#75 - Unlocking Incrementally in a Privacy-First World | Real Talk about Marketing an Acxiom Podcast

In this episode of Real Talk with Anant Veeravalli, the discussion revolves around the evolving data landscape and the necessity for strategic partnerships to achieve holistic measurement. The team unpacks the importance of ethical data sourcing, privacy compliance, and the utilization of clean room environments like Snowflake and Databricks to bridge data gaps. Enabling secure and scalable data connectivity and facilitating real-time data sharing is key for brands to derive meaningful intelligence, including predictive modeling and AI-driven insights. This episode is essential listening for anyone focused on governance, security, and future-proofing data systems.

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Transcript

Kyle Holloway (00:00):

Hello, I'm your host Kyle Holloway, and I'm here with co-host Dustin Rainey. The world of advertising and marketing continues to evolve rapidly, which is both exciting and frustrating at the same time. So I'm excited to dive back into the topic of analytics and data on today's episode. But before that, Dustin, we are thrilled to let you know that real talk about marketing has won a silver award from W three for our podcast episode titled Driving the Future of Automotive with Aaron Schaefer of General Motors. As a reminder, you can find it on your favorite podcast player, or you can also find us on Axioms YouTube channel. So if you missed the episode when it first came out earlier this year, we encourage you to give it a watch or listen. So with that said, welcome to Rail Talk and today we've got a really good one lined up.

(00:51):

Joining us is Anant Veeravalli Global Chief Analytics Officer at Axiom and Media Brands, and he's someone who's been deep in the analytics world for a long time, helping brands make sense of a pretty wild and ever-changing data landscape. So we're going to talk about how clients' needs have changed because let's be honest, the past few years have completely flipped the ecosystem on its head. We'll dig into how advertisers are proving that what they're doing is actually driving real incremental business results and how clean room solutions like Snowflake and Databricks fit into all of that. Anand's also got some great perspectives on how brands are using their first party data in more strategic ways and what kinds of partnerships are making analytics stronger, including some unique ones Axiom has to access. So Dustin, you ready to dive in?

Dustin Raney (01:43):

Let's go and yeah, analytics, we all need to know a little bit more about what's working and what's not working. Right.

Kyle Holloway (01:51):

There you go. So Anant welcome and really appreciate you joining the podcast today. So do you want to give us a quick snapshot of your background and how you came to your current role?

Anant Veeravalli (02:02):

Yeah. First of all, thank you Kyle and Dustin for having me on your show. I'm excited to be here. My journey has been one of those growth your ranks agency guy who started in the marketing and advertising for about 20 or so years. I come from a very traditional data management and data practice background, so I've been always grounded in the world of data. All my advertising and marketing carrier, just like anyone who's been in the agency world long enough, it feels like you become someone who's more like a Swiss Army knife. You get thrown into various challenges, various client problems, client needs, and over time you start to kind of dabble into a multitude of things, whether it's things around industry, verticals, products, solutions, local, regional, global delivery, you name it, over a period of time if you're lucky enough. And I feel blessed in that regard that I've had the opportunity to do this for the last two decades. You get to kind of experience firsthand the world of advertising and marketing. So I've also been quite lucky to be in three of the five or six advertising holding companies.

(03:16):

So I started my s tateside career at dentsu performance marketing or customer experience arm Merkel back in the late two thousands. And after a pretty long career there I had moved over to Omnicom. And as my career has progressed, my foundational data and tech background has groomed into starting to get closer to where the problems lie and where the decisions are made. And the path that I chose of moving from data tech to orchestration and campaigns to analytics has been a journey in the making for the last two decades. And in my current role, as you dually introduce myself, I lead analytics globally for Axiom and media brands. We have a very strong army of specialists of about 1100 people across the globe that are supporting our clients in every corner doing a multitude of things from being consultative in a growth mindset to audience analytics to data science and any advanced analytics as well as data engineering and visualization. So it's been a humbling journey so far.

