Speaker 1: Today we're going to be talking about the future of
Speaker 1: artificial intelligence in digital advertising with Sherry Backstein, who is
Speaker 1: global head of Watson Advertising and the Weather Company, an
Speaker 1: IBM business. If you want to hear previous entries in
Speaker 1: the series, you can simply look up the episodes labeled
Speaker 1: smart Talks in our feed from earlier this year. And
Speaker 1: you can also check out the episodes of smart Talks
Speaker 1: on the I Heart Media podcast Tech Stuff, same thing,
Speaker 1: just look for the ones that's say smart Talks. And
Speaker 1: now let's jump right into our conversation about advertising and
Speaker 1: AI with Sherry Backstein. Sherry Backstein, Welcome to the podcast. Hi,
Speaker 1: great to be here. Let's start with your professional background.
Speaker 1: How did you end up doing what you do now? Well,
Speaker 1: that's a really interesting story because I actually started my
Speaker 1: career in journalism, um and worked for one of the
Speaker 1: national networks doing the news and then got recruited by
Speaker 1: the Weather Channel TV way back in my career, and
Speaker 1: that's really how I it all started from there. Um,
Speaker 1: you know, left the Weather Company after a while, went
Speaker 1: to the digital world as we saw changes in television coming,
Speaker 1: and then came back to the Weather Company about thirteen
Speaker 1: years ago on the digital side of the company and
Speaker 1: have been here ever since. So why are you passionate
Speaker 1: about the work you do? The work that we do
Speaker 1: at at the Weather Company. It makes a difference in
Speaker 1: people's lives. It helps people make the right decisions for
Speaker 1: them personally, for their families, for their businesses as it
Speaker 1: relates to the weather. And what's interesting is and before
Speaker 1: I had the role I have today, and early on
Speaker 1: in my career, I was actually a storm chaser for
Speaker 1: the Weather Channel, and so I got to go out
Speaker 1: into the field and cover all these weather events that
Speaker 1: were happening, and it was fascinating work, um. And it
Speaker 1: was heart wrenching work as well, because you saw the
Speaker 1: destruction and the power that weather has. And there wasn't
Speaker 1: a type of storm I didn't get to cover, between
Speaker 1: tornadoes and hurricanes, nor easter's and you know, it was
Speaker 1: really just it was fascinating work. And you know, it's
Speaker 1: it's interesting that when, um, you're in the middle of
Speaker 1: a storm and one day I was covering it was
Speaker 1: a F five tornado. The tornado was a mile wide,
Speaker 1: ripped through Norman, Oklahoma, and I went there the day after.
Speaker 1: I couldn't believe the destruction. It was just it's something
Speaker 1: that unless you see it yourself, you just really can't
Speaker 1: believe it. And you know, I was talking to people
Speaker 1: that had impacted and these people would just come up
Speaker 1: and they would just hug you, and they would say,
Speaker 1: if it wasn't for you and the Weather Channel, we
Speaker 1: would not be here today. It's your alerts, your you know,
Speaker 1: the information you provide is so critical. And so when
Speaker 1: you get to experience something like that and you get
Speaker 1: to touch another human being in that way and to
Speaker 1: help them, you become very passionate about your work. And
Speaker 1: I have to say everybody at the Weather Company feels
Speaker 1: this passion for what we do. That we are saving
Speaker 1: lives um and then and then doing something just as
Speaker 1: simple as helping people plan their day. So that's part
Speaker 1: of the reason I'm so passionate about it. Well, to
Speaker 1: all of the weather reporters, storm Chaser's meteorologists out there,
Speaker 1: from from the bottom of my heart, I say a
Speaker 1: genuine thanks, well, we appreciate that, and you know, again
Speaker 1: we're happy to provide the service. So IBM bought the
Speaker 1: Weather Company in I'm curious about that. What's the relationship
Speaker 1: between the two companies. Well, it really came down to data.
