:55,000 Speaker 5: it could go into different types of sports and different 92 00:05:55,040 --> 00:05:59,200 Speaker 5: audiences and sports, and then we've got the technology underlying 93 00:05:59,440 --> 00:06:02,599 Speaker 5: technology services that can support them in delivering some of 94 00:06:02,600 --> 00:06:06,480 Speaker 5: those outcomes. And so we've been working on business outcomes 95 00:06:06,480 --> 00:06:08,720 Speaker 5: and solutions with Deloitte for a really long time. I 96 00:06:08,760 --> 00:06:11,440 Speaker 5: think that's where the partnership comes to life, is how 97 00:06:11,480 --> 00:06:14,160 Speaker 5: do we deliver an outcome to a customer. And that's 98 00:06:14,200 --> 00:06:17,160 Speaker 5: what's really exciting about the converged platform is the outcome 99 00:06:17,360 --> 00:06:21,239 Speaker 5: is a differentiated and unique fan experience, and it's in sports, 100 00:06:21,240 --> 00:06:23,000 Speaker 5: but it can actually apply more broadly than that. 101 00:06:23,120 --> 00:06:28,080 Speaker 2: Yeah, I think AWS is uniquely positioned given there obviously 102 00:06:28,120 --> 00:06:31,119 Speaker 2: the infrastructure associated with cloud, but also with their AI 103 00:06:31,400 --> 00:06:35,560 Speaker 2: enabled tools. It allows us to tap into their subject 104 00:06:35,560 --> 00:06:39,320 Speaker 2: matter expertise, their engineers, They support us from a product perspective. 105 00:06:39,720 --> 00:06:40,560 Speaker 3: So for us, it's. 106 00:06:40,400 --> 00:06:43,159 Speaker 2: Always easy to go with best in breed and so 107 00:06:43,560 --> 00:06:46,200 Speaker 2: partnering with AWS is always a great choice. 108 00:06:45,920 --> 00:06:47,880 Speaker 4: Right, And I think, I mean obviously if you have, 109 00:06:48,040 --> 00:06:51,240 Speaker 4: if Deloitte has put a billion dollars into this UC, 110 00:06:51,640 --> 00:06:54,239 Speaker 4: massive opportunity on the horizon, I think. 111 00:06:54,040 --> 00:06:57,680 Speaker 5: So like AI is everywhere here and can and. 112 00:06:57,640 --> 00:07:00,440 Speaker 2: You would imagine that a festival of creativity would be 113 00:07:00,480 --> 00:07:04,719 Speaker 2: about you know human you know innovation. However, it's about 114 00:07:04,720 --> 00:07:08,200 Speaker 2: how you partner with the tech and with the technology 115 00:07:08,240 --> 00:07:12,800 Speaker 2: to technology to transform the creative process for a different outcome. 116 00:07:13,160 --> 00:07:15,880 Speaker 2: And it's no different with fans in sport, right they 117 00:07:15,960 --> 00:07:20,080 Speaker 2: expect a personalized experience and that's what Converge does is 118 00:07:20,120 --> 00:07:24,360 Speaker 2: it enables fans to identify, raise their hand and then 119 00:07:24,560 --> 00:07:28,240 Speaker 2: for us to basically understand what is their affinity, what 120 00:07:28,360 --> 00:07:31,920 Speaker 2: is their propensity to engage to buy a ticket, How 121 00:07:31,960 --> 00:07:35,400 Speaker 2: do we you know, understand your fandom score of which 122 00:07:35,480 --> 00:07:39,920 Speaker 2: is effectively lifetime value of those fans to both sports 123 00:07:40,040 --> 00:07:43,760 Speaker 2: organizations as well as to partners who or brands who 124 00:07:44,320 --> 00:07:47,520 Speaker 2: really are about the coming together of the league the team. 125 00:07:48,000 --> 00:07:50,640 Speaker 2: So I feel like, you know, there's a bit of 126 00:07:50,680 --> 00:07:54,200 Speaker 2: a push towards all things fandom and the ability to 127 00:07:54,400 --> 00:07:57,480 Speaker 2: measure it in a way that historically we cannot. 128 00:07:57,760 --> 00:08:00,600 Speaker 5: And just maybe Michelle building on that, because I do 129 00:08:00,720 --> 00:08:05,360 Speaker 5: think with Generator AI and personalization, partnerships are the future. 130 00:08:05,520 --> 00:08:09,280 Speaker 5: It is the only way to provide that personalized experience. 131 00:08:08,840 --> 00:08:11,600 Speaker 4: Because nobody has all the data and all of the 132 00:08:11,680 --> 00:08:13,240 Speaker 4: ability that makes it. 133 00:08:13,280 --> 00:08:15,920 Speaker 5: There's just much more value out of bringing the data 134 00:08:15,920 --> 00:08:19,000 Speaker 5: together and the insights from it. So actually a service 135 00:08:19,040 --> 00:08:22,880 Speaker 5: that Deloitte uses as aws clean Rooms, and this is 136 00:08:22,880 --> 00:08:26,320 Speaker 5: one where you can bring data from multiple parties and 137 00:08:26,840 --> 00:08:30,160 Speaker 5: the underlying raw data is still secure and owned by 138 00:08:30,160 --> 00:08:32,720 Speaker 5: the entity that brought that data, but you can draw 139 00:08:32,800 --> 00:08:35,959 Speaker 5: insights from the collective data, and so that allows us 140 00:08:36,000 --> 00:08:38,920 Speaker 5: to get insights. One of our customers is Coca Cola, 141 00:08:38,920 --> 00:08:41,480 Speaker 5: for example, and they're able to use it to provide 142 00:08:41,520 --> 00:08:45,840 Speaker 5: aggregate insights to their advertising team, their marketing teams and 143 00:08:45,880 --> 00:08:49,040 Speaker 5: then be able to provide a personalized experience to their customers. 144 00:08:49,240 --> 00:08:51,680 Speaker 5: Another customer I would call out is the weather channel. 145 00:08:51,800 --> 00:08:56,360 Speaker 5: They worked with Latima on a travel and hospitality customer 146 00:08:56,360 --> 00:08:59,400 Speaker 5: of THEIRS, and they're pulling data from a bunch of 147 00:08:59,400 --> 00:09:03,080 Speaker 5: different source and using clean rooms. They care about the insight, 148 00:09:03,160 --> 00:09:05,520 Speaker 5: not the underlying data. They care about the answer, which 149 00:09:05,559 --> 00:09:09,800 Speaker 5: is this individual a low frequency flyer or a high 150 00:09:09,800 --> 00:09:12,920 Speaker 5: frequency flyer? What is their brand affinity? What are the 151 00:09:12,920 --> 00:09:15,600 Speaker 5: brands that they are associated with? Do they travel by 152 00:09:15,679 --> 00:09:18,800 Speaker 5: air or land or by sea? And then as a 153 00:09:18,840 --> 00:09:22,000 Speaker 5: result of that those attributes, they're able to actually get 154 00:09:22,000 --> 00:09:24,640 Speaker 5: those insights. I think the number I saw was ninety 155 00:09:24,679 --> 00:09:29,360 Speaker 5: eight percent faster, and the entire process and the cost 156 00:09:29,400 --> 00:09:32,480 Speaker 5: to run it is seven times more efficient than what 157 00:09:32,520 --> 00:09:34,880 Speaker 5: they were doing before. And so that's the power of 158 00:09:34,920 --> 00:09:38,480 Speaker 5: partnerships is you get the answer faster, it's lower cost, 159 00:09:38,640 --> 00:09:42,000 Speaker 5: but more importantly, you're able to provide that personalized experience 160 00:09:42,080 --> 00:09:45,599 Speaker 5: to that end customer. And I think that for consumers, 161 00:09:45,679 --> 00:09:50,040 Speaker 5: for customers, for advertisers, this is table stakes today, and 162 00:09:50,120 --> 00:09:52,960 Speaker 5: so the more data you have, the better insights you have, 163 00:09:53,120 --> 00:09:54,439 Speaker 5: and you can be differentiated. 