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Brands, It’s Time To Test Those Alt IDs
00:40:57
Agencies now have more breathing room to think carefully and be strategic about signal loss solutions, because signal loss isn’t just a third-party cookie thing, says Sisi Zhang, chief data and analytics officer at Razorfish. Specifically, she says,...
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- [00:00]
A
Foreign welcome to Ad Exchanger Talks, the podcast devoted to examining the issues and trends in advertising and marketing technology that matter most to you.
- [00:33]
B
Hello everyone, and thanks for tuning in to this week's episode of Ad Exchanger Talks. I'm Melissa Boyle, Ad Exchanger Senior Editor and your host for today's episode. Signal loss in the advertising stratosphere has spurred a scramble for more data and identity solutions, from first party data and alternative IDs to a heightened focus on shopper data and retail media networks. The scramble for data is somewhat behind a wave of M and A and consolidation that's been hitting the shores of the ad tech world over the past few months. But as far as signal loss solutions are concerned, what's actually working, what came up at CES last month, and what the heck is going on with third party cookies? Here to help shine some clarity onto the confusion is today's guest, CC Zhang, Chief data and Analytics Officer at the Publicis owned agency Razorfish. Thanks so much for joining us today, Cici.
- [01:23]
A
Thank you so much for having us. Or having me.
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B
Royal we right. I like to help our listeners get to know our guests a bit better before we get into what I call the nerdy nitty gritty. So when you're not thinking about signal loss and data solutions for your clients, what do you do? What do you do for fun?
- [01:40]
A
Yes, outside of work, when I'm not thinking about data, I'm usually baking. So typically baking any kind of bread products or pastries or cookies is what I like to do outside of work.
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B
Oh lovely. I don't bake, but I've been watching a lot of baking shows I watch. Is it cake? And it's people have to make super realistic cakes, right? It's a sewing machine and you have to figure out which one's the cake. It's riveting. Anyway, so to take a step back, you're about a year into your promotion at Razorfish to Chief Data and Analytics Officer and you help advise, you know, really big national clients on actively managing signal loss. So what has the past year been like? What has happened or changed in the past year about how some of these clients have to deal with signal loss?
- [02:22]
A
Yeah, that's a great question. I would say the biggest change for us as Razorfish and also for our clients is a little bit surprisingly, Google's reversal of the announcement that they had around their third party cookie deprecation. That is a big change because of course, over the past few years we've been advising and counseling clients to prepare for signal Deprecation, which is still something they should prepare for and I'll touch on that in a little bit. But I think the reversal from Google has been interesting because it's provides a bit of an opportunity for clients to think outside of just, you know, a timeline that was given over the past couple years. It was pushed back a couple of times, of course, but really thinking through, well, what does signal deprecation mean, especially when there isn't a specific timeline that they have to work towards. And so that has been a big shift in terms of how we're thinking about what that means for our clients, ecosystems, what that means for them in terms of future proofing, data privacy, data signal considerations, etc. But that was for sure the biggest shift in 2024.
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B
Absolutely. And actually, you know, that day that Google announced, you know, that, that, that their cookie deprecation plan wasn't the plan anymore, I remember being out that day and it was just, oh my God, I remember coming back into the office, like I really picked the worst day. Wow. So I also like that what you pointed out about how, you know, this is a chance for marketers to think beyond just the scope of cookies. Because I was also going to point out that, I mean, privacy and signal loss conversations have been going on for a long time and I remember when they centered around things like Apple's app tracking transparency framework and the state level privacy laws cropping up. So I guess given the major news with Google deciding not to drop third party cookies unilaterally, what has this, what impact has that had on signal laws in the last year? Has the conversation kind of quieted down? Is there less urgency to it? Are you finding yourself as an agency trying to have to convince marketers that they should still care, or do you feel like marketers have already been aware that this is just happening?