Speaker 1: So as the weather company, we have a tremendous amount
Speaker 1: of data from a forecasting perspective, from a weather perspective,
Speaker 1: and so it came down to really providing businesses with
Speaker 1: a weather strategy. So every business should of a weather
Speaker 1: strategy because it impacts really everything you do around your
Speaker 1: supply chain in most businesses. And so it really came
Speaker 1: down to being able to take our weather data and
Speaker 1: infuse it into all of our customers within IBM to
Speaker 1: better help them make decisions around their business. And then
Speaker 1: of course as the Weather Channel, we have such a
Speaker 1: large consumer business as well, and so it is a
Speaker 1: really great touch point for IBM to reach consumers at
Speaker 1: such a mass scale. We have three and fifty million
Speaker 1: users every month that use our platforms. But it's just
Speaker 1: a great way for IBM then to to reach a consumer.
Speaker 1: Um you know, from that perspective. So one of the
Speaker 1: reasons then you're saying is that, um, the weather base
Speaker 1: is data that can be used by IBM, but also
Speaker 1: does it go the other way or their overlaps in
Speaker 1: the world today between artificial intelligence that serve say the
Speaker 1: weather forecasting side. So we have before we actually became IBM,
Speaker 1: we used AI in our forecasting and we still today.
Speaker 1: But what we have seen is we've been able to
Speaker 1: accelerate the use of AI in other areas of our business.
Speaker 1: For example, we are able to predict uh, you know,
Speaker 1: the flu in areas at risk associated with the flu um,
Speaker 1: you know, health risk associated with allergies by using Watson's AI.
Speaker 1: So that's a great example. And then most recently, we
Speaker 1: are leveraging AI on the advertising side of our business
Speaker 1: to create AI driven advertising solutions, not only for us
Speaker 1: as a publisher, but then these solutions that could be
Speaker 1: used by other publishers or marketers or others within the ecosystem.
Speaker 1: So with the Weather Company being part of IBM, it's
Speaker 1: been very beneficial from not only an AI perspective, but
Speaker 1: we just put out a brand new Weather model earlier
Speaker 1: this year and it leverages the Power nine supercomputer of IBM,
Speaker 1: and so if we were not IBM, we wouldn't have
Speaker 1: been able to do that. So there has been you know,
Speaker 1: benefits on both sides of the companies and us joining together.
Speaker 1: So you brought up advertising, and obviously that's one of
Speaker 1: the main things that we wanted to talk about today,
Speaker 1: So um to set the stage. We know that today's
Speaker 1: Internet and technology sphere is paid for or at least
Speaker 1: subsidized in huge part by advertising. And people argue about
Speaker 1: whether there could be a better model, but for better
Speaker 1: or worse, this is the one that's in play today.
Speaker 1: Give us a picture of what that landscape is like,
Speaker 1: how does the Internet and the technosphere make money through
Speaker 1: advertising and how is that changing? So you're right, most
Speaker 1: content on the Internet and digital content is underwritten by advertising,
Speaker 1: and it's done that way so people can get it
Speaker 1: for free so they don't have to pay for it.
Speaker 1: And certainly we've seen an upcrease and subscription where people
Speaker 1: are paying for that content because they don't want to
Speaker 1: see advertising. So there's really those two choices. But for decades,
Speaker 1: advertising has really underwritten all that is content. And what's
Speaker 1: important is advertising has changed through the years. Is kind
Speaker 1: of twofold one for the user. So you're on a platform,
Speaker 1: you're using an app, using a website, and the ads
Speaker 1: that are targeted to you. It's a better experience if
Speaker 1: they're relevant, if they're something that you might be interested in,
Speaker 1: and so targeting has improved over the years, and that's
Speaker 1: become very very important in the industry. On the marketer side,
Speaker 1: that targeting is important because as a marketer, you only
Speaker 1: have so much budget to reach audience that are are
Speaker 1: going to be meaningful, audience are that are going to
Speaker 1: take the action that you want to take, and so
Speaker 1: being able to target just to that specific audience is
Speaker 1: really important from an efficiency perspective and you know the
Speaker 1: best case for results for you as well. So that's
Speaker 1: really the advertising landscape UM as we know it today, UM,
Speaker 1: and it's changed through the years. You know, it started