164 00:09:54,640 --> 00:09:59,800 Speaker 4: Right, you are going light years beyond age, gender general 165 00:09:59,800 --> 00:10:03,040 Speaker 4: g To your point, it doesn't matter whether that person 166 00:10:03,160 --> 00:10:06,760 Speaker 4: who flies all the time, you know, lives in Cleveland 167 00:10:06,840 --> 00:10:08,000 Speaker 4: or lives in Los Angeles. 168 00:10:08,040 --> 00:10:09,880 Speaker 3: I guess some of it matters according to their airport. 169 00:10:09,920 --> 00:10:13,360 Speaker 4: But the larger point is that you're able to just 170 00:10:13,760 --> 00:10:19,680 Speaker 4: find those those discrete pockets of potential audience in a 171 00:10:19,720 --> 00:10:22,960 Speaker 4: way that is just it's fascinating. It's three dimensional chests 172 00:10:23,040 --> 00:10:26,200 Speaker 4: versus when I started, I would get a fax, so 173 00:10:26,280 --> 00:10:28,680 Speaker 4: this dates when I started covering up television. 174 00:10:28,720 --> 00:10:31,040 Speaker 3: I would literally get a fax of. 175 00:10:30,960 --> 00:10:34,560 Speaker 4: The overnight ratings, and when I could see the grainy numbers, 176 00:10:34,559 --> 00:10:37,480 Speaker 4: sometimes they'd blur together. I could see the grainy numbers 177 00:10:37,520 --> 00:10:40,320 Speaker 4: and I'd look market by market. They did fifty you know, 178 00:10:40,880 --> 00:10:44,280 Speaker 4: people in television know this, fifty markets or fifty six markets. 179 00:10:44,280 --> 00:10:45,720 Speaker 3: It got to fifty six markets. 180 00:10:45,760 --> 00:10:49,240 Speaker 4: That was seventy percent of the country, and we'd scrutinize 181 00:10:49,240 --> 00:10:52,160 Speaker 4: that and look for the patterns. But this is just 182 00:10:52,320 --> 00:10:55,080 Speaker 4: incredibly written large Now with Converge, one of the big 183 00:10:55,120 --> 00:11:01,000 Speaker 4: big use cases for you is really really connecting sports teams, events, 184 00:11:01,160 --> 00:11:04,680 Speaker 4: games and sponsors and just making them forgive me home 185 00:11:04,760 --> 00:11:08,520 Speaker 4: runs every time. All the sports analogies I'm coming really 186 00:11:08,559 --> 00:11:10,760 Speaker 4: showing some discipline here and what. 187 00:11:10,720 --> 00:11:13,240 Speaker 2: Happened to your baseball hat you had it on earlier. 188 00:11:13,320 --> 00:11:15,200 Speaker 2: I think we're going to need that back for this 189 00:11:15,320 --> 00:11:18,200 Speaker 2: many sports analogies. It's a great point because if you 190 00:11:18,520 --> 00:11:21,839 Speaker 2: are a sponsor of a team or a league, what 191 00:11:21,920 --> 00:11:25,720 Speaker 2: you were interested in is that then diagram of the team, 192 00:11:26,000 --> 00:11:29,200 Speaker 2: the league, and your own brand plus what Converge then 193 00:11:29,280 --> 00:11:34,840 Speaker 2: offers you is a basically a data fabric of two 194 00:11:34,880 --> 00:11:38,200 Speaker 2: hundred and sixty million adult Americans. We know who you are, 195 00:11:38,360 --> 00:11:40,520 Speaker 2: we know what you care about, and then we use 196 00:11:40,640 --> 00:11:45,840 Speaker 2: our propensity modeling orip our understanding of sports and fandom 197 00:11:46,360 --> 00:11:49,719 Speaker 2: to hand that off to sponsors so that they're then 198 00:11:49,800 --> 00:11:51,679 Speaker 2: able to activate. 199 00:11:51,200 --> 00:11:52,720 Speaker 5: In a more personalized way. 200 00:11:52,760 --> 00:11:56,600 Speaker 2: To your point, right, so it's about what player do 201 00:11:56,640 --> 00:11:59,839 Speaker 2: you care about, what merchandise do you buy as a result, 202 00:12:00,200 --> 00:12:03,320 Speaker 2: and how do I get you to kind of engage 203 00:12:03,360 --> 00:12:06,960 Speaker 2: with the brand across the lifetime of fandom in a 204 00:12:07,000 --> 00:12:12,560 Speaker 2: way that makes engagement feel more personal and like it's 205 00:12:12,600 --> 00:12:15,600 Speaker 2: going to continue to engage me with both my team 206 00:12:16,080 --> 00:12:16,800 Speaker 2: and the brand. 207 00:12:16,920 --> 00:12:20,360 Speaker 4: At that intersection, the metric that everybody's looking for is 208 00:12:20,400 --> 00:12:21,439 Speaker 4: that time spent? 209 00:12:22,200 --> 00:12:23,040 Speaker 3: Would you say, is that? 210 00:12:23,280 --> 00:12:27,319 Speaker 2: So we've actually evolved this idea of lifetime value into 211 00:12:27,360 --> 00:12:30,680 Speaker 2: something called a fandom score. So it looks at all 212 00:12:30,720 --> 00:12:33,959 Speaker 2: of the attributes associated with your engagement. So that might 213 00:12:34,000 --> 00:12:37,120 Speaker 2: be in person, that might be online, that might be social, 214 00:12:37,440 --> 00:12:39,720 Speaker 2: it might be your sentiment that you're putting out into 215 00:12:39,720 --> 00:12:43,080 Speaker 2: the universe, into the social universe. And then it applies 216 00:12:43,600 --> 00:12:46,760 Speaker 2: some of your behaviors, whether that's purchase behavior or it's 217 00:12:47,080 --> 00:12:50,439 Speaker 2: browsing behavior in a way that is, you know, frankly 218 00:12:50,640 --> 00:12:54,200 Speaker 2: a bit evolved than the traditional time spent viewing, which 219 00:12:54,240 --> 00:12:56,240 Speaker 2: we both come from a TV background. I spend lots 220 00:12:56,280 --> 00:12:57,559 Speaker 2: of years at Turner Broadcasting. 221 00:12:57,920 --> 00:12:59,880 Speaker 5: Like Nielsen used to. 222 00:12:59,800 --> 00:13:02,280 Speaker 2: Be the metric and it was yes or no? Did 223 00:13:02,320 --> 00:13:03,720 Speaker 2: your eyeballs watch this content? 224 00:13:03,840 --> 00:13:04,240 Speaker 5: Yes or no? 225 00:13:04,360 --> 00:13:06,280 Speaker 2: So now to think about, we now have a metric 226 00:13:06,320 --> 00:13:10,640 Speaker 2: that is twenty four different data attributes associated with how 227 00:13:10,760 --> 00:13:14,960 Speaker 2: much you love a brand in your fandom. Is you know, 228 00:13:15,120 --> 00:13:17,120 Speaker 2: light years ahead of where we were, you know, just 229 00:13:17,160 --> 00:13:18,120 Speaker 2: a few years ago. 230 00:13:18,280 --> 00:13:22,440 Speaker 4: Rubert, were there anything in helping Deloitte put Converge together? 