#76 - Asking Marketing Questions that Technology Can Answer | Real Talk about Marketing an Acxiom Podcast

In this episode, Erin Foxworthy of Snowflake joins the podcast to discuss the future of media and advertising, including the importance of first-party data, governance and privacy, and the role of AI in modernizing marketing infrastructure. The Erin, Kyle and Lorel also delve into democratizing data, common organizational challenges around measurement, and how to leverage cloud technology for faster, more governed, and collaborative insights. 

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Transcript

Announcer (00:00):

Welcome to Real Talk about Marketing an Acxiom podcast where we explore real challenges and emerging trends marketers face. Today at Acxiom, we love to solve and helping the world's leading brands realize the greatest value from data and technology.

Kyle Hollaway (00:18):

Hello everyone. Thank you for joining us on another real talk about marketing episode. I'm your host, Kyle Holloway, and I'm joined today by very special co-host L'Oreal Wilhelm. Lre. Welcome back to the show.

Lorel Wilhelm-Volpi (00:31):

Thank you, Kyle. It's lots of fun to be

Kyle Hollaway (00:34):

Here. Well, awesome man. It's a pleasure to have you joining us today. So can you give our listeners a quick overview of your role here at Acxiom?

Lorel Wilhelm-Volpi (00:41):

Yeah, happy to. So I look after our partner in services marketing for Acxiom, so I'm super excited about our episode today and to be here with Erin.

Kyle Hollaway (00:52):

Yeah, well we do have a special guest today from Snowflake. We have Erin Foxworthy, global head of marketing and advertising. So Erin, welcome to the show.

Erin Foxworthy (01:02):

Thank you for having me.

Kyle Hollaway (01:04):

Why don't you give our listeners some background on you, how you got to Snowflake and a little bit about your history in the industry.

Erin Foxworthy (01:13):

Sure, love to, I won't say how many years I've been in it. Let's just say right out of college I landed in digital advertising, which was Prego, so I'm going to leave it there. Spent the early part of my career in the early agency world. Most of my career, almost all my career has been in Los Angeles. And it was interesting time then because digital budgets were a tiny percentage of the fraction of a marketing organization in Los Angeles, I got the opportunity to work across a lot of different types of industries, but automotive entertainment tend to be large ones there and I spent a lot of my time in these large enterprises, one very, let's say more data rich, some data poor. And so it was really interesting to see how different industries approached their marketing services. I spent a lot of time in a lot of different types of businesses, which I think was really helpful in my early career.

(02:09):

I had an interesting opportunity later after working in agency side is to either go client side or to go technology side. And I loved the digital advertising slash technology intersection that was happening at the time and I got a really amazing opportunity to go work at Microsoft. And in that role, similar to the role I have now, it was a role called category development. And this is when Microsoft Advertising was being born. So if you think about Jeff Green's at ECN was there, they had the aqua purchase. This is pre being, they're building a really large advertising division. So my role was to kind come in as a, let's just call it a marketing expert to help the field sell in some of the services across the Microsoft portfolio. And so we did that for many years, rolled that out globally and then kind of sat back after Microsoft was unraveling a little bit of their footprint within the advertising world.

(03:05):

And I tend to do this to myself. I get really curious about things I don't know, which I know they say is a good thing sometimes that's not necessarily always a good thing. And I was watching kind of the unfolding of programmatic, I was watching the unfolding of a lot of data-driven marketing. We had just started that and Microsoft advertising was fascinated by that space. And so I did a little bit of time very short at, it was called Rubicon Project, which is now Magnetite, so on the SSP side. And that was really interesting. But right in the moment I was there, I had an opportunity from a very good friend to kind of come back agency side, which I swear I would never do. But she had this amazing opportunity that I couldn't pass up was there was a brand new movie studio had no legacy data and she wanted to start to unify her own, her MarTech stack to ad stack.

