The American auto market has often been driven by emotional purchases: You see a car that you love, and you drive it off the lot the same day. Cars have long held an intimate place in the American imagination, aided by a media industry that loves auto—just think of iconic cars like the 1961 Ferrari 250 GT SWB California Spyder from Ferris Bueller’s Day Off, James Bond’s 1964 Aston Martin DB5, or the 1966 Ford Thunderbird from Thelma & Louise.
Consumers aren’t used to waiting to bring home their new wheels—but that’s exactly what many have had to do in recent years, thanks to a global semiconductor shortage that upended not just the supply chain, but also the auto retail model that’s existed for over 50 years in the US.
Fortunately, it seems that the worst of these supply chain issues is behind us, and vehicle inventory is forecast to reach pre-pandemic norms in 2024. While recent years have been marked by higher prices and interest rates, keeping many consumers out of the market, vehicle prices should decrease in 2024 as the supply chain recovers and the industry is forecast to see constrained growth, giving automotive brands an opportunity to capitalize on pent-up consumer demand.
For marketing and advertising leaders, the key to making the most of this opportunity will be to understand their target audiences’ behaviors, preferences, and perspectives, and to adjust their strategies accordingly.
While 66% of consumers are interested in purchasing a vehicle within the next three years, affordability is still top of mind as prices and interest rates remain high. That doesn’t mean consumers are unwilling to invest in new vehicles, though: In fact, the majority of in-market consumers intend to purchase a new vehicle, which represents a shift from previous years.
To earn consumer dollars, advertisers must understand what their specific audiences care about. Millennials, in particular, present a notable opportunity as not only the largest demographic group in the US, but one that’s demonstrating significant interest in purchasing vehicles in the near future.
As pent-up demand drives purchases in 2024, auto marketers should focus on nurturing brand loyalty, addressing consumer interest in electric vehicles, and making the most of digital advertising opportunities to reach audiences where they spend their time.
In a crowded marketplace where consumers have a wide array of options, dealers and brands must carefully consider how they can cut through the noise and foster brand loyalty.
Today’s consumers want to know what causes and core beliefs they’re supporting when they buy from a certain company. Gen Z and millennials, in particular, have indicated they want to support brands who do more than just sell goods and services—they want to build relationships with companies that are making a difference in the world, making brand values a worthy differentiator in creative messaging.
For some brands, leading with brand values could mean highlighting certain social causes, such as sustainability, as almost half of consumers who either currently own a vehicle or intend to buy one in the next three years favor brands that support social issues and are environmentally conscious.
Be wary, however, of coming across as inauthentic. Consumers today have sensitive radars for insincerity, and if you choose to focus on brand values in your marketing, it’s essential your messaging aligns with your actions behind the scenes.
Speaking of environmentally conscious consumers, demand for hybrid and electric vehicles (EVs) is on the rise: Revenue for EVs will rise 18% this year compared to 2023, and according to a GWI/Basis Technologies survey, close to half of consumers think EVs are the future of transportation. While gas-powered vehicles continue to reign supreme for now, the majority of in-market consumers are willing to consider fully electric or hybrid cars, and adoption is set to grow in the coming years as these models become more affordable.
The clamor around EVs comes against a backdrop of ballooning gas prices and growing consideration and sentiment around sustainability. And from an automaker’s perspective, laws in both California and New York requiring all new car and light truck sales to be EV or emissions-free by 2035, and a new federal regulation intended to guarantee that most new passenger cars and light trucks sold in the US are either all-electric or hybrids by 2032, are providing additional incentive. Throw in better, next-generation battery technology, and the future of auto really does look electric. As such, automakers and dealers are preparing for a future driven by EVs: Many of the industry’s major players have already started making EVs en masse, and they’re putting some serious dollars behind marketing those offerings.
Still, the road to a future powered by EVs isn’t obstacle-free, due in part to lack of charging infrastructure and shortages of the raw materials needed to build batteries. As a result, advertisers can expect the rise of EVs to develop at a more moderate pace. For example, this February, sales of hybrid vehicles rose 62% year-over-year, while YoY EV sales fell.
As interest around EVs evolves, marketers will need to focus on creating greater awareness around their electric vehicles, educating consumers about the benefits of electric mobility and emphasizing their brands’ commitment to sustainability. Both brands and dealers must also find ways to usher EVs into their marketing strategy without cannibalizing or alienating the still critical traditional gas-powered vehicle buyer.
In the current auto retail market, industry marketers will want to leverage digital opportunities to their fullest potential. Consumers are embracing an increasingly digital buying journey, with close to 30% of consumers open to purchasing their next car via an entirely digital process, and 23% preferring to order online but also wanting the benefit of physical touchpoints, such as a test drive. Consumers in the market to lease vehicles are even more open to an entirely online ordering process. Considering this, marketers need to ensure they have a robust presence online to meet audiences where they are.
Leveraging digital advertising is especially important for reaching younger audiences and first-time car buyers who spend much of their time online. This is a significant demographic for auto advertisers, as younger audiences are more likely to buy a car in the short-term future. Digital marketing also allows advertisers to serve targeted, personalized messages to groups of consumers that have the highest likelihood of converting.
Personalization is quickly becoming the norm across the digital ecosystem, with 56% of consumers expecting offers to always be personalized. To earn pent-up consumer dollars, auto marketers will need to understand their consumers on a granular level, reach them at specific moments, in specific places, and on specific devices, and create individualized customer experiences at scale. As such, a data-driven approach to digital marketing will be critical for building and reaching high-quality automotive audiences.
The key to creating a personalized, stress-free car buying experience is consumer data—and with third-party cookies on their way out, marketers will need to set up new systems for gathering information about their customers and meeting them in their moment.
For auto dealers and brands, first-party data offers an avenue for providing personalization at scale. Advanced customer data infrastructure, for example, can collect and unify first-party data from multiple sources—including CRM, website, and ads—to build a single, coherent, and complete view of each customer and their journey. Marketers can then use the collective data to create targeted and personalized marketing campaigns that enable one-to-one communication with consumers.
If first-party data is the wheels that enable marketers to connect with consumers, advertising automation tools are the engines that allow marketers to use that data effectively.
Personalization strategies are inherently nuanced and achieving them at scale requires a level of flexibility and efficiency that is nearly impossible to achieve manually. The fragmented and complex marketing media landscape means advertisers are often slowed down at several stages of the campaign, including planning, performance optimization, and measurement. Advertising automation reduces manual labor and streamlines the campaign life cycle, empowering auto marketers with the agility required to align and shift ad spending in a turbulent market, and ensuring ads are reaching high-value targets to drive measurable outcomes.
After a turbulent few years, automotive advertisers should be able to enjoy a return to some semblance of normalcy in 2024. Making the most of pent-up consumer demand in today’s market will require a deep understanding of today’s consumer base, along with a prioritization of strategies that meet that audience’s behaviors and preferences. Advertisers who take strides in this direction by promoting their brand values, preparing for an EV-focused future, and embracing personalized digital marketing will find themselves well-positioned to earn the business of consumers who are excited to finally purchase a new vehicle this year.