Speaker 1: where it was really more direct sale is where you
Speaker 1: a sales person would go into a marketer or a
Speaker 1: brand and they would make this relationship and they would
Speaker 1: sponsor certain segments on an app or a website. And
Speaker 1: then about eight to ten years ago, programmatic came on
Speaker 1: the scene which automated that process UM and so you
Speaker 1: didn't have to have as much face to face contact
Speaker 1: as a publisher, you know, to a marketer, but it
Speaker 1: can all be done through an automatic exchange. And so
Speaker 1: that's where we are today as far as the advertising
Speaker 1: industry is. But that is changing. And what's the role
Speaker 1: of what's known as third party data in all of this,
Speaker 1: what is that and how does it work? So third
Speaker 1: party data, So most websites have first party data. So
Speaker 1: it's that relationship between the user and the brand. So
Speaker 1: in the case of the Weather channel, you know, you
Speaker 1: come to our platform, you type in a zip code,
Speaker 1: or you let us take your location, you give us permission,
Speaker 1: and so that's first party data. We know where you're
Speaker 1: interested in getting the weather, we know where weather is
Speaker 1: where you live, if you click on allergy or flu,
Speaker 1: that's all first party data. Well, not every publisher has
Speaker 1: a lot of first party data or means to collect it,
Speaker 1: and so then you can leverage the actions that you
Speaker 1: do on one platform and that can travel with you
Speaker 1: to another platform. So then that first party data becomes
Speaker 1: third party data on someone else's platform. So when someone
Speaker 1: from maybe a news publisher comes to us, we know
Speaker 1: what they were interested in maybe on the news publishers site,
Speaker 1: what sections they went to, and then advertisers can target
Speaker 1: them appropriately on our website. And so that's how the
Speaker 1: third party data has grown and it's become again valuable
Speaker 1: in order to provide that specific targeting to make your
Speaker 1: experience from advertising perspective relevant to who you are to
Speaker 1: where you are and what your interests might be. That
Speaker 1: was the case in the in the ad supported web
Speaker 1: we have today for it, content that really the gold
Speaker 1: standard is targeted advertising. I mean is untargeted advertising, uh
Speaker 1: sort of going nowhere? Well, untargeted advertising isn't very desirable
Speaker 1: from anybody's perspective. I mean, I'm sure you have seen
Speaker 1: ads that just seem so obscure to maybe what your
Speaker 1: interests are, just you know, um, something that you would
Speaker 1: not be interested in all. And from a marketer perspective,
Speaker 1: you know, it's kind of like you're just throwing an
Speaker 1: ad out there and just hoping you reach someone that
Speaker 1: might be interested. So it's a waste of money. And
Speaker 1: so really targeting advertising is beneficial for both the consumer
Speaker 1: and the marketer. You know. It's as a publisher, we
Speaker 1: talk to our users a lot, do a lot of surveys,
Speaker 1: and we've asked them what they prefer, if they prefer
Speaker 1: relevant ads or they just prefer they don't really care
Speaker 1: what ads they see, and they do tell us in
Speaker 1: most cases they prefer relevant ads, um things that they're
Speaker 1: interested in that they actually might want to purchase or
Speaker 1: might want to find more information about. And so it
Speaker 1: does improve that user experience. But I guess, UM, one
Speaker 1: of the distinctions here is whether the data that's being
Speaker 1: used to target the user is somehow unique to them
Speaker 1: or something that people would feel is personal or private information.
Speaker 1: And one of those things I think might be mobile identifiers.
Speaker 1: Could you talk about the idea of mobile identifiers and
Speaker 1: what role they play in UH targeted advertising today? Yeah,
Speaker 1: So the mobile identifiers essentially an I D that's associated
Speaker 1: with that device, and so then whatever you do on
Speaker 1: that device can be tracked through advertising, and there's permissions
Speaker 1: around that. So it's not that it's done in any
Speaker 1: kind of secret way because on your UM you know,
Speaker 1: in the case of I D f A, that's that's
Speaker 1: an iOS supported identifier, you have to limit, you know,
Speaker 1: put that AD tracking on, or you can say I
Speaker 1: want to limit the AD tracking. So that user has
Speaker 1: always had the decision on the iOS device, UM, you know,
Speaker 1: to make that decision whether they want that tracking or
Speaker 1: not to happen. But that identifiers just like your I D.