(03:52):

It was an early vision and the promise was most studios had no data and they wanted first party data, they wanted a relationship with their consumer. And how did we start to build that? And for me, coming from more of a media background, but wanting to be more first party Rich was really curious about this time in that space and I got really lucky. So we built a team that was almost in-house and I was at Horizon Media at the time and we actually kind of took this company on the journey of the CDP deployment. So how do you build first party? What does that look like? What does CDPs mean? How do we start to think about email and loyalty and push notifications and actually build a relationship with our customers? How do we even gamify a little bit? What do we reward them with some more loyalty, which I had never explored before, but what that was doing was also feeding our media.

(04:39):

Our first party segments was also feeding into our media buying because any media buyer knows first party will always perform amazingly, sometimes limited, but it works. And so that was kind of the impetus was the more we build up our own channels, the more we have first party for our paid channels. And what was really interesting is that we were really struggling with the early CDPs. They weren't flexible, we couldn't see our data. The cookie loss hurt a lot of them. And so what we ended up doing was migrating off of CDPs and saying, okay, where do we get more transparency and visibility into our data and kind of avoiding to the lock-in. And that brought us to Snowflake actually. So the solution for us was a data warehouse at the time, which much more than that now that plus an identity spine so that we could have much more control in the visibility of our audiences.

(05:31):

And at the time, even activating out of a data warehouse was a very unknown thing to do. It was just more of like a BI tool. And so we were kind of pushing the boundaries of what that meant. We were also at the same time, because I had an amazing team doing really great data science work and a lot of the clean rooms. So we were testing a DH, we were testing Meta's clean room, we were testing really early testers of Amazon's clean room. When I saw Snowflake roll out that they were going to have kind of clean room, I was like, oh gosh, my data's already here. My first part is here. I'm segmenting here and my identity here. If I can now collaborate in a privacy safe way, that's where this is going. And so I got really lucky at that time of my interest, there was a position open, which is this role now. It's a very small team that kind of sits across Snowflake, but has this conversation with marketers. So that same journey I went through, now all marketers are going through across all enterprises, big, small across the board, how are you joined and paid and owned? What's your first party data strategy? How does Snowflake help? And so I lead a team now that has those conversations across the organization.

Kyle Hollaway (06:35):

Wow, I love that you

Lorel Wilhelm-Volpi (06:35):

Were describing that. Yeah, I was thinking you could not have had a better foundation for where you are now. I mean truly.

Kyle Hollaway (06:43):

Yeah, I think it does seem like there's a convergence there of your story and Snowflake and where Snowflake's heading and what we're really seeing them work towards at this point. Just a great convergence there. So that's really cool. So if we think about the big picture, you said it's a small team right now, but very impactful. I know you're having lots of conversations and a lot of influence there, but from now the Snowflake perspective, what are these trends that you're really starting to see shape up around the future of media and advertising?

Erin Foxworthy (07:19):

Yeah, I think that this is probably no surprise to you. The first one is it's the identity imperative, which I put that in the first party world, how do we get better fidelity of our first party and understand our consumers? So I'll put that in that bucket. I would say, and you guys know this too very much based on where regulation is headed, governance and privacy, right? That's forefront. We see those two just happening a lot there. We'll talk about how Snowflake answers some of those challenges. And then the third one now is just it's AI foundation. How are we setting ourselves up for the future of ai? Hard to predict, but there's some very key things that I think an organization needs to do to be ready, which is you have to have your data ready for AI to be able to use it. So those are definitely the three paradigms I would say that we're seeing in market right now.

Lorel Wilhelm-Volpi (08:07):

So as I'd like to take a deep dive for just a second, in the first one that you mentioned that importance of first party data. So can you talk to us a little bit about how Snowflake is helping brands really navigate that shift into third first party strategies?

Erin Foxworthy (08:23):

So the interesting thing about when you think about cloud infrastructure, which a lot of times I know from marketers lives in the world of their IT department, but the reason why it's so important, if just take it from marketer's perspective and a consumer's perspective, what we know is how fragmented everything is now. So in our world, you hear, you'll hope people talk about structured and unstructured data. Really it's about consumers are on apps, they're on web, they're in your retail stores, they're in your call centers, they're looking at video messages, they're listening to audio. All of those touchpoint are things that marketers want to use because what we do is we understand behavior, we understand consumer behavior better than anyone else. I love the way that marketers brains think, but to be able to think about how to respond to those touch points, we have to have the data in a place that can handle it.