Dustin Raney (04:21):

Yeah, thanks Anant man. And just thinking back over your career as you were describing that, starting in that data foundation, going into campaigns and stuff and now culminating in analytics, would you say that the sophistication like your careers culminated to the epitome of where the biggest challenge is? Have you seen that analytics and you landing here? Did you see that coming?

Anant Veeravalli (04:52):

That's a great question. I would say more recently, yes, if I was to dial back about five to seven years, it felt like I've been very intentional in my path in analytics. As you could imagine, there are many facets of analytics and it's important to understand how you get well grounded on how every element of analytics adds value to a client's business objective and outcome. But the journey has been quite interesting just because as I have grown in advertising and marketing career, it feels like the advertising and marketing ecosystem itself has grown as well quite dramatically and drastically, if I may say, everybody is probably seeing and failing some of this. But the fundamental changes have come in the form of AI disruption, which seemingly is at top of mind for almost every marketer across the globe. Data deprecation with third party cookies has been a big topic these last couple of years. We know digital fragmentation and retail media proliferation has created a lot more disruption in understanding continuity of data and consumers are getting very privacy conscious and by design that means more and more countries are starting to regulate privacy loss, which is rewriting the rules of how marketers and advertisers need to use and source data ethically to drive tangible connectivity with their consumers. So I would say that the evolution is just not personal, but it feels very professional the way the ecosystem has evolved as well.

Kyle Holloway (06:27):

So that's some really great points there. And so the question is also for brands, historically analytics was kind of way buried deep in the back office. I mean it was like, oh yeah, there's those quants that sit back there somewhere and they're doing something and then you had more of the client facing side that was, there's kind of a chasm there and we've seen that dramatically change over the last few years where it feels like that kind of the analytics capabilities and even the department itself has moved much closer to the front of office. Absolutely. When you're working with clients, because you work with a lot of clients and you've seen a broad piece of landscape, how are they embracing that? I mean, is it going well? Do you see that that's functioning up to the level that we may be expecting it to or hoping it to? Where does it sit?

Anant Veeravalli (07:26):

Yeah, it's a great question and I don't think there is anything wrong in being either of those personas that you explained, right? I think we need both types of personas to be successful. What I would say is fundamentally what clients are expecting to get in terms of the value portion out of analytics is very different now than it has been in the past even two to five years as I have had the luxury and opportunity of interacting with a multitude of clients, both regional to us but also globally and having had exposure to a multitude of industry verticals, there is probably three or four things that is almost foundationally consistent in how marketers have evolved in their optics of analytics. It's become very obvious that marketers are no longer just satisfied with what we call vanity metrics. Like media efficiency for example, used to be a big thing like five years ago.

(08:25):

That's a gold standard of what people are looking for. But now marketers really want to understand what is driving growth, how do I retain my customers? How do I drive incremental lifetime value? So it feels like marketers are starting to get more intentional about connecting the dots between the data, the relationships, as well as the objectives and outcomes that they're looking to accomplish. We also know that in many instances clients are struggling wanting to get more expeditious insights. I hear time and again that as an organization, CMOs come and say, I want to be an insight led organization, and insight needs to come with agility and speed, right? People want insights in real time, they prefer more self-serve accessibility platform as a service is becoming much more near and dear gone are those days where we would build static dashboards or refresh data or elts and like a monthly or quarterly cycle and still expect clients to intelligently action on those data points that we provide them.

(09:33):

Also, it's becoming important that having a more unified view of a consumer is critical in a client's mind to be successful in engaging effectively with the type of connectivity they aspire to have with their consumers. And by design, that means we need to break down a lot of fragmentation and silos that exists within tech stacks or technical organizations. It feels like there is a very fundamental need to have a unified view of all of the paid owned and earned touch points, not just within a client's ecosystem, but across the client's ecosystem, any vendor partner agency that they work with. I would also say that clients are traditionally used to looking at analytics, and I think you Julie stated this towards the beginning of your question is it's looked at as something that talks about what happened in the past, but clients are starting to look at it as what is going to happen looking forward, which is becoming more predictive and not just retrospective.

