Cecilia Dones:

Yeah, so first of all, thank you so much for this opportunity and also thank you to the listeners for listening. So I am a qual quant researcher, very much focused in telling stories about people using data. So everything I’ve ever done in my career, all my tools in my toolkit is really about how do I tell the story about people, about these persons and utilizing data to help me tell that story. And so I’m qualitative in the sense that I do still do focus groups, I still do interviews. I’m also quantitative in what that means is my technical background. I’m a statistician. And so blending both worlds and applying that in the space of marketing and advertising and then amplifying that and accelerating that with all of the advancements in ai, it’s fun, exciting, nauseating, all the feelings being in this space. So I’m really excited to be here.

Dustin Raney:

Awesome. I love the way you bring in the storytelling aspect of your response there and who you are and what you represent. It does get really techy real fast, but at the end of the day we do, like Colin, and I always say it’s like human psychology at scale. We’re part psychologists to understanding people behaviors. How do people one want to be treated? What are they actually in market for? We’re all people. So to bring that kind of empathetic aspect to what you’re doing in such a critical role in marketing, I think it’s really unique and cool. So let’s start maybe with a broader question about marketing. You’ve stated until every marketing program has research, analytics and measurement in place, I will continue to advocate for these methods. So how do marketers need to rethink measurement strategies in light of complex marketing tech stacks, third party cookies, wall gardens, hedge gardens?

Cecilia Dones:

So I’m a bit pragmatic when it comes to research analytics and data sciences in the sense that if a measurement which we can also interpret as feedback from our consumers, if it will lead us to take an action that is different than we would normally take, by all means let’s run the experiment, let’s do the analytics, let’s find the data and make it happen. However, if what we are doing is just to measure things because we can measure, and that is very common in the digital space, I would argue could I spend my dollar better elsewhere? Probably probably messaging to your consumer while I view research and analytics, very, very important and it is my bread and butter and it’s why I’m so curious about the space. I also recognize at the end of the day, we have to deliver business results. So if that measurement helps me to either demonstrate that business result or take a different action to improve the experience for my consumer, great. If not, do we really need to measure it? And I think that’s where I humbly admit there needs to be a partnership with our marketers as well as their marketing research counterparts. No one can do this alone. I’ll say the exact same thing about data counterparts as well. No one can actually do this alone. It is too complex. You do need a village to make this happen. And so marketers being able to create those relationships with those specialists that surround them is such a key, I would argue capability for the future of marketing, meaning it will only get more complex, you will have even more specialists. So navigating that complexity of relationships as well as expertise as a team is so critical.

Kyle Holloway:

So you said their measurement for the sake of measurement is a common practice and one that has questionable value ultimately to the business. Why do you think that has kind of been the status quo to date and how do we begin to change that mindset within the broader marketing populace?

Cecilia Dones:

So it comes from a good place and the good place it comes from is, oh, we’re marketers. We’re making an impact on our consumer. And at the end of the day, if we don’t, the product’s not going to move off the shelf. The service is not going to be purchased. And so it’s coming from a place of I want to demonstrate my accountability, how I’m really driving the business. The challenge has historically been, and in the cases where I’ve been able to successfully bring together two different teams, having marketing work very closely with their finance counterparts, such that when I measure something, my financial counterpart says, yes, that is true. That is the incremental value you delivered. So when the difficult conversations happen around budget, tough decisions have to be made all the time. Trade-offs have to be made all the time. It’s very clear when we’re making choices to up or down any particular budget. But I think it comes from the fact that marketers have historically struggled other than here’s my sales number to demonstrate, here’s the impact on the consumer. And why is it difficult? It’s because the relationship with our consumer isn’t a one-time deal. It’s a relationship. So by definition it takes time. So unless it’s purely a utilitarian transactional relationship, there are ebbs and flows. There are times where the consumer is going to need your brand and there are times when they’re not. And so managing that relationship over time, that means there’s long-term impact with our consumers. And sometimes that’s very difficult to measure. And so sometimes that means we reach for things we can measure so that way we can demonstrate some level of impact. So it comes from a good place. I think there’s always opportunity to improve it,