(09:12):

So a platform that can handle the speed, the data comes in, the format, the data comes in, the scalability of how to leverage that data that happens in the cloud. And so what Snowflake does is allow you to bring all those different elements in a way that you can transform it to make it something that can be usable by the business. And that's really what Snowflake does. It was built to do that the way that it was architected really early was meant to be exceptionally fast and scalable platform. And so for consumer data, which is probably the fastest moving data set, I would argue we really set ourselves up to be that foundation.

Kyle Hollaway (09:47):

And as we've seen that shift kind of to the first party data and especially with deprecation as you mentioned of cookies and such that a lot of signals were lost in the third party space, but there's still a rich set of providers on the third party data aspect. Talk a little bit about how Snowflake is bringing those together.

Erin Foxworthy (10:10):

So we work with a lot of some of the most amazing companies in the world around their data sets, Acxiom obviously being one of them. So Snowflake has a marketplace similar to some other marketplaces. The interestingly one about Snowflake's marketplace is that if you think about data coming instantly to an actionable database, that's what it does. It allows the organization to either bring a third party dataset. We work with many of the top leading third party data providers, geolocation data, social listening, data like name a lot. You can land that instantaneously into a snowflake table. Now that's different because in the past when I was a media buyer, I would click a box, write in a platform, and it would be my data set and it would activate it. The neat thing about when you buy and purchase data sets in Snowflake is it lands in a table, you physically see it, it is standing right there and you actually physically see it and you work with it right away.

(11:00):

And not only do we work with, this is really interesting part, and Axio plays a big role here. Not only can you buy third party data sets, all kinds, you can actually buy applications. You can buy actual IP that comes directly instantaneously to your data. So this is where identity resolution would come in. So you're buying the actual function of identity resolution without having to send your data out. So that's amazing. You set that foundation and now there's a term we'll talk about called Data Gravity, which is that idea, these applications, these services identity resolution can come to you, resolve right there and allow you to understand those signals of your consumers better. Your household information, your mobile IDs and your email addresses as an example. So whether that's third party data or whether it's identity resolution, those are some examples that Snowflake allows.

Lorel Wilhelm-Volpi (11:50):

Oh, that's awesome. You've mentioned Acxiom a few times, so I would love to take just a second to talk about our partnership, if you don't mind. So why would Snowflake want to work with Acxiom? What makes us strategic partners? I know from our side, but I'd love for you to talk about Snowflake's perspective in this.

Erin Foxworthy (12:11):

Yeah, I mean, I don't know an organization I don't talk to that thinks about the fidelity of their data and world class identity resolution is paramount to the industry and that's where Acxiom's lived for many, many, many years. So not only the long history and understanding consumer data, the reputation of governance and being privacy first has been really important too. So there's not that many organizations in the world that know that Acxiom. And so I think that that was a natural progression of whether it's ID resolution, whether it's hygiene, all kinds of different applications for that partnership to be there. And I think what I appreciate is organizations. I think it's hard for some companies to see this shifting where someone will say to you, I'm not going to move my data. I'm a marketer. It's going to get it staying. And so I think the organizations who realize that and take advantage of the fact that this dynamic shift is happening is the leaders and Acxiom was right on top of that. They saw that, they see this, they work with some of thoses governing privacy data in the world. They know that the best thing for everyone is that data not to move. And so I think that that just created a natural synergy for our companies to work together.

Lorel Wilhelm-Volpi (13:21):

Yeah, couldn't agree more. Are there, I would love to hear your perspective on some of the outcomes that you've seen and some of our joint solutions or joint implementations.

Erin Foxworthy (13:35):

Yeah, the first one I don't want to understate, which is time to value.