Speaker 1: So then when you visit maybe a different app or
Speaker 1: you go to a web browser on your phone, that
Speaker 1: I D would follow you UM, and so then your
Speaker 1: behaviors and things that you're interested in kind of build
Speaker 1: on that one I D. So then advertisers can target
Speaker 1: that I D. So this brings us to the relationship
Speaker 1: between targeted advertising and privacy. What is the conflict here
Speaker 1: as people receive it? So it's an interesting question because
Speaker 1: as I mentioned, like with I d f A, users
Speaker 1: have always had the ability to turn it off. What's
Speaker 1: changing is that instead of that being opted in for
Speaker 1: a user, it's now going to be opted off. And
Speaker 1: so then the user has to explicitly say that they
Speaker 1: want to share their information, which I personally agree with. UM,
Speaker 1: as you know, a pub lasher myself, our consumers privacy
Speaker 1: should be respected and they should be able to make
Speaker 1: that decision on what information they want to share and
Speaker 1: to be able to control that data. So I think
Speaker 1: it's critically important. So that's really what's changing on I
Speaker 1: d f A is the opted off versus what has
Speaker 1: been opted in. And I think we all know when
Speaker 1: we get our cell phones, um, there's so much to
Speaker 1: go through when setting up a phone, UM, and there's
Speaker 1: so many, you know, different areas you can go in
Speaker 1: with an operating system, and most people just don't do it.
Speaker 1: So then what happens is you have articles that come
Speaker 1: out or media that comes out that starts scaring people
Speaker 1: that all of these things are happening and and and
Speaker 1: you're being tracked in in you know, in certain ways
Speaker 1: that make you feel uncomfortable. And I'm not saying that
Speaker 1: there's not bad players out there, but for the most part,
Speaker 1: people are doing it to support all the free content
Speaker 1: UM that consumers are are using and the services in
Speaker 1: order to fund that, and so because of that, it's
Speaker 1: all about transparency. And so the transparency about what's being
Speaker 1: done UM is really improving, and I think that that's
Speaker 1: really important. Yeah, I think we certainly take digital privacy
Speaker 1: concerns very seriously on the show, and personally, I tend
Speaker 1: to find that with targeted advertising, people don't seem to
Speaker 1: mind it when the mechanisms are clear to them, when
Speaker 1: it seems based on their consent. And I think that
Speaker 1: means uh, very importantly for people, their conscious consent meaning
Speaker 1: that they understand what they're agreeing to and not just signing, say,
Speaker 1: a big agreement with lots of fine print. Uh. But
Speaker 1: what I think people often don't like is that the
Speaker 1: feeling that they have been observed without expecting to be
Speaker 1: observed or that they are being manipulated in a way
Speaker 1: that feels sneaky or hidden or tactically arcane. Would you
Speaker 1: basically agree with that? I would absolutely agree with that.
Speaker 1: I think private see really boils down to that value
Speaker 1: exchange between the consumer or the user and the content
Speaker 1: or tech provider. And so you know, if you look
Speaker 1: at a privacy policy, they certainly can be daunting um
Speaker 1: and and rarely I think people look at them. So
Speaker 1: companies that are trying to make that more digestible UM
Speaker 1: and really helping to understand what you're doing with that data.
Speaker 1: That's really important and it's easy to do, and it's
Speaker 1: it's the right thing to do, so people understand. Because
Speaker 1: the more fear we take out of it, the better
Speaker 1: off I think everyone will be, not only from you know,
Speaker 1: making a better user experience, but then that that content
Speaker 1: camera remain free because the fear is that you know,
Speaker 1: companies have to underwrite their content with advertising or else
Speaker 1: they'll probably turn to a subscription model UM and then
Speaker 1: you know, consumers will have to pay for that. And
Speaker 1: companies that have both options and we certainly do, because
Speaker 1: there's some users that just don't want ads, they're they're
Speaker 1: intrusive to their experience. They have that option, which I
Speaker 1: think is great. How have companies like Apple and Google
Speaker 1: played a role in privacy versus targeting, well, both from
Speaker 1: an iPhone and um android perspective. I mean, they own
Speaker 1: the operating system and so they are the ones in
Speaker 1: the case of like I d f A UM they're
Speaker 1: making the change that says, hey, we should have users
Speaker 1: opt into this and versus them opting out of it.