Advertisers and brands are hungry to capitalize on AI. But attempts to market and adopt the technology have grown so omnipresent that it seems the industry has skipped past gaining a solid understanding of what AI actually is.
Alex Castrounis is the Founder and CEO of Why of AI, an AI consultancy that educates and advises businesses on investing in AI in impactful ways. In this episode, he lays out the foundational knowledge marketers need to effectively harness this much hyped emerging technology.
Noor Naseer: Artificial intelligence has been a buzzword for a minute now and it will continue to be one across 2024 and beyond. The speed with which people have been talking about it, you might think you'd be well versed at this point. The reality is most people aren't and probably could use a solid primer to understand what it is from an unbiased source. And who better to discuss the topic than a real subject matter expert. Our guest today is Alex Castrounis. He's the best-selling author of a book on Artificial intelligence called “AI for People and Business - a framework for better human experiences in business success”. He's also a professor at Northwestern University's Kellogg McCormick MBAI program that is focused on innovation. He runs a consultancy and organization called Why of AI which consults clients and businesses on all things artificial intelligence and how they can leverage it in the smartest ways possible. Alex shares a ton of information on what AI is and its implications, purposes, and use cases are for ad tech and beyond. This episode with Alex to get the 101 on AI starts right now.
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Alex thanks for joining me today. I know you're a busy guy. There's a lot of stuff happening in the artificial intelligence space, so the time is much appreciated.
Alex Castrounis: Of course, yeah thanks for having me today.
NN: AI has been around for a long time but this new revolution or renaissance around it has really just started. What has really changed about AI now compared to the AI that's been around for the last several years?
AC: Yeah, I mean so as you said AI has been around a long time. In fact the term AI was coined in 1956. And even then, the origin of the idea of artificial intelligence included things, concepts and potential techniques like neural networks and things that we see today that are very much associated with artificial intelligence. So, indeed it's not new. Although AI as a field has gone through different kinds of periods of lots of investment, lots of innovation, lots of progress, followed by what they call “AI Winters” where things slow down a bit then pick up again and so on. At one point in time, we started to have a lot more data available, and the advent of the world wide web, and just the ability to transfer and move data around in much greater quantities and store more data. And more computing power and so on sort of led to this sort of growth of AI and ML capabilities.
And a lot of what was going on was really around things like forecasting or predictive analytics whether it's trying to predict numbers or sort automatically classify things. And then fromthere, other techniques started to gain traction and get more advanced as well like computer vision and natural language processing and so on. I think what led to this moment was really when certain kinds of what I would call “architecture” it's what most people refer to is kind of neural networks or deep learning architectures, started to be developed by researchers, like “The Transformer”. I'm not sure if you're familiar with that at all. But the Transformer sort of model and architecture that underlies models like large language models that we hear about today that power ChatGPT and GPT4, and now Claude and llama and Bard with Google and Palm; and the list just now goes on and on. It really is where this transition really happened in terms of capabilities from a generative perspective with language, as well as these models being able to do things people didn't explicitly train them to do. So, in other words, they could do different tasks almost on the fly and become very specialized or what they call ‘conditioned’ based on the intent that you have for them. So, just by introducing this idea of ‘prompts’ you could take a model and take the base model, and then condition it or specialize it in a certain way using certain kinds of examples. But also, you could ask it to do tasks that it was never explicitly trained on. And it turned out that these models are actually much more versatile and generalizable than people originally sort of expected them to be.
Once that became better understood a lot of that came out of the research. In fact, papers like “Attention is All You Need”, the original Open AI GPT2 paper and the Open AI GPT3 paper where some of these concepts were really brought to the forefront are why we're at this point right now where you know exponential increase worldwide in terms of global AI awareness interests. And quite honestly people are sort of scrambling to figure out, “What is this stuff? How do we understand it? How do we demystify it? How do we use it?” I think it is largely because of the Open AI ChatGPT for example. And when they launched GPT 3 and some things like that started to add kind of a usability characteristic to this stuff, sort of a UX, UI if you will like a user interface that's easy to understand and use.
With GPT3, it was a little more complex because the interface still required you to tune certain parameters and things like that that maybe the majority of people wouldn't be as familiar with. But I think with ChatGPT that launch sort of brought the honestly quite remarkable capabilities of these large language models to the public in an interface that's super easy to use quickly, super easy to understand and see results right away. It doesn't require any sort of tuning or configuration on the user's part. And I think just that helps really helps people sort of the light bulb go off and go, “Oh wow this is pretty remarkable stuff”. And now the potential applications and use cases are a little bit clearer; at least in the generative and large language model sense.
NN: So, there's folks like you Alex who are in the bucket of people with deep subject matter expertise around artificial intelligence. Or even something tangential like they work in the predictive analytics space. They're knowledgeable about what neural networks are or they're just deeply researched and they're educating themselves. And then there's folks on the other side of the spectrum where there's curiosity and that might be the greatest extent to which I've tried Chat GPT. So, a lot of folks maybe want to be moving away from being all the way on one side and moving a little bit closer. They're not going to become subject matter experts, but they want to know more about the medium. How should people educate themselves? What recommendations would you make to folks besides just reading the next AI article that pops up in your newsfeed?
AC: It's a great question because it does really depend largely on sort of what your goals are in terms of the understanding. On the one hand there's the practitioners. There's the data scientists, the data engineers, the machine learning engineers, the AI researchers and so on. In which case if that's of interest or doing any of the coding or understanding sort of these learning algorithms models and technical detail that go into these things, if that's something that someone's interested in then the path to learning is very different, than let's say you're a decision maker or business leader or entrepreneur or whatever the case may be. In which case for me in my company Why of AI actually focuses much more on that education piece with that type of audience. The more business folks' entrepreneurs, innovators, basically non-practitioners and not necessarily technical folks that still need to understand AI machine learning to some degree- sort of what I would refer to as the appropriate level for what they need to understand.
One of the challenges with it is that AI and ML are huge fields. Even though right now a lot of people have like what I would call horse blinders on, in terms of this very focused view of AI as like generative AI or large language models or Chat GPT but there's still like a very large field of AI which is like other aspects of natural language processing, computer vision, unsupervised techniques like clustering and segmentation that are often used in marketing and advertising, and so on. There's forecasting, there's classification. There's personalization, recommender systems and sort of the list kind of goes on and on.