(13:40):

So in a marketing organization it's speed. Your promotions are up or down, your consumers are moving and shifting. The dynamics are changing so quickly in a marketing organization. So the first part is just that standing up this identity resolution on a dataset and a data foundation that's already established, the amount of speed we save just to kind of move to market is pretty massive. So I would say that's number one. The second one, and I know there's still a lot of organizations that struggle with this, is really kind of increasing, especially in the paid ad space, like the fidelity of your match rate. So we still know these platforms don't have all the signals and for them to perform well, they need both from a activation perspective and also from a measurement, from a conversion API perspective, they need this data to be able to do what the platforms do well, which is understand their audiences, but they don't have all the signals. So I think marketers are like, how do I continue to make sure I have high fidelity data where I'm enriching these audiences into downstream platforms that get me the best bang for the buck? I definitely see an improvement of that type of signal as we work together

Kyle Hollaway (14:47):

And talking about working together. Obviously we have a shared set of clients where we are approaching really the marketing and advertising arms of world's largest brands and such. But you mentioned earlier talking about cloud tends to be in the IT department execution and everything lives in the marketing department.

(15:17):

You're sitting at the middle of that and certainly Acxiom's long time role has been bridging the gap between those two. So it's really interesting where you are coming at it from a architectural, even a kind of fundamental level of how to enable technology for these purposes, but then communicating that to a more non-technical, certainly the marketing departments have become much more technical over the last even five years in my experience. And so you're bringing that, and I love that snowflake's really leaning in by bringing on practitioners like yourself to help with that. So talk a little bit about how Snowflake is approaching this aspect of being a technology company and a core component, but how they're delivering that into the marketing departments.

Erin Foxworthy (16:19):

Yeah, it's not easy. That's what I'm going to start with. There you go. And the reason why, I mean I good answer. Agree because I've been in the office of CMOs many times. They are under the gun. The pressure, we know the average life cycle of a CMO is very short the amount that is sitting on them. And then for them to sit back and understand, oh, and need to understand cloud, which is highly technical. And by the way, it's own massive ecosystem, right? It's funny, it's like we have a lot of drama sitting in MarTech and adtech. We have the same type of drama in cloud. It's there's all kinds of competitors and things happening moving all the time. So you really kind of have to stay on top of it. But I think that that's what we're hoping to do, and you guys, axon does this so well is when we talk about Snowflake, I can talk about elasticity like I did in scale and structured unstructured, but what I'm really saying is do you want a fast event trigger for someone that's abandoned a group?

(17:10):

So we try to get to that business level and we need a whole ecosystem of partners to get us there, but we are solutioning for that modern need of a marketer. A lot of it's speed, a lot of it's governance, a lot of it's privacy, A lot of it's better measurement and visibility into their data. Every CMO can understand that. It's a little bit of a translation. And so the other part that's really been I think driving this too is that Snowflake, one of our key parts of our ecosystem is it's collaboration. Whether that's for second party data, whether that's collaboration, to add platform endpoints or MarTech providers. Snowflake was built actually to a collaborative platform. And so I think what you're starting to see is that when a marketer goes to say, Hey, I want to collaborate with maybe a tangential sister agency or company, and I want to think about maybe a really interesting co-promotion on data, I think what they're hearing is IT department's going like, okay, that's great, but we're going to do it through a data share or we're going to do it through a data clean realm because we have governance concerns.

(18:12):

And so I think they're hearing sometimes Snowflake come into the conversation around collaboration and sometimes that's where it's starting. And then that's allowing us to come in and actually saying, well, actually your entire foundation's here and that's why it's important to you to care. So I think we come in sometimes with a conversation like that a lot, which is great if the way we come in and that's the way CMOs understand us, is through collaboration. That's a great opportunity for us to educate as well.

Kyle Hollaway (18:39):

Yeah, I think that leads back to your original kind of three points, first party data regulation and then now AI on top of that. So adding one more level of complexity and challenge is bringing that and we're again, pulling those technical aspects and considerations and then placing them more into the hands of the marketer on how to leverage those in a way that still allows the IT department to do their thing and to still provide that governance and that oversight and everything that they want to do, but in a manner that allows the marketer to really take advantage of, like I said, landing it directly into an actionable table and being able to visualize that

Erin Foxworthy (19:25):

And

Kyle Hollaway (19:25):

Be able to then decision off of that and execute without the historical baggage of, Hey, we've got to move this data from one place over to a partner, then to maybe an execution platform and then

Erin Foxworthy (19:41):

Months

Kyle Hollaway (19:41):

So on, so forth. Yeah, exactly. Yeah, exactly. And it'll eventually get there. So talk a little bit about, because I know over the last period of time you've kind of repositioned a bit from just data cloud to AI data cloud and really leaning into that, and I think it's more than just a moniker. I mean there's real meat behind why, but talk just a little bit about how you're integrating that AI specifically into media and advertising workflows.