231 00:13:22,480 --> 00:13:25,160 Speaker 3: Were there any was there any R and D any. 232 00:13:24,920 --> 00:13:29,640 Speaker 4: Innovations that AWS did to empower what they wanted to do. 233 00:13:30,080 --> 00:13:32,560 Speaker 5: Yeah, there's a wide variety of services, but maybe a 234 00:13:32,600 --> 00:13:36,240 Speaker 5: couple that I'll dig into is Amazon Personalization, which actually, 235 00:13:36,440 --> 00:13:38,240 Speaker 5: you know, you can put in the data. Whatever the 236 00:13:38,280 --> 00:13:41,839 Speaker 5: customer is puts in the data. It then pull is 237 00:13:41,960 --> 00:13:46,760 Speaker 5: data like user their interactions, clicks, time spent somewhere, any 238 00:13:46,800 --> 00:13:49,760 Speaker 5: purchasing behavior. So it's not just what you tell me 239 00:13:49,800 --> 00:13:51,440 Speaker 5: you want to do, but what you actually do that 240 00:13:51,440 --> 00:13:54,200 Speaker 5: we're now measuring. And that's I think what the personalization 241 00:13:54,320 --> 00:13:57,840 Speaker 5: is all about. Then taking that and it actually selects 242 00:13:57,840 --> 00:14:01,080 Speaker 5: the data to train a model that is based on 243 00:14:01,160 --> 00:14:04,280 Speaker 5: the user's actual behavior, and then that model is used 244 00:14:04,320 --> 00:14:07,280 Speaker 5: now to predict what the next user is going to 245 00:14:07,320 --> 00:14:10,720 Speaker 5: do based on similar attributes. So that's just one example 246 00:14:11,880 --> 00:14:14,240 Speaker 5: of what we're using. And then the other piece is 247 00:14:14,640 --> 00:14:17,600 Speaker 5: Deloitte's been an amazing partner with us with Generative AI 248 00:14:17,720 --> 00:14:21,360 Speaker 5: and using Amazon Bedrock, which is our managed service that 249 00:14:21,600 --> 00:14:25,520 Speaker 5: allows the customer and the partner to utilize whatever large 250 00:14:25,600 --> 00:14:28,480 Speaker 5: language model is fit for purpose for that use case. 251 00:14:28,840 --> 00:14:31,600 Speaker 5: So whether the fit for purpose is analyzing image data, 252 00:14:32,000 --> 00:14:35,320 Speaker 5: or taking real time speech and then converting it to text, 253 00:14:35,840 --> 00:14:38,600 Speaker 5: or getting a whole bunch of data points and putting 254 00:14:38,640 --> 00:14:42,200 Speaker 5: out insights. So one example we were talking about earlier 255 00:14:42,240 --> 00:14:46,080 Speaker 5: Cynthia was Formula One. We worked with Formula one on 256 00:14:46,960 --> 00:14:51,160 Speaker 5: Track plus and in this solution you've got it's not 257 00:14:51,240 --> 00:14:53,760 Speaker 5: like there's one ball in a stadium, not that we 258 00:14:53,800 --> 00:14:56,840 Speaker 5: don't like ballsports, but this is a sport with twenty drivers. 259 00:14:57,200 --> 00:14:59,320 Speaker 5: They're going up to two hundred and thirty miles per 260 00:14:59,360 --> 00:15:02,880 Speaker 5: hour around a racetrack, and there are one million data 261 00:15:02,880 --> 00:15:08,040 Speaker 5: points per car per second. Now imagine your former commentator self. 262 00:15:08,080 --> 00:15:11,280 Speaker 5: That's like reviewing that and trying to give a fan 263 00:15:11,480 --> 00:15:14,440 Speaker 5: some useful information. Oh and by the way, this isn't 264 00:15:14,480 --> 00:15:17,920 Speaker 5: the only race they've raced in. There's historical data they've 265 00:15:18,120 --> 00:15:21,120 Speaker 5: switched the tires because it's wet or dry, or maybe 266 00:15:21,160 --> 00:15:23,640 Speaker 5: they chose not to switch the tires for whatever reason. 267 00:15:24,280 --> 00:15:27,920 Speaker 5: All of that information now can be provided in real time, 268 00:15:28,320 --> 00:15:32,400 Speaker 5: contextualized historically and giving the why behind it. And now 269 00:15:32,480 --> 00:15:35,520 Speaker 5: the experience for fans is much richer. And this is 270 00:15:35,560 --> 00:15:39,720 Speaker 5: a very global audience. Five hundred million plus fans and 271 00:15:39,760 --> 00:15:41,840 Speaker 5: I think anyone that watches Drive to Survive like that 272 00:15:41,920 --> 00:15:46,040 Speaker 5: took the viewership up, so very diverse group. And now 273 00:15:46,080 --> 00:15:49,520 Speaker 5: you're able to tailor these insights to that audience. 274 00:15:49,880 --> 00:15:50,840 Speaker 3: That is fascinating. 275 00:15:51,080 --> 00:15:53,600 Speaker 5: Think about it. From a fan experience because some fans 276 00:15:53,720 --> 00:15:56,960 Speaker 5: want the stats and the technical details, some want like 277 00:15:57,040 --> 00:15:59,440 Speaker 5: the history, and then there are some that really want 278 00:15:59,440 --> 00:16:01,640 Speaker 5: the drama. I mean they want like you know, they 279 00:16:02,920 --> 00:16:05,600 Speaker 5: bang the steering wheel or they were really upset or 280 00:16:05,600 --> 00:16:08,560 Speaker 5: through their helmets or whatever. You know. That was actually 281 00:16:08,560 --> 00:16:11,920 Speaker 5: something that we learned working with Bundeslega, which is the 282 00:16:12,200 --> 00:16:16,320 Speaker 5: German Football Association professional football sairly rabid fans, yes, yes, 283 00:16:16,600 --> 00:16:20,320 Speaker 5: but very diverse, right. There are some who want the metrics, 284 00:16:20,360 --> 00:16:22,680 Speaker 5: they want to know every play and how does that 285 00:16:22,840 --> 00:16:25,680 Speaker 5: rank versus other players and history, and then there are 286 00:16:25,720 --> 00:16:27,400 Speaker 5: some that want the drama and they want to know 287 00:16:27,440 --> 00:16:30,160 Speaker 5: about any fights or they want to know about injuries. 