So, it does depend on what it is but ultimately the key thing is that the way I help people understand it especially in the business sense, is ultimately it has to line up to some goals that you have either for your business. Or for a certain department within your business-like sales, operations, marketing, HR whatever it is. Or maybe you're looking at how to solve certain problems for your specific products or services or for your customers or users. So, there's going to be goals associated with those different areas, goals, needs, gaps or challenges. So, the question then becomes which sort of areas, and which specific types of tasks can you accomplish using artificial intelligence/machine learning depending on what those needs have been. And usually, it comes down to less about the really technical details of the models, or the algorithms or the tools or whatever and more about what are you trying to do exactly. Are you trying to predict something? Are you trying to augment something? Are you trying to answer certain questions based on some data you have? Are you trying to extract information in a certain way? Are you trying to categorize things in a certain way or recognize or detect things in images or things like that? So, I think part of it is learning more about how AI and machine learning help you functionally solve these problems. And what do those real-world use cases and applications look like for your business products, services, departments, whatever.
So yeah, it depends on what you're trying to learn and what you're trying to keep up to date with. But the bare minimum, I think everyone should have some degree of understanding at this point of generally what artificial intelligence kind of means, what does machine learning mean and what are some of those different areas and how might they be used to accomplish certain goal-driven or goal-aligned tasks and results.
NN: If an organization has come to the conclusion that you suggested, which is that their first responsibility is to figure out what their business objectives are that could leverage the upside of AI. Tell me the more granular details. How are organizations doing that before they're turning to you or other subject matter experts in AI space? Are they doing an audit across departments? I'll use an example specific to the advertising space where a lot of people work in sales. And a lot of people if they work at agencies there's a pitch and sales side of things. And then there's also the workflow piece, the processes piece, and you talked about operations where I think efficiencies are deeply desired. How do you help people help you if that makes sense when they're trying to share their business objectives? Because I think if you started asking everybody in your company, people could come up with an endless list of challenges they're trying to solve for.
AC: Well that's exactly it that's spot on. I mean, at this point you being an organization can benefit from AI and machine learning especially now with the generative stuff that's really accelerated some of this. How you can see results and value pretty quickly depending on what you're trying to do across the organization. You can help your organization at large you can help with every single business function you have. You can help implement and incorporate AI into product features that you have or your processes or customer experience. I mean, you just name it. So, you're right in that the options are sort of endless.
I think what it comes down to is trying to really figure out where the biggest needs are at the moment, where the biggest gaps, challenges, needs, goals, objectives. Like what are the most important things? A big part of it is sort of a bit of a prioritization exercise. In terms of that and filtering down a little bit and trying to narrow things down.
Going back to your first question though. In terms of what I see out there with companies and how they're approaching it, it's all over the place, quite honestly. In many ways it comes down to what I often refer to as “AI readiness” and “AI maturity”. There's a spectrum, there's a scale. There's everything from companies that have not done anything with artificial intelligence and machine learning, that just want to know more about and start to get their feet wet and get going. Then there's companies sort of in between—they prototyped some things, they've done some things but not necessarily gotten AI solutions into production and commercialized or at scale in any appreciable way. Then there's organizations that have a kind of a mature and experienced and sophisticated AI or machine learning team. But often what I see even in really big companies is they tend to be very narrowly focused in certain areas of AI machine learning based on sort of their core company offerings, let's say. So, they're sort of experts in specific things around what the core companies like products or services do. And so, they've developed these very sophisticated models and ways to maintain them and manage them, and improve them and monitor the results of them and so on. But they're in sort of a similar boat whereas AI advances literally at this point on a day-to-day week- to week basis and they're hearing about generative AI and large language models and all that. They're not necessarily either already doing stuff with it or have resources that have that kind of bandwidth to just tackle those problems either. And so, sometimes organizations get around that sort of thing by setting up centers of excellence or emerging technology innovation centers, things like that. But generally, yeah, it's all over the place. So, it really depends on the organization how much they've been doing with AI/ML, if at all. And do they only specialize in certain areas and still have a lot of opportunities to branch out and sort of figure out how to do more with it?
NN: My guess is that for people who are in the AdTech and advertising space or the agency space, they’re less likely to be in the position where they've got in-house data scientists or people who are managing learning models that they're building out that are custom and to themselves. And it's more likely that they're going to leverage those tools. So, there's so many tools that have popped out of the woodwork in the last couple of months. So, I think a lot of people what they're doing is assessing those tools. So, you've mentioned a couple of times Chat GPT which is very visible. Google’s Bard as well, and I think a handful of other free tools. But I also wonder if there's some charlatans out there just selling snake oil. They're trying to jump on a hot new trend and get in with folks that don't have deep subject matter expertise. Have you seen anything like that out there Alex, like things that people should be wary of in the AI space? Or maybe it's not even their intention to offer something that is so lacking in legitimacy but it's just not as fruitful as what maybe people are looking to gain by looking at AI tools?
AC: Yeah. Absolutely. And I don't think that's new right, like if you think back even quite a while ago there became a trend of everyone saying on their website their product was powered by AI in some way. You saw that a lot actually in things like advertising or marketing tools and platforms and whatever. And often they're not necessarily powered by AI. So, in that case I mean it could be a little hard to assess because companies aren't always 100% transparent. And nor should they be necessarily because that's kind of their bread-and-butter, secret sauce, confidential sort of proprietary information. If you're sort of like, “Hey, what exact algorithms or models are using or this or that?” So, sometimes it could be a little tricky to determine.
I will say that there are a lot of—to your point, I think one of the trends we started to see even before sort of this explosion of AI and machine learning interest was around no-code low-code. So there was a big movement to make coding more accessible to companies so that they could set up a website quicker like with Squarespace or Wix or something like that without necessarily having this programmer in house. Because to your point a lot of companies didn't necessarily have a software development team either and with that requires you know UX UI designers, QA folks, product managers and this big list. So, whenever you can kind of abstract away some of those like technical complexities and make these technologies a bit more accessible to others to use, that tends to be very attractive particularly for organizations that don't have that sort of expertise or core competency if you will in-house. I think we're seeing the same movement now with AI tools as well. So, we're seeing these platforms whether they’re cloud-based platforms that sort of can help manage the end-to-end process of AI and machine learning development and deployment of models and so on. Also, APIs that you can use on demand sort of like Open AI API and some of the other ones we're seeing like Claude and Anthropic and some other ones sort of making these tools accessible via API calls where you don't have to necessarily roll this all out yourself.
Hugging Face is a great example as well. I don’t know if you're familiar with Hugging Face. But there's also the open- source movement and that's been around for quite some time. One of the organizations that's really big in the AI space right now is called Hugging Face and they've basically created this very capable and sort of comprehensive open-source Python-based library that wraps these Transformer models. So that organizations don't have to like sort of train or build these models from scratch, but they can benefit from this and use them in a much sort of simpler way within their own sort of tools.