Erin Foxworthy (20:12):

Yeah, it's really so exciting to watch in the progression that we've seen marketers explore this. So going back to what we talked about first, the hardest part and the most important part, and I talked to a lot of marketers about we need to be control freaks. We need to think about our data and control it at all costs. That is your ip, that foundation of your data will be the future of your organization, and this is across the enterprise, but really for the cmo, right? You need to guard it at all costs. Those are your consumers. And setting up that foundation is hard, right? It's not easy to come in and get all of those API integrations, all those endpoints transformed and normalized in a way that can be leveraged for the business. And those that have done that, they're sprinting, we're watching 'em, they're sprinting to the industry.

(20:56):

What that did, that foundation, it set them up to basically build something what we call semantic layer. So just think about a business user layer that AI reads to tells the AI what to do, and it has all your governance and all your controls and all the understanding of your consumer there. And once that's built, it allows the orchestration of AI and agents. And so that's something that Snowflake has naturally been able to build and bring to a lot of organizations. Now, one of the things that we're doing, which I get really excited about, obviously Snowflake is a highly technical SQL based platform, but what we just released is an example of where I get excited where we're probably going to continue to head is to something called Snowflake intelligence. So just went

(21:38):

Ga. Think about that as a natural language super agent that learns over time on all your data that's sitting in Snowflake that you can ask any questions to. So I'll give you some examples of some early use cases we've seen, we've seen the questions of, Hey, I want to understand I have social sentiment data coming in. I have at some from APIs, I have things coming off of blog chatter, I have it coming off of some social platforms. I just want to understand what's the sentiment that's happening around my brand. Maybe I had a trailer drop, maybe I had a game release, maybe I had a Friday promotion. What's happening now? We know that there's a lot of SaaS applications out there that kind of provides social listening, but now instantaneously in Snowflake, you can ask questions because that data's landed and it's structured and it's yours.

(22:24):

And so you have that beautiful UI layer now to ask the questions to and learns over time. And so whatever data sources you want to feed that it gives you the understanding of that. Think about that on top of measurement data instantaneously asking questions over what's my highest lifetime value, what's my best performing segment? When you start to combine that across what's happening across my call center, give me a cohort of users that are unhappy with their last experience that I can think about segmenting. So it's powerful. There's a lot of chat bots, there's many of them, but ours comes with the intelligence of all the data within the enterprise and the learnings. And so it's really exciting for us to watch that and kind of see us as being the place of democratizing all the data that the marketer has never been able to access. I love that as a business user. It's so fun to be able to see the ability for Snowflake to really democratize the data that they've never had before. And I'm really, I'm even processing this a lot as a marketer, what does that mean for images? What does it mean for audio? What does it mean for all this data? I've never had this unstructured data that's coming in now. I'm excited to see some of those use cases too.

Lorel Wilhelm-Volpi (23:45):

What have some early reactions been demo as you've demoed Snowflake intelligence, I got to see it this morning and it's amazing. It's amazing.

Erin Foxworthy (23:55):

I mean, marketers are, we like UIs. We're visual. And so think I can talk all day about a semantic data layer and then I could say, look what it can do to answer questions you've never seen before. We walks into a gaming company, we've talked to a movie studio, and it's all of a sudden it's like, do you want to do box office predictions, ticketing data, and also tie that to your measurement of all your ad spend all in one UI layer? And they're like, yes. Right? Yes, please, yes, please. So I couldn't demo that. We've been pulling up SQL and we'd be showing 'em code and then hopefully attaching it to a beautiful BI tool. But now we can just do that instantaneously with natural language. And so it's really, it's fun because as marketers, we know the questions and now what we just have to do is make sure you have the data in a format that can be answered.