- [04:29]
A
Anyway, I think all great questions, I think to start with, even though Google's policy has changed, it doesn't necessarily change the fact that data privacy, protecting consumers, data thinking through alternative identifiers to bring different data sets together, those are still very valid considerations for organizations. So to your point about urgency, the fact that there isn't necessarily again, this timing specifically for Google's IDs to be deprecated, that gives a lot of brands and advertisers the opportunity to think through maybe different creative solutions that weren't necessarily available before, just by virtue of the timeline that they were looking to implement. And so when I think about what this means for the industry overall for signal deprecation, it's really this idea that first, consumers are still very aware of how data is collected, probably more so than they were before. So Google's announcement doesn't necessarily change that. Second, advertisers and brands are still very incentivized to develop best in class experiences for their consumers. These experiences that are really relevant based on different data signals that they get. And even without Google's announcement, there's still a lot of deprecation across the entire overall digital ecosystem and also, you know, the offline ecosystem. So there's some considerations there for what that actually means for advertisers that they still have to stay ahead of. And then third, I think one of the biggest things that you and I've talked about a little bit in the past is this idea that different organizations will approach their views and their setups for data consolidation, data ingestion, and their data ecosystem design very differently. And so this really creates an opportunity for these brands, for these organizations to think through the right solutions for them. Whether that means building something in house and having a little bit more time to do that, because there isn't that necessarily that urgency there. Whether it means partnering with different vendors, evaluating different services and ID partners, et cetera. It's really a great opportunity now because that need to really think through what thoughtful data collection, thoughtful data signal connections, what those look like that still exists independent of whether or not Google's redacting of the IDs is going away or not.
- [06:43]
B
Yeah, and I think this has me thinking about a lot because, well, first of all, I mean, I think I very much agree with this idea that, you know, hey, there's no strict timeline on when this particular signal is going away. That gives a lot of, you know, room, a lot of wiggle room for brands to just take their time and figure out, you know, what, what actually would work for them, what might be a good creative solution to address certain aspects of signal loss so they have the advantage of time in a way, which I think, you know, explains why, you know, this is still an important topic, even though there's not this sense of urgency around it anymore. And also your point that this is happening anyway and consumers, you know, we're all consumers in our daily lives when we're not thinking about signal loss all day. You know, I have a lot of friends that point out, you know, that they won't get, you know, period, tracking apps now. They're all worried about what that data can do in the wrong hands and how likely it is to end up in the wrong hands. Depending on, you know, where you are or, you know, other factors of course. But you know, as a consumer I definitely see, you know, other consumers, my friends, my circle, like being much more wary about how data is being collected and what services they're willing to use as a result of that. So you also mentioned, you know, creative solutions being something that brands are able to lean into now that they have the advantage of time. Can you elaborate on what types of, you know, tech maybe some of these marketers are building in house, what types of partners they're prioritizing, for example?
- [08:08]
A
Yeah, absolutely. So I think in terms of creative solutions, it's really this idea again that there really isn't necessarily one size fits all approach for different brands. And so we've been a big proponent at Razorfish of testing different opportunities and capabilities again, partners, vendors, platforms, etc. And so I think what it comes down to is really thinking through for a client, an organization that has a first party data asset and also is starting to collect zero party data and preference information, et cetera, what does that look like and what can really add value to that data set when thinking through the types of signals that you can get throughout the consumer experience. And again, you know, the idea that the consumer experience is linear is really not the case anymore. So it's thinking through what are the different data signals that you can get through a very kind of fragmented ecosystem at this point. And then what are those signals, how they're coming into the environment, how they need to be joined together, how they need to be matched in a way again that's very privacy, safe and respectful of consumers preferences, if they're opting into different experiences, that really creates this again opportunity for brands and organizations to test what the right solutions are for them. So if, whether it's doing, you know, a small proof of concept with a particular vendor, whether it's trying to look for a specific use case for activation, either for personalization or maybe even targeting suppression that can really give them an idea of how the data signals coming in to their environment is, are really adding value to their overall ecosystem. I think those are really great ideas and steps that brands can take to think through. What does this long term plan look like for them. And I think at the core of this is really again this idea that the data ecosystem, the stronger that it is, the more it's designed to think through signal deprecation, again this idea that the consumer journey is more fragmented and people are entering experiences at different points in time, the better the ecosystem is designed to account for that, the more thoughtful it is, the more it's able to be adaptable to different changes in the environment and the industry. Because the industry will always keep evolving as it pertains to data, the better brands and organizations will be to really give those thoughtful experiences to consumers. And going back to your earlier point about how people are opting in and thinking through their data, consumers now are always seeing different kind of options to manage their preferences. And I think that's something that they'll always keep doing. And so that should be at the forefront of brands minds when they're thinking through, okay, how are we collecting this data again? How are we joining together? What are the right use cases for either personalization or just to get a better idea of what the consumer is looking for to give them that relevant experience at the time that makes most sense for them?