You're right in terms of, especially with generative AI, there's new companies coming out nonstop right they're saying they're doing generative AI stuff or natural language stuff. And then the question really becomes like are they really differentiated in any particular way or are they just a wrapper around something like Open AI's API. Because anyone could do that. Anyone can sort of just build a front end, connect it to Open AI's API and then collect some language somehow from a user whether they speak to the app or platform or they type something in. Send it off to the Open AI API, get the results back and then just show it to the user. In that case if that's all it is it's a wrapper more or less but if they're doing other things like maybe they're fine-tuning models or they're doing specific kind of sophisticated prompt engineering behind the scenes, or they're connecting not only to those kinds of APIs but also to some sort of database to like not just have all the outputs that you're returning to the user be generated purely by these large language models through their parameters; which is kind of like a statistical thing where the output is purely based on the parameters of the model. And what's the most probable output for the prompt you gave it. But rather either combining or shifting between outputs that come from real data that's relevant to that particular application, versus outputs that come purely from the model statistically generating the most likely probable output for whatever it received through the user interface or conversational interface. So, it's a bit Wild West out there, to summarize.
NN: You use the words that I was searching for that there are some existing accessible and sometimes free tools like Chat GPT and anything else in that category that's now been popularized. And another organization has put a wrapper on it customized or repositioned it a little bit. And now they're putting that out there for some companies that might be worthwhile. Something else you had mentioned earlier on that people have concern about or they raise an eyebrow to is when people do not distinguish between what is machine learning versus what is AI. And I think we touched on this a little bit. It's been talked a lot about in the trades that some organizations are going out of their way to distinguish between one versus the other. How do you really describe the difference between them at a time when so many folks are just jumping on the bandwagon to associate themselves with artificial intelligence?
AC: Yeah, I mean the way I've always sort of defined these things and explained these concepts is with artificial intelligence I sort of always go back to the sort of how you would define intelligence in general. If you look up the definition of intelligence for humans for example like human intelligence or animal type intelligence, it always boils down to something along the lines of you learn you understand things, and then you use that understanding to carry out tasks or accomplish goals and things like that. So, when we're babies were born with sort of a blank slate and then we learn from our parents, our school, our friends. As kids we do a lot of trial and error and experimenting. We just keep learning. We develop more and more knowledge that our brain sort of remembers and encodes if you will. That becomes accessible to us and it also gives us what we call common sense, which is really that we over time buy a world model that we just have operating in the background all the time, even if we don't think about it. All of that allows us to do things like have conversations so getting back to that thing of doing something with that learning and understanding. So, we can have conversations, we can get to work every day and get home. We can do the tasks we need to do as part of our job. We can assist clients in a consulting fashion if that's what we do and so on and so forth.
Machine learning though is the learning part of that bigger picture equation. So that's intelligence as we think of humans and animals, that sort of thing. Artificial intelligence is literally just a natural extension of that by saying intelligence exhibited by machines. So, if you could get machines to also learn then understand and be able to carry out tasks like predict a stock price. Predict whether an email is spam. Make recommendations of songs you might be interested in listening to. Determine whether a skin lesion is cancerous or not. Figure out what's the best price or promotion for a given sort of target market for a given product as well. That's artificial intelligence. And the learning part of that for the machines, when it's machines that are exhibiting intelligence, comes from machine learning. So, really all machine learning is is certain algorithms, what they call “learning algorithms” that as long as you have data and usually that data is somewhat domain specific or industry specific or maybe it's functionally specific like in the case of sales or marketing. Or domain in the sense of advertising or insurance or financial services. Then these learning algorithms that fall under the machine learning umbrella can kind of automatically learn the underlying correlations, relationships patterns and so on that's encoded into that data, such that you can use it in an AI solution to do those tests.
So, machine learning in and of itself is the learning part and the outcome of the learning part from those learning algorithms is usually a model. That model is then that understanding part. Like we said with AI or intelligence in general there's learning then there's understanding and then there's doing and carrying out tasks. These learning algorithms do the learning the understanding, and machine learning comes in the form of models. So, in the case of ChatGPT and GPT4 I use those as examples regularly just because people are now very familiar with them. Those are large language models that have already been pre-trained, and they've been made available via their API or their user interface. But those are just a bunch of model parameters. In the case of GPT4, it's like a trillion model parameters or something like that that was learned during that learning process. That model once you have it represents that sort of understanding of human language.
Then what you do with it is what makes it AI. If you don't do anything with it, if all you do is learn, use learning algorithms to learn from data and create a trained model and the model just sits there on the shelf or does nothing, then that's not AI. It has to then predict something or classify something or automate something or help someone carry out certain tasks at their job every day or whatever the case may be.
NN: Yeah, I think what I've seen a lot in the adtech space is that people have just a bandwagon to say, “we've always had AI”. And on some level, it's much more machine learning optimization than it is in fact artificial intelligence. There's also the intent to do dynamic customization or the dynamic delivery of ads. I don't have enough subject matter expertise to say how much they lean towards AI versus machine learning but I'm just harboring a guess that it's much more machine learning at this time than it is in fact artificial intelligence.
Another question I have for you is just about not embracing AI. I think there have been some things in the past, in the recent past where businesses, marketers, advertisers. People from any other walk of life and business they've raised an eye and said, “You know this emerging technology, it's a trend, it's a fad it's not really going to become a part of our day-to-day”. Have you seen any of that skepticism showing up for companies where they're not taking it seriously? And if they're not taking it seriously do you have fears for companies that are refusing to put time and energy into understanding how they can adopt AI?
AC: I love that question because yeah totally in the past I saw that a lot more where people like—you know it's funny ‘cause there's a lot of people like myself that have actually been working in this field for quite some time and we're sort of saying, “Hey there's this AI thing and machine learning thing and it can do these things that could be beneficial.” A lot of companies weren't taking it very seriously, or they just didn't understand it or they didn't get like what are those real world applications and use cases or whatever the case may be. So, there was more of that. And there was hesitation around it in general just sort of like “Oh, AI”.
Then again, I think now the opposite happened because of the arrival of ChatGPT in these models but also genuinely the capabilities of these models like it really has advanced it's not just hype. These models do kind of remarkable things and if you understand sort of how they work under the hood and how they get to the point of being able to do these things. Not just with language but also with things like Dall-E where you can type text and it generates an image. It's pretty remarkable how we've gotten to this point and it's not stopping there. It's continuing to go. So, all of that combined sophistication has gotten a lot better. The advancements are a lot better and more capable; the interest awareness buzz is so much greater. It's less now of people like “Yeah, I don't know about if we need this AI thing”. It's more like scrambling everywhere. It seems like more and more everywhere I turn or people I talk to or organizations I talk to or hear about they're more scrambling now. They're very much worried about missing the boat on this thing or getting behind or somehow losing advantage or something like that.
The two biggest questions I get today hands down are build versus buy. Sort of everybody wants to know should they build or buy solutions now in AI and then secondly is should we wait to build. So, it's not so much we don't think we need it or we're worried about going down that path or anything. It's more we're scrambling. We need it. We have horse blinders on and to us now AI just is synonymous with large language models and Chat GPT. And in some ways almost ignoring the rest of this, like a much bigger landscape that falls under the AI umbrella. But a concern now is more how do we invest time, effort and money in building solutions when all we're hearing is the stuff is advancing so quickly. And if we go and build on something and then it's out of date or deprecated or obsolete three weeks from now or two months from now or six months from now, have we sort of created problems for ourselves?