288 00:16:30,240 --> 00:16:33,360 Speaker 5: And it's you know, now with the Bundesloga app, you 289 00:16:33,400 --> 00:16:37,360 Speaker 5: can actually drive up engagement because you are personalizing the 290 00:16:37,400 --> 00:16:40,320 Speaker 5: content that's delivered to the user based on what they want. 291 00:16:40,960 --> 00:16:43,080 Speaker 5: And that's what's really exciting is it's a personalized fan 292 00:16:43,120 --> 00:16:47,160 Speaker 5: experience and they're seeing engagement go up significantly. The time 293 00:16:47,280 --> 00:16:50,520 Speaker 5: that the app users are spending there going up because 294 00:16:50,560 --> 00:16:52,120 Speaker 5: it's the stuff they are interested in. 295 00:16:52,320 --> 00:16:54,680 Speaker 4: In terms of just pure connectivity, the fact that the 296 00:16:54,760 --> 00:16:57,160 Speaker 4: drivers can speak to their pit crews and speak to 297 00:16:57,200 --> 00:16:59,240 Speaker 4: each other on the and and you know we at 298 00:16:59,240 --> 00:17:02,600 Speaker 4: times we can hear that definitely adds to the drama, Michelle, 299 00:17:02,720 --> 00:17:03,240 Speaker 4: no I was. 300 00:17:03,200 --> 00:17:05,240 Speaker 2: Just going to add the tech piece associated with that. 301 00:17:05,320 --> 00:17:07,760 Speaker 2: You know, we use something called a non supervised clustering 302 00:17:07,760 --> 00:17:11,240 Speaker 2: algorithm to have the machine basically look at the data 303 00:17:11,280 --> 00:17:13,919 Speaker 2: in a way that humans don't. So to your fact's 304 00:17:13,960 --> 00:17:16,080 Speaker 2: reference where you used to sit and do the analysis 305 00:17:16,160 --> 00:17:19,760 Speaker 2: yourself that you know, we're now allowing the machine to 306 00:17:19,840 --> 00:17:24,920 Speaker 2: train itself to understand who those audiences are and then 307 00:17:25,119 --> 00:17:28,639 Speaker 2: using jen Ai and Betrock and in this case stage 308 00:17:28,640 --> 00:17:34,080 Speaker 2: Maker two also an AWS product to basically name those segments. 309 00:17:34,080 --> 00:17:37,359 Speaker 2: So something that used to take marketers, you know, in 310 00:17:37,400 --> 00:17:40,600 Speaker 2: some cases weeks to do the analysis to name the 311 00:17:40,640 --> 00:17:41,520 Speaker 2: actual segment. 312 00:17:41,840 --> 00:17:44,040 Speaker 5: We're now letting the machine do it for us. 313 00:17:44,119 --> 00:17:47,000 Speaker 2: And then it's the When it names the segment, marketers 314 00:17:47,040 --> 00:17:49,159 Speaker 2: are then able to act on it and push that 315 00:17:49,240 --> 00:17:51,840 Speaker 2: data downstream to do the outreach, whether it be via 316 00:17:51,920 --> 00:17:55,120 Speaker 2: mobile or social or app, whatever it might be. The 317 00:17:55,160 --> 00:17:57,920 Speaker 2: machine is now doing that for you, and it's closing 318 00:17:57,960 --> 00:18:01,399 Speaker 2: the loop via mL operations to continue to train it 319 00:18:01,920 --> 00:18:03,560 Speaker 2: on what those fans want. 320 00:18:03,840 --> 00:18:06,399 Speaker 4: The loop or you know where the line ends between 321 00:18:06,440 --> 00:18:09,840 Speaker 4: the machine learning, the training and the generative AI. That 322 00:18:10,000 --> 00:18:13,320 Speaker 4: is going to be a science going forward for any 323 00:18:13,520 --> 00:18:15,720 Speaker 4: number of applications. And I think this is a really 324 00:18:15,760 --> 00:18:16,560 Speaker 4: interesting one. 325 00:18:17,040 --> 00:18:19,920 Speaker 1: Don't go anywhere. We'll be right back with more from 326 00:18:19,960 --> 00:18:22,040 Speaker 1: Strictly Business Live from can Lion. 327 00:18:25,880 --> 00:18:28,680 Speaker 2: While brand alone used to build fan engagement and loyalty, 328 00:18:29,040 --> 00:18:32,800 Speaker 2: today's fan expectations have shifted and organizations can be challenged 329 00:18:32,840 --> 00:18:36,560 Speaker 2: to deliver interconnected fan experiences. What if you could give 330 00:18:36,600 --> 00:18:41,040 Speaker 2: fans the experience they want by seamlessly integrating touch points 331 00:18:41,080 --> 00:18:45,439 Speaker 2: like ticketing, athlete interactions, game streaming, and more within a 332 00:18:45,480 --> 00:18:49,720 Speaker 2: single intuitive platform. Imagine a fan data platform that takes 333 00:18:49,720 --> 00:18:52,760 Speaker 2: your customer data and puts it to work, creating advantage 334 00:18:52,800 --> 00:18:57,200 Speaker 2: for your team and stakeholders. Converged by Deloitte for Sports 335 00:18:57,640 --> 00:19:02,879 Speaker 2: empowers organizations to design personalized digital experiences at scale to 336 00:19:03,000 --> 00:19:05,720 Speaker 2: delight your fan base with holistic experiences. 337 00:19:08,840 --> 00:19:11,639 Speaker 1: And we're back with Strictly Business Live from Canline. 338 00:19:12,359 --> 00:19:12,719 Speaker 3: Michelle. 339 00:19:12,800 --> 00:19:15,120 Speaker 4: Are there anything so if I understand right, Converge has 340 00:19:15,160 --> 00:19:17,080 Speaker 4: been it's been in the works for a while, it's 341 00:19:17,080 --> 00:19:19,160 Speaker 4: been active in the marketplace for about a year. 342 00:19:19,920 --> 00:19:22,080 Speaker 3: Anything you know, any top. 343 00:19:21,880 --> 00:19:24,800 Speaker 4: Line kind of something that surprised you about you know 344 00:19:24,880 --> 00:19:28,360 Speaker 4: the behavior of sports fans or what you know, what 345 00:19:28,520 --> 00:19:31,800 Speaker 4: motivated sponsorship or you know, things that maybe things that 346 00:19:31,880 --> 00:19:34,640 Speaker 4: weren't you know, obviously intuitive. 347 00:19:35,080 --> 00:19:39,040 Speaker 2: Well, so first converge consumer was the first converge. So 348 00:19:39,240 --> 00:19:43,159 Speaker 2: we took that same data fabric that we talked about 349 00:19:43,200 --> 00:19:47,280 Speaker 2: around retail and consumer product data, and we learned from their. 350 00:19:47,280 --> 00:19:49,439 Speaker 5: General behavior and share of wallet. 351 00:19:49,600 --> 00:19:52,520 Speaker 2: Right, So we took all our learnings from retail, and 352 00:19:52,600 --> 00:19:58,240 Speaker 2: then we applied sports specific data around ticket purchase as 353 00:19:58,280 --> 00:20:03,680 Speaker 2: well as t affinity based on primary research or digital research, 354 00:20:04,160 --> 00:20:08,000 Speaker 2: and then brought the two together to inform the fandom 355 00:20:08,040 --> 00:20:11,280 Speaker 2: score that I mentioned. And so the thing that I 356 00:20:11,280 --> 00:20:14,520 Speaker 2: find most interesting about fandom is that your fandom is 357 00:20:14,560 --> 00:20:19,000 Speaker 2: not unique. Right, It crosses it crosses music, it crosses 358 00:20:19,480 --> 00:20:23,560 Speaker 2: your retail and buying habits, it crosses all the areas 359 00:20:23,600 --> 00:20:26,040 Speaker 2: of your life. So you know you think about that, 360 00:20:26,040 --> 00:20:29,199 Speaker 2: that's intuitive. Yeah, Like sports fans are not you know, 361 00:20:29,240 --> 00:20:33,560 Speaker 2: they're not a monolith. They are they are rabbit about teams, 362 00:20:33,680 --> 00:20:35,959 Speaker 2: leagues in different sports. So how do you look at 363 00:20:36,000 --> 00:20:40,119 Speaker 2: them holistically as a fan and then act upon that 364 00:20:40,320 --> 00:20:43,000 Speaker 2: unique fandom? In that audience of one. 365 00:20:43,200 --> 00:20:46,200 Speaker 5: Just to add to that, Cynthia, because it's it goes 366 00:20:46,280 --> 00:20:52,480 Speaker 5: beyond a personalized experience, it's actually a personalized monetization experience. 