Speaker 1: And so that's how they're definitely evolving their privacy. They've
Speaker 1: always been very privacy conscious, UM at least at least
Speaker 1: Apple from that perspective, has always been. But they're making
Speaker 1: it even more transparent now, and so I think that
Speaker 1: that is certainly important UM, and that people understand how
Speaker 1: their their data is being used, especially you know, with Google,
Speaker 1: because they're not only an operating system, but they are
Speaker 1: one of the major ad platforms, UM, you know, in
Speaker 1: the world. This may be kind of a tangent, but
Speaker 1: I'm a little bit curious did this conflict between targeted
Speaker 1: advertising and people's perception of their you know, the limits
Speaker 1: of their privacy. Did this arise in as a surprise
Speaker 1: or has this been widely predicted back in the earlier
Speaker 1: days of the Internet. That's a really good question. I
Speaker 1: think the changes that we see are really changes that
Speaker 1: big tech companies are making to be more transparent, and
Speaker 1: by that I mean Apple and Google because they are
Speaker 1: the ones that are now making those changes to impose
Speaker 1: these new rules that then publishers and marketers have to
Speaker 1: adhere to. And so that's really I think where most
Speaker 1: of this privacy changes are stemming from is a result
Speaker 1: of that. But I do feel consumers are becoming more
Speaker 1: privacy conscious as time goes on, as more information as
Speaker 1: being shared, and you know, as they see, um, certain
Speaker 1: companies coming under attack, you know, from from different points
Speaker 1: of view, from a legislative part of view as well,
Speaker 1: and so there's I think more awareness now of what
Speaker 1: actually is happening, and people are getting more educated on it. Um.
Speaker 1: You know, information and knowledge is always you know, as
Speaker 1: time goes on, is really important and the more people
Speaker 1: understand it, the more than they're taking more control over
Speaker 1: their own personal privacy and making those decisions. So the
Speaker 1: way things stand today under the current model, publishers need
Speaker 1: ad targeting in order to pay the bills and target
Speaker 1: effectively while a lot of individual people want more privacy
Speaker 1: and control over their data, and I believe you're suggesting
Speaker 1: that AI could help accommodate both of these needs at
Speaker 1: the same time. Can you explain that, Yes, So the
Speaker 1: changes that are are happening in our industry with the
Speaker 1: identifiers and with cookies doesn't mean we can't have targeted advertising.
Speaker 1: It just means that those identifiers are not going to
Speaker 1: be used in the future, so we have to find
Speaker 1: new ways to target advertising. And at IBM what's in advertising,
Speaker 1: we believe AI is going to be the new backbone
Speaker 1: of the advertising industry, and that's because it doesn't rely
Speaker 1: on identifiers, and so it can be anonymous, and AI
Speaker 1: can look at a lot of different unstructured data and
Speaker 1: it can help be more instead of being just deterministic,
Speaker 1: it can be probabilistic, and it can help be predictive
Speaker 1: and take that data and make insights for marketers. And
Speaker 1: there's a lot of ways to do that. You can
Speaker 1: use it by combining other data. So in our case,
Speaker 1: we can take our weather data and combine it with
Speaker 1: other shoppable data like from Nielsen to create look alike
Speaker 1: segments that then we can be able to target people
Speaker 1: that way without ever having to know anything about that
Speaker 1: user other than their behavior on our platforms, that relationship
Speaker 1: again that they have between us as a publisher and
Speaker 1: them using our platform, and then you can use it
Speaker 1: in social So social influencers are really hot right now
Speaker 1: for marketers UM and the marketers are benefiting a lot
Speaker 1: from having influencers showcase their products. The challenge there with
Speaker 1: the brands is finding that right influencer. So you might
Speaker 1: know a handful of influencers, but with a I, you
Speaker 1: can look at all the influencers. You can look at
Speaker 1: their tone, you can look at the content that they supply,
Speaker 1: and you can decide which one of these influencers is
Speaker 1: the best to represent my brand. That's going to be
Speaker 1: brand safe, because that's critically important as as you're fixing
Speaker 1: that picking that right influencer for your brand. So AI
Speaker 1: can comb through all of that mass amount of data
Speaker 1: which would take you know, humans days and weeks to
Speaker 1: be able to do, and it can really augment that
Speaker 1: human process to get a better result and a faster result,
Speaker 1: and then it can adapt over time and improve over time.