NN: Yeah, the build or buy piece like that sounds exactly right and it's less of people saying “We're totally going to ignore this thing”. Because the way I'm seeing AI today it's kind of like saying you're going to ignore the internet and that it's not going to be a part of your business. It just doesn't make sense. It's baked into our expected vision for the future. Knowing whether or not you're spinning your wheels and wasting your time doing something that you don't need to be doing because there's something available for you and you could be wasting time and resources. Or acquiring resources that could otherwise be better spent doing other things that are necessary for your business. So, I imagine that's something that you're giving a lot of advice on, and that people are trying to get recommendations or referrals for what they can do at this moment in time. Is that fair?
AC: Absolutely. It's completely fair. And you're right it is like the internet. I think that's a great analogy. The other thing is whether we like it or not or want it or not. Everyone is interacting with AI today now all the time- with all sorts of different tools that they use and software that they use. Even the people that are being served the ads. In the case of digital advertising there's AI algorithms behind the scenes there. When you're setting up campaigns there's AI algorithms sort of optimizing where those ads show up and so on, and when and all this sort of thing. So, it's just baked into so much at this point too. I don't think it really benefits anyone to ignore it.
I think the bigger thing isn't so much whether you ignore AI or choose not to use it or something like that. But rather just making sure that you're using it responsibly, fairly, safely in a trustworthy way. So, I think the bigger thing is really just at the same time you're trying to figure out what is this AI/ML stuff and how do we use it for our business to benefit either our business, our customers, our users and so on. It's also how we do that in a very safe and sort of fair and responsible way. And, we create trustworthy solutions that we're confident in and that we trust.
NN: I'll have to say any follow-up questions about potential concerns are at AI for another time. But just in the last few minutes that we have together, I do want to ask this question. For organizations that are small, that don't have dev teams available that want to make sure that they're staying on top of what they can like you mentioned people are developing Centers of Excellence or task force things of that nature. What should people do today when they are let's say less equipped and they don't have as many resources at their avail? What should those smaller teams be doing to stay on top of AI as best as they can?
AC: I mean so shameless plug here so my company Why of AI, certainly helps at least on the education piece and the strategy consulting piece. We don't build AI/ML solutions, we work with partners that do. But I think one of it is if you want to start learning through a workshop type of thing whether it's us or someone else. There's that kind of things like getting help in terms of workshops or some sort of courses for small teams that sort of thing to understand AI and ML at the right level- again not super technical depending on whether you're a practitioner or not. But in terms of keeping up I have to say it's really hard. This is something I spend a tremendous amount of time on and it's not a trivial task uh to keep up with AI and machine learning today. So, I think the question is really more what aspects you are trying to keep up with. If you're not so focused on necessarily all the super technical details or the latest and greatest models or this or that. But you are in like advertising or digital marketing or you're in financial services or health care or whatever. I would really recommend at the minimum gaining enough sort of high-level understanding at least of the general concepts of AI and machine learning. Not the super technical stuff. Not the really in the weeds jargon. But like a high enough understanding that you can read some of the articles that come out or the news you're seeing in a specific industry that's relevant to you. And you can understand it.
The other day I saw this really amazing thing where they're using sound to listen in the oceans for fish activity around coral reefs as a measure of whether the coral reef is healthy or not or dying or has died. And use that information to then take actions to sort of help maintain healthier coral reefs. Things like that. You don't necessarily need to know all the technical models and algorithms that are powering this solution. But you start to get a feeling of like, “Oh I get how you can use audio in certain ways. You can use images and video in certain ways. You can use text in certain ways. You can use structured data that might, maybe you have in spreadsheets or tables or a relational database like your CRM or your sales data in certain ways and so on”.
So, I think the biggest thing is if you're not a technical person or a practitioner is really just understanding what this stuff is at the right level. And how is it being used in real world use cases and applications and so on that are creating actual positive impacts and benefits and outcomes that are relevant to you. That would be a good starting place because otherwise the whole thing is just too massive.
NN: It's great framing and perspective. I'll leave it here Alex. I know you have to run to another meeting. AI calls. Duty calls for artificial intelligence. So, this is just a kickoff point for us on this topic. There's much more to learn for everyone. So, maybe we'll touch base with you later on in future episodes.
AC: Well, I can't thank you enough for having me join the show today. And thank you so much. Best of luck and I hope to talk again soon.
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NN: That's it for this episode. Thanks to Alex Castrounis, Founder and CEO of Why of AI for all his insight. I'll just say I did not want to get a primer from anyone in adtech who might spin it to speak to a product or a product pitch or a product release at this time. It's a little bit questionable how AI-centric some of those releases are and I think this is a topic that has so many implications and is going to impact this industry for years to come and so many others. So, a lot more to be seen. We'll be touching on AI again and again. So, expect to hear it brought up in multiple episodes in the future. Until next time, more Adtech Unfiltered real soon.
Artificial intelligence (AI) has played a significant role in digital advertising for years now. Initially used for basic data analytics and targeting, the technology has evolved considerably since its first applications in advertising, and its use has grown more advanced and widespread in kind. Today, digital advertisers rely on AI for campaign automation, data-driven decision-making, creative optimization and personalization, audience insights, and more.
Over the last several months, a specific type of AI has been making big waves—specifically, the kind that can write and perform songs, turn images into poetry, and clone individuals’ voices with an alarming level of accuracy. Since generative AI (GenAI)’s public debut in late 2022, leaders have begun to test its new features within their campaigns, particularly those related to content creation, design, and creative optimization/personalization. And given GenAI’s pattern recognition and data processing abilities, this technology also has the potential to have a significant impact on analysis, media buying, and even strategic decision making. All in all, it’s not hard to imagine a world where the efficiency, speed, and ease of launch GenAI offers shapes nearly every aspect of the digital marketing process.
But with the opportunities it offers come warnings and concerns from a variety of experts, as well as questions around its appropriate usage and regulation. With GenAI regulation in its beginning stages, leaders must understand what aspects of GenAI use will likely become regulated and stay abreast of legislative developments in order to make the most of the technology while maintaining compliance and fostering consumer trust.
In summer 2023, the EU came out with the world’s first comprehensive AI regulation: the EU Artificial Intelligence Act. The law was approved by the European Parliament in March 2024, and the EU has since established an AI Office that is tasked with implementing the regulation.
The EU AI Act approaches AI regulation by classifying different AI technologies and outlining specific obligations for providers of those technologies according to their level of risk. Beyond outright banning certain types of high-risk AI systems, it also establishes regulation for lower risk and general purpose GenAI. For instance, the act requires that GenAI providers comply with existing copyright laws and disclose the content used to train their models. It also requires that companies disclose when their content has been manipulated by AI.