367 00:20:52,600 --> 00:20:54,560 Speaker 3: So you can think it doesn't like it. 368 00:20:56,520 --> 00:20:59,919 Speaker 5: Right, You've got you know, subscribers, maybe businesses that have 369 00:21:00,000 --> 00:21:03,800 Speaker 5: subscription service, or businesses that are selling certain content, or 370 00:21:03,840 --> 00:21:06,480 Speaker 5: an ADS based business, and you can now with the 371 00:21:06,520 --> 00:21:10,000 Speaker 5: fandom score, be able to apply that score or any 372 00:21:10,000 --> 00:21:14,840 Speaker 5: of those insights provided by Generative AI to determine, Hey, 373 00:21:15,040 --> 00:21:17,719 Speaker 5: is this consumer do they have Maybe they have a 374 00:21:17,800 --> 00:21:22,600 Speaker 5: low propensity to subscribe, but they have a high propensity 375 00:21:22,680 --> 00:21:25,720 Speaker 5: for or they have a high AD score, and we'd 376 00:21:25,760 --> 00:21:28,080 Speaker 5: be able to make we as a vendor, whomever the 377 00:21:28,160 --> 00:21:31,919 Speaker 5: vendor is, could make money off of advertising. So it's okay, 378 00:21:32,119 --> 00:21:35,400 Speaker 5: if they don't subscribe, maybe we'll let them pass through 379 00:21:35,400 --> 00:21:37,520 Speaker 5: the paywall to get the content because the AD is 380 00:21:37,560 --> 00:21:40,520 Speaker 5: going to generate more revenue for us than the subscription 381 00:21:40,840 --> 00:21:43,760 Speaker 5: even though they're a low propensity to subscribe. So thinking 382 00:21:43,760 --> 00:21:46,119 Speaker 5: through how to use that type of information in the 383 00:21:46,160 --> 00:21:49,200 Speaker 5: monetization model and to do that in real time based 384 00:21:49,240 --> 00:21:51,760 Speaker 5: on the person I think is really valuable. 385 00:21:52,119 --> 00:21:55,879 Speaker 4: I'm curious about the score is Is it a numerical score, 386 00:21:56,080 --> 00:21:59,639 Speaker 4: is it a like certain traits about a person? How 387 00:21:59,680 --> 00:22:01,520 Speaker 4: do you you say the fandom score? How do you 388 00:22:01,520 --> 00:22:03,760 Speaker 4: exactly calculate it? And how do you express it in 389 00:22:03,800 --> 00:22:06,920 Speaker 4: a way that marketers can interpret it? Does it expresses. 390 00:22:06,440 --> 00:22:10,800 Speaker 2: Itself as a number one, two, one hundred, And it's 391 00:22:10,840 --> 00:22:16,400 Speaker 2: always in relationship to the brand you're talking about and 392 00:22:16,720 --> 00:22:20,119 Speaker 2: the sports league you're talking about, right, So, but the 393 00:22:20,160 --> 00:22:24,000 Speaker 2: good news is that you can adjust it. It's obviously 394 00:22:24,080 --> 00:22:26,399 Speaker 2: a weighted metric. You can adjust it based on the 395 00:22:26,440 --> 00:22:29,919 Speaker 2: behaviors that your sponsor might be interested in. Right, So, 396 00:22:30,080 --> 00:22:34,240 Speaker 2: if a sponsor is interested in selling more live event tickets, 397 00:22:34,680 --> 00:22:38,520 Speaker 2: then you can wait it based on discretionary income available 398 00:22:38,600 --> 00:22:41,679 Speaker 2: to purchase tickets. And obviously an F one ticket is 399 00:22:41,720 --> 00:22:45,600 Speaker 2: a much more pricier spend than a Dodger's game. So 400 00:22:45,880 --> 00:22:48,159 Speaker 2: you can look at that in a way that is, 401 00:22:48,480 --> 00:22:52,680 Speaker 2: you know, that downstream enablement and enactment of you know, 402 00:22:53,280 --> 00:22:57,480 Speaker 2: the score as the you know, kind of numerical representation. 403 00:22:57,840 --> 00:23:00,160 Speaker 4: And I would imagine although with you know, some three 404 00:23:00,240 --> 00:23:04,440 Speaker 4: hundred million people in certainly in the US, monitoring those 405 00:23:04,440 --> 00:23:07,760 Speaker 4: scores would be would be challenging but I would bet 406 00:23:07,840 --> 00:23:11,240 Speaker 4: that the concept of that that people are would are 407 00:23:11,440 --> 00:23:12,720 Speaker 4: very eager to look at. 408 00:23:12,680 --> 00:23:14,760 Speaker 3: And see what those scores are for folks. 409 00:23:14,800 --> 00:23:16,879 Speaker 4: And I would imagine that if you're really high in 410 00:23:16,920 --> 00:23:19,680 Speaker 4: the fan engagement, you're going to have other attributes. You 411 00:23:19,760 --> 00:23:22,720 Speaker 4: might be an early adopter of technology. I would imagine 412 00:23:22,720 --> 00:23:24,760 Speaker 4: that you're able to draw those kind of parallels. 413 00:23:24,800 --> 00:23:25,399 Speaker 3: Absolutely. 414 00:23:25,560 --> 00:23:30,400 Speaker 2: The algorithm basically surfaces the attributes that are associated with 415 00:23:30,800 --> 00:23:36,560 Speaker 2: certain behaviors, right, So this attribute drives live event propensity, right, 416 00:23:36,720 --> 00:23:39,479 Speaker 2: So it tells you and we also use the gen 417 00:23:39,560 --> 00:23:43,680 Speaker 2: AI to say, this is a high and tending population 418 00:23:44,080 --> 00:23:48,119 Speaker 2: who care about, to your point, early technology adoption. So 419 00:23:48,160 --> 00:23:51,840 Speaker 2: give them an immersive experience that looks different than you know, 420 00:23:51,960 --> 00:23:55,280 Speaker 2: somebody who might be cost conscious who does not want 421 00:23:55,280 --> 00:23:56,480 Speaker 2: a technology component. 422 00:23:56,600 --> 00:23:56,800 Speaker 3: Right. 423 00:23:56,840 --> 00:24:01,640 Speaker 2: So the ability to give those i'll say in person 424 00:24:01,800 --> 00:24:06,199 Speaker 2: activations in a way that the sponsor cares about is 425 00:24:06,400 --> 00:24:08,000 Speaker 2: important to CMOS everywhere. 426 00:24:08,040 --> 00:24:11,119 Speaker 5: I'll add one more thing that's with the emergence of 427 00:24:11,160 --> 00:24:15,600 Speaker 5: agentic AI, where it's in some cases it's not eyeballs, 428 00:24:15,320 --> 00:24:19,639 Speaker 5: it's it's actually machines. You know, how do you know 429 00:24:19,880 --> 00:24:22,439 Speaker 5: whether it's a machine or a person. And then what 430 00:24:22,600 --> 00:24:24,840 Speaker 5: data if it's a machine scraping to get an insight 431 00:24:24,960 --> 00:24:28,119 Speaker 5: to give to someone, versus it's a person that's actually 432 00:24:28,119 --> 00:24:32,359 Speaker 5: engaging and wants to giving. Yeah, yeah, so how do 433 00:24:32,400 --> 00:24:35,000 Speaker 5: you you know? We're using AI to do that. And 434 00:24:35,080 --> 00:24:37,520 Speaker 5: actually I was on a panel yesterday with a couple 435 00:24:37,600 --> 00:24:40,959 Speaker 5: of our partners, and Adobe had mentioned that they've been 436 00:24:41,040 --> 00:24:45,439 Speaker 5: measuring the number of machines that are interacting with their 437 00:24:45,760 --> 00:24:49,360 Speaker 5: their platform and in the last six months it went 438 00:24:49,480 --> 00:24:52,760 Speaker 5: up something like three thousand, five hundred percent. Maybe don't 439 00:24:52,800 --> 00:24:54,600 Speaker 5: quote me exactly on the number. It was over three 440 00:24:54,680 --> 00:24:58,960 Speaker 5: thousand of machines versus people. The machines are doing something useful, right, 441 00:24:58,960 --> 00:25:02,000 Speaker 5: They're getting information and sending it somewhere to someone who's 442 00:25:02,000 --> 00:25:05,520 Speaker 5: going to use it. But how you present content for 443 00:25:05,600 --> 00:25:07,639 Speaker 5: that is very different. How you monetize that it is 444 00:25:07,720 --> 00:25:08,200 Speaker 5: very different. 