(10:37):

And we are seeing a slew of our advanced analytics capabilities come to bear, especially in the world of AI and ML that is starting to look at a lot of predictive outcomes models and forecasting and helping guide smart or intelligent decision making for our clients that need to happen in a more expeditious manner. And we know that budgets are continuing to shift or shrink year over year given the larger macroeconomic or sociopolitical landscape. And that uncertainty creates a lot of uncertainty for our marketers as well in terms of the type of budget or the type of freedom that they have in their decision making to be able to drive strategic insights in decision-making, in planning and in optimization. And then the last thing I would say is brands recognize that there's a lot of competition today in the marketplace. If you look at any segment, there is a slew of traditional players who have been around for a while and then there is a slew of new boutique bespoke startup type of companies that are coming in and disrupting what used to be status quo.

(11:45):

And brands recognize that this is starting to create a threat to what they used to take for granted. And by are starting to kind of challenge themselves and their partners to be able to understand how do we drive more visibility and accountability across the entirety of the funnel that gives them both visibility and clarity around brand and performance, which largely used to be separate conversations, even five years in the view. And that unified strategy of looking at both short-term, midterm and long-term implications and impact of both brand and performance is becoming a key linchpin and having what I would call a growth mindset or a growth

Dustin Raney (12:27):

Man, there's a lot to unpack right there. I know. No, it's incredible. I think our listeners will be able to stop and take notes. You basically just laid out the thesis of what brands need to be looking for and you've seen over your career over the past 20 years, it used to be easier when you didn't have so many channels. The communications just gotten so bifurcate all the different streaming. Now it's not just linear tv, I've got all these different places that I can go buy media on, and I think one of the things that marketers at the end of the day, they're like, okay, I've got this dollar. Where do I spend this dollar to get the next best return, the most return on the dollar and incremental gain, you said incremental as you were talking, incrementality is so hard to actually unlock. How are you seeing your expertise, people like ax, what you guys are doing help unlock that incrementality, true incrementality, where do I actually spend these dollars to get an incremental return in the sense that I know that my customers are probably going to see my brand on maybe meta or some social channel, maybe on CTV and search, but where should I spend more providing that incremental

Anant Veeravalli (13:59):

Return? Absolutely, and I think it's a very fundamental question and candidly I would say incrementality has become a fundamental standard. It's no longer a nice to have and there's an expectation that we don't rely too much on what we would call traditional ways of measuring success, which used to be either a last click or multi-touch type of attribution models that we know are quite challenged in this day and age of privacy and fragmentation. And it feels like if you go talk to, you mentioned Googles and Metas, there is the trifecta of what they call unified measurement methodologies, which is your 300 monster or three musketeers of marketing mix modeling, some sort of experimentation and an always on incrementality setup. And those three approaches have largely bundled themselves between providing clarity on how I can be more nimble and agile so that I am doing the type of experiments that you mentioned, whether it's geo lift or match market test or audience holdouts in a much quicker expeditious manner, be able to learn from those experiments and be able to course correct tweak, optimize, or just take current course depending on how we can truly identify where that net incremental impact of our marketing lies.

(15:33):

Also, if you think about incrementality, if you think about the type of platforms that we have to bear, right? You mentioned Google's metas, Amazons, there is an expectation that we are partnering with these large tech organizations to be able to effectively build what I call a co-authored learning agenda library of sorts. Because a lot of times what happens is there's this perception of the partner of vendor grading their own homework and sometimes it's valuable, they are the best to kind of tell what happened. But it's always good to have what I would call an unbiased perspective into what we are learning, but more importantly how we put those learnings into practice in terms of decision making. So having some sort of a continuous learning agenda that allows us to recalibrate some of our experiments and models that allow us to basically learn over time but learn in a meaningful way over time has become extremely important.