Speaker 1: So there's just two examples of how AI can help
Speaker 1: brands better target um and better. Um, you know have
Speaker 1: targeted type advertising. So could you give a hypothetical example
Speaker 1: of how data about whether might be used to target
Speaker 1: a specific add experience to a user. Sure, So we
Speaker 1: know from research that weather and mood have a connection,
Speaker 1: so people's moods and what they do when they feel
Speaker 1: a certain way, and so when we look at data
Speaker 1: around whether, and if you can match that with buying
Speaker 1: behavior data, then you can predict what somebody might be
Speaker 1: interested in purchasing based on the weather because maybe their
Speaker 1: their mood is indicative of them wanting to spend at
Speaker 1: a certain time. So kind of a really simple example
Speaker 1: is when it's chili outside, we know that people are
Speaker 1: in the mood for soup for dinner. Right, super simple example, UM,
Speaker 1: But then there's more examples like that that maybe aren't
Speaker 1: so simple. So we have a partner that was, you know,
Speaker 1: a big retailer, and we did a lot of analysis
Speaker 1: on their purchasing data and what people were purchasing in
Speaker 1: their store. We combine that with the weather data and
Speaker 1: we could see that when certain weather conditions existed, for example,
Speaker 1: when humidity was at a certain level in a certain
Speaker 1: region of the country, that strawberries sold so well compared
Speaker 1: to other weather times, and so what they did is
Speaker 1: help that retailer be more prepared from the supply chain
Speaker 1: perspective to say, when this weather condition is approaching, we
Speaker 1: should put strawberries out, you know, on the end cap
Speaker 1: of the counter, because we know that they're going to
Speaker 1: sell and we have to make sure that we have
Speaker 1: enough supply. Since one simple example that wouldn't be as
Speaker 1: obvious as the cold weather and the soup example. And
Speaker 1: so by looking at those insights, you can use AI
Speaker 1: to gather all of these insights. Then a retailer can
Speaker 1: make sure that they have enough stock on hand, they
Speaker 1: can make sure they have enough workers on hand, or
Speaker 1: it can help them better promote products within their store
Speaker 1: to get them to move off the shelf. Could you
Speaker 1: tell us a bit about what's in the ads. How
Speaker 1: does that fit into the role of AI in future advertising.
Speaker 1: So that's a really great advertising product that we've provided
Speaker 1: our marketers, and essentially what that is, it's it's a
Speaker 1: chat bought technology that leverages AI. So this allows a
Speaker 1: user on our platform to directly interact with the marketer
Speaker 1: on our site. And it can be many examples different
Speaker 1: you know partners that we've had. But the chat bought
Speaker 1: would provide information to the user and the user could
Speaker 1: ask it questions and using AI, the AI anticipates the questions,
Speaker 1: provides the answer, and provides additional information. So it's a
Speaker 1: really great interactive marketing tool that can be used on
Speaker 1: our site. And then that marketer gets all of that
Speaker 1: information from that interaction with the user with their permission,
Speaker 1: that then they can use to provide insights and help
Speaker 1: them to better market their products. And so it could
Speaker 1: be an insurance company that could be given quotes right
Speaker 1: on the side. It could be a company that's um
Speaker 1: telling you how to use their products in certain recipes
Speaker 1: based on what your likes are. So there's a lot
Speaker 1: of uses for it UM that we've seen between CpG
Speaker 1: brands and other retailers insurance um and it's worked really
Speaker 1: really well for them as a way to not just
Speaker 1: display their ad but to actually have that interaction with
Speaker 1: the potential customer right there on our platform. Now, of course,
Speaker 1: today one of the most obvious natural world of what's
Speaker 1: going on is the pandemic. How is that played into
Speaker 1: these operations? When the pandemic started and everyone was looking
Speaker 1: for information around the risk of COVID in your area,
Speaker 1: whether it was the number of cases or the number
Speaker 1: of people hospitalized. We saw that that data was coming
Speaker 1: in at a state level well being the Weather Company
