1 00:00:00,120 --> 00:00:02,440 Speaker 1: Today we're going to be talking about the future of 2 00:00:02,560 --> 00:00:07,560 Speaker 1: artificial intelligence in digital advertising with Sherry Backstein, who is 3 00:00:07,640 --> 00:00:11,800 Speaker 1: global head of Watson Advertising and the Weather Company, an 4 00:00:11,800 --> 00:00:14,520 Speaker 1: IBM business. If you want to hear previous entries in 5 00:00:14,520 --> 00:00:17,000 Speaker 1: the series, you can simply look up the episodes labeled 6 00:00:17,040 --> 00:00:19,720 Speaker 1: smart Talks in our feed from earlier this year. And 7 00:00:19,760 --> 00:00:22,120 Speaker 1: you can also check out the episodes of smart Talks 8 00:00:22,160 --> 00:00:25,200 Speaker 1: on the I Heart Media podcast Tech Stuff, same thing, 9 00:00:25,320 --> 00:00:27,680 Speaker 1: just look for the ones that's say smart Talks. And 10 00:00:27,720 --> 00:00:31,200 Speaker 1: now let's jump right into our conversation about advertising and 11 00:00:31,360 --> 00:00:37,640 Speaker 1: AI with Sherry Backstein. Sherry Backstein, Welcome to the podcast. Hi, 12 00:00:37,800 --> 00:00:41,080 Speaker 1: great to be here. Let's start with your professional background. 13 00:00:41,320 --> 00:00:44,400 Speaker 1: How did you end up doing what you do now? Well, 14 00:00:44,440 --> 00:00:47,640 Speaker 1: that's a really interesting story because I actually started my 15 00:00:47,800 --> 00:00:51,159 Speaker 1: career in journalism, um and worked for one of the 16 00:00:51,200 --> 00:00:55,520 Speaker 1: national networks doing the news and then got recruited by 17 00:00:55,600 --> 00:00:59,120 Speaker 1: the Weather Channel TV way back in my career, and 18 00:00:59,160 --> 00:01:03,680 Speaker 1: that's really how I it all started from there. Um, 19 00:01:03,720 --> 00:01:06,600 Speaker 1: you know, left the Weather Company after a while, went 20 00:01:06,640 --> 00:01:10,279 Speaker 1: to the digital world as we saw changes in television coming, 21 00:01:10,760 --> 00:01:13,640 Speaker 1: and then came back to the Weather Company about thirteen 22 00:01:13,720 --> 00:01:16,840 Speaker 1: years ago on the digital side of the company and 23 00:01:17,480 --> 00:01:20,280 Speaker 1: have been here ever since. So why are you passionate 24 00:01:20,319 --> 00:01:22,520 Speaker 1: about the work you do? The work that we do 25 00:01:22,600 --> 00:01:25,560 Speaker 1: at at the Weather Company. It makes a difference in 26 00:01:25,560 --> 00:01:29,600 Speaker 1: people's lives. It helps people make the right decisions for 27 00:01:29,680 --> 00:01:32,760 Speaker 1: them personally, for their families, for their businesses as it 28 00:01:32,800 --> 00:01:39,240 Speaker 1: relates to the weather. And what's interesting is and before 29 00:01:39,240 --> 00:01:41,520 Speaker 1: I had the role I have today, and early on 30 00:01:41,560 --> 00:01:43,920 Speaker 1: in my career, I was actually a storm chaser for 31 00:01:43,959 --> 00:01:47,199 Speaker 1: the Weather Channel, and so I got to go out 32 00:01:47,280 --> 00:01:51,640 Speaker 1: into the field and cover all these weather events that 33 00:01:51,680 --> 00:01:55,880 Speaker 1: were happening, and it was fascinating work, um. And it 34 00:01:56,000 --> 00:02:00,480 Speaker 1: was heart wrenching work as well, because you saw the 35 00:02:00,600 --> 00:02:05,520 Speaker 1: destruction and the power that weather has. And there wasn't 36 00:02:05,600 --> 00:02:08,240 Speaker 1: a type of storm I didn't get to cover, between 37 00:02:08,280 --> 00:02:12,600 Speaker 1: tornadoes and hurricanes, nor easter's and you know, it was 38 00:02:12,639 --> 00:02:16,200 Speaker 1: really just it was fascinating work. And you know, it's 39 00:02:16,240 --> 00:02:19,320 Speaker 1: it's interesting that when, um, you're in the middle of 40 00:02:19,320 --> 00:02:22,840 Speaker 1: a storm and one day I was covering it was 41 00:02:22,919 --> 00:02:26,440 Speaker 1: a F five tornado. The tornado was a mile wide, 42 00:02:26,840 --> 00:02:31,519 Speaker 1: ripped through Norman, Oklahoma, and I went there the day after. 43 00:02:32,000 --> 00:02:35,480 Speaker 1: I couldn't believe the destruction. It was just it's something 44 00:02:35,480 --> 00:02:38,040 Speaker 1: that unless you see it yourself, you just really can't 45 00:02:38,080 --> 00:02:41,680 Speaker 1: believe it. And you know, I was talking to people 46 00:02:41,760 --> 00:02:44,280 Speaker 1: that had impacted and these people would just come up 47 00:02:44,280 --> 00:02:46,760 Speaker 1: and they would just hug you, and they would say, 48 00:02:46,800 --> 00:02:49,760 Speaker 1: if it wasn't for you and the Weather Channel, we 49 00:02:49,800 --> 00:02:53,600 Speaker 1: would not be here today. It's your alerts, your you know, 50 00:02:53,960 --> 00:02:57,160 Speaker 1: the information you provide is so critical. And so when 51 00:02:57,480 --> 00:03:00,720 Speaker 1: you get to experience something like that and you get 52 00:03:00,760 --> 00:03:03,400 Speaker 1: to touch another human being in that way and to 53 00:03:03,520 --> 00:03:07,360 Speaker 1: help them, you become very passionate about your work. And 54 00:03:07,400 --> 00:03:11,040 Speaker 1: I have to say everybody at the Weather Company feels 55 00:03:11,080 --> 00:03:14,400 Speaker 1: this passion for what we do. That we are saving 56 00:03:14,480 --> 00:03:18,079 Speaker 1: lives um and then and then doing something just as 57 00:03:18,080 --> 00:03:20,919 Speaker 1: simple as helping people plan their day. So that's part 58 00:03:20,919 --> 00:03:23,520 Speaker 1: of the reason I'm so passionate about it. Well, to 59 00:03:23,760 --> 00:03:28,120 Speaker 1: all of the weather reporters, storm Chaser's meteorologists out there, 60 00:03:28,120 --> 00:03:30,040 Speaker 1: from from the bottom of my heart, I say a 61 00:03:30,040 --> 00:03:33,200 Speaker 1: genuine thanks, well, we appreciate that, and you know, again 62 00:03:33,400 --> 00:03:36,960 Speaker 1: we're happy to provide the service. So IBM bought the 63 00:03:36,960 --> 00:03:40,680 Speaker 1: Weather Company in I'm curious about that. What's the relationship 64 00:03:40,720 --> 00:03:43,840 Speaker 1: between the two companies. Well, it really came down to data. 65 00:03:44,400 --> 00:03:48,040 Speaker 1: So as the weather company, we have a tremendous amount 66 00:03:48,080 --> 00:03:51,680 Speaker 1: of data from a forecasting perspective, from a weather perspective, 67 00:03:52,080 --> 00:03:57,119 Speaker 1: and so it came down to really providing businesses with 68 00:03:57,360 --> 00:04:00,480 Speaker 1: a weather strategy. So every business should of a weather 69 00:04:00,560 --> 00:04:04,280 Speaker 1: strategy because it impacts really everything you do around your 70 00:04:04,280 --> 00:04:07,680 Speaker 1: supply chain in most businesses. And so it really came 71 00:04:07,720 --> 00:04:10,600 Speaker 1: down to being able to take our weather data and 72 00:04:10,720 --> 00:04:14,280 Speaker 1: infuse it into all of our customers within IBM to 73 00:04:14,440 --> 00:04:17,360 Speaker 1: better help them make decisions around their business. And then 74 00:04:17,400 --> 00:04:19,560 Speaker 1: of course as the Weather Channel, we have such a 75 00:04:19,640 --> 00:04:22,760 Speaker 1: large consumer business as well, and so it is a 76 00:04:22,800 --> 00:04:26,160 Speaker 1: really great touch point for IBM to reach consumers at 77 00:04:26,200 --> 00:04:29,040 Speaker 1: such a mass scale. We have three and fifty million 78 00:04:29,160 --> 00:04:32,640 Speaker 1: users every month that use our platforms. But it's just 79 00:04:32,680 --> 00:04:35,440 Speaker 1: a great way for IBM then to to reach a consumer. 