Though agency leaders and brands not operating in the EU aren’t legally required to comply with this legislation, they can benefit from understanding, and perhaps even embracing aspects of, the AI Act. For example, some teams may want to disclose when their content has been AI-generated or modified—not just because the AI act requires companies working in the EU to do so, but because 75% of consumers feel it’s important. Whether or not businesses working outside the EU choose to comply with parts of the AI Act, understanding its requirements for advertisers is beneficial, as they may reflect consumer preferences around AI, and may eventually be adopted in US legislation.
The US, on the other hand, has yet to implement any nationwide, comprehensive AI regulation. But that doesn’t mean it hasn’t been a topic of significant discussion and focus.
Over the last few years, Congress has held committee hearings on oversight of AI, and in September 2023, Senate Majority Leader Chuck Schumer convened a closed-door AI insight forum where tech leaders, two-thirds of the Senate, and labor and civil rights leaders gathered to discuss major AI issues and implications.
Since then, many bills have been introduced aimed at regulating AI. Additionally, House leaders recently announced a new, bipartisan AI task force that will explore how Congress can balance innovation and regulation as AI technology continues to evolve—with a focus on its intersection with safety and security, civil rights issues, transparency, elections, and more.
Beyond these developments on Capitol Hill, President Joe Biden signed an executive order in late October 2023 on the “safe, secure, and trustworthy development and use of artificial intelligence.” Though this order outlines clear action steps for the oversight and regulation of AI—including implementing standardized evaluations of AI systems, addressing security-related risks, tackling questions related to novel intellectual property, and more—these are just strong recommendations at present and would require congressional action to become enforceable law.
This order also tasks the Department of Commerce with developing a report that outlines potential solutions to combat deepfakes and to clearly label artificial content. Though the results of this report are forthcoming, brand and agency leaders should be aware that its outcomes could have an impact on how they label marketing collateral that is AI-generated. The executive order specifically cites watermarking as a potential way to label such content, and it’s possible that marketing teams could be responsible for watermarking all AI-generated content in their campaigns in the future.
Additionally, the Federal Trade Commission (FTC) has made it clear that AI oversight and regulation is one of their current areas of focus. They have proposed new AI-related protections, and, at the IAB’s recent Public Policy & Legal Summit, they emphasized how critical it is for advertising leaders to be aware of the risks of bias, privacy, and security posed by GenAI, and to regularly conduct AI-focused risk assessments to help mitigate these potential risks.
In terms of US copyrighting-related regulations, AI-generated content currently cannot be copyrighted. However, the Copyright Office recognizes that “public guidance is needed,” especially when it comes to works that include both human-generated and AI-generated content. As such, they have launched an agency-wide initiative to further explore these issues.
At the state level, nearly all US legislatures in session are considering AI-related bills. Many of these are focused on algorithmic discrimination, which is when an AI-powered tool treats an individual or group of people differently based on protected characteristics. Like the EU’s AI Act, several of these bills approach AI regulation by distinguishing between high-risk AI systems vs. more general-purpose AI models, with different regulatory requirements depending on a tool’s classification.
Though AI-related regulation in the US remains primarily in the realm of guidance for now, advertising leaders can proactively utilize this guidance to plan for the impacts of forthcoming regulations. By building out systems to safeguard consumer safety and trust against the risks posed by AI now, advertising leaders can foster an environment of ethical AI usage, and set their teams up to adapt effectively as regulation becomes more concrete.
In many ways, what we’ve seen so far is just the beginning of AI regulation, and advertisers can expect to see a lot of movement in this space in the months and years ahead. Those brands and agencies that seek to understand current guidance to develop ethical AI practices will be well-positioned to adapt as these new regulations and recommendations arise.
At present, advertising and marketing leaders can benefit from expanding their knowledge and understanding of new GenAI tools, as well as their potential risks. Digital advertising leaders should be aware of the top threats GenAI poses to advertisers, including its ability to:
To navigate these risks, it can be helpful for teams to conduct AI-focused risk assessments and to request their partners/vendors do the same, so they can identity and proactively address any challenges specific to the tools they are using. And, when it comes to using AI-generated content, simply ensuring that all materials are reviewed and edited by a human can help prevent biased content from ever leaving the chat box or image generator, and can halt the spread of mis- and disinformation. By implementing these processes now, brands and agencies will have a leg up as more concrete AI regulation develops in the future.
As generative AI continues to evolve, so too will the regulations that govern it. Marketing and advertising leaders will be well-served to approach this technology in a balanced way that allows them to both harness its power and navigate its risks. By putting systems in place to evaluate and assess AI tools and to address their potential risks head-on, leaders will not only ensure they’re using this technology in safe and productive ways but will also prepare their teams for complying with the types of legislation we’re likely to see coming down the line.
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Want insights on how marketers and advertisers are using generative AI and how they think it will change the industry moving forward? We surveyed over 200 marketing and advertising professionals from top agencies, B2B and B2C companies, non-profits, and publishers to understand how industry professionals feel about GenAI’s impact on the advertising industry—and how it could shape the future of marketing.
Every two years, US political campaigns descend upon the advertising landscape, feasting on inventory and bringing with them a host of challenges for nonpolitical advertisers—challenges that only seem to intensify with each election cycle.
2024 is no exception. A highly divisive Presidential race, hotly contested Senate battles, and polarizing ballot measures are converging to drive unprecedented political ad spend. At the same time, advancements in generative AI could potentially create a landscape characterized by groundbreaking volumes of mis- and disinformation, especially on social media.
Marketing and advertising leaders from outside the world of politics will need to navigate that landscape and determine how to make the most of their spend amidst high demand and localized inventory scarcity, while simultaneously protecting their clients or brands from the potential pitfalls of adjacency to negative political content and AI-generated mis-and disinformation.
To do so, marketers must understand when and where these issues will affect them (and their ad dollars) most acutely, and should dial up their placement control to protect brand safety.
This year’s political ad spend is forecast to land between $10.2 billion and $12 billion—numbers that would demonstrate a 13%-to-30% increase from the 2019-2020 election cycle. For advertisers, this glut of money can bring higher CPMs—especially during certain time periods, in certain locations, and on certain channels. Inventory scarcity is another consideration, although there’s enough inventory out there that price will be most advertisers’ primary concern.
Political ad spend stats from Basis platform in 2022 and 2020 show that about 50% of the year’s political dollars ran in the 30 days leading up to the election, with 25% running in the ten days leading up to Election Day. In 2024, we may see that run on political ads start a bit earlier as a result of early in-person and mail-in voting, but the election likely won’t dominate advertising too much sooner than that. Historically, we have seen gradual increases in the numbers of individuals using these early voting methods, but those early voters are generally not the undecided voters that political advertisers most want to reach. That means that advertisers will see the highest prices and the scarcest inventory for all of October and into November.