445 00:25:08,720 --> 00:25:11,480 Speaker 4: You're using AI to ferret out the machines that are 446 00:25:11,600 --> 00:25:14,960 Speaker 4: confounding the AAI that is that that is very. 447 00:25:15,640 --> 00:25:16,760 Speaker 3: Indicative of our times. 448 00:25:16,840 --> 00:25:19,640 Speaker 2: Yeah, it's the future, right, agents acting on your behalf 449 00:25:20,200 --> 00:25:23,840 Speaker 2: is the evolution of AI gen AI that are you know, 450 00:25:23,880 --> 00:25:28,400 Speaker 2: doing schedule optimization and always looking at the scores, making recommendations, 451 00:25:28,520 --> 00:25:32,400 Speaker 2: running in the background, Those algorithms and agents are are 452 00:25:32,480 --> 00:25:36,720 Speaker 2: the future of how fans are going to engage with Historically, 453 00:25:36,720 --> 00:25:38,920 Speaker 2: maybe it was statistics, or it was you know, any 454 00:25:38,960 --> 00:25:43,040 Speaker 2: sort of live event. Even right how you're engaging with 455 00:25:43,960 --> 00:25:47,720 Speaker 2: the device at all times is going to radically change 456 00:25:47,760 --> 00:25:48,480 Speaker 2: in the next year. 457 00:25:48,800 --> 00:25:51,679 Speaker 5: What's cool about agents, especially for in this space, is 458 00:25:51,680 --> 00:25:54,640 Speaker 5: it they can allow you to run multiple campaigns at once, 459 00:25:54,760 --> 00:25:57,439 Speaker 5: do A B testing way faster than you could before. 460 00:25:58,080 --> 00:26:01,000 Speaker 5: I mean, they can parallelize the entire higher workload. And 461 00:26:01,040 --> 00:26:03,440 Speaker 5: so I mean, I think kind of traditional A B 462 00:26:03,640 --> 00:26:07,000 Speaker 5: testing maybe you can do two tests per week, you know, 463 00:26:07,119 --> 00:26:09,720 Speaker 5: per analyst, and now with agents, I mean that number 464 00:26:09,800 --> 00:26:12,879 Speaker 5: can grow exponentially, and then you can, you know, you 465 00:26:12,920 --> 00:26:15,199 Speaker 5: can figure out which of these campaigns or which of 466 00:26:15,240 --> 00:26:19,000 Speaker 5: these experiences is actually driving better results because the agent 467 00:26:19,080 --> 00:26:22,120 Speaker 5: was actually able to modify the campaign that you were 468 00:26:22,119 --> 00:26:24,600 Speaker 5: going to put out there, test it, get the data, 469 00:26:24,920 --> 00:26:27,720 Speaker 5: give you the results, and then you keep pushing and. 470 00:26:27,760 --> 00:26:31,200 Speaker 4: All that we're talking about, which is again so personalized, 471 00:26:31,240 --> 00:26:36,880 Speaker 4: so individualized. It does underscore the need for massive, massive 472 00:26:37,000 --> 00:26:40,720 Speaker 4: amounts of cloud storage. It really does, because somebody has 473 00:26:40,760 --> 00:26:43,840 Speaker 4: to maintain that, and I know, you know, there's costs 474 00:26:43,880 --> 00:26:46,200 Speaker 4: to maintaining that. There's you have to keep it. I 475 00:26:46,320 --> 00:26:49,159 Speaker 4: understand you have to. It's something that you have to 476 00:26:49,240 --> 00:26:51,560 Speaker 4: keep it fresh. It can't get too stale, otherwise you 477 00:26:51,960 --> 00:26:55,440 Speaker 4: compromises some of the efficacy of the. 478 00:26:54,960 --> 00:26:57,880 Speaker 5: The data challenge is probably you know, step number one 479 00:26:58,040 --> 00:27:00,280 Speaker 5: is you have to have a data foundation. If you 480 00:27:00,320 --> 00:27:02,399 Speaker 5: look at and I don't want to say legacy media 481 00:27:02,440 --> 00:27:06,479 Speaker 5: companies because it actually plagues every single industry frankly, is 482 00:27:06,520 --> 00:27:08,719 Speaker 5: the data is not all in one place. So if 483 00:27:08,720 --> 00:27:12,840 Speaker 5: you're thinking about personalization by aggregating data from the ads team, 484 00:27:12,920 --> 00:27:16,280 Speaker 5: from the marketing team, from the content team, today many 485 00:27:16,359 --> 00:27:20,119 Speaker 5: of them still sit in silos. The functions are in silos, 486 00:27:20,320 --> 00:27:23,840 Speaker 5: their data is in silos, and the tech is also siloed. 487 00:27:24,119 --> 00:27:27,000 Speaker 5: And so being able to bring that together is step 488 00:27:27,080 --> 00:27:29,840 Speaker 5: number one is getting your data in order, put it 489 00:27:29,840 --> 00:27:33,040 Speaker 5: all in one place, being able to use these tools 490 00:27:33,280 --> 00:27:35,800 Speaker 5: that can give you the insights it is really key. 491 00:27:35,880 --> 00:27:38,040 Speaker 5: And so that's why actually I love the name Converge. 492 00:27:38,280 --> 00:27:40,879 Speaker 5: I'm not a marketing professional or brand person, but I 493 00:27:40,880 --> 00:27:42,480 Speaker 5: think you guys did a great job with that name, 494 00:27:42,880 --> 00:27:44,760 Speaker 5: because that is what it's all about, is bringing that 495 00:27:44,840 --> 00:27:46,800 Speaker 5: data together to converge and insight, and. 496 00:27:46,760 --> 00:27:49,480 Speaker 4: With large organizations, we've all been there in meetings, the 497 00:27:49,800 --> 00:27:54,360 Speaker 4: possibility of a small communications lapse or somebody didn't talk 498 00:27:54,400 --> 00:27:57,280 Speaker 4: to somebody and you're looking for something and then a 499 00:27:57,320 --> 00:27:59,680 Speaker 4: week later, somebody's in a meeting saying, oh, I have 500 00:27:59,800 --> 00:28:02,000 Speaker 4: that right here, but you couldn't put your hands on 501 00:28:02,080 --> 00:28:04,040 Speaker 4: it because it was all in different spots. 