Kyle Holloway:

And I think that’s a great point there on the consumer engagement being over time. And I would also argue over channel, there’s many different ways you engage with the consumer depending on your business model, but even a very basic business model probably has multiple channels in which you’re engaging, whether it’s in person through some digital means, even through sell through an app, whatever it may be. So the innate desire to show value, I totally resonates. I mean even we as Acxiom as a partner to brands showing values what we want to do,

Kyle Holloway:

I think you really point out a great point is it’s hard to at times quantify where that value is realized. There may be just some end number, did you sell more widgets or did you acquire more customers? That’s great, but what was the journey that got you there? And then how to realize where to apply additional emphasis along that journey to continue to drive that value up is certainly a challenging problem. Dustin, you’re with brands a lot. Is that kind of what you see in those conversations?

Dustin Raney:

Yeah, absolutely. And I was going to share in a previous life, I said on the brand side where I actually and cc your comment about the financial aspect of measurement, and that is what’s actually working is so true because I would have all these vendors come to me and talk about incremental return, right? We’re going to bring you incremental sales or revenue on top of what you’re doing today. But provability is a whole other thing to be able to say, oh yeah, programmatic partner, you produced an actual lift above and beyond what I could have done without you had I just spent in another channel. So I think that really resonates and it honestly gave me the ability to empathize with our clients that I go speak to every day. It’s like the last thing I want to do is go in and lie to them, lie to a brand and say, oh, we’re going to bring incrementality without proof. So cc, with that said, I know that this is an area of pretty deep expertise on your side. There’s a lot of different methods of proving what works and what doesn’t. One of the old school methods was more like more the control groups, randomized controlled trials and things like that. Are there some other methodologies that you’re teaching today or leveraging in your practice to help in this area of provability?

Cecilia Dones:

Yeah, no worries. So a few thoughts. When I think about holding partners accountable, it’s always extra helpful when they come to you with resources. So yes, yes, I would never turn that away. However, coordinating the answer and deduplicating and triangulating what’s really the incremental value, that’s really tough. Being on the brand side, I’ve been a brand as well. And I think the way to think about it that kind of removes some of the pressure sometimes that we feel being brand marketers is that I liken it to a symphony. And what I mean by that is we create music as marketers, real an orchestra of different media content experience touch points. And so I would always be very reticent to say, oh, well we have an orchestra now. We no longer need strings because some measurement tools said, no more strings. We’re just going to be all about percussion and winning instruments. Now that changes the symphony. I don’t even know, do we even call it a symphony? And so when I think about it measuring and specifically individual partners, I think more holistically like, oh, do I have strings? Do I have percussion? Do I have, when instruments is the music from a consumer standpoint, the consumer’s experience, I am more than happy to always walk a business line leader, a business unit leader through this is actually what your consumer is experiencing when you open the mobile app. See how long it’s taking to load beautiful experience. Maybe we should work on that as opposed to trying to cut particular suppliers or trying to find out the exact precision of incremental measurement. And this is what I mean, I’m pretty pragmatic when it comes to measurement, even though I really, really hard it in terms of things in my toolkit that I would always recommend when we’re thinking about measurement, randomized control tiles, so our RCT very common we learned, this actually really came from biology and medicine, so it’s a very standard tool in media land. I would say geo-based testing is particularly interesting and a good technique to think about. And the reason why I say this, especially if you’re in goods, so less so services, but mostly goods, the distribution of your product across whatever geographies is non-uniform. So you don’t have all your products in every single store and every single store is not exactly equidistant in terms of distance from your consumer. And so because you have that variability of what’s happening in real life, geo-based testing, totally a good idea. If you have highly complex situations where you have all sorts of media offline, online, you have paid, owned, earned, all of these channels, just continuously creating music around your consumers, econometric modeling is something that I also recommend. Sorry, that might have been, that’s the fancy word. We can also say market mix modeling. So m’s, very common and that’s very good for FMCG. So I come from A CPG space, much more beauty and luxury. And so that’s a very common method. On the qual side, again, I am a big fan of listening to my consumers because you never know what you can learn about how they experience your products, experience your services, unless you actually listen and you don’t have to always listen at scale. I know it is tempting to try to listen at scale, but sometimes a focus group, sometimes a shop along, sometimes listening to the customer call center on Sunday evening when there’s some hair disaster that happens and you have to go into the office the next day listening to the consumer trying to explain their issue as someone’s trying to help them from your organization. That provides so much contextual insight that as a marketer, so when you’re thinking about how do I create experiences that resonate with my consumer’s lives, and as a measurement person, I’m always interested where did that data come from? What was the data generating process? Oh, this is the context of those ones in zeros. Oh, okay, maybe it makes sense to use it in this model versus that model. So I think about things like that all the time, always from the perspective of the consumer experience.