There is also a chance that some of these races might not be determined on Election Day, as some states may end up having runoffs, like Georgia did in 2020. If that is the case, these inventory and brand safety issues will continue for advertisers in those specific locations until those elections are completed in the traditionally retail-heavy months of November and December.
Speaking of location, geography is the other major factor that will impact advertisers during this year’s election cycle. Political campaigns will heavily target certain swing states and counties, and ad rates and inventory will be much more affected in those areas than those that reliably vote blue or red.
For the presidential election, there are about seven states that will be hotly contested and see an increased proportion of presidential campaign ads: Nevada, Arizona, Michigan, Wisconsin, Georgia, Pennsylvania, and North Carolina. Within those states, most political consultants think certain counties will swing the election, and hyperlocal targeting within those counties will mean higher CPMs and scarcer inventory.
Beyond the Presidential election, key Senate races and divisive ballot measures will also lead to high volumes of political advertising in cities like Las Vegas, Philadelphia, Phoenix, Reno, Pittsburgh, Missoula, Billings, Boston, Wilkes Barre-Scranton, Butte-Bozeman, Detroit, Los Angeles, Charlotte, Atlanta, Cleveland, Cincinnati, Harrisburg, DC, and Raleigh-Durham.
Marketers must research the states, counties, and cities in which they plan to advertise this year to determine how divisive the races and issues on the 2024 ballot will be in those locales, as that will correspond to how acutely political advertising will reshape the media landscape.
Political advertisers love video, so the increased demand for advertising space across media will be felt most acutely on CTV, linear, online display video, and those social networks that accept political ad dollars. The impact on CTV will be especially pronounced: 45% of all digital political ad spend in 2024 will go to CTV, up a whopping 26% from the 2020 election cycle.
Additionally, advertising on linear television comes with the possibility of actually getting crowded out of ad slots. Because of the FCC’s Equal Time Rule, if a candidate wants to advertise and buy 60 seconds on a certain channel, that channel must offer other candidates a comparable amount of time and a comparable placement. This can end up bouncing other advertisers in situations where there is limited inventory, and while somewhat unusual, it is most likely to occur during the leadup to Election Day in competitive locations.
The election season poses multiple brand safety threats, manifesting in the form of possible adjacency to negative political content and/or to political disinformation and misinformation.
Political ads are often negative (or come with negative baggage), and political content can be divisive, so advertisers should consider how comfortable they feel having their ads show up nearby. One survey found that exposure to negative political advertising provokes “extremely negative emotions” in viewers, and that those feelings can have a detrimental impact on how consumers perceive brands whose ads run alongside them. To avoid this, advertisers may want to take measures to avoid adjacency to political ads and content (more on this in a moment).
While negative political advertising has been around for a long time, the volume of election-related mis- and disinformation expected this year poses a newer challenge for advertisers. This kind of content was an issue in 2020, but experts expect it to be even more of a threat in 2024 as a result of the emergence of generative AI, which makes it easy for bad actors to generate huge amounts of false or misleading content. Even outside of the context of this year’s election cycle, close to 100% of advertisers agree that GenAI presents a brand safety and misinformation risk for digital marketers, with 84.2% viewing it as a moderate to significant risk.
Compounding this is wave of layoffs in recent years affecting the trust and safety teams at Meta, X, Alphabet, and Amazon, including employees tasked with addressing mis- and disinformation. It’s particularly worrisome considering that social media is the channel where misinformation gets amplified most. Social platforms have promised to release new tools designed to protect consumers and advertisers from misinformation this year, and advertisers should keep an eye out for those capabilities, but social could present unique brand safety risks this election season.
Considering this environment, leaders will need to dial up their placement control, and there are a variety of ways to accomplish this. Advertisers can work with partners like ComScore, Oracle, and Peer39 to ensure their ads are shown in premium, non-divisive environments, and implement allow lists and block lists—such as dynamic MFA block lists—to avoid political and low-quality websites. Utilizing smart contextual targeting is another way to make sure messages only show up in desired environments, and investing in programmatic guaranteed and PMPs can further secure premium placement. Finally, teams may want to avoid certain social platforms during the lead-up to Election Day—for example, X will likely be a hub of political conversation, which could come with increased brand safety concerns. Instead, advertisers may opt to shift their spend toward places that don’t accept political advertising, such as Netflix, Disney+, Pinterest, and LinkedIn, during this time period.
Additionally, marketers who want their brands to stand out from negative and weighty political content can embrace lighter, more upbeat, and humor-driven creative to better appeal to weary consumers.
Overall, it’s critical that advertisers factor in these challenges when planning their campaigns so that their messages show up in places that can effectively—and positively—reach consumers.
In what promises to be a tumultuous election year with record political ad spend, marketers who take a “business as usual” approach to their campaigns risk misallocating their spend, alienating potential customers, and taking major hits to brand perception. It’s essential that leaders plan their campaigns around how political advertising will impact the landscape—especially during the start of Q4.
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While advancements in generative AI are driving many of the brand safety threats facing advertisers in 2024, they’ve also brought new opportunities for efficiency and speed in the campaign process. Check out Generative AI and the Future of Marketing to learn how your peers are utilizing GenAI.
In March 2024, US employment across advertising, public relations, and related fields reached an all-time high, with ad agencies accounting for the largest portion of these jobs. However, this job growth doesn’t negate the longstanding challenge of high turnover within the advertising industry, particularly among junior-level employees.
To help mitigate turnover, the vast majority of hiring managers—in advertising and beyond—say they plan to hire in 2024. And as agencies and brands approach either filling newly-created positions on their teams or replacing those left vacant by employee departures, it’s increasingly likely that many of their candidates will be members of Generation Z.
The last several years have seen an influx of Gen Z workers: 17.1 million joined the US workforce in 2023, and they are forecast to overtake the number of baby boomers entering the workforce in 2024. While each generation is distinct, Gen Z is particularly so, having been shaped by the digital age, prolonged economic turbulence, and the COVID-19 pandemic. Because of this, agency and brand leaders who fail to adapt their hiring, engagement, and retention strategies for Gen Z may struggle to meet their distinct needs and expectations, which could in turn lead to long-term workforce challenges.
Born approximately between 1997 and 2012, Gen Z is the first generation of true digital natives, with all its members having grown up at a time when the internet was a ubiquitous part of daily life. Members of this generation are also more racially and ethnically diverse than any prior generation, as only a slim majority—52%—are non-Hispanic whites. As a result, they care deeply about inclusivity and embracing diverse perspectives in their interactions and decision-making. Gen Zers are also adaptable and resilient, as a result of events like the 2008 financial crisis, the COVID-19 pandemic, and subsequent economic uncertainty shaping many of their formative years.
Further, Gen Z has established itself as a socially conscious generation, one that cares about action over words and isn’t afraid to take a stand on social and political issues. Members of this generation expect brands to do the same, with mental health, caring for the environment, and racial and gender equity being 3 of the top values that Gen Zers want brands and companies to support—in honest and authentic ways. And given the high expectations they have for brands as consumers, it’s likely those who pursue careers in the advertising industry will hold their employers to the same standards.