502 00:28:04,240 --> 00:28:06,560 Speaker 5: That's the number one bottleneck. And a B testing is 503 00:28:06,600 --> 00:28:09,199 Speaker 5: the analyt doing the work doesn't have access to the 504 00:28:09,280 --> 00:28:11,159 Speaker 5: data and it you know, the person that needs to 505 00:28:11,160 --> 00:28:13,760 Speaker 5: provide them the data hasn't checked their email and all 506 00:28:13,800 --> 00:28:15,720 Speaker 5: of that. And so the idea of getting your data 507 00:28:16,119 --> 00:28:19,480 Speaker 5: in one place and having a comprehensive data strategy, which 508 00:28:19,520 --> 00:28:23,200 Speaker 5: we do constantly with Deloitte when we support our customers 509 00:28:23,320 --> 00:28:24,520 Speaker 5: is really step number one. 510 00:28:24,640 --> 00:28:26,959 Speaker 2: Yeah, and we've gotten to the point where, you know, 511 00:28:27,080 --> 00:28:30,960 Speaker 2: prompt engineering it's a misnomer for sure, It's just how 512 00:28:31,000 --> 00:28:33,920 Speaker 2: you interact with the model and ask the questions. Has 513 00:28:34,000 --> 00:28:36,280 Speaker 2: made it so that you know, when you can't find 514 00:28:36,280 --> 00:28:39,080 Speaker 2: that piece of data or research, it now spans your 515 00:28:39,160 --> 00:28:42,440 Speaker 2: organization scours it for you and then surfaces it with 516 00:28:42,640 --> 00:28:45,000 Speaker 2: an insight or an action. You now need to take 517 00:28:45,080 --> 00:28:48,280 Speaker 2: as opposed to having to crunch the number and read the. 518 00:28:48,160 --> 00:28:49,719 Speaker 3: Facts really understanding. 519 00:28:49,840 --> 00:28:52,880 Speaker 4: Just as people that understand code now have a skill, 520 00:28:53,240 --> 00:28:55,680 Speaker 4: the coding of the future is going to be prompts. 521 00:28:55,920 --> 00:28:57,680 Speaker 5: There is the prompt side of it, which is a 522 00:28:57,720 --> 00:29:01,120 Speaker 5: reactive engagement mode. So like I, I put in a 523 00:29:01,160 --> 00:29:05,040 Speaker 5: prompt and you know the the LM is reacting and 524 00:29:05,040 --> 00:29:08,800 Speaker 5: giving me an answer. But the agentic kind of wave 525 00:29:09,000 --> 00:29:12,120 Speaker 5: that is upon us is just proactive. It's just doing 526 00:29:12,160 --> 00:29:15,400 Speaker 5: things on our passing it, and that's you an end goal. 527 00:29:15,720 --> 00:29:18,640 Speaker 4: I'm still wrapping my mind around. You know, there will 528 00:29:18,640 --> 00:29:22,480 Speaker 4: come a time everything is becoming Hollywood and Hollywood everybody 529 00:29:22,480 --> 00:29:25,160 Speaker 4: has an agent. Soon everybody will have an agent, and 530 00:29:25,280 --> 00:29:28,480 Speaker 4: somebody will have to store all that information or maybe 531 00:29:28,640 --> 00:29:30,800 Speaker 4: maybe the state of that art will be finding more 532 00:29:30,800 --> 00:29:32,680 Speaker 4: efficient ways to store it in a way that you 533 00:29:32,720 --> 00:29:34,880 Speaker 4: can put it over here, but grab it when the person, 534 00:29:35,000 --> 00:29:36,880 Speaker 4: either the person needs it or the person putting in 535 00:29:36,960 --> 00:29:37,440 Speaker 4: the prompt. 536 00:29:37,600 --> 00:29:40,320 Speaker 5: The cool thing about agents is they're kind of optimized 537 00:29:40,400 --> 00:29:43,880 Speaker 5: for a task or a part of a workflow. And 538 00:29:43,960 --> 00:29:46,440 Speaker 5: so if you think about someone in a marketing function, 539 00:29:46,520 --> 00:29:49,400 Speaker 5: you know they've got to think about multiple workflows and processes, 540 00:29:49,440 --> 00:29:51,960 Speaker 5: and you can have agents that support a sub segment 541 00:29:52,000 --> 00:29:55,560 Speaker 5: of that and so having them all work together on 542 00:29:55,680 --> 00:29:58,920 Speaker 5: behalf of that individual to figure out, hey, what is 543 00:29:58,960 --> 00:30:01,680 Speaker 5: the most optimal If you're optimizing for cost or if 544 00:30:01,720 --> 00:30:04,960 Speaker 5: you're optimizing for reach, or whatever the outcome you're trying 545 00:30:05,000 --> 00:30:09,280 Speaker 5: to achieve, multiple agents can come together. We have multiple 546 00:30:09,320 --> 00:30:13,520 Speaker 5: partners in our AWS partner network, and they're all contributing 547 00:30:13,640 --> 00:30:18,880 Speaker 5: agents to a broader network because you can't do it 548 00:30:18,920 --> 00:30:22,120 Speaker 5: with one company. I think it does require partnerships and 549 00:30:22,200 --> 00:30:25,240 Speaker 5: experts in certain verticals or in certain processes or in 550 00:30:25,280 --> 00:30:28,760 Speaker 5: certain workflows, and bringing all those agents together to interoperate 551 00:30:28,800 --> 00:30:33,040 Speaker 5: and deliver that optimized process. I think that is going 552 00:30:33,120 --> 00:30:34,840 Speaker 5: to be the future. And it is all proactive, like 553 00:30:34,880 --> 00:30:38,560 Speaker 5: they're doing it on their own, and the business leader's 554 00:30:38,760 --> 00:30:40,920 Speaker 5: job is now to ask the right question and what 555 00:30:41,080 --> 00:30:43,080 Speaker 5: the key outcome is that they're trying to achieve. 556 00:30:43,440 --> 00:30:46,720 Speaker 4: Michelle, is there any next frontier for convergence? 557 00:30:46,960 --> 00:30:50,200 Speaker 2: So the next frontier is media, of course, so converge 558 00:30:50,240 --> 00:30:53,600 Speaker 2: media not too far off, but applying the same concepts, 559 00:30:53,680 --> 00:30:57,760 Speaker 2: right of measurement, engagement. What does it mean for media 560 00:30:57,880 --> 00:31:03,320 Speaker 2: organizations who care about all things fandom for potential subscribers 561 00:31:03,480 --> 00:31:07,000 Speaker 2: and who is next as a subscriber and who doesn't 562 00:31:07,040 --> 00:31:09,520 Speaker 2: subscribe today? And how do I talk to them in 563 00:31:09,560 --> 00:31:12,480 Speaker 2: a tone of voice that gets them down the funnel 564 00:31:12,520 --> 00:31:18,000 Speaker 2: to convert faster. I think your fandom in characters, titles, 565 00:31:18,160 --> 00:31:23,920 Speaker 2: worlds all influences your purchasing behavior when you think about 566 00:31:24,320 --> 00:31:27,160 Speaker 2: streaming and subscription, and so for US, that's the next 567 00:31:27,160 --> 00:31:29,760 Speaker 2: frontier to take. All that we've learned and done with 568 00:31:29,840 --> 00:31:34,320 Speaker 2: AWS is to apply to yet another industry for US. 569 00:31:34,480 --> 00:31:36,680 Speaker 4: And I wanted to ask you, is this largely US 570 00:31:36,720 --> 00:31:39,000 Speaker 4: centric at this point? Are you largely focusing on the 571 00:31:39,120 --> 00:31:40,320 Speaker 4: US consumer today? 