80 00:04:35,960 --> 00:04:39,320 Speaker 1: Um you know, from that perspective. So one of the 81 00:04:39,360 --> 00:04:42,320 Speaker 1: reasons then you're saying is that, um, the weather base 82 00:04:42,800 --> 00:04:45,680 Speaker 1: is data that can be used by IBM, but also 83 00:04:45,720 --> 00:04:47,960 Speaker 1: does it go the other way or their overlaps in 84 00:04:47,960 --> 00:04:51,960 Speaker 1: the world today between artificial intelligence that serve say the 85 00:04:51,960 --> 00:04:57,039 Speaker 1: weather forecasting side. So we have before we actually became IBM, 86 00:04:57,120 --> 00:05:00,560 Speaker 1: we used AI in our forecasting and we still today. 87 00:05:01,480 --> 00:05:03,560 Speaker 1: But what we have seen is we've been able to 88 00:05:03,600 --> 00:05:07,680 Speaker 1: accelerate the use of AI in other areas of our business. 89 00:05:07,800 --> 00:05:12,000 Speaker 1: For example, we are able to predict uh, you know, 90 00:05:12,400 --> 00:05:17,000 Speaker 1: the flu in areas at risk associated with the flu um, 91 00:05:17,040 --> 00:05:21,039 Speaker 1: you know, health risk associated with allergies by using Watson's AI. 92 00:05:21,200 --> 00:05:24,960 Speaker 1: So that's a great example. And then most recently, we 93 00:05:25,040 --> 00:05:28,400 Speaker 1: are leveraging AI on the advertising side of our business 94 00:05:28,520 --> 00:05:32,840 Speaker 1: to create AI driven advertising solutions, not only for us 95 00:05:32,839 --> 00:05:35,560 Speaker 1: as a publisher, but then these solutions that could be 96 00:05:35,680 --> 00:05:40,160 Speaker 1: used by other publishers or marketers or others within the ecosystem. 97 00:05:40,200 --> 00:05:43,800 Speaker 1: So with the Weather Company being part of IBM, it's 98 00:05:43,800 --> 00:05:48,080 Speaker 1: been very beneficial from not only an AI perspective, but 99 00:05:48,640 --> 00:05:52,480 Speaker 1: we just put out a brand new Weather model earlier 100 00:05:52,520 --> 00:05:57,240 Speaker 1: this year and it leverages the Power nine supercomputer of IBM, 101 00:05:57,640 --> 00:05:59,640 Speaker 1: and so if we were not IBM, we wouldn't have 102 00:05:59,640 --> 00:06:02,120 Speaker 1: been able to do that. So there has been you know, 103 00:06:02,640 --> 00:06:05,920 Speaker 1: benefits on both sides of the companies and us joining together. 104 00:06:06,560 --> 00:06:09,320 Speaker 1: So you brought up advertising, and obviously that's one of 105 00:06:09,360 --> 00:06:11,760 Speaker 1: the main things that we wanted to talk about today, 106 00:06:11,800 --> 00:06:15,040 Speaker 1: So um to set the stage. We know that today's 107 00:06:15,240 --> 00:06:18,800 Speaker 1: Internet and technology sphere is paid for or at least 108 00:06:18,800 --> 00:06:22,360 Speaker 1: subsidized in huge part by advertising. And people argue about 109 00:06:22,360 --> 00:06:24,719 Speaker 1: whether there could be a better model, but for better 110 00:06:24,800 --> 00:06:27,599 Speaker 1: or worse, this is the one that's in play today. 111 00:06:27,760 --> 00:06:30,360 Speaker 1: Give us a picture of what that landscape is like, 112 00:06:31,200 --> 00:06:34,000 Speaker 1: how does the Internet and the technosphere make money through 113 00:06:34,000 --> 00:06:37,320 Speaker 1: advertising and how is that changing? So you're right, most 114 00:06:37,440 --> 00:06:42,000 Speaker 1: content on the Internet and digital content is underwritten by advertising, 115 00:06:42,760 --> 00:06:45,039 Speaker 1: and it's done that way so people can get it 116 00:06:45,080 --> 00:06:46,920 Speaker 1: for free so they don't have to pay for it. 117 00:06:47,200 --> 00:06:51,239 Speaker 1: And certainly we've seen an upcrease and subscription where people 118 00:06:51,240 --> 00:06:53,040 Speaker 1: are paying for that content because they don't want to 119 00:06:53,040 --> 00:06:57,719 Speaker 1: see advertising. So there's really those two choices. But for decades, 120 00:06:57,839 --> 00:07:01,520 Speaker 1: advertising has really underwritten all that is content. And what's 121 00:07:01,600 --> 00:07:05,599 Speaker 1: important is advertising has changed through the years. Is kind 122 00:07:05,600 --> 00:07:09,960 Speaker 1: of twofold one for the user. So you're on a platform, 123 00:07:10,000 --> 00:07:12,720 Speaker 1: you're using an app, using a website, and the ads 124 00:07:12,760 --> 00:07:16,280 Speaker 1: that are targeted to you. It's a better experience if 125 00:07:16,280 --> 00:07:19,440 Speaker 1: they're relevant, if they're something that you might be interested in, 126 00:07:19,840 --> 00:07:22,960 Speaker 1: and so targeting has improved over the years, and that's 127 00:07:23,000 --> 00:07:27,520 Speaker 1: become very very important in the industry. On the marketer side, 128 00:07:27,720 --> 00:07:31,560 Speaker 1: that targeting is important because as a marketer, you only 129 00:07:31,600 --> 00:07:35,000 Speaker 1: have so much budget to reach audience that are are 130 00:07:35,000 --> 00:07:37,560 Speaker 1: going to be meaningful, audience are that are going to 131 00:07:38,040 --> 00:07:40,480 Speaker 1: take the action that you want to take, and so 132 00:07:40,600 --> 00:07:44,320 Speaker 1: being able to target just to that specific audience is 133 00:07:44,360 --> 00:07:47,520 Speaker 1: really important from an efficiency perspective and you know the 134 00:07:47,520 --> 00:07:50,640 Speaker 1: best case for results for you as well. So that's 135 00:07:50,680 --> 00:07:55,000 Speaker 1: really the advertising landscape UM as we know it today, UM, 136 00:07:55,040 --> 00:07:57,600 Speaker 1: and it's changed through the years. You know, it started 137 00:07:57,640 --> 00:08:00,960 Speaker 1: where it was really more direct sale is where you 138 00:08:01,320 --> 00:08:03,920 Speaker 1: a sales person would go into a marketer or a 139 00:08:03,960 --> 00:08:06,600 Speaker 1: brand and they would make this relationship and they would 140 00:08:06,680 --> 00:08:10,960 Speaker 1: sponsor certain segments on an app or a website. And 141 00:08:11,000 --> 00:08:14,520 Speaker 1: then about eight to ten years ago, programmatic came on 142 00:08:14,560 --> 00:08:18,240 Speaker 1: the scene which automated that process UM and so you 143 00:08:18,240 --> 00:08:21,320 Speaker 1: didn't have to have as much face to face contact 144 00:08:21,640 --> 00:08:24,320 Speaker 1: as a publisher, you know, to a marketer, but it 145 00:08:24,400 --> 00:08:27,440 Speaker 1: can all be done through an automatic exchange. And so 146 00:08:27,560 --> 00:08:30,440 Speaker 1: that's where we are today as far as the advertising 147 00:08:30,440 --> 00:08:33,800 Speaker 1: industry is. But that is changing. And what's the role 148 00:08:33,840 --> 00:08:36,880 Speaker 1: of what's known as third party data in all of this, 149 00:08:37,000 --> 00:08:39,440 Speaker 1: what is that and how does it work? So third 150 00:08:39,440 --> 00:08:43,000 Speaker 1: party data, So most websites have first party data. So 151 00:08:43,080 --> 00:08:46,000 Speaker 1: it's that relationship between the user and the brand. So 152 00:08:46,120 --> 00:08:48,400 Speaker 1: in the case of the Weather channel, you know, you 153 00:08:48,480 --> 00:08:51,240 Speaker 1: come to our platform, you type in a zip code, 154 00:08:51,320 --> 00:08:54,280 Speaker 1: or you let us take your location, you give us permission, 155 00:08:54,679 --> 00:08:57,280 Speaker 1: and so that's first party data. We know where you're 156 00:08:57,360 --> 00:09:00,560 Speaker 1: interested in getting the weather, we know where weather is 157 00:09:00,600 --> 00:09:03,480 Speaker 1: where you live, if you click on allergy or flu, 158 00:09:03,600 --> 00:09:08,319 Speaker 1: that's all first party data. Well, not every publisher has 159 00:09:08,360 --> 00:09:11,040 Speaker 1: a lot of first party data or means to collect it, 160 00:09:11,360 --> 00:09:14,600 Speaker 1: and so then you can leverage the actions that you 161 00:09:14,640 --> 00:09:17,679 Speaker 1: do on one platform and that can travel with you 162 00:09:17,720 --> 00:09:21,160 Speaker 1: to another platform. So then that first party data becomes 163 00:09:21,160 --> 00:09:25,240 Speaker 1: third party data on someone else's platform. So when someone 164 00:09:25,400 --> 00:09:29,080 Speaker 1: from maybe a news publisher comes to us, we know 165 00:09:29,160 --> 00:09:32,120 Speaker 1: what they were interested in maybe on the news publishers site, 166 00:09:32,160 --> 00:09:35,560 Speaker 1: what sections they went to, and then advertisers can target 167 00:09:35,559 --> 00:09:38,680 Speaker 1: them appropriately on our website. And so that's how the 168 00:09:38,760 --> 00:09:41,679 Speaker 1: third party data has grown and it's become again valuable 169 00:09:42,360 --> 00:09:47,320 Speaker 1: in order to provide that specific targeting to make your 170 00:09:47,320 --> 00:09:51,680 Speaker 1: experience from advertising perspective relevant to who you are to 171 00:09:51,760 --> 00:09:54,000 Speaker 1: where you are and what your interests might be. That 172 00:09:54,160 --> 00:09:58,240 Speaker 1: was the case in the in the ad supported web 173 00:09:58,280 --> 00:10:02,680 Speaker 1: we have today for it, content that really the gold 174 00:10:02,760 --> 00:10:08,000 Speaker 1: standard is targeted advertising. I mean is untargeted advertising, uh 175 00:10:08,200 --> 00:10:13,160 Speaker 1: sort of going nowhere? Well, untargeted advertising isn't very desirable 176 00:10:13,240 --> 00:10:16,959 Speaker 1: from anybody's perspective. I mean, I'm sure you have seen 177 00:10:17,160 --> 00:10:20,640 Speaker 1: ads that just seem so obscure to maybe what your 178 00:10:20,679 --> 00:10:24,040 Speaker 1: interests are, just you know, um, something that you would 179 00:10:24,080 --> 00:10:27,640 Speaker 1: not be interested in all. And from a marketer perspective, 180 00:10:27,960 --> 00:10:30,280 Speaker 1: you know, it's kind of like you're just throwing an 181 00:10:30,320 --> 00:10:33,760 Speaker 1: ad out there and just hoping you reach someone that 182 00:10:33,840 --> 00:10:36,800 Speaker 1: might be interested. So it's a waste of money. And 183 00:10:36,880 --> 00:10:41,240 Speaker 1: so really targeting advertising is beneficial for both the consumer 184 00:10:41,320 --> 00:10:45,640 Speaker 1: and the marketer. You know. It's as a publisher, we 185 00:10:45,840 --> 00:10:48,240 Speaker 1: talk to our users a lot, do a lot of surveys, 186 00:10:48,280 --> 00:10:51,280 Speaker 1: and we've asked them what they prefer, if they prefer 187 00:10:51,440 --> 00:10:54,280 Speaker 1: relevant ads or they just prefer they don't really care 188 00:10:54,440 --> 00:10:56,840 Speaker 1: what ads they see, and they do tell us in 189 00:10:56,920 --> 00:11:00,560 Speaker 1: most cases they prefer relevant ads, um things that they're 190 00:11:00,600 --> 00:11:03,599 Speaker 1: interested in that they actually might want to purchase or 191 00:11:03,920 --> 00:11:06,480 Speaker 1: might want to find more information about. And so it 192 00:11:06,520 --> 00:11:09,959 Speaker 1: does improve that user experience. But I guess, UM, one 193 00:11:09,960 --> 00:11:13,560 Speaker 1: of the distinctions here is whether the data that's being 194 00:11:13,679 --> 00:11:17,080 Speaker 1: used to target the user is somehow unique to them 195 00:11:17,240 --> 00:11:20,920 Speaker 1: or something that people would feel is personal or private information. 196 00:11:21,040 --> 00:11:25,120 Speaker 1: And one of those things I think might be mobile identifiers. 197 00:11:25,160 --> 00:11:27,440 Speaker 1: Could you talk about the idea of mobile identifiers and 198 00:11:27,480 --> 00:11:30,920 Speaker 1: what role they play in UH targeted advertising today? Yeah, 199 00:11:31,000 --> 00:11:34,600 Speaker 1: So the mobile identifiers essentially an I D that's associated 200 00:11:34,640 --> 00:11:38,680 Speaker 1: with that device, and so then whatever you do on 201 00:11:38,720 --> 00:11:42,840 Speaker 1: that device can be tracked through advertising, and there's permissions 202 00:11:42,880 --> 00:11:46,280 Speaker 1: around that. So it's not that it's done in any 203 00:11:46,320 --> 00:11:49,800 Speaker 1: kind of secret way because on your UM you know, 204 00:11:49,880 --> 00:11:51,680 Speaker 1: in the case of I D f A, that's that's 205 00:11:51,720 --> 00:11:56,200 Speaker 1: an iOS supported identifier, you have to limit, you know, 206 00:11:56,320 --> 00:11:58,360 Speaker 1: put that AD tracking on, or you can say I 207 00:11:58,360 --> 00:12:00,800 Speaker 1: want to limit the AD tracking. So that user has 208 00:12:00,880 --> 00:12:04,440 Speaker 1: always had the decision on the iOS device, UM, you know, 209 00:12:04,480 --> 00:12:07,280 Speaker 1: to make that decision whether they want that tracking or 210 00:12:07,280 --> 00:12:10,439 Speaker 1: not to happen. But that identifiers just like your I D. 211 00:12:11,160 --> 00:12:13,960 Speaker 1: So then when you visit maybe a different app or 212 00:12:14,000 --> 00:12:16,520 Speaker 1: you go to a web browser on your