Kyle Hollaway (24:46):

Yeah, I love that. And that leads to the enabling component, which is your native apps. You touched on those earlier and certainly that's been a place where Acxiom has really leaned in because from an enterprise perspective, as you've noted, having the data all there and then being able to have Snowflake intelligence, the AI agent on top of that is awesome. But then that aspect of how do we bring one data hygiene into that bring in the aspect of, as you said, identity resolution to really know who the individuals are within that ecosystem and start to stitch all those disparate components together. One thing just to have all the data there, that's another to effectively stitch it together and then augment it or enrich it with those third party assets like we talked about, all while not leaving your own ecosystem,

(25:48):

Bringing those capabilities to the data. And that's really been a great strategy for us at Acxiom in conjunction with your native applications, is being able to take what has historically been send your data to us and then we'll return it, of us being able to bring our capabilities still in a very privacy conscious. We've always been at the very forefront of privacy legislation and ensuring compliance. And so allowing us in a privacy conscious manner to bring our capabilities to the data in a privacy conscious ecosystem has really been a game changer and one that I'm really excited to see continue to grow out over time. And so yeah, that native application framework that you guys have brought to the table, I think has just been a very strategic component.

Erin Foxworthy (26:49):

It's really interesting you say that because processing native apps have been around for a little while. You guys were leading this space there. And when I think about identity resolution and I think about hygiene and I think about a better customer view, we talked to us earlier a lot of times to test and understand if that was helping, was a downstream activation to a match rate, and then maybe there's some measurement and then you understood like, oh, this is really helping get a better view of my data. I could see it in my match rates and kind of my media as an example. But if you think about now if I can ask natural language questions in my audiences, you're going to see immediately the value of it because you're going to know through the questions, the accuracy of the data, which is exciting. It's going to make, I think that that investment of identity resolution and hygiene and some Acxiom even more important because it's going to immediately give the marketers the visibility into the data. Not even necessarily for activation, but just for understanding of audiences. So I think that's going to be a really an interesting value that Acxiom is going to bring to Snowflake intelligence for sure.

Lorel Wilhelm-Volpi (28:00):

Yeah. So in all of the conversations that you have with marketers across the industry, Erin, are there any common threads that you're hearing, common challenges you're seeing, especially on the media side that leaders are facing? And just talk to us a little bit about those and how Snowflake helps address them.

Erin Foxworthy (28:23):

Yeah, there's a couple. I mean, some of the challenges, they've been consistent. How do I make sure I get full visibility of my data specifically in the traditional wall garden space? That's obviously difficult. Those are wall gardens to begin with. So I think that that challenge is something that we see, but we are seeing, I would say a lot in the measurement space. Kind of that question come up of, and you guys are probably hearing this quite a bit too, the measurement space is shifting really quickly. I think that we're hearing a lot of the conversation around triangulation of measurement, which I think is really interesting. You can go in platform, you can do your holdouts, you can do your optimization, you can use the tools in the platform. The second phase up from that is your holdouts, your controls, your incrementality measurement. So how do you get that back even in aggregate, and then do your holdout and controls and do incrementality testing?

(29:10):

We have a lot of marketers using our clean room as a way to access into that incrementality, into these ecosystems where the publisher doesn't have to avail their entire log set, protect their ip, and you could still bring incrementality to those audiences and those platforms. And then the nice part about that is that if you're using the same incrementality model and hold holdout across all of your different endpoints, now you're getting a true sense of incrementality, right? It's not the model, the holdout of the platform, it's yours. And so you got to bring that across. And then the last one, so that piece that's giving you channel level incrementality, maybe not at a bidded level or a granular optimization level, but enough to know the channels, bringing incremental audiences, and then that is feeding into MM models, which is also something you can run in snow park.

(29:57):

And there's a lot of open source ones. Now we have people kind of building their own using a lot of our ML libraries. So now you can run that same data that's feeding the incrementality, the data that's coming off the platforms and all the enterprise data. So if you have your POS data coming in and sitting and you have some call center data, you have other data sets in Snowflake, but all can now start to feed a faster churn. MM models have been hard because I remember specifically in a fast turn business, if I have to know, I have an up and down very fast sales cycle, an MMM model comes back to me in nine months. That's not going to help me.