Many Gen Zers were wrapping up college, obtaining internships, and securing their first jobs as the COVID-19 pandemic surged.
This timing led some members of this generation to take time off college or delay graduation; for others, their internships or first job offers were rescinded as employers in advertising and beyond were forced to make cutbacks; still others started their jobs in under-resourced, overworked, and all-remote environments, contributing to high levels of burnout.
Even after the pandemic peaked, this tumult didn’t end—the years that followed were characterized by the Great Resignation, which was especially pronounced in the advertising industry. All in all, Gen Zers’ first experiences in the workplace were largely characterized by layoffs, thoughts of quitting, or actual quitting.
Given their turbulent entry to the workforce, it’s no shock that Gen Z workers aren’t as engaged as older generations, and that they continue to experience higher levels of job-related stress and burnout. Additionally, many managers say they struggle to connect meaningfully with their Gen Z employees, with 3 in 4 managers reporting that Gen Z employees are difficult to work with, and nearly half experiencing this difficulty all or most of the time.
Given Gen Z’s increasing presence in the workforce, current employment growth in the advertising industry, and the fact that agency leaders are already articulating concerns about recruiting and engaging workers from this generation, brand and agency leaders must be proactive and intentional as they implement strategies to support their Gen Z employees.
Like every other generation, Gen Z has been shaped by the unique societal and technological context of their coming of age. Brand and agency leaders who seek to understand this, and then embrace strategies that meet this new generation’s needs, can enhance productivity, bolster engagement, foster creativity, and improve retention in the years ahead.
This generation has grown up immersed in technology, making them adept at navigating digital platforms, adapting to ever-changing tech, and understanding online trends. Agencies and marketing teams can tap into this expertise in a variety of ways. For instance, leaders can empower their Gen Z workers to play a meaningful role on digital campaigns or projects on platforms they’re intimately familiar with, such as TikTok, Instagram or Snapchat. Managers can also give Gen Z employees the chance to showcase their knowledge around the latest digital trends, platforms, and tools they use with their broader teams, such as through collaborative workshops.
Though many organizations seem to be walking back their commitments to diversity, equity, and inclusion (DEI), agencies and brands looking to support their Gen Z employees (and, frankly, all their employees) should be doing the opposite. Prioritizing and investing in these programs is not only good for employees, but also for businesses’ bottom lines.
As the most diverse and educated generation in the workforce, Gen Z is increasingly advocating for diversity and inclusion with their employers. In fact, 56% of Gen Zers say they would not accept a job without diverse leadership, and 68% feel their employer is not yet doing enough on this front.
Brand and agency leaders looking to promote diversity among their teams can start by ensuring their hiring practices are attracting and supporting a diverse talent pool. This could include evaluating job descriptions for potential biases, building diverse interview panels, and doing outreach to underrepresented groups.
Beyond their hiring practices, leaders can also invest in regular training and education, such as workshops or summits, for all employees. Focusing on education can help raise awareness, build empathy, and equip all employees with the tools to foster an inclusive workplace.
“We’ve learned in the last few years that Gen Z employees value flexibility,” says Goretti Duncker Joseph, Director of Total Rewards at Basis Technologies. “Flexibility builds trust and loyalty—employees want to work for a company that cares about their wellbeing and demonstrates that through their policies,” she continues.
When leaders hear “flexibility,” many might think this only refers to where employees work—and that being flexible means adopting an all-remote approach. But Gen Zers, within advertising and beyond, have indicated that this isn’t the case: In fact, a recent study found that only 11% of Gen Z workers want to be fully remote.
While a hybrid work approach might work for some businesses, employers can also foster flexibility by allowing employees to work earlier or later than the traditional 9 to 5 time frame, to help foster balance between their work and personal lives and mitigate potential burnout.
“Flexibility isn’t just about when and where employees are working,” adds Duncker Joseph. “We’ve found that the Gen Z population also values flexible benefits that meet their unique needs.” This could look like providing student debt repayment programs, since many Gen Zers are working to pay off student loans, offering telemedicine benefits alongside traditional health insurance, or including mental health benefits such as free access to virtual therapy and life coaching. By leaning into opportunities to grant their workers flexibility, agency and brand leaders can help foster the trust and loyalty that is foundational to long-term retention and engagement—particularly among Gen Z.
In a recent conversation with Gen Zers on their struggles working within the advertising industry, many shared they felt a lack of personal connection with their teammates and craved mentorship. Many said they feel they’re not given enough direction, and want guidance and support as they develop their skills. Given their entrance to the workforce during the isolation and upheaval of the early days of the COVID-19 pandemic, this desire for mentorship and connection with teammates beyond their own generation makes sense.
To support this desire for personal connection and mentorship, agency and brand leaders might consider implementing formal mentorship programs within their organizations. These programs can pair Gen Z employees with experienced professionals who can offer guidance, share industry insights, and provide constructive feedback. At the same time, they create space for Gen Zers to share their own unique expertise. Creating a structured mentorship framework helps foster meaningful connections and facilitates knowledge transfer.
“We’re bridging the mentorship gap for our Media Operations team, which is largely composed of early-career professionals, by connecting them with seasoned revenue team members at Basis,” says Marissa Enfield, Group VP of Media & Ad Operations at Basis Technologies. “By facilitating a combination of one-on-one and group learning over 6 months, we aim to enhance our team’s understanding of our campaign workflow and address vital career themes like burnout prevention and goal setting.”
Additionally, leaders can support their Gen Z talent by ensuring these employees have a clearly defined manager or leader to check in with. This framework can help to provide Gen Z employees with clear expectations, and create space for consistent conversations around alignment, growth, and any challenges that might arise. By providing opportunities like these for mentorship, organizations can nurture Gen Z's professional growth, confidence, and engagement in the workplace.
It's crucial for agency and brand leaders to adapt their strategies to meet the unique needs and expectations of generation Z, given that these individuals are making up an increasing portion of the workforce. By leaning into Gen Z's strengths as digital natives, prioritizing diversity, equity, and inclusion, creating flexible work environments, and providing meaningful mentorship opportunities, organizations can enhance productivity, boost engagement, and improve retention.
As the industry evolves, embracing change and investing in Gen Z's success will be key to navigating future challenges and driving long-term outcomes. Plus, embracing these approaches not only supports Gen Z's professional growth and well-being, but also fosters a vibrant, inclusive, and resilient workplace for all employees.
As seismic shifts reshape the advertising industry, marketers are reinvesting in strategies that have stood the test of time.
What shifts, exactly, are pushing advertisers back towards these old-school approaches? To start, there’s the matter of signal loss, driven by factors such as Apple’s App Tracking Transparency, data privacy regulations, the consumer demand for data privacy, and Google’s plans to