phone, that 213 00:12:16,679 --> 00:12:19,760 Speaker 1: I D would follow you UM, and so then your 214 00:12:19,800 --> 00:12:23,080 Speaker 1: behaviors and things that you're interested in kind of build 215 00:12:23,200 --> 00:12:26,319 Speaker 1: on that one I D. So then advertisers can target 216 00:12:26,360 --> 00:12:29,040 Speaker 1: that I D. So this brings us to the relationship 217 00:12:29,080 --> 00:12:33,120 Speaker 1: between targeted advertising and privacy. What is the conflict here 218 00:12:33,160 --> 00:12:37,079 Speaker 1: as people receive it? So it's an interesting question because 219 00:12:37,120 --> 00:12:39,920 Speaker 1: as I mentioned, like with I d f A, users 220 00:12:39,960 --> 00:12:42,760 Speaker 1: have always had the ability to turn it off. What's 221 00:12:42,880 --> 00:12:47,600 Speaker 1: changing is that instead of that being opted in for 222 00:12:47,720 --> 00:12:51,320 Speaker 1: a user, it's now going to be opted off. And 223 00:12:51,360 --> 00:12:54,280 Speaker 1: so then the user has to explicitly say that they 224 00:12:54,320 --> 00:12:59,000 Speaker 1: want to share their information, which I personally agree with. UM, 225 00:12:59,040 --> 00:13:02,960 Speaker 1: as you know, a pub lasher myself, our consumers privacy 226 00:13:03,000 --> 00:13:06,120 Speaker 1: should be respected and they should be able to make 227 00:13:06,200 --> 00:13:10,040 Speaker 1: that decision on what information they want to share and 228 00:13:10,080 --> 00:13:12,160 Speaker 1: to be able to control that data. So I think 229 00:13:12,200 --> 00:13:16,280 Speaker 1: it's critically important. So that's really what's changing on I 230 00:13:16,400 --> 00:13:19,680 Speaker 1: d f A is the opted off versus what has 231 00:13:19,720 --> 00:13:22,160 Speaker 1: been opted in. And I think we all know when 232 00:13:22,200 --> 00:13:25,640 Speaker 1: we get our cell phones, um, there's so much to 233 00:13:25,720 --> 00:13:28,440 Speaker 1: go through when setting up a phone, UM, and there's 234 00:13:28,600 --> 00:13:31,560 Speaker 1: so many, you know, different areas you can go in 235 00:13:31,760 --> 00:13:34,439 Speaker 1: with an operating system, and most people just don't do it. 236 00:13:34,880 --> 00:13:37,440 Speaker 1: So then what happens is you have articles that come 237 00:13:37,480 --> 00:13:40,520 Speaker 1: out or media that comes out that starts scaring people 238 00:13:40,600 --> 00:13:43,120 Speaker 1: that all of these things are happening and and and 239 00:13:43,160 --> 00:13:46,800 Speaker 1: you're being tracked in in you know, in certain ways 240 00:13:46,880 --> 00:13:49,679 Speaker 1: that make you feel uncomfortable. And I'm not saying that 241 00:13:49,720 --> 00:13:52,760 Speaker 1: there's not bad players out there, but for the most part, 242 00:13:53,240 --> 00:13:56,680 Speaker 1: people are doing it to support all the free content 243 00:13:57,280 --> 00:14:01,240 Speaker 1: UM that consumers are are using and the services in 244 00:14:01,360 --> 00:14:05,520 Speaker 1: order to fund that, and so because of that, it's 245 00:14:05,520 --> 00:14:09,560 Speaker 1: all about transparency. And so the transparency about what's being 246 00:14:09,600 --> 00:14:13,640 Speaker 1: done UM is really improving, and I think that that's 247 00:14:13,679 --> 00:14:17,880 Speaker 1: really important. Yeah, I think we certainly take digital privacy 248 00:14:17,880 --> 00:14:22,040 Speaker 1: concerns very seriously on the show, and personally, I tend 249 00:14:22,120 --> 00:14:26,360 Speaker 1: to find that with targeted advertising, people don't seem to 250 00:14:26,400 --> 00:14:29,320 Speaker 1: mind it when the mechanisms are clear to them, when 251 00:14:29,320 --> 00:14:32,400 Speaker 1: it seems based on their consent. And I think that 252 00:14:32,440 --> 00:14:36,640 Speaker 1: means uh, very importantly for people, their conscious consent meaning 253 00:14:36,680 --> 00:14:40,480 Speaker 1: that they understand what they're agreeing to and not just signing, say, 254 00:14:40,520 --> 00:14:43,440 Speaker 1: a big agreement with lots of fine print. Uh. But 255 00:14:43,520 --> 00:14:45,640 Speaker 1: what I think people often don't like is that the 256 00:14:45,720 --> 00:14:48,600 Speaker 1: feeling that they have been observed without expecting to be 257 00:14:48,640 --> 00:14:52,160 Speaker 1: observed or that they are being manipulated in a way 258 00:14:52,200 --> 00:14:56,200 Speaker 1: that feels sneaky or hidden or tactically arcane. Would you 259 00:14:56,320 --> 00:14:59,000 Speaker 1: basically agree with that? I would absolutely agree with that. 260 00:14:59,080 --> 00:15:02,000 Speaker 1: I think private see really boils down to that value 261 00:15:02,040 --> 00:15:06,520 Speaker 1: exchange between the consumer or the user and the content 262 00:15:06,640 --> 00:15:10,080 Speaker 1: or tech provider. And so you know, if you look 263 00:15:10,080 --> 00:15:13,440 Speaker 1: at a privacy policy, they certainly can be daunting um 264 00:15:13,560 --> 00:15:16,360 Speaker 1: and and rarely I think people look at them. So 265 00:15:16,480 --> 00:15:20,680 Speaker 1: companies that are trying to make that more digestible UM 266 00:15:20,960 --> 00:15:26,359 Speaker 1: and really helping to understand what you're doing with that data. 267 00:15:26,720 --> 00:15:30,000 Speaker 1: That's really important and it's easy to do, and it's 268 00:15:30,040 --> 00:15:32,720 Speaker 1: it's the right thing to do, so people understand. Because 269 00:15:32,760 --> 00:15:35,520 Speaker 1: the more fear we take out of it, the better 270 00:15:35,600 --> 00:15:38,520 Speaker 1: off I think everyone will be, not only from you know, 271 00:15:38,600 --> 00:15:41,920 Speaker 1: making a better user experience, but then that that content 272 00:15:42,000 --> 00:15:45,880 Speaker 1: camera remain free because the fear is that you know, 273 00:15:46,000 --> 00:15:50,680 Speaker 1: companies have to underwrite their content with advertising or else 274 00:15:50,720 --> 00:15:54,120 Speaker 1: they'll probably turn to a subscription model UM and then 275 00:15:54,480 --> 00:15:57,000 Speaker 1: you know, consumers will have to pay for that. And 276 00:15:57,080 --> 00:16:00,360 Speaker 1: companies that have both options and we certainly do, because 277 00:16:00,840 --> 00:16:03,280 Speaker 1: there's some users that just don't want ads, they're they're 278 00:16:03,360 --> 00:16:06,320 Speaker 1: intrusive to their experience. They have that option, which I 279 00:16:06,360 --> 00:16:09,640 Speaker 1: think is great. How have companies like Apple and Google 280 00:16:09,680 --> 00:16:13,640 Speaker 1: played a role in privacy versus targeting, well, both from 281 00:16:13,720 --> 00:16:17,080 Speaker 1: an iPhone and um android perspective. I mean, they own 282 00:16:17,120 --> 00:16:20,440 Speaker 1: the operating system and so they are the ones in 283 00:16:20,440 --> 