Lorel Wilhelm-Volpi (30:31):

The

Erin Foxworthy (30:31):

Reason why that modeling's so hard, it's the aggregation of the data.

(30:35):

It makes sense that a platform like Snowflake's going to allow you now to bring in faster MMM. So I think it's that piece, right? It's like how do I continue to optimize and platform? How do I know that the channels are working? And then how do I think about the big picture of allocation? And so we talk a lot about that measurement framework with marketers. I think that whole industry is kind of changing a little bit on how people are doing that. That's definitely a big one we're hearing in market. And I would say, we talked about the very tip of this, Kyle A. Little bit is, and this is organizational, it's the convergence of ad tech and MarTech.

(31:06):

It's like that your first party coming between your owned and paid, it's the same audiences and they should be respected the same way. You should understand how you're hitting 'em in downstream media channels and advertising as much as you are thinking about them like we do with an email and SMS, yes, this is more personal and but how you think about even suppression, what's working between those channels and how do I say, okay, I've hit you over the head with six emails, you haven't opened it. Maybe I need to suppress that and think about finding you in another channel. That sounds basic. That's very hard for a modern organization to do that. So I think we see that convergence happening quite a bit on Snowflake as well, which is exciting. A lot of that, those org design, we have these disparate orgs in a marketing organization. So it's not all technology. There's a lot of humans. It has to happen too.

Kyle Hollaway (31:56):

Absolutely. So I'll be maybe a little provocative and such in this. So that was a great little segue there where you're talking about the media mix modeling and the platforms and measurement and all this. There may be some aspect of someone say like, Hey, because of the focus on first party data, and especially at the publisher side, we're seeing kind of a proliferation of many walled gardens where they're kind of saying, oh no, this is mine. I now have this first party data that is more accurate than what traditionally was, and I want to monetize it. So now we're, and certainly Snowflake is a key component to their architecture. So do you see within the industry more fragmentation? Do you see that there is still a collaborative layer there where people are actually buying into this aspect of, because measurement's going to have to take place across multiple mini walled gardens and plus the open internet. So the complexities increasing, not withstanding the technical aspect of Snowflake that could enable that. Do you think industry wise, do you think we're going to get there where people will actually embrace

Erin Foxworthy (33:29):

That? I mean, you're probably teeing this up. This is where an Acxiom really makes the difference. There has to be universal stitching of this complexity back to a single source for the buy side, because I see the fragmentation. You're right, snowflake sits on both sides. We enable some of that fragmentation. I do think data cleaner rooms try to help with that. I do think that level of transparency of like, Hey, I want to run an incrementality model. I am a marketer. I'm spending a lot of money on your platform. I have the right to come in and create measurement with restrictions of privacy to know how my ads are performing. I think that does help a little bit, but it's a lot of work, a data clean room. We're all making it easier. We're continuing to make it easier. But it's not as simple as an API or a floodlight tag.

(34:20):

It's a little bit more complicated than that. I do think that the fragmentation is continuing, and I think that we need organizations, IP, G and Axio to continue to push to say, wait a second, we need to have an identifier we collaborate on that can bring everything back. And we need that transparency to push the ecosystem from fragmenting too much. Actually, snowflake helps technology wise, of course, but we don't have necessarily the industry, what's the right word? Demand. We're not an ad spender, you know what I mean? We're not representing any type of transactional demand in conversation. So I think about it a lot as a marketer, it is a lot more complex, and I do think that we need companies specifically Acxiom, to help continue to be that stitching of everything. It's really important.

Kyle Hollaway (35:13):

Yeah, that's great. I appreciate that perspective. I think that is kind of the reality of it. There's the art of the possible, which there's a lot being enabled in the possibility

Erin Foxworthy (35:25):

Space,

Kyle Hollaway (35:26):

And then it's the

Erin Foxworthy (35:27):

Reality,

Kyle Hollaway (35:27):

The behavioral reality of who we are. And I was just reading an article earlier today just around, it was actually IP to him analysis that was done. And there was a statement in there that was like, it's hard when people are paid to not understand because they're focused on reach and then there's precision and they understand the challenges with precision. So kind of not understanding how it all works allows us to keep going. They're paid on reach, right? So there are some behaviorals, like you're saying, it's not just technology that it's organizational, it's behavioral, it's just industry expectation and breaking some old habits where we were measuring off of things that were not necessarily things we need to measure off of going forward. So very dynamic time and very interesting. If you were to give a piece of advice to CMO media leaders who you're, and you're probably doing this every day, give me devices, this is who you're talking to, what would be that one piece around modernizing of their infrastructure that you'd really give to them?