(16:33):

And even in the world of marketing measurements. So the MMS have become very agile and granular. So in a very intentional push of gravitating away from MTA, more and more companies have invested in figuring out how do I build speed and agility into the MMS that traditionally used to run every six months, every quarter now to monthly or even weekly basis. In fact, within the Axiom IPG family, we have real world examples where clients use what they call real time MMM, which gives them a view not just on a weekly basis but on a daily basis of combining media sales macro data for almost near realtime ROI visibility because you could argue getting realtime insights. Does that translate to realtime decision making? I would argue it's 50 50. So I would say the trifecta of unified measurement driven off of mms, some sort of an always on learning agenda with our partner ecosystem and platform ecosystem and preferably some sort of an experimentation engine that is built for speed.

(17:47):

And within IPG, both of you're probably familiar, we do have the experimentation lab, which becomes a portal of sort where we can kind of crowdsource the various types of experiments that we can bring to bear, but also allow for clients to have an easy access to all the types of experiments and the learnings from those experiments in a one-stop shop manner. And I think it's proving to be extremely powerful, not just for us to be able to expedite decision making, but also to be able to give clients the data points, the proof points that they're looking for to truly feel confident that incrementality is moving in the direction that they would like for it too.

Kyle Holloway (18:23):

Alright, yeah, it does feel like we're getting a PhD here, right? Because you're giving so much. Sorry, so much great information. Let's peel the onion just a little bit and talk a little bit of how tos talking about the partner ecosystem, all this data, it doesn't just sit in a partner platform, it doesn't just sit even in the first party context, it may be other partners involved and you're trying to bring it all together. And then to your previous comments around privacy and a lot of the considerations, we've seen this proliferation and focus towards clean rooms in particular with some of our really great partners like Snowflake or Databricks. Do you want to unpack that a little bit? How's that working? What do brands need to be thinking about in that context and what are you seeing in the marketplace?

Anant Veeravalli (19:20):

Yeah, I think that's quite a loaded question as well. Kyle. I think you guys are teeing up the right questions and it kind of spills out a lot of ideas in my head. Partnerships candidly have become an essential necessity of sorts for organizations like mine to be successful. So if you just think about any brand, any client, any organization around the world, not one brand, not one agency, not one vendor has all the data that a client or a brand is looking for to be able to measure performance holistically or measuring the brand holistically. The world that we live in, as we've moved from being more terrestrial to more digital budgets have shifted largely I would say 60 40, 70 30 digital terrestrial. Now it feels like fragmentation is too far gone north and with privacy and compliance challenges on top of it, the necessity to be more connected with our partners, the necessity to be more collaborative is becoming more and more necessary.

(20:28):

Otherwise, you are all going to have silos queued view of what the truth is and the truth is not necessarily always actionable In the world of Axiom, I would say the partnerships have been key in a few areas. The first and foremost would be in our multi-sourcing data strategy. If you think about it, we have one of the largest ethically sourced part of data in the world, and we are one of the only agencies that has a Forester five auto rating on privacy and security that puts a lot of responsibility and ownership, but also the authority to be able to lead into our partners and drive credible connectivity and interoperability with them in terms of bringing the connectivity. These partnerships allow us to bridge the gap between first party, second party and third party data in a privacy safe environment. And that's where the notion of clean rooms come into play, right?

(21:28):

If you think about our integrations with Snowflake and Databricks and by extension with other partners as well, right? If it's Google's a DH, meta AA or Amazon Marketing Cloud, we can act as that trusted kind of liaison, the trusted connector between a brand's first party data and the broader marketing ecosystem. And we are entrusted with enabling privacy compliant, ethically sourced, safe, scalable kind of data connectivity, which by design enables a more full or whole measurement or analytics enablement on top of 'em. We do have in many instances through our enterprise partnerships on the data side, exclusive data deals that allow us to give scale and reach of data across a multitude of markets across the globe. And that exclusivity allows us to bring more data in terms of prospecting data, in terms of enrichment to our clients. It also allows us to be able to build prefab connectors.