- [10:49]
B
Yeah. And continuing on your point too about collecting data and matching it in a privacy safe way, I also wanted to ask you about clean rooms because I feel that as, you know, the same way that signal loss overall doesn't feel like it has this sense of urgency throughout the industry anymore, I also feel that the clean room conversation seems to have, you know, changed and almost quieted down a little bit. In a sense, I feel like I hear about it less in the past year than I did the year prior. I'm wondering what you think is going on with the clean room conversation also because, you know, do you think it's, you know, kind of the same, I guess in the same vein of there's less urgency but it's just something that kind of happens in the background. Are we still kind of testing and learning? Are they going out of style?
- [11:32]
A
I definitely don't think they're going out of style. I definitely there's a lot of testing and learning and again, that it gives. The announcement from Google last year does give brands some more time to think through again what their clean room solutions look like. And there's different clean rooms out there for different purposes. I do think what it enables in terms of the clean room discussion is there's always going to be a need for clean room when we think again about data privacy, consumer preferences, et cetera. So I don't think that necessarily goes away. I think the way that brands think about clean rooms and how they are integrated into their overall timeline to build out their data ecosystem that might change because again, there's less of this immediate urgency to solve a specific need for signal redaction. But there's a longer term play probably in place of thinking through, again, how do these different components work in our overall ecosystem? How can we be more thoughtful about them? Because at the end of the day, clean rooms are a big technical investment when you think about the different elements that need to be mapped together, stitched together, worked through with the different providers, et cetera. So it's actually a good thing that there's more time to be thoughtful again about how these solutions fit in an ecosystem.
- [12:41]
B
Yeah, I think another trend I've been noticing about clean rooms, and this is, I will admit, like from the streaming TV publisher perspective, but I've noticed that more publishers seem to be promoting or putting out clean room technology, but not so much, you know, clean rooms themselves, if that makes sense. So they're more so focusing on, oh, this is a clean room product or a clean room solution. But they aren't necessarily calling their clean room tech just clean rooms. And I feel like that makes me think of that conversation about how fragmented clean rooms were. And there was always this conversation throughout the TV industry of, you know, am I going to buy, you know, an ad from this publisher and not know what impact that has on this other publisher? Do you think that that trend also is, you know, just kind of resulting from more, you know, flexibility and time given to the clean room conversation to do it more efficiently, or do you think that's more of just the natural evolution of clean rooms?
- [13:39]
A
I think it's a little bit of both. I think the natural evolution of the clean room actually points to kind of more collaboration across different clean rooms and different types of solutions that publishers and partners provide. And then to your point about, you know, the urgency in the timeline, the timing is such that there can be different solutions now in the market so that these cleanrooms can interact a little bit more with one another. I think one example of this not exactly clean room per se, but, you know, Disney's announcement at CES may think through how they're looking at solving the data fragmentation. And I think what we'll see is more of this collaboration and kind of consortium of different vendors, different platforms, different partners, et cetera. Really thinking through what does this look like for data access to be available to consumers, you know, whether that's a clean room, whether that's just access to different data in general. I know connected tv, something that you and I've talked about as well, in terms of some of the fragmentation there. And so I do think to clean room specifically, the idea that the timing is a little bit longer and the Runway is longer, gives these companies and these publishers more time to think through. Well, what does this actual technical solution look like? Is there an opportunity for us to maybe partner with other solutions to again, provide a little bit more robustness and interoperability before going out to clients and providing a view on what this clean room and what these IDs and what these signals you provide can do for them. And so I do think there's, there's a little bit of that going on that we'll see throughout this year and beyond.