00:16:23,320 Speaker 1: the case of like I d f A UM they're 284 00:16:23,360 --> 00:16:27,320 Speaker 1: making the change that says, hey, we should have users 285 00:16:27,320 --> 00:16:30,120 Speaker 1: opt into this and versus them opting out of it. 286 00:16:30,960 --> 00:16:34,520 Speaker 1: And so that's how they're definitely evolving their privacy. They've 287 00:16:34,520 --> 00:16:37,400 Speaker 1: always been very privacy conscious, UM at least at least 288 00:16:37,400 --> 00:16:40,680 Speaker 1: Apple from that perspective, has always been. But they're making 289 00:16:40,680 --> 00:16:43,680 Speaker 1: it even more transparent now, and so I think that 290 00:16:43,680 --> 00:16:47,720 Speaker 1: that is certainly important UM, and that people understand how 291 00:16:47,800 --> 00:16:51,520 Speaker 1: their their data is being used, especially you know, with Google, 292 00:16:51,560 --> 00:16:54,080 Speaker 1: because they're not only an operating system, but they are 293 00:16:54,120 --> 00:16:57,640 Speaker 1: one of the major ad platforms, UM, you know, in 294 00:16:57,720 --> 00:17:00,120 Speaker 1: the world. This may be kind of a tangent, but 295 00:17:00,560 --> 00:17:04,480 Speaker 1: I'm a little bit curious did this conflict between targeted 296 00:17:04,520 --> 00:17:07,520 Speaker 1: advertising and people's perception of their you know, the limits 297 00:17:07,520 --> 00:17:11,200 Speaker 1: of their privacy. Did this arise in as a surprise 298 00:17:11,359 --> 00:17:14,000 Speaker 1: or has this been widely predicted back in the earlier 299 00:17:14,080 --> 00:17:18,159 Speaker 1: days of the Internet. That's a really good question. I 300 00:17:18,200 --> 00:17:23,520 Speaker 1: think the changes that we see are really changes that 301 00:17:23,640 --> 00:17:26,960 Speaker 1: big tech companies are making to be more transparent, and 302 00:17:27,720 --> 00:17:30,880 Speaker 1: by that I mean Apple and Google because they are 303 00:17:30,880 --> 00:17:34,840 Speaker 1: the ones that are now making those changes to impose 304 00:17:34,920 --> 00:17:38,320 Speaker 1: these new rules that then publishers and marketers have to 305 00:17:38,359 --> 00:17:41,840 Speaker 1: adhere to. And so that's really I think where most 306 00:17:41,880 --> 00:17:46,000 Speaker 1: of this privacy changes are stemming from is a result 307 00:17:46,119 --> 00:17:50,160 Speaker 1: of that. But I do feel consumers are becoming more 308 00:17:50,359 --> 00:17:55,080 Speaker 1: privacy conscious as time goes on, as more information as 309 00:17:55,160 --> 00:17:59,160 Speaker 1: being shared, and you know, as they see, um, certain 310 00:17:59,200 --> 00:18:04,639 Speaker 1: companies coming under attack, you know, from from different points 311 00:18:04,640 --> 00:18:07,240 Speaker 1: of view, from a legislative part of view as well, 312 00:18:07,720 --> 00:18:10,399 Speaker 1: and so there's I think more awareness now of what 313 00:18:10,520 --> 00:18:14,760 Speaker 1: actually is happening, and people are getting more educated on it. Um. 314 00:18:14,800 --> 00:18:18,080 Speaker 1: You know, information and knowledge is always you know, as 315 00:18:18,119 --> 00:18:21,920 Speaker 1: time goes on, is really important and the more people 316 00:18:22,080 --> 00:18:26,200 Speaker 1: understand it, the more than they're taking more control over 317 00:18:26,240 --> 00:18:29,800 Speaker 1: their own personal privacy and making those decisions. So the 318 00:18:29,840 --> 00:18:33,040 Speaker 1: way things stand today under the current model, publishers need 319 00:18:33,080 --> 00:18:36,199 Speaker 1: ad targeting in order to pay the bills and target 320 00:18:36,240 --> 00:18:40,680 Speaker 1: effectively while a lot of individual people want more privacy 321 00:18:40,680 --> 00:18:44,080 Speaker 1: and control over their data, and I believe you're suggesting 322 00:18:44,119 --> 00:18:47,400 Speaker 1: that AI could help accommodate both of these needs at 323 00:18:47,400 --> 00:18:50,480 Speaker 1: the same time. Can you explain that, Yes, So the 324 00:18:50,600 --> 00:18:53,960 Speaker 1: changes that are are happening in our industry with the 325 00:18:54,000 --> 00:18:58,240 Speaker 1: identifiers and with cookies doesn't mean we can't have targeted advertising. 326 00:18:58,760 --> 00:19:02,000 Speaker 1: It just means that those identifiers are not going to 327 00:19:02,040 --> 00:19:04,200 Speaker 1: be used in the future, so we have to find 328 00:19:04,240 --> 00:19:10,000 Speaker 1: new ways to target advertising. And at IBM what's in advertising, 329 00:19:10,119 --> 00:19:13,280 Speaker 1: we believe AI is going to be the new backbone 330 00:19:13,320 --> 00:19:17,359 Speaker 1: of the advertising industry, and that's because it doesn't rely 331 00:19:17,400 --> 00:19:21,320 Speaker 1: on identifiers, and so it can be anonymous, and AI 332 00:19:21,440 --> 00:19:26,359 Speaker 1: can look at a lot of different unstructured data and 333 00:19:26,720 --> 00:19:30,199 Speaker 1: it can help be more instead of being just deterministic, 334 00:19:30,480 --> 00:19:33,960 Speaker 1: it can be probabilistic, and it can help be predictive 335 00:19:34,000 --> 00:19:37,800 Speaker 1: and take that data and make insights for marketers. And 336 00:19:37,800 --> 00:19:39,640 Speaker 1: there's a lot of ways to do that. You can 337 00:19:39,720 --> 00:19:42,840 Speaker 1: use it by combining other data. So in our case, 338 00:19:42,920 --> 00:19:46,080 Speaker 1: we can take our weather data and combine it with 339 00:19:46,240 --> 00:19:51,040 Speaker 1: other shoppable data like from Nielsen to create look alike 340 00:19:51,200 --> 00:19:53,879 Speaker 1: segments that then we can be able to target people 341 00:19:54,400 --> 00:19:57,480 Speaker 1: that way without ever having to know anything about that 342 00:19:57,640 --> 00:20:02,160 Speaker 1: user other than their behavior on our platforms, that relationship 343 00:20:02,200 --> 00:20:05,159 Speaker 1: again that they have between us as a publisher and 344 00:20:05,200 --> 00:20:08,720 Speaker 1: them using our platform, and then you can use it 345 00:20:08,760 --> 00:20:13,200 Speaker 1: in social So social influencers are really hot right now 346 00:20:13,240 --> 00:20:16,880 Speaker 1: for marketers UM and the marketers are benefiting a lot 347 00:20:17,040 --> 00:20:21,240 Speaker 1: from having influencers showcase their products. The challenge there with 348 00:20:21,680 --> 00:20:25,800 Speaker 1: the brands is finding that right influencer. So you might 349 00:20:25,880 --> 00:20:29,480 Speaker 1: know a handful of influencers, but with a I, you 350 00:20:29,520 --> 00:20:32,199 Speaker 1: can look at all the influencers. You can look at 351 00:20:32,240 --> 00:20:35,800 Speaker 1: their tone, you can look at the content that they supply, 352 00:20:36,760 --> 00:20:40,639 Speaker 1: and you can decide which one of these influencers is 353 00:20:40,680 --> 00:20:43,439 Speaker 1: the best to represent my brand. That's going to be 354 00:20:43,560 --> 00:20:47,400 Speaker 1: brand safe, because that's critically important as as you're fixing 355 00:20:47,440 --> 00:20:50,679 Speaker 1: that picking that right influencer for your brand. So AI 356 00:20:50,840 --> 00:20:53,639 Speaker 1: can comb through all of that mass amount of data 357 00:20:53,720 --> 00:20:57,840 Speaker 1: which would take you know, humans days and weeks to 358 00:20:57,840 --> 00:21:00,359 Speaker 1: be able to do, and it can really augment that 359 00:21:00,480 --> 00:21:04,360 Speaker 1: human process to get a better result and a faster result, 360 00:21:04,680 --> 00:21:07,760 Speaker 1: and then it can adapt over time and improve over time. 361 00:21:07,800 --> 00:21:10,640 Speaker 1: So there's just two examples of how AI can help 362 00:21:10,760 --> 00:21:14,359 Speaker 1: brands better target um and better. Um, you know have 363 00:21:14,400 --> 00:21:18,160 Speaker 1: targeted type advertising. So could you give a hypothetical example 364 00:21:18,320 --> 00:21:23,240 Speaker 1: of how data about whether might be used to target 365 00:21:23,320 --> 00:21:27,239 Speaker 1: a specific add experience to a user. Sure, So we 366 00:21:27,359 --> 00:21:32,560 Speaker 1: know from research that weather and mood have a connection, 367 00:21:32,640 --> 00:21:35,400 Speaker 1: so people's moods and what they do when they feel 368 00:21:35,440 --> 00:21:38,920 Speaker 1: a certain way, and so when we look at data 369 00:21:39,720 --> 00:21:43,320 Speaker 1: around whether, and if you can match that with buying 370 00:21:43,400 --> 00:21:48,040 Speaker 1: behavior data, then you can predict what somebody might be 371 00:21:48,080 --> 00:21:52,159 Speaker 1: interested in purchasing based on the weather because maybe their 372 00:21:52,200 --> 00:21:55,640 Speaker 1: their mood is indicative of them wanting to spend at 373 00:21:55,640 --> 00:21:59,280 Speaker 1: a certain time. So kind of a really simple example 374 00:21:59,560 --> 00:22:03,720 Speaker 1: is when it's chili outside, we know that people are 375 00:22:03,760 --> 00:22:09,480 Speaker 1: in the mood for soup for dinner. Right, super simple example, UM, 376 00:22:09,520 --> 00:22:12,040 Speaker 1: But then there's more examples like that that maybe aren't 377 00:22:12,080 --> 00:22:16,760 Speaker 1: so simple. So we have a partner that was, you know, 378 00:22:16,920 --> 00:22:20,120 Speaker 1: a big retailer, and we did a lot of analysis 379 00:22:20,240 --> 00:22:24,280 Speaker 1: on their purchasing data and what people were purchasing in 380 00:22:24,320 --> 00:22:28,359 Speaker 1: their store. We combine that with the weather data and 381 00:22:28,400 --> 00:22:32,800 Speaker 1: we could see that when certain weather conditions existed, for example, 382 00:22:33,359 --> 00:22:36,919 Speaker 1: when humidity was at a certain level in a certain 383 00:22:37,000 --> 00:22:41,880 Speaker 1: region of the country, that strawberries sold so well compared 384 00:22:41,920 --> 00:22:44,919 Speaker 1: to other weather times, and so what they did is 385 00:22:44,960 --> 00:22:47,880 Speaker 1: help that retailer be more prepared from the supply chain 386 00:22:47,920 --> 00:22:51,600 Speaker 1: perspective to say, when this weather condition is approaching, we 387 00:22:51,600 --> 00:22:54,520 Speaker 1: should put strawberries out, you know, on the end cap 388 00:22:54,560 --> 00:22:56,960 Speaker 1: of the counter, because we know that they're going to 389 00:22:57,000 --> 00:22:58,919 Speaker 1: sell and we have to make sure that we have 390 00:22:59,040 --> 00:23:02,560 Speaker 1: enough supply. Since one simple example that wouldn't be as 391 00:23:02,600 --> 00:23:06,439 Speaker 1: obvious as the cold weather and the soup example. And 392 00:23:06,520 --> 00:23:09,399 Speaker 1: so by looking at those insights, you can use AI 393 00:23:09,560 --> 00:23:13,680 Speaker 1: to gather all of these insights. Then a retailer can 394 00:23:13,720 --> 00:23:16,200 Speaker 1: make sure that they have enough stock on hand, they 395 00:23:16,200 --> 00:23:19,800 Speaker 1: can make sure they have enough workers on hand, or 396 00:23:19,840 --> 00:23:22,760 Speaker 1: it can help them better promote products within their store 397 00:23:22,800 --> 00:23:24,800 Speaker 1: to get them to move off the shelf. Could you 398 00:23:24,840 --> 00:23:26,960 Speaker 1: tell us a bit about what's in the ads. How 399 00:23:26,960 --> 00:23:31,040 Speaker 1: does that fit into the role of AI in future advertising. 400 00:23:31,400 --> 00:23:34,920 Speaker 1: So that's a really great advertising product that we've provided 401 00:23:34,920 --> 00:23:38,159 Speaker 1: our marketers, and essentially what that is, it's it's a 402 00:23:38,280 --> 00:23:44,000 Speaker 1: chat bought technology that leverages AI. So this allows a 403 00:23:44,160 --> 00:23:48,399 Speaker 1: user on our platform to directly interact with the marketer 404 00:23:48,560 --> 00:23:52,560 Speaker 1: on our site. And it can be many examples different 405 00:23:52,800 --> 00:23:55,800 Speaker 1: you know partners that we've had. But the chat bought 406 00:23:56,040 --> 00:23:59,240 Speaker 1: would provide information to the user and the user could 407 00:23:59,280 --> 00:24:04,359 Speaker 1: ask it questions and using AI, the AI anticipates the questions, 408 00:24:04,600 --> 00:24:08,480 Speaker 1: provides the answer, and provides additional information. So it's a 409 00:24:08,560 --> 00:24:12,440 Speaker 1: really great interactive marketing tool that can be used on 410 00:24:12,480 --> 00:24:15,840 Speaker 1: our site. And then that marketer gets all of that 411 00:24:15,960 --> 00:24:20,840 Speaker 1: information from that interaction with the user with their permission, 412 00:24:21,040 --> 00:24:24,320 Speaker 1: that then they can use to provide insights and help 413 00:24:24,359 --> 00:24:26,840 Speaker 1: them to better market their products. And so it could 414 00:24:26,840 --> 00:24:29,640 Speaker 1: be an insurance company that could be given quotes right 415 00:24:29,640 --> 00:24:32,960 Speaker 1: on the side. It could be a company that's um 416 00:24:33,000 --> 00:24:36,240 Speaker 1: telling you how to use their products in certain recipes 417 00:24:36,720 --> 00:24:38,760 Speaker 1: based on what your likes are. So there's a lot 418 00:24:38,800 --> 00:24:42,200 Speaker 1: of uses for it UM that we've seen between CpG 419 00:24:42,440 --> 00:24:47,040 Speaker 1: brands and other retailers insurance um and it's worked really 420 00:24:47,080 --> 00:24:50,239 Speaker 1: really well for them as a way to not just 421 00:24:50,359 --> 00:24:53,800 Speaker 1: display their ad but to actually have that interaction with 422 00:24:53,880 --> 00:24:57,440 Speaker 1: the potential customer right there on our platform. Now, of course, 423 00:24:57,440 --> 00:25:00,200 Speaker 