Erin Foxworthy (36:49):

I think that when I talk a lot to marketers, I want them to understand that there's technology now that can help them that they didn't really know existed. Meaning I think when I'm a marketer in the past, when I'm moving so fast, what I wasn't thinking about is that every time I send a copy to an API or anytime I send data out, I was kind of losing the relationship with that consumer. And I didn't, don't think I would've known that there was options. Now, there's options to protect your data and still have the orchestration of it without moving it and having full control. You don't have to hand over how your data is structured or your consumer data to have a relationship with endpoints. And I think that that's what Cloud now does, and that reducing your silos and choosing a foundational platform that allows you to do that, again, that's the IP of not only the marketing, but your enterprise and lean into that.

(37:43):

I think that we've seen a lot of what lean industry calls it a lot like shadow it. They kind of went off and kind of, I need this and I'm moving fast and I'm going to break away and I'm going to do my thing. Which I think we had to move fast at the time, but I think it's different now. I think that a lot of the organizations are catching up. My favorite conversation is when come into a meeting and it's the CTO or CCIO O and the CMO in the room, and they're working and orchestrating together, it's just magic. It's magic to watch it. Here's what I need, here's what I can do, here's what I need, this is what we can do. And then the organization just accelerates. So it's reach out and ask the questions because the technology's there, the information's there. So I it control freak cmo, be controlling of your data. You don't have to give away the keys to the kingdom. You are in control of your customer data and it's your ip. So I coach a lot around that.

Lorel Wilhelm-Volpi (38:37):

I think if you walk in a room and all of those leaders are there, you just described it really is the blending of the art of the possible and reality kind of bringing it to fruition. Yeah.

Kyle Hollaway (38:49):

Well, we are about out of time, which it always goes way faster than everything it does. So I'm going to actually ask this to both of you. So if all the data about Erin and LRE were in the Snowflake intelligence, and I asked the question of give me three words that describe you, what would come back? What would Snowflake intelligence tell me about you.

Erin Foxworthy (39:14):

Well,

Lorel Wilhelm-Volpi (39:15):

You go first because I have to think about it. Oh, man, you saw me writing down as soon as you said, I'm going to ask, man. Okay, so this is a hot take because I didn't know you're going to ask that.

Kyle Hollaway (39:29):

Exactly.

Lorel Wilhelm-Volpi (39:31):

So I think if AI were to describe me in three words, it would probably be quirky. My kids would agree with that for sure. I hope it would say kind and curious.

Erin Foxworthy (39:46):

We have kind of similar. I was saying I was thinking genuine and kind. Those are kind of two together. Curious was my definite number two. And it's funny, I don't know if you are, but I'm an Aquarian, so that has a little, so maybe not quirky, but a little bit of aloof. I try work, but I'm a thinker, right? I'm kind of my nature, so I get a little bit of aloofness too. So it's really similar. That's kind of funny. I had the same three kind of ideas. That is funny.

Kyle Hollaway (40:11):

That is awesome. Well, great. Well, it has been a pleasure having both of you here, and certainly Erin gave me a lot to think about. I mean, I love your insights and certainly your expertise both in the industry at large, and then what you're bringing specifically Snowflake, I think is hugely valuable. I think our listeners are going to take a lot from that. So thank you, thank you. Thank you for being here. And Laurel, thank you so much for jumping in and joining us today. Thank you. And so for those that are listening, just thank you for being with us. We hope today's episode was informative and thought provoking. So we would appreciate if you could leave a review on iTunes or Apple Podcast. And with that, we're going to sign off for today. Thank you very much.

Announcer (41:01):

Thanks for listening to Real Talk about Marketing an Acxiom podcast. You can find all our podcasts at Acxiom.com/realtalk or on your favorite podcast platform.