- [15:08]
B
Okay, yeah, that'll be interesting to keep, you know, keep an eye out. I like the, you know, 2025 look ahead angle. Here it is, you know, beginning of the year still. So another question that I have about just something trending in, you know, the realm of signal loss, as you call it. When we last spoke, we kind of talked a lot about, you know, consolidation being, you know, a likely result of signal loss. And here we are now, you know, like so many acquisitions already into the first month of, of 2025. And when we were talking about that, you'd mentioned that there's a lot of data decentralization on marketers minds. So the ability to use different data sets for like, measurement versus targeting, for example, and you know, consolidation is trendy because it lets bigger players get, you know, smaller players with different tech for different use cases. Do you think, like, where do you think that trend is going now? I mean, we're seeing the consolidation happen in real time. To what extent do you think data decentralization has something to do with this?
- [16:08]
A
I definitely think data decentralization has a big role to play. I think when we think about, you know, what this looks like, again, for data sets to interact with one another, to really be providing value, there's always going to be different solutions in the market that needs to be again, mapped to other solutions in a way that can provide these capabilities at scale for brands, for organizations, for targeting, for personalization, et cetera. And so I think data decentralization really plays a key here because when you think about the different types of data that are needed to understand a consumer's preferences, understand again, where they are in the consumer journey and the life cycle of either purchasing a product or maybe becoming aware of a brand for a first time, the idea is that you really need to think through again, these different data sets and how they're bringing value and providing some clarity into what the consumer's intentions are. And so I think data centralization plays a key role here because when you think about the overall landscape of how data sets are built and how they're connected to one another. What we need to be thinking about and what we're really recommending our clients think about is this idea, especially not thinking through clean rooms and privacy safety and all those elements of how these components come together at a point in time to give visibility into the consumer journey and then can be more consistent and persistent over time as that journey evolves. But then also the data can be existing in its own different silo, so that it's not necessarily something that has to be around forever, that follows the consumer around. And so I think here, one of the key ideas is that the consolidation that you're seeing in the marketplace and that we're seeing today is really this idea around. Like I was saying before, it's a lot of technical investment to create these solutions. And when you think about the data sets that work well with one another, I think there's going to be more consolidation in the industry as identifiers, as signals start becoming a lot more available and more persistent in these client ecosystems. And so I think what we're seeing is more consolidation around those lenses and then this idea that this provides a better level of service and visibility for clients as long as they are ingesting the data in a way that is thoughtful and again, predicated on their own first party data.
- [18:32]
B
Mm. Something that's coming to mind while we're talking about this too is that, you know, clearly with signal loss happening, there's a lot of growth and a lot of business opportunity emerging as a result of that. Which is interesting because, you know, signal loss, you know, began as, you know, like a doom cloud where we were all afraid of it and we thought, okay, this is going to reduce the amount of data that helps marketers actually justify their investments and then that could affect the economy, et cetera, et cetera. So it was a really scary concept. But now there's a lot of innovation growth that's actually, you know, arguably some of it maybe wouldn't have happened otherwise. But so I guess what is your, what is your perspective on the benefit that the signal loss aftermath is having on the industry from, from a business and growth perspective?