Speaker 1: and people coming to our site. It's people need local information,
Speaker 1: right and and the same with COVID. While it was
Speaker 1: interesting to know what was happening in your state, people
Speaker 1: really just want to know what's happening around me or
Speaker 1: around where people that I love live. And so we
Speaker 1: set out to aggregate county level COVID data from multiple sources,
Speaker 1: and these were all approved sources, government sources, and the
Speaker 1: sources that were official sources. The problem with that data
Speaker 1: it came in all forms. One site may have data
Speaker 1: in a PDF, one site may have it in a graph,
Speaker 1: someone else had it in a map. And so the
Speaker 1: power of Watson was able to take all of that data,
Speaker 1: no matter what structure it was in, pull it together
Speaker 1: and aggregate it so then we could put it on
Speaker 1: our platform and so on weather dot Com and a
Speaker 1: weather Channel app, we had a COVID section that provided
Speaker 1: this information down to a county level. So from that perspective,
Speaker 1: that was what we initially started and how we wanted
Speaker 1: to use that data to provide that public service. As
Speaker 1: we started to continue to collect the data, our marketers
Speaker 1: were interested in also having that data to help them
Speaker 1: drive their business and to help manage their business, because
Speaker 1: what a marketer did not want to do was to
Speaker 1: market in areas where the pandemic was really really high.
Speaker 1: They wanted to be sensitive to their messaging, uh, to
Speaker 1: you know, their potential customers and be sensitive to the
Speaker 1: pandemic going on, and so they didn't want to make
Speaker 1: that mistake and to you know, target advertising in places, um,
Speaker 1: you know, where they were really seeing this influx of
Speaker 1: the illness. And so we were able to take that data,
Speaker 1: similar to using weather data to help build triggers, so
Speaker 1: they could appropriately market in areas where they were starting
Speaker 1: to see recovery. They could appropriately market, you know, depending
Speaker 1: on what their products were, two areas that maybe needed
Speaker 1: some of their products or services that they offered. So
Speaker 1: that was just another way of using AI to build
Speaker 1: a trigger for marketers, um you know, so what they
Speaker 1: were doing was relevant and what they were doing, you know,
Speaker 1: was sensitive to the situation that was going on. Now
Speaker 1: that's that's that's very interesting because I think in anyone
Speaker 1: who's ever worked in newspapers or knows anything about the
Speaker 1: newspaper business of old, you know that you always have
Speaker 1: to be careful about where where an advertisement is popping
Speaker 1: up next two ways, say a problematic story and so forth.
Speaker 1: And so this this is kind of like a geographic
Speaker 1: version of that to a certain extent, making sure that
Speaker 1: that that the advertising is is sensitive in the way
Speaker 1: it targets individuals. Uh, that's interesting. Absolutely, that's important. And
Speaker 1: then if you look at other illnesses, so we're coming
Speaker 1: upon flu season, and so we also then have data
Speaker 1: around flu that's actually predictive. We were not able to
Speaker 1: get there with COVID uh just yet um, but with flu,
Speaker 1: because you have so much historical data, you can actually
Speaker 1: put together an algorithm using AI that actually can predict it.
Speaker 1: And when I talk about the risk of flu, there
Speaker 1: is a correlation between flu and weather, and so we
Speaker 1: can look at weather data points and see where flu
Speaker 1: trends based on this weather data points and tell people
Speaker 1: that there's a risk of the flu maybe in that area.
Speaker 1: And because our forecast goes out fifteen days, we can
Speaker 1: predict out fifteen days, and so that's really benefits shoal,
Speaker 1: you know, in order for people to perhaps go get
Speaker 1: flu shots if that's you know, what they choose, or
Speaker 1: at least to have medications on hand that can help
Speaker 1: in case, you know, the flu you know, does hit
Speaker 1: their family um and they've be able to, uh, you know,
Speaker 1: at least be prepared. So, looking towards the future, what
Speaker 1: challenges do you see concerning digital privacy in the future.