(22:33):

If you think about it in the space of platform as a service, clients are looking for more turnkey solutions. And what these partnerships with Snowflake and Databricks have allowed us to do is that it has allowed us to create more what I would call ready to solution type of kind of product offerings that we are able to easily plug into a customer's ecosystem, bring in client data into the platform, look at cross-platform connectivity and be able to add value. And our role with Snowflake and Databricks candidly, even with Snowflake recently, we have been partnering with them to enable Snowflake intelligence for our clients. And that is now bringing to the forefront a lot of their cortex models to be able to drive AI conversational insights and dynamic visualization at the turn of a button, not only to the technical users, but also to the business users. So beyond the notion of clean rooms being a privacy safe environment, having these wrappers on top of those clean rooms are allowing our clients, both business and non-business users to get access and visibility into what happened, but more importantly, what is going to happen synonymous with what an agency or a vendor or partner would've done traditionally.

Dustin Raney (23:48):

Man, that's great. And just for our listeners to maybe visualize a little bit, especially on the marketing side, they're trying to activate these high value audiences now they're doing a different way maybe within Snowflake or Databricks and publishers have their data there as well. So basically what you're saying is these linkages, the very things necessary for you to understand or even close the loop, we're actually able to in many ways kind of harden or make those better, a higher fidelity match, even in the advertising ad tech ecosystem in a very privacy compliant way, which then allows that sharing across advertiser to publisher to be brought back into an analytic environment in some ways, like a one-to-one, you know that I wanted to advertise maybe on Disney or Conde or wherever it is and say they have their data sitting in snowflakes as well. This enables some of those use cases, right? A

Anant Veeravalli (25:02):

Hundred percent. And with Snowflake specifically, as more and more clients enterprise data store moving into Snowflake and a lot of their heavy lifting is done through Databricks are able to enable zero data movement, which allows for real time data sharing between client and partner ecosystems has become a game changer. Gone are those days where we would spend days at length transferring through secure means like s ft p or any other means like SharePoint, large dumps of data in a transactional manner from one environment to another, extract that information, load it into a system, transform it, and make it accessible in some sort of a presentation layer. A lot of that kind of middleware has largely disappeared because of a lot of the sophistication that our partners like Snowflake and Databricks have brought to bear. And now strap on the AI element to weight. The power that these platforms are offering is brilliant.

(26:05):

It drives scalability, it drives interoperability, it drives advanced modeling. It brings a lot of the machine learning powers from these platforms, more turnkey to our clients. Now think about the power that this can drive, right? This can drive things like how do I predict customer lifetime value? How do I predict customer churn? What do I do when I need to start thinking about next best actions? Things that have been challenges because of the archic nature of having to bring complex data make sense out of that data both in structured and unstructured manner and being able to create meaningful outcomes out of that has largely been condensed through the sophistication that these players like Snowflake and Databricks have brought to us.

Kyle Holloway (26:52):

So in conjunction with that then Snowflake obviously being a partner, we've recently kind of announced the collaboration between Axiom, the Trade Desk, leveraging Snowflake as the backend rolled out as true intelligence. Why don't you give our listeners a recap of what's being brought to the table and why it's transformative?

Anant Veeravalli (27:15):

Yeah, super excited about this and it's something that we've been working on in 2025. True Intelligence is a solution. It's a close loop person level measurement solution that finally connects ad exposure to both online and offline conversions. And this is done even without requiring extensive first party data or even compromising privacy. There are three elements to it. It combines Axiom's real id, as you all know, it's our durable privacy safe identity spine, the Trade desk coca platform, which allows us to get access to log level and exposure data. And then the UID 2.2, which is an open source identity layer that ensures interoperability across the open ecosystem. So together with these three elements, we are able to provide our advertisers and marketers with true incrementality measurement. Think about it, not just attribution, but true incrementality measurement that allows our clients and brands to understand which campaigns, which audiences, what creatives actually drove new incremental business outcomes.