- [19:20]
A
Yeah, I think it's really to your point, it's really around innovation. I think there's always been a push for brands, you know, for our clients to better understand their consumers. And sigma loss created this inflection point where brands really needed to think about, well, what data is really relevant to me as a brand, understand what my consumer is thinking about again, understand where they are in the purchase journey and the consumer lifecycle. And so I think when you think about data signals and what that looks like, it's really again this idea of breaking through a lot of the noise and so being able to think through what is truly resonant with consumers, knowing that there's a ton of data being generated a day. And so, you know, signal loss kind of creates that need to think through. Okay, well I don't have the signals that I maybe had before. Maybe some of them were useful, maybe some of them weren't as useful, but I really need to think through, you know, as a brand, how do I actually collect and organize and connect the data that I have with this notion that it's not going to be always complete. How do I collect all the data that I do have and then how do I think of bolstering it? Because there's the challenges with signal laws and think through the things in a more innovative way that may not have been accessible before just because the default was that you're collecting everything all the time. And so I do think it creates this opportunity for innovation because by default brands have to be more thoughtful and creative about how they're thinking through their data again, how they're thinking through managing their consumer preferences and then how they're developing those experiences to consumers. So I think a key theme that we'll also see for this year is this idea of these signals are coming together, we're being more thoughtful about what actually matters to consumers. And then maybe it's not necessarily that we need to drive hyper personalization as a result of the signals that we have, but maybe we have a better idea of the consumer that needs a little bit more of a nurturing journey flow versus a specific product. I think the signal loss conversation over the past few years has really given brands this ability to think more about what those different types of journeys are and those experiences can be as a result of thinking through data collection in a more thoughtful and a smarter way.
- [21:36]
B
Yeah, I think like you made a really interesting point that there, you know, there, there is nuance to the conversation and also that, you know, there is such thing as too much data or rather too much targeting that can happen, which is interesting to think about because know, consumers just probably wouldn't like that anyway and it would have the adverse effect. Okay, well we covered a lot here so we're going to pause for a quick break before we move on to discuss some more specifics about first party data and alternative identifiers. Stick with us. And we're back. So I wanted to zoom into more specifics about some of the solutions that we're talking about here. You know, especially like including first party data and alternative identifiers. Are there particular solutions that your clients seem to be more focused on or excited about?
- [22:41]
A
Yeah, I'd say in general, clients have been testing alternative identifiers. So as I mentioned before, one of the things that we do recommend is, especially with the lack of urgency right now, is focusing on different tests to determine different alternative identifiers, different types of data sets that can be connected to their first party data. So I'd say in general we're seeing a lot of clients opt into that in terms of looking at the different vendors that offer those capabilities. So I do think alternative identifiers, that's going to be something that seems stays around again with this idea of making sure that, excuse me, even without the urgency and the pressure of signal application immediately over the next couple months, that these alternative identifiers have some more time also themselves to ramp up and develop their own capabilities that make sense for the client. And so we're recommending that clients test these different solutions and then we're also excited to see how those roadmaps evolve over time, given that there's a little bit more time now to be more thoughtful of how different specific capabilities are rolled out.
- [23:45]
B
Okay. Yeah, so there's more attention on alternative identifiers. I know you can't name names, but just for the sake of some context and examples. So there's UID 2, there's ramp ID, Yahoo has connect ID, there's ID 5, and there's actually literally scores of other identifiers out there. So cc, about a year ago when we last spoke, you said that there wasn't necessarily one ID leading the way just yet. So a year later, has your perspective changed?
- [24:13]
A
I think it's pretty similar. I think the reason why, you know, I say that is again with this, this additional Runway in this timeline for these different identifiers to really bolster their capabilities, it'll be interesting to see, you know, what they are able to support in terms of ability to provide better again identity resolution for their clients on behalf of the consumers that are visiting, you know, their properties and then also think through what that technology can look like in terms of the actual data collection and then how that data is mapped back into the client's own environment. So I do think right now it'll be really interesting to see just how these, these technologies evolve, the IDs themselves and then also what additional data Signals are captured as a result of the evolution because there isn't necessarily this urgency right now to solve everything at this moment.
- [25:03]
B
Mm, right. Yeah. So there's more time for testing. There's no winners and losers yet. But eventually, you know, there can't. Well, you know, I guess there can't be scores of alternative identifiers. So eventually there's gonna have to be some, you know, some that kind of rise to the top. So from a tech and data perspective, what do you think ultimately IDs need to get ahead? Like, what will determine which IDs can succeed?