1: today one of the most obvious natural world of what's 424 00:25:00,240 --> 00:25:03,160 Speaker 1: going on is the pandemic. How is that played into 425 00:25:03,320 --> 00:25:07,560 Speaker 1: these operations? When the pandemic started and everyone was looking 426 00:25:07,600 --> 00:25:12,439 Speaker 1: for information around the risk of COVID in your area, 427 00:25:12,480 --> 00:25:14,920 Speaker 1: whether it was the number of cases or the number 428 00:25:14,920 --> 00:25:19,399 Speaker 1: of people hospitalized. We saw that that data was coming 429 00:25:19,400 --> 00:25:23,160 Speaker 1: in at a state level well being the Weather Company 430 00:25:23,200 --> 00:25:28,200 Speaker 1: and people coming to our site. It's people need local information, 431 00:25:28,359 --> 00:25:30,960 Speaker 1: right and and the same with COVID. While it was 432 00:25:31,000 --> 00:25:33,439 Speaker 1: interesting to know what was happening in your state, people 433 00:25:33,520 --> 00:25:36,600 Speaker 1: really just want to know what's happening around me or 434 00:25:36,640 --> 00:25:40,920 Speaker 1: around where people that I love live. And so we 435 00:25:41,000 --> 00:25:47,000 Speaker 1: set out to aggregate county level COVID data from multiple sources, 436 00:25:47,040 --> 00:25:50,359 Speaker 1: and these were all approved sources, government sources, and the 437 00:25:50,440 --> 00:25:55,080 Speaker 1: sources that were official sources. The problem with that data 438 00:25:55,200 --> 00:25:58,720 Speaker 1: it came in all forms. One site may have data 439 00:25:58,760 --> 00:26:01,480 Speaker 1: in a PDF, one site may have it in a graph, 440 00:26:01,680 --> 00:26:04,080 Speaker 1: someone else had it in a map. And so the 441 00:26:04,160 --> 00:26:07,840 Speaker 1: power of Watson was able to take all of that data, 442 00:26:08,040 --> 00:26:11,560 Speaker 1: no matter what structure it was in, pull it together 443 00:26:11,680 --> 00:26:14,119 Speaker 1: and aggregate it so then we could put it on 444 00:26:14,160 --> 00:26:16,760 Speaker 1: our platform and so on weather dot Com and a 445 00:26:16,840 --> 00:26:20,840 Speaker 1: weather Channel app, we had a COVID section that provided 446 00:26:20,880 --> 00:26:24,680 Speaker 1: this information down to a county level. So from that perspective, 447 00:26:25,200 --> 00:26:27,919 Speaker 1: that was what we initially started and how we wanted 448 00:26:27,920 --> 00:26:32,359 Speaker 1: to use that data to provide that public service. As 449 00:26:32,440 --> 00:26:36,680 Speaker 1: we started to continue to collect the data, our marketers 450 00:26:36,880 --> 00:26:40,440 Speaker 1: were interested in also having that data to help them 451 00:26:40,520 --> 00:26:44,040 Speaker 1: drive their business and to help manage their business, because 452 00:26:44,040 --> 00:26:47,000 Speaker 1: what a marketer did not want to do was to 453 00:26:47,200 --> 00:26:51,359 Speaker 1: market in areas where the pandemic was really really high. 454 00:26:51,520 --> 00:26:54,760 Speaker 1: They wanted to be sensitive to their messaging, uh, to 455 00:26:55,080 --> 00:26:58,600 Speaker 1: you know, their potential customers and be sensitive to the 456 00:26:58,640 --> 00:27:01,520 Speaker 1: pandemic going on, and so they didn't want to make 457 00:27:01,600 --> 00:27:06,280 Speaker 1: that mistake and to you know, target advertising in places, um, 458 00:27:06,320 --> 00:27:08,520 Speaker 1: you know, where they were really seeing this influx of 459 00:27:09,040 --> 00:27:12,159 Speaker 1: the illness. And so we were able to take that data, 460 00:27:12,800 --> 00:27:16,399 Speaker 1: similar to using weather data to help build triggers, so 461 00:27:16,480 --> 00:27:20,080 Speaker 1: they could appropriately market in areas where they were starting 462 00:27:20,119 --> 00:27:23,880 Speaker 1: to see recovery. They could appropriately market, you know, depending 463 00:27:23,880 --> 00:27:26,840 Speaker 1: on what their products were, two areas that maybe needed 464 00:27:26,920 --> 00:27:29,439 Speaker 1: some of their products or services that they offered. So 465 00:27:29,480 --> 00:27:32,680 Speaker 1: that was just another way of using AI to build 466 00:27:32,680 --> 00:27:36,399 Speaker 1: a trigger for marketers, um you know, so what they 467 00:27:36,400 --> 00:27:39,560 Speaker 1: were doing was relevant and what they were doing, you know, 468 00:27:39,960 --> 00:27:43,960 Speaker 1: was sensitive to the situation that was going on. Now 469 00:27:43,960 --> 00:27:47,639 Speaker 1: that's that's that's very interesting because I think in anyone 470 00:27:47,640 --> 00:27:50,480 Speaker 1: who's ever worked in newspapers or knows anything about the 471 00:27:51,320 --> 00:27:54,040 Speaker 1: newspaper business of old, you know that you always have 472 00:27:54,080 --> 00:27:56,919 Speaker 1: to be careful about where where an advertisement is popping 473 00:27:57,000 --> 00:28:01,200 Speaker 1: up next two ways, say a problematic story and so forth. 474 00:28:01,240 --> 00:28:05,000 Speaker 1: And so this this is kind of like a geographic 475 00:28:05,119 --> 00:28:09,520 Speaker 1: version of that to a certain extent, making sure that 476 00:28:09,520 --> 00:28:11,880 Speaker 1: that that the advertising is is sensitive in the way 477 00:28:11,880 --> 00:28:16,760 Speaker 1: it targets individuals. Uh, that's interesting. Absolutely, that's important. And 478 00:28:17,040 --> 00:28:19,160 Speaker 1: then if you look at other illnesses, so we're coming 479 00:28:19,240 --> 00:28:23,240 Speaker 1: upon flu season, and so we also then have data 480 00:28:23,280 --> 00:28:27,040 Speaker 1: around flu that's actually predictive. We were not able to 481 00:28:27,080 --> 00:28:31,040 Speaker 1: get there with COVID uh just yet um, but with flu, 482 00:28:31,280 --> 00:28:34,560 Speaker 1: because you have so much historical data, you can actually 483 00:28:34,560 --> 00:28:38,120 Speaker 1: put together an algorithm using AI that actually can predict it. 484 00:28:38,560 --> 00:28:41,320 Speaker 1: And when I talk about the risk of flu, there 485 00:28:41,400 --> 00:28:44,360 Speaker 1: is a correlation between flu and weather, and so we 486 00:28:44,400 --> 00:28:47,680 Speaker 1: can look at weather data points and see where flu 487 00:28:48,000 --> 00:28:51,160 Speaker 1: trends based on this weather data points and tell people 488 00:28:51,160 --> 00:28:53,719 Speaker 1: that there's a risk of the flu maybe in that area. 489 00:28:54,240 --> 00:28:57,120 Speaker 1: And because our forecast goes out fifteen days, we can 490 00:28:57,160 --> 00:29:00,280 Speaker 1: predict out fifteen days, and so that's really benefits shoal, 491 00:29:01,000 --> 00:29:03,520 Speaker 1: you know, in order for people to perhaps go get 492 00:29:03,600 --> 00:29:06,680 Speaker 1: flu shots if that's you know, what they choose, or 493 00:29:06,720 --> 00:29:09,400 Speaker 1: at least to have medications on hand that can help 494 00:29:09,440 --> 00:29:11,800 Speaker 1: in case, you know, the flu you know, does hit 495 00:29:12,120 --> 00:29:15,920 Speaker 1: their family um and they've be able to, uh, you know, 496 00:29:15,920 --> 00:29:19,840 Speaker 1: at least be prepared. So, looking towards the future, what 497 00:29:19,960 --> 00:29:23,280 Speaker 1: challenges do you see concerning digital privacy in the future. 