Kyle Holloway:

Well, that’s great, and when you’re walking through all of that, certainly the challenge that I hear is that’s a lot of different data points coming from a lot of different types of systems or even different types of data. You may be talking about data that’s more quantifiable. You’re talking about sentiment analysis maybe, or then even how do you take the logs of a call on a more micro scale to actually extract some kind of knowledge from that, or where do you put that? So in your experience, pragmatically speaking, where are the biggest obstacles in actually achieving that? Is it a technological obstacle? Is it more of an operational obstacle or even kind of a sociological where just getting people to change their mindset, what

Cecilia Dones:

All of the above? Yeah, that’s a good answer. When I think about this from a data perspective, I always think about the data discipline as people processing platform. I find in most cases it’s typically not the platform or the technology is the problem. Typically, the initial blockers are the people in the process. And you’ve already alluded to part of the answer, maybe there’s a cultural value that, oh, if I own my data, I get to be able to own my narrative. So I am able to tell what success means. And so, oh wait, that’s a cultural norm that we have to change. So that has to come from leadership and changing cultural values over time. Or it could be, well, this is the process of how the data flows through the organization and we’ve always done it this way and the data is safe and it fits all the security requirements. So why do you want to tap it? Why do you want to change it? So then maybe it’s more of a process thing. We don’t have a process for dealing with other users who want to utilize the data. I would say the partnership with your data teams, the partnership with your IT teams is so critical. Again, this is not a you do it all by yourself kind of thing. It is very much we need a village to be able to do this. And I would say that what’s beautiful about the recent developments in architecture and technologies, data mesh is a thing. So being super worried about having your datas in silos and they can’t talk to each other at all, ever, that’s less so of an issue. And so there are ways to deal with it. I’m not saying that’s the silver bullet. It has to always be bespoke to your specific organization’s requirements. And that’s where I always depend on my IT counterparts, my data counterparts to help, but there are solutions in place. I would also say when it comes to, Ooh, this data is in a weird format, or oh goodness, that data doesn’t look clean, what do we do? What do we do? This is where it gets very exciting with AI technologies because it does help us. And I would say this is my low hanging fruit ish, low-ish hanging fruit in terms of a use case, especially for marketing teams. Marketing teams are sitting on a pile of data that is being underutilized, and this is your strategic moat that makes you different from your competitors because it’s your data. The challenge is it doesn’t look like it fits in a beautiful spreadsheet. It doesn’t look like it fits in an Excel file or a Google Sheets or whatever software to use. It looks like a PowerPoint. It may look like a transcript, it may be in a file format that we don’t really use anymore. And so this unstructured data can sometimes be a hindrance. And again, this is where I get excited about some of these newer AI technologies. There are solutions in place to help address those unstructured data issues. And so working with your IT and data counterparts, suddenly you go from 20 20% structured data to opening up the remainder 80% of unstructured data. And so then, oh, now you can really release your analytics folks and your research folks and be really creative about how can we extract more value from our data. And so I would say it’s that partnership again, that’s so critical and building the processes and the trust between the teams to be able to navigate some of this complexity as a result of having a lot of data, but sometimes it’s not as usable as we want it to be.