- [25:28]
A
Yep, that's a great question. I think there's a few key components. I think the first is really, again with the idea of data priv. Data centralization. First and foremost, the IDs that will be able to be both persistent but privacy safe in the sense that I think last time we spoke, we talked a little bit about federated data ecosystems, where data comes together for the specific purpose at a point in time to either determine if a consumer has a specific preference for something that a brand is offering from a product perspective, or for personalization, but then the data goes back to its original kind of table or ecosystem that it's from. So I think the IDs have to be able to work within that environment because as we think through again, all the different data that's being produced, all the different challenges and constraints of how different ecosystems are designed for different brands and organizations for their specific purposes, the IDs that will be the most successful are the ones that will be able to have really a strong depth of understanding of the consumer signals that are attached to the id, but also be able to play and operate within a client's environment, such that it's data that's coming together for a specific purpose and then data that's going back out into their own environments to be able to be again, privacy safe and compliant over the years to come. So I think that's really what we're looking for, is those two capabilities is just the strength of the signal and the ID capabilities. And then the second is the ability from a technical perspective, to work through, through the different considerations for a brand.
- [27:03]
B
Yeah, I think federated data is something I've heard quite a bit more about in the last couple months. So just for context too. So I think the clean room conversation has evolved in the sense that, you know, clean rooms have, I guess, positioned themselves to be a place where data doesn't have to leave. And some of them are becoming cloud based with the idea that there's less privacy risk, maybe if data doesn't have to leave its environment. Whereas it sounds like, you know, federated data is for the purpose of go somewhere to complete a task and then it goes back to where it lives. Do you think those two concepts kind of contradict each other or do you think there's a way they can work together?
- [27:39]
A
I definitely think they work together. I think probably the biggest thing that I can think about in terms of how they would coexist is there's some cases where it's just not. It's unwieldy to have data all in clean rooms or in one environment. And everything is done in one environment. And so in that case especially, you have a lot of data. Again, going back to this idea that the journey is fragmented, there is a ton of data that's being generated at any point in time when a consumer is entering and exiting a journey and an experience. It's not necessarily always going to be the most efficient to have that in one clean room environment. And so I think there's opportunities again where the IDs and the Federated learning and the clean rooms, they all work together. Again, it might not be for every single use case, but there are probably specific ones where for clients it's a lot more efficient and can be effective to design their ecosystem that way.
- [28:31]
B
Yeah, maybe slightly off topic, but I mean, I'm out of cloud storage in my Gmail and I kind of like relate to this idea of, you know, hey, there's only so much data and so much storage that's actually possible. Otherwise, you know, you will hit capacity and have to make a Yahoo account. So one more question about alternative identifiers too is that especially a year ago, a lot of, you know, panel discussions at events kind of centered on this idea that alternative identifiers didn't quite have enough scale yet. And this is maybe a year ago or more. What do you feel that clients think now about the scale that's possible with alternative identifiers? Like has it improved or maybe only for a select few?
- [29:12]
A
I think it's definitely improved. Again, I think it depends on client specific use cases. So what might have improved for some clients might not be applicable to other clients. But I think going back to this idea that there's more time for these identifiers to continue to improve and kind of what you were alluding to earlier, that doesn't necessarily mean that all identifiers will be successful. There is going, there are going to be some that are just by virtue of investments and capabilities and different data signals over time and different technologies will end up being a lot more scalable. But I think the idea is that again, there was time now for the IDEs to improve and to provide a different set of value offerings to clients that necessarily, maybe again, a year ago, wasn't as clear because everybody was kind of trying to solve the same challenge at the same time. Whereas now, again, there's. One of the themes that you and I have been talking about today is this idea of innovation. So there's, now there's opportunity for the different identifiers to really think through again from an innovation perspective, what can their specific alternative ideas provide to brands? What are the data sets that they can bring in in terms of just again, the knowledge of the consumer? Because ultimately that's at the forefront of these ideas. It's not to have an ID for an ID sake, but really to know what the consumer's experience is, what they, what they're looking for, their preferences, how to really create experiences that are resonant to them in this privacy safe way. That's the most important. And I think really when brands think about that at the forefront and when they're evaluating these different technologies with that in mind, that will help determine which ones of these IDs have this, the scalability that they need.