(28:29):

So this is an evolution on the question that we talked a few minutes ago, which is around incrementality. This is kind of putting the nail in the head and saying this is a closed loop person level way of doing incremental measurement to drive incremental kind of business outcome for our clients. There are probably about three or four steps, I'm not going to go into too much detail here, but it basically allows us to go through a very progressive four step process. I would say first and foremost is brands activate their audiences using either their own first party data or Axiom data within Trade Desk coka. After the campaign flighting is done, we get log level data from the trade desk using the UID 2.0 and they're matched to conversion data using Axiom's Real id. This is where the magic happens, which is getting to more accurate person level measurement of incremental lift.

(29:24):

And we had in our initial pilots when we did this, we saw lifts up to 85% for activity that was run through trade desk, which in comparison is at least 30 to 40% higher in fidelity than what has been seen in the past. So clients are quite excited at the prospects of how much fidelity this is able to offer them. We also then apply advanced machine learning and statistical modeling, audience modeling to uncover which segments, what creatives, what media drove the results. And ultimately this helps us be more tactical and providing recommendations to optimize subsequent campaigns. And the beauty is you mentioned Snowflake, all of this happens in a very privacy conscious clean room environment. So this means that we're maintaining compliance to the T while scaling truly across the open web and this scalable measurement strategy that combines identity, combines marketing data and outcomes in one interoperable privacy compliant architecture is the beauty of the true intelligent solution.

Dustin Raney (30:32):

Man, we really couldn't have time that question any better. It was like a culmination of the entire conversation. Incrementality all the different components of modern marketing and analytics and the ability to tie more impressions to conversion events, retrain some of those lookalike models even to go back in and enhance who you go after and communicate with next. And that is media optimization at its finest with privacy wrapped around it. So super excited about seeing what happens with true intelligence and seeing more case studies come out. So we'll be sure to get you back on or not. With that said, we are starting to get a little bit low on time. So we do have a standard wrap up question. If you fed all the data about Anant into ai, what are the three words it would produce to describe you?

Anant Veeravalli (31:39):

Oh wow. I don't know if three words would unravel the complicated me speaking. You're talking about almost five decades worth of intelligence. I don't know if I've given it enough context or not for the AI to actually have meaningful answers, but jokes apart. I would say it would come back with three objectives. One is probably ambitious throughout my career. I've always challenged the status quo in a meaningful way, right? I'm not a rebel, but I'm looking to kind of push the boundaries of what I can possibly do, but in the context of helping myself and the organization that I'm representing. So ambitious is definitely that would come to mind. Two, intentional. I always lead with intent, but I am someone who also leads by example. And I do want to make sure that we're being very thoughtful and strategic in the way we approach the world of advertising and marketing, but from the lens of using analytics and measurement as a key tool in driving value for our clients.

(32:53):

So I would say intentional and strategic would be the second bucket. And third is, this is something that we embody here at Axiom and IPG, so I'm going to use that as a cliche, is AI would say that I am the connective tissue, right? Our own kind of transformative model has been the three Cs, which is connected insights, connected platforms, and connected people. And I am someone who is intent in bringing together the people platform and data to ensure that we are able to travel this vast ecosystem of structured and unstructured information to drive meaningful intelligence for our brands alike. So I would say that would be the last kind of objective that comes to mind.

Kyle Holloway (33:38):

Yeah, I love that. And I would affirm that all of those are definitely a descriptor of you. I mean, you say those and it's like, well, yeah, of course that's anon, that's how I've always known them. And I think they're also interesting that they almost triangulate around analytics, right? It

Anant Veeravalli (33:57):

Does. I'm a data-driven person, Kyle, you are. It's fortunate or unfortunate. That's me.

Kyle Holloway (34:02):

No, that's great. And at the forefront of it, an ambitious part of, it's not like just old tried and true methodologies, but you're always pushing the boundary. You're always looking for new ways to do things and to really drive more and more value so that incrementality not just in what the brands are trying to do, but you're always trying to bring incrementality to their business just to help them move forward. So man, it's been great having you on. I really appreciate your insights. Like I said, I think we'll definitely need to use AI to pull out all the key nuggets of what you said because you put so much out there. It was like a gold mine of stuff. So thank you, and hopefully our listeners were able to get a lot from that and really appreciate everyone joining us on these podcasts.