Dustin Raney:

Yeah, I would say that from that perspective, an industry that I’m super excited about is healthcare, right? If you think about that, your experience of going to your practitioner, your doctor or the knee surgeon or whatever, and how many times you have to complete the same information over and over again, the facilities there might have leveraged archaic technology in capturing that information. It might still be on a piece of paper, but the ability of these new tools and AI to bring that information and make it usable to help with wellness, with your health to drive efficiencies in that entire industry, is that an area where you’re seeing a lot of growth and opportunity? What specific, and I know you mentioned that you’re more on the retail side of the fence, but maybe share some ways that you’re seeing some of these applications being used.

Cecilia Dones:

Okay, this is the fun part. So more recently when I was brand, I was more FMCG, but more recently I’m helping large and small organizations across sectors in terms of their AI practice. So it could be as, oh my goodness, where do we start with ai? We don’t even have a maturity curve to, can you run a generative AI workshop to, oh goodness, I have a whole bunch of real experts here, but they don’t really know how to do prompt engineering help. Or I have an executive team that uses AI as a verb. AI is not a verb. How do we kind of demystify this and make it relatable and pragmatic? So I help organizations cross sectors do that. So in the healthcare space, I am super excited. The reason why I’m super excited is that a personalized medicine is definitely going to be one of those areas where it will change the way we live, in the sense that imagine if there were preventative medications or preventative procedures protocols, so we don’t have to be on medications that were bespoke to your metabolism, metabolism in your metabolic profile. So every single person, we have our own unique profile. So the way I digest medicines or the way I digest sugar or protein is going to be different If I went to the gym an hour ago or I haven’t eaten since yesterday, or I’m hung over from last night, I don’t know why that became an example, but all those things. So those are all things that today we don’t have ways necessarily to make medicine more bespoke. So imagine if we could make more medicine bespoke to what’s happening in your life right now in the context of where you are. So for example, humidity, weather data, how humidity it is in New York as opposed to in Texas as opposed to in California, very different. So because of that, maybe the amount of dehydration someone experiences will be quite different. And so this is again where we can utilize alt data and all of these newer signals to help make medicine more bespoke. I’m also a very, very big advocate in the mental healthcare space. And so mental healthcare tech I think is another area that is going to be one that’s growing. So you can imagine individuals that maybe are struggling and they feel alone. This is where we can get resources to them at scale using technologies. And for individuals who may be more traditional methods of addressing whatever their concerns are are not working, we can explore new ways of addressing those issues with technology. And so that’s where I get very hopeful. So imagine someone who’s struggling to identify emotions. So There are a class of conditions in which that is true. So imagine with augmented reality glasses, now you have the computer utilizing machine vision to help you see the person you’re talking to. Are they bored? Are they interested? Are they happy? Do they want you to say more? Imagine how that could facilitate communication between individuals. It could be a way of creating connection that for that person who’s struggling to communicate it changes their life. I mean, okay, let’s go into something a little bit more fun. So imagine in the dating scene you’re like, I don’t know what to say. Is this person bored? Was that the right wine to purchase for this dinner? Should I have purchased wine at all? I can’t remember. Do they not drink those kinds of questions? Again, this is where AI technologies could potentially help change the way we interact with each other in a positive way. I’ve described in a kind of fun and flirtatious way as the dating example, but also in a negative way. And so this is where I always, always caution. So I teach and lecture a lot in AI ethics, and I’m always, there are two ideas that I tend to talk about a lot. The first one is data is dynamic. It is not static. So the ones and zeros, they change, and you need context to use it appropriately. So just grabbing something off the shelf and saying, oh, I know this data. No, you don’t know until you understand the data generating process because you don’t know if the conditions have changed, the context has changed, which makes it appropriate or not appropriate for use case. The second thing I would say, and I think this is important regardless if you’re in marketing or not, is that technology is neutral. It’s a machine, it’s inert. What is not neutral is people. So we have a choice to practice agency to decide do we use these technologies to help each other or do we choose to utilize these technologies, make different choices. But the good news is we have that choice and we still have that choice. Even now, despite all the noise that you hear in the marketplace, we still have that choice,