- [30:47]
B
Yeah, it kind of reminds me of this historical debate of whether we should have art for art's sake. And a lot of people thought, hey, we should probably have meaning behind what we're doing. So it kind of reminds me of what we're talking about now, which is, you know, not having an ID for an IDs sake, but having an ID that's actually like differentiated and can actually, you know, solve for a specific issue. Because, you know, like we said before, there's scores of them, so you have to stand out. So I guess moving slightly away from alternative identifiers, I wanted to just put a little bit more light on first party data. I think I remember a year ago there was, you know, some, some more concern vocalized in the industry about how, you know, after testing first party data and different use cases, sometimes it's not always reliable, sometimes it's extremely costly to get it. So it's not always the most reliable, you know, data set. But how has, how has the landscape of first party data changed in the last year? Like how have marketers kind of grappled with those concerns?
- [31:45]
A
I think that's still true today as it was a year ago, in that first party data is a very kind of Time intensive asset to build over time. And so it's something that brands and marketers need to be aware of in terms of how they're collecting that data. And really, again, going back to this idea that there's a lot of noise in data sets, including first party data, what elements of that first party data are really relevant to their specific use cases for marketing personalization or for any other use cases that they need for activation? So I do think the idea of first party data, it's not necessarily, it's not going to be a quick fix for brands. It's going to be something that needs to be again, thoughtful and invested in over time and really thought through. Again, what are the elements from a first party data perspective that are going to be key for brands that are going to be the most relevant? What are the elements from a consumer perspective that we think can be more persistent over time? So we have this idea of this first party data is attached to this consumer and we think it's going to be that same consumer over time. I think that's going to be something that marketers need to be thoughtful of and that ties right back into the data privacy component of how do we make sure that the signals that we're getting are truly representative of the consumer segments that we have. So first party data, I think is always going to be something that's ever evolving. Again, I think we're at a great point right now because there's lots of that urgency where brands and marketers can really think through what are those data elements that make the most sense to collect from a first party data perspective. And some of them might not be as relevant even though they were collected over time for many years. And this is a really great opportunity for brands to think through what that looks like and then also think through how do we build up experiences for consumers so that we can bolster the data sets that we're getting to supplement what we have for first party.
- [33:42]
B
Yes. Amidst all this, you know, testing and learning and trying new data sets, how does the, you know, just the rise of state level privacy laws cropping up left and right does that. How much is that complicating the process?
- [33:57]
A
Great question. It's definitely challenging. I think that's something that brands should just be kind of aware that it's probably going to be the case that there's going to be different constraints and different considerations for different areas that they're operating in, whether that's in the U.S. in different states, whether if they're global brands operating internationally, et Cetera, So it does complicate things. And that goes back to this idea, again, of making sure that data is collected for very specific reasons, for, you know, specific, again, data that's collected in one area might not look like the same as data and collected in other areas. So very specific design around what that looks like overall for an ecosystem. And all of these activities typically are quite time intensive and quite require a lot of collaboration across multiple teams within an organization. And I think the opportunity now is, there's the time now to really spend thinking through what this might look like and how to think through creative solutions to make sure that, again, it's not really going to be a lot of this technical investment to duplicate these different data sets or duplicate these different types of data collection capabilities. But think through what this might look like holistically at an enterprise level and where there might be some nuances depending on the market that they're in.
- [35:09]
B
Yeah, and also, so, you know, with Razorfish being at ces, you know, of course I know you're not, you can't name clients or, you know, discuss specific conversations, but overall, what, what types of, you know, trends relating to data and signal laws have come up, you know, from, from marketers during the conference?