498 00:29:23,640 --> 00:29:27,200 Speaker 1: I'm not sure I would say there's challenges with digital privacy. 499 00:29:27,440 --> 00:29:32,640 Speaker 1: I think that brands, publishers like ourselves are going to 500 00:29:32,720 --> 00:29:38,320 Speaker 1: become more and more transparent and think of privacy first. 501 00:29:39,040 --> 00:29:42,720 Speaker 1: So privacy by design and consumer privacy is going to 502 00:29:42,760 --> 00:29:47,360 Speaker 1: become even more important because it's really important as a 503 00:29:47,400 --> 00:29:51,760 Speaker 1: publisher or brand to establish trust with their user and 504 00:29:51,800 --> 00:29:54,920 Speaker 1: their consumer. So that part of it, I think is 505 00:29:54,960 --> 00:29:57,880 Speaker 1: going to change, but I think it's a very positive change. 506 00:29:58,320 --> 00:30:01,760 Speaker 1: As a result of that, some of the tactics and 507 00:30:01,880 --> 00:30:04,520 Speaker 1: some of the processes that we've had to use around 508 00:30:04,560 --> 00:30:08,560 Speaker 1: advertising are just simply going to have to evolve. And 509 00:30:08,760 --> 00:30:13,960 Speaker 1: fortunately there's great technology out there like AI that can 510 00:30:13,960 --> 00:30:17,880 Speaker 1: help move us into a new era of advertising, which 511 00:30:17,920 --> 00:30:20,240 Speaker 1: is really where we need to go. So I think 512 00:30:20,360 --> 00:30:24,440 Speaker 1: this change is positive because it will force marketers and 513 00:30:24,560 --> 00:30:29,520 Speaker 1: publishers to use these incredible new technology like AI to 514 00:30:29,640 --> 00:30:32,960 Speaker 1: drive their business. And actually I find it to be 515 00:30:33,080 --> 00:30:36,120 Speaker 1: very exciting. The products that are coming out, and we've 516 00:30:36,160 --> 00:30:40,480 Speaker 1: been building a suite of products for this, they're very exciting, 517 00:30:40,520 --> 00:30:42,520 Speaker 1: and they can do so much more because they can 518 00:30:42,560 --> 00:30:48,120 Speaker 1: be more probabilistic. It's more than just automation that programmatic provides. 519 00:30:48,440 --> 00:30:51,880 Speaker 1: It's more insightful, it's more intelligent UM And so I 520 00:30:51,920 --> 00:30:54,240 Speaker 1: think it's going to be as exciting time as the 521 00:30:54,280 --> 00:30:57,640 Speaker 1: advertising industry adjust to the privacy changes. And do you 522 00:30:57,640 --> 00:30:59,840 Speaker 1: think the trend is going to be towards more personal 523 00:31:00,040 --> 00:31:04,800 Speaker 1: lation or less. I think there's opportunities for equal to 524 00:31:04,880 --> 00:31:08,680 Speaker 1: more personalization. I think we will go back to doing 525 00:31:08,760 --> 00:31:11,040 Speaker 1: some of the things that we did years ago, especially 526 00:31:11,040 --> 00:31:16,440 Speaker 1: around like contextual advertising as an example, UM to how 527 00:31:16,440 --> 00:31:21,080 Speaker 1: to target. But I don't believe that again targeting is 528 00:31:21,080 --> 00:31:22,960 Speaker 1: going to way. It's just a different way that we 529 00:31:23,040 --> 00:31:28,400 Speaker 1: will do that. And again, the most valuable user is 530 00:31:28,480 --> 00:31:32,120 Speaker 1: one that a marketer can have a relationship with and 531 00:31:32,240 --> 00:31:35,280 Speaker 1: they can serve relevant ads to, and so we have 532 00:31:35,440 --> 00:31:40,320 Speaker 1: to keep solving for that problem as identifiers go away. 533 00:31:40,360 --> 00:31:43,720 Speaker 1: But again there's there's technology to do that that's actually 534 00:31:43,880 --> 00:31:47,400 Speaker 1: smarter and more privacy safe. So really I feel it's 535 00:31:47,400 --> 00:31:49,959 Speaker 1: going to be a win win for everyone. What are 536 00:31:49,960 --> 00:31:54,240 Speaker 1: the opportunities you see in advertising today and especially looking 537 00:31:54,280 --> 00:31:57,479 Speaker 1: toward the future, What what would be uh, you know, 538 00:31:57,920 --> 00:32:01,640 Speaker 1: when in your wildest dreams the industry could go in 539 00:32:02,080 --> 00:32:05,160 Speaker 1: whatever direction you chose, what would be the kinds of 540 00:32:05,240 --> 00:32:08,280 Speaker 1: changes we would see come about, and what would be 541 00:32:08,280 --> 00:32:12,120 Speaker 1: the kinds of opportunities that we would embrace. So the 542 00:32:12,240 --> 00:32:15,320 Speaker 1: changes that we're on the path for that I really 543 00:32:15,360 --> 00:32:20,440 Speaker 1: strongly believe have to continue is that consumer privacy and 544 00:32:20,520 --> 00:32:25,680 Speaker 1: consumers feeling safe with brands, So that's really important. So 545 00:32:25,800 --> 00:32:29,840 Speaker 1: I feel that the changes that we're seeing with identifiers 546 00:32:29,840 --> 00:32:33,840 Speaker 1: going away, it's not a crisis in the advertising industry. 547 00:32:33,920 --> 00:32:36,960 Speaker 1: I see it as a tremendous opportunity for us to 548 00:32:37,080 --> 00:32:42,160 Speaker 1: evolve and to really use some amazing technology that can 549 00:32:42,200 --> 00:32:46,760 Speaker 1: help serve the same purpose of underwriting content so it 550 00:32:46,920 --> 00:32:51,040 Speaker 1: remains free for users, but also providing marketers and brands 551 00:32:51,200 --> 00:32:55,840 Speaker 1: a safe and trusted way to reach their potential customers. 552 00:32:56,360 --> 00:33:00,240 Speaker 1: So it's really about this you know, evolution as well. 553 00:33:00,280 --> 00:33:03,080 Speaker 1: That's happening in the industry and we should embrace it. 554 00:33:03,600 --> 00:33:06,240 Speaker 1: Sherry Backstein, thank you so much for joining us today. 555 00:33:06,240 --> 00:33:11,200 Speaker 1: It's been great talking. Thank you. I've enjoyed it. Thanks 556 00:33:11,200 --> 00:33:13,840 Speaker 1: again to Sherry Bastein for chatting with us today. Again. 557 00:33:13,880 --> 00:33:16,479 Speaker 1: If you want to hear more from this series, just 558 00:33:16,520 --> 00:33:18,840 Speaker 1: look up the episodes of our show as well as 559 00:33:18,840 --> 00:33:22,480 Speaker 1: episodes of tech Stuff labeled smart Talks, and if you 560 00:33:22,560 --> 00:33:24,440 Speaker 1: want to learn more about the series itself, you can 561 00:33:24,480 --> 00:33:28,200 Speaker 1: go to IBM dot com slash smart Talks to read 562 00:33:28,320 --> 00:33:31,320 Speaker 1: more about IBM's work with AI and advertising. 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