572 00:31:40,440 --> 00:31:43,040 Speaker 2: Our data fabric is focused on the US consumer. We 573 00:31:43,120 --> 00:31:48,200 Speaker 2: definitely have ambitions on global more global markets, but as 574 00:31:48,240 --> 00:31:52,800 Speaker 2: you know, data around individuals is variable by country, so 575 00:31:52,880 --> 00:31:55,880 Speaker 2: our ability to stitch it together in a fabric is 576 00:31:55,920 --> 00:31:57,960 Speaker 2: going to take a little work. But yeah, we're definitely 577 00:31:58,000 --> 00:31:59,960 Speaker 2: working on global and other industries as well. 578 00:32:00,000 --> 00:32:00,840 Speaker 3: Well. Ruba. 579 00:32:00,960 --> 00:32:04,200 Speaker 4: Anything on the horizon for AWS that you'd like to 580 00:32:04,200 --> 00:32:05,120 Speaker 4: talk about. 581 00:32:05,000 --> 00:32:07,480 Speaker 5: Yeah, I think it's all about, you know, bringing together 582 00:32:07,720 --> 00:32:11,280 Speaker 5: the different capabilities and then providing new experiences. And actually, 583 00:32:11,320 --> 00:32:13,600 Speaker 5: you know, Cynthia was speaking with you and Michelle earlier 584 00:32:13,640 --> 00:32:16,680 Speaker 5: about something I'm really excited about that we're showingcasing here 585 00:32:16,720 --> 00:32:20,880 Speaker 5: in the Amazon port and that's taking multiple different large 586 00:32:20,920 --> 00:32:25,520 Speaker 5: language models. So Amazon Nova's family of large language models 587 00:32:25,560 --> 00:32:28,040 Speaker 5: that are optimized for you know, speech to text or 588 00:32:28,160 --> 00:32:32,080 Speaker 5: text to analytics, or creating an image or creating a video, 589 00:32:33,160 --> 00:32:35,520 Speaker 5: and you can go in and have an experience where 590 00:32:35,600 --> 00:32:40,280 Speaker 5: you tell this machine what inspires you, what are the 591 00:32:40,480 --> 00:32:43,280 Speaker 5: fragrances that you like, or what are notes that you like, 592 00:32:43,480 --> 00:32:47,520 Speaker 5: and then it'll create an ad campaign for your personalized 593 00:32:47,560 --> 00:32:50,800 Speaker 5: perfume with a name, with an image, with a video 594 00:32:51,440 --> 00:32:53,800 Speaker 5: and then actually there's a perfumer there that will make 595 00:32:53,880 --> 00:32:56,320 Speaker 5: it based on the notes there. And so think about 596 00:32:56,320 --> 00:33:00,720 Speaker 5: how generative AI is providing personalization across all aspects of 597 00:33:00,760 --> 00:33:03,320 Speaker 5: our lives and taking something that isn't just this it's 598 00:33:03,360 --> 00:33:05,080 Speaker 5: all in the cloud. We don't know what that is. 599 00:33:05,160 --> 00:33:08,800 Speaker 5: It's you know, big data or data analytics, machine learning agents. 600 00:33:09,240 --> 00:33:12,400 Speaker 5: It makes it something tangible that you can touch. And 601 00:33:12,400 --> 00:33:15,000 Speaker 5: that's what I love about the possibilities years. It's giving 602 00:33:15,080 --> 00:33:17,280 Speaker 5: you things that you can only imagine, and actually you 603 00:33:17,280 --> 00:33:19,360 Speaker 5: didn't imagine it. The machine imagined it and made it 604 00:33:19,400 --> 00:33:20,800 Speaker 5: real for you, I would you. 605 00:33:20,800 --> 00:33:24,520 Speaker 4: Know the idea of creating your own perfume, and to 606 00:33:24,560 --> 00:33:27,200 Speaker 4: the point of even having an ad, the ability to 607 00:33:27,280 --> 00:33:31,440 Speaker 4: train it to exactly your agents or your large language 608 00:33:31,440 --> 00:33:34,000 Speaker 4: model is to exactly what you need. I think that 609 00:33:34,000 --> 00:33:37,200 Speaker 4: that is we're just just starting to scratch the surface 610 00:33:37,240 --> 00:33:39,480 Speaker 4: of that, and certainly in media and entertainment, but just 611 00:33:39,600 --> 00:33:41,360 Speaker 4: in personal living, our lives. 612 00:33:41,400 --> 00:33:43,720 Speaker 3: So conversations like this express. 613 00:33:43,400 --> 00:33:46,400 Speaker 4: That there is so much there is so much opportunity 614 00:33:46,400 --> 00:33:50,600 Speaker 4: to unleash with new technology. It is not something to fear, 615 00:33:50,720 --> 00:33:53,640 Speaker 4: it's something to harness in a way that you've given 616 00:33:53,720 --> 00:33:56,600 Speaker 4: us some great use cases and listeners. Since you can't 617 00:33:56,600 --> 00:33:59,000 Speaker 4: see it, I just want to say that we have 618 00:33:59,080 --> 00:34:01,400 Speaker 4: been talking here a lot of about clouds. There is 619 00:34:01,480 --> 00:34:04,360 Speaker 4: not a cloud in the blue sky here in Can. 620 00:34:04,640 --> 00:34:08,360 Speaker 4: But only in Can would we have this deeply technical 621 00:34:08,400 --> 00:34:11,920 Speaker 4: conversation on a yacht where the yacht next door they're 622 00:34:11,960 --> 00:34:15,960 Speaker 4: setting up for a party with disco balls and discombobulated heads. 623 00:34:16,360 --> 00:34:17,400 Speaker 3: That's Can for you. 624 00:34:17,880 --> 00:34:20,479 Speaker 4: Thank you all so much, Really appreciate your time, really 625 00:34:20,480 --> 00:34:21,520 Speaker 4: appreciate your thoughts. 626 00:34:21,719 --> 00:34:28,120 Speaker 5: Thanks for having us, Thank you, thanks for listening. 627 00:34:28,520 --> 00:34:31,480 Speaker 6: Be sure to leave us a review at Apple podcasts 628 00:34:31,640 --> 00:34:35,080 Speaker 6: or Amazon Music. We love to hear from listeners. Please 629 00:34:35,120 --> 00:34:37,600 Speaker 6: go to Variety dot com and sign up for the 630 00:34:37,640 --> 00:34:42,040 Speaker 6: free weekly Strictly Business newsletter, and don't forget to tune 631 00:34:42,080 --> 00:34:46,080 Speaker 6: in next week for another episode of Strictly Business. 632 00:34:53,360 --> 00:34:56,120 Speaker 2: In an era when fan engagement has direct implications on 633 00:34:56,200 --> 00:35:00,440 Speaker 2: revenue and brand loyalty, understanding and quantifying fandom becomes crucial 634 00:35:00,520 --> 00:35:02,120 Speaker 2: for businesses across industries. 635 00:35:02,760 --> 00:35:04,319 Speaker 3: When teams, leagues. 636 00:35:04,000 --> 00:35:07,720 Speaker 2: And brands understand where their fans and potential partners align, 637 00:35:07,880 --> 00:35:11,640 Speaker 2: it creates a unique opportunity. With a deeper understanding of 638 00:35:11,680 --> 00:35:15,480 Speaker 2: their fans, organizations can determine which fans to engage at 639 00:35:15,480 --> 00:35:18,480 Speaker 2: the right time with the right content, and how to 640 00:35:18,560 --> 00:35:23,400 Speaker 2: transform that engagement into value for stakeholders. For example, younger 641 00:35:23,440 --> 00:35:26,760 Speaker 2: consumers are more likely to say their fandoms are important 642 00:35:26,760 --> 00:35:30,960 Speaker 2: to their identity. According to Deloitte's Digital Trends Report, fandom 643 00:35:31,000 --> 00:35:33,319 Speaker 2: can fuel revenue, but only if you know how to 644 00:35:33,360 --> 00:35:33,839 Speaker 2: harness it.