Speaker 1: I'm not sure I would say there's challenges with digital privacy.
Speaker 1: I think that brands, publishers like ourselves are going to
Speaker 1: become more and more transparent and think of privacy first.
Speaker 1: So privacy by design and consumer privacy is going to
Speaker 1: become even more important because it's really important as a
Speaker 1: publisher or brand to establish trust with their user and
Speaker 1: their consumer. So that part of it, I think is
Speaker 1: going to change, but I think it's a very positive change.
Speaker 1: As a result of that, some of the tactics and
Speaker 1: some of the processes that we've had to use around
Speaker 1: advertising are just simply going to have to evolve. And
Speaker 1: fortunately there's great technology out there like AI that can
Speaker 1: help move us into a new era of advertising, which
Speaker 1: is really where we need to go. So I think
Speaker 1: this change is positive because it will force marketers and
Speaker 1: publishers to use these incredible new technology like AI to
Speaker 1: drive their business. And actually I find it to be
Speaker 1: very exciting. The products that are coming out, and we've
Speaker 1: been building a suite of products for this, they're very exciting,
Speaker 1: and they can do so much more because they can
Speaker 1: be more probabilistic. It's more than just automation that programmatic provides.
Speaker 1: It's more insightful, it's more intelligent UM And so I
Speaker 1: think it's going to be as exciting time as the
Speaker 1: advertising industry adjust to the privacy changes. And do you
Speaker 1: think the trend is going to be towards more personal
Speaker 1: lation or less. I think there's opportunities for equal to
Speaker 1: more personalization. I think we will go back to doing
Speaker 1: some of the things that we did years ago, especially
Speaker 1: around like contextual advertising as an example, UM to how
Speaker 1: to target. But I don't believe that again targeting is
Speaker 1: going to way. It's just a different way that we
Speaker 1: will do that. And again, the most valuable user is
Speaker 1: one that a marketer can have a relationship with and
Speaker 1: they can serve relevant ads to, and so we have
Speaker 1: to keep solving for that problem as identifiers go away.
Speaker 1: But again there's there's technology to do that that's actually
Speaker 1: smarter and more privacy safe. So really I feel it's
Speaker 1: going to be a win win for everyone. What are
Speaker 1: the opportunities you see in advertising today and especially looking
Speaker 1: toward the future, What what would be uh, you know,
Speaker 1: when in your wildest dreams the industry could go in
Speaker 1: whatever direction you chose, what would be the kinds of
Speaker 1: changes we would see come about, and what would be
Speaker 1: the kinds of opportunities that we would embrace. So the
Speaker 1: changes that we're on the path for that I really
Speaker 1: strongly believe have to continue is that consumer privacy and
Speaker 1: consumers feeling safe with brands, So that's really important. So
Speaker 1: I feel that the changes that we're seeing with identifiers
Speaker 1: going away, it's not a crisis in the advertising industry.
Speaker 1: I see it as a tremendous opportunity for us to
Speaker 1: evolve and to really use some amazing technology that can
Speaker 1: help serve the same purpose of underwriting content so it
Speaker 1: remains free for users, but also providing marketers and brands
Speaker 1: a safe and trusted way to reach their potential customers.
Speaker 1: So it's really about this you know, evolution as well.
Speaker 1: That's happening in the industry and we should embrace it.
Speaker 1: Sherry Backstein, thank you so much for joining us today.
Speaker 1: It's been great talking. Thank you. I've enjoyed it. Thanks
Speaker 1: again to Sherry Bastein for chatting with us today. Again.
Speaker 1: If you want to hear more from this series, just
Speaker 1: look up the episodes of our show as well as
Speaker 1: episodes of tech Stuff labeled smart Talks, and if you
Speaker 1: want to learn more about the series itself, you can
Speaker 1: go to IBM dot com slash smart Talks to read
Speaker 1: more about IBM's work with AI and advertising. You can
Speaker 1: check out IBM dot com slash Watson Dash Advertising and
Speaker 1: if you'd like to check out more episodes of our show.
Speaker 1: You can find Stuff to Blow your Mind wherever you
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Speaker 1: audio producer Seth Nicholas Johnson. If you would like to
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