- [35:29]
A
I think the biggest one is, again, making sure that we're getting through the core of data that is the most relevant and the most resonant for consumers. So data fragmentation, key theme that we think through of how brands and marketers can identify the right solutions, you know, coming out of, again, what's likely going to be just a lot more data collection this year and beyond. So I think that's a key theme, is just really thinking through again in the marketplace, what are the solutions that can help us really think through what the actual consumer journey and the consumer experience is, because it's no longer linear before you might think somebody sees a TV ad, they go search for a product, etc. It was a little bit more discreet and concrete in terms of what that overall consumer experience is, but now the consumer experience can start and stop really, at any point. And so that creates a lot of confusion in addition to the other considerations that we've already talked about. And so there's confusion in the fragmentation. And so I think what's really a key theme of focus for this year is simplifying a lot of what that is. And that comes down to identifying the use cases, identifying the ecosystem that clients and brands want to build and need to build to better understand what their consumers are looking for and to better service where they are at any point in the consumer journey. Awesome.
- [36:51]
B
And also so looking ahead too, and I know we talked about some of these, you know, trends to expect where, you know, consolidation is concerned or, you know, more attention and alternative identifiers, ad targeting and personalization. What do you think will be the, I guess, the most top of mind trend or priority for marketers in 2025 as it pertains to signal us?
- [37:17]
A
So I think a top of mind trend is actually something similar, similar that we've been saying over time. It's really the right message, the right person at the right time. But the reason why I say that now for 2025 is when we think through again, the wealth of the data signals and we think through the consumer journey and the experience. And to what you were saying earlier, people are more aware of how their data is being collected and how it's being used. And they're also probably a little bit more aware of, you know, what targeting and personalization looks like. Even if they might not necessarily be able to identify what that is in that moment, they can know that if they're searching for something and they're being retargeted, it's related in some capacity. And so I do think what we'll see is this idea of really, okay, maybe there is a consumer that really will benefit from the personalization in the experience based on what we know, because we've seen all these different touch points. You know, we've looked at the data, this consumer is part of this segment that we think will really benefit from a more lower funnel kind of activity. But then there's also consumers that are maybe just browsing. And I think from a marketing perspective, we also have to be able to identify that not everybody necessarily wants hyper personalization all the time. And so when we're talking about, you know, right message, right time to the right person, part of that might just be, hey, just let them, just let them browse, let them learn more about the brand as opposed to necessarily putting a lot of people down the same kind of experience funnel if that's not what they're looking for. And so I think that'll be a key theme, especially as we're thinking through, you know, 2025, what that looks like this year, what it looks like beyond as we get more signals and as you kind of see some of that, the differences in the journeys for different consumers and what ends up sticking and being resonant with them is that it might not be hyper personalization, it might be something a little bit more Simple.
- [39:04]
B
Yeah, I think that's a great point because here's one silly example. But so I'm obsessed with cats. I do not have a cat, but I look at them a lot online. So now I get emails every single day for cat trees and I'm not clicking on them because I don't have a cat every single day. So at what point does a marketer realize, hey, maybe this customer is not buying a cat tree right now. Maybe I should think of something else, a cat themed item for somebody who doesn't have a cat. Maybe, I don't know.
- [39:29]
A
Right. And then you think about the inverse of that. You as a consumer, it's probably very frustrating to you and it's not necessarily the best experience. And so I think when you think about like the personalization, it's ways to really identify what the consumer is truly looking for based on the signals that are provided and thinking through thoughtfully. Well, what to your point, it's maybe not a catch rate, it's maybe something else and thinking through what that might be. And so I think there's going to be a lot more thoughtfulness around the marketing ecosystem and the activations for this year and beyond.
- [39:59]
B
Yeah, especially when, you know, when consumers actually respond, then that also can help guide what types of signals are more relevant for certain consumers. Maybe a cat pillow would work, maybe a cat themed rug.
- [40:10]
A
Right.
- [40:11]
B
But great. So this is a really fun conversation. We talked a lot about just what to expect in the coming year. More testing of alternative identifiers, more collection and use of what kinds of first party data really works. Also just dealing with the slow wave of signal loss as it comes with privacy laws and who knows what else is in our future. So there's a lot to look forward to. And yeah, really appreciate you being on this week's episode. Thank you so much. And we'll see you next week.