Dustin Raney:

Man. So much to talk about here. You just opened up a Pandora’s box of things that I want to, this might be an extended version of real talk, Kyle. I don’t know. I don’t know how we’re going to keep this packaged. So going down that path of choice, I tie that to a big word that’s being thrown around these days in consent. So to have choice now, if we’re flipping the ownership and control of data from big organizations to more self-sovereign like us having choice of how my data is used, do you see AI playing a big role in enabling that? And then on I guess the other side of that, how do we alleviate the concern that when all this data is out there, that big brother isn’t going to take it and start using it against it? So maybe some thoughts around consent and the use of data and the control.

Cecilia Dones:

Yeah, first of all, I love the alliteration, so thank you. Again, thinking about it from a consumer standpoint, I think the challenge, at least I have as a consumer is that I’m always trying to balance the trade-off between convenience. Here, please take my data because I want you to remember my name, so it’s faster for me to replenish whatever I was purchasing versus control. Oh, well, no, I gave you my data for this transaction, but please don’t. I know you have it, but please don’t use it. And I think that’s the challenge for most consumers and myself included in the sense that I don’t have an intuition around is this really okay and do I really want it for that reason? Because I can always imagine scenarios where maybe it is appropriate or maybe it’s completely inappropriate. So because data is dynamic and always require context, I think the idea of consent, I think it’s a tricky one. I do not have the answers, but I think we need to start to nuance the idea of consent such that it’s not a checkbox to try to make the big box go away. So you can actually read the article, but more so a meaningful consent. So really translating whatever is the tiny print into something that is more meaningful for the average consumer, trying to understand why do I have to click this box in the first place? So when I think about privacy consent, I also tend to think about confidentiality, and I think confidentiality is actually much more of a pragmatic way to think about things in the sense that my data is out there, I have a digital footprint. I am very curious to find the magical person who does not have a digital footprint. And because I take that as a given that we cannot erase our digital footprints and there is ghosts and exhausts and all the fancy charms we want to utilize to describe that, okay, my data is going to be out there, then what I want is not privacy. What I want is if you’re going to have it, you better make sure that if I want it confidential, you keep it confidential. I don’t want this data breach stuff. I don’t want this data leak stuff where my information is going out there. We’re going to have to figure out a way for me to trust you that if you’re going to hold my data, you’re holding it safe. And when I want you to keep it confidential, you’re able to keep it confidential. So it’s a slight nuance on the current conversation around privacy and consent, but I think it’s one that is even more relevant now, especially since you have large language models that are gobbling up everything in the internet. You have all these other AI technologies utilizing and creating as much data as possible. And so I think nuancing our definitions, given the advancements we have today, I think is a fair conversation, not an easy conversation, but I think it’s fair to start thinking about it.

Kyle Holloway:

Yeah, I think that whole conversation is such a double-sided conversation. There’s so many aspects to it. Even as you’re going through there, it’s like, yes, you want to be able to ensure a degree of privacy or of security of the data. At the same time, I want Claude to actually answer correctly of things, and those things may require that data. And so now how do we navigate that in that both large language and even small language models require lots of data and very specific data. So then how do you authorize your data potentially to go into an ecosystem that is informing a very targeted LLM that’s going to bring value? And so then it becomes that balance between the privacy and the security and the value that may be presented. I mean, it is just become a very complex world in that aspect. And there are no easy answers because you can go all the way down to, okay, well, we’re going to put everything into digital wallets and secure, and we’re going to monetize people’s consent by saying we’ll pay you to give us some of your data. But even that just creates then a world where not everything is known. And then therefore you have to understand that there may be gaps in the knowledge of the services that are being provided back to you. And so how do you reconcile that? So yeah, it’s a really interesting concept. To dovetail from that a little bit. I talking about trust and you say you talk a lot about AI and teach and train on AI and stuff. Where do you think trust is going with that? I mean, there’s a lot of information around hallucinations and awkward results coming out of AI platforms. Unfortunately, I was even reading one recently in the healthcare space on the denial rate based on some ai, and it was like the analysis I was doing, it was determined to be like 90% false positive, and so it wasn’t responding correctly. So where do you see that going and how do you apply that even into going back to our marketing and advertising ecosystem, that degree of trust with ai?

Cecilia Dones:

Okay, so I don’t have a magic, what do you call it, that crystal ball. And if I did, I’ll be very honest with you, maybe we wouldn’t be having this conversation. We’d be on a beach exactly discussing

Kyle Holloway:

Really wealthy.

Cecilia Dones:

But what I can say in the near term that I think is critically important to think about. So I talk a lot, and this is a very standard framework. I talk a lot about human in the loop, which means making sure throughout the life cycle of an AI project, the life cycle of an analytics project, life cycle of a data project, you’re always making sure that there are inflection points of feedback from people, from experts, from diverse individuals to help kind of guide, does this make sense? Is this intuitive? Is there potentially an angle that could be missing? And so human in the loop is something I am a very big fan of. There are very simple ways of just mapping a workflow and saying, okay, let’s think about this. Okay, here, this is where we’re going to insert a person to just kind of check in. Is this happening as we expect it to happen? Or is it producing emergent properties that we weren’t expecting like hallucinations and therefore how do we correct for that? So human in loop, big fan, very much regardless of the project, regardless of the sector that we’re talking about. I’m also a fan of, and this isn’t, I would’ve said a year ago this was really novel, but now it’s starting to mature, which is excellent, which means we’ve learned a lot of things. And therefore, if this is new for your organization, good news, there’s lots of resources around it. I’m a big fan of rag. So retrieval, augmented, generative models. And what I mean by that is do we want the entire internet to be producing a specific piece of content for your consumer? Maybe not. Maybe directing a rag model to look first inside of your organization’s data assets and inside the corpus of institutional knowledge and firm specific knowledge to generate the appropriate prompt. And then utilizing the workflow to produce an output could be a way of, I’m oversimplifying, so if anyone is really technical that’s listening, feel free to roast me on it later. But pre-filtering some of the information that would be utilized by the models such that when you look at the output, it’s less likely to be a little bit awkward. It’s less likely to be a hallucination, it’s less likely to be an unexpected outcome. So HT, HITL, human in loop and then rag RAG, I would say these are two methods that if you’re a marketer that’s interested in collaborating with your data and IT counterparts, first of all, it’s fun alphabet soup. So let’s just add to the jargon list. But I would also say it’s a way of extending that curiosity to say, wow, that’s kind of cool what you do in your space, help me understand that better because this is the consumer experience I want to create in the marketing space, and how do we help each other make that happen for our consumers?

Dustin Raney:

And I would say that brands represent a persona and you have a brand image. So being able to speak in a brand voice almost requires that you don’t want the open internet speaking for you, like you said, you want to kind of keep that messaging on brand, on who you want to project to your consumers one ethically, making sure you’re not out there lying to your customers about things, the hallucinations, but just in the nuance of how you say things matters, how all three of us are going to have the same conversations differs because we’re all diverse and unique and we have our own persona. A brand. Rick kind of represents a culmination of their customers. So I think that’s spot on. I think where brands should focus in the AI space. Cci, I’m going to do something that I would never do. I’m going to come off of a super interesting topic of AI to something that’s so 2020, okay, I can’t believe I’m doing this. I’m talking about cookies, I’m talking about cookies. Third party cookies. Now you’re an assistant professor at Columbia. At university, Kyle and I have been preaching, we could write a complete, what’s it called when you graduate and get your master’s, a dissertation on the lifecycle of cookies in advertising? Are you talking about this with your classes? Do they even care anymore? Have you guys moved on? Because when it comes to measurement in the utopic state, everything’s addressable. Everything can come back to some form of id, and the cookie was the easy button. So any thoughts there?

Cecilia Dones:

So always going to teach the framework. So zero party, first party, second party or third party data, even fourth party data. That’s a fun one. Still teach it. And the reason why is I think it helps to conceptualize the level of abstraction from the relationship with the consumer. Oh no, I said fancy words. Basically, the more you’ve provided value as a brand to exchange for information with your consumer, the closer it is to zero and one, and that’s the highest quality data because the consumer has made a meaningful consent with you to say, yes, I want to give you my birthday because I really want that free whatever when it is my birthday, versus some probabilistic model that’s a third party data source that may sort of get your gender correct, may sort of get other demographic features correct. And so that’s important I think, because I think it helps to conceptualize how close to, I’m going to say lowercase T truth we are when it comes to that data asset when thinking about the different types of data that we can generate from consumers. What I would say, and I say this, the reason I’m pausing is I don’t think I say this often enough to students, but I do say this in practice. So when I’m speaking to clients and running workshops and things like that, I’m very reticent to go into the somewhat detailed details of, look at the different hierarchies of data, look at the different categorizations of data. This sounds like a topology. I just said topology. What does that mean? I tend to try to focus everyone on what is that value exchange with your consumer? What is why your consumer would want to give you this data? And then I would say, put yourself in your consumer’s shoes. Would you give this data or is it hashtag cringe? Hashtag hashtag creepy, hashtag whatever. It gives you the ick. So that’s where I always want to focus marketers on your consumer, so well, your brand. So well play to your strengths, be the voice of your consumer when everyone else is trying to abstract them into ones and zeros,

Dustin Raney:

Man. Well, you stayed on point on keeping this about people, keeping it about humans, and that’s awesome, cc, and I think it’s certainly a very, in some worlds, a kind of provocative way of approaching a role in this techie marketing space is to keep it on center. It’s like how you want to be treated, treat other people like you want to be treated. You’re a marketer who cares about cookies unless they’re in the pantry. We are running low on time, unfortunately, we could go much deeper and certainly hope to have you back as a return guest. One of the kind of standard wrap up questions that we always like to ask as we part is if you fed the data about CC into ai, what are the three words that would produce to describe who you are?

Cecilia Dones:

Curious, compassionate, I would say evolving.

Dustin Raney:

I can see all three. Those they’re great words, and I think it absolutely describes at least what we see already in having met you and having you on the show today. Any closing thoughts that you want to leave an audience? I know you speak on these topics all day, all the time. Anything that you would like to just leave as a last leading statement?

Cecilia Dones:

I say this to students all the time because I know with the AI question mark, everyone’s expecting disruption. Everyone is expecting uncertainty, everyone’s expecting maybe the sky is swelling for some, the message I want to say is, you are enough and we’re in this together.

Kyle Holloway:

Love it. Yeah, that’s a great parting statement there because it is a very dynamic world, and that’s great to hear. So cc, thank you so much for your time. Very excited to really continue to absorb all this information that you’ve provided today, and I know it’s super meaningful to our listeners, and I think it’ll really give them a lot to mull over, maybe sitting at a Starbucks, drinking a coffee, and just really thinking about some of these because there are some pretty topics there, and it’ll probably take some time apart to really feed in. But thank you so much for joining us. Do look forward to having you on again. And for our listeners, thank you for being with us and just want to say you are enough. I love that. Hopefully this is just something that can help drive you forward in your own careers. And so you can certainly find all of our podcast episodes at Acxiom.com/real talk or find us on your favorite podcast platform. So thank you and everybody, have a good day.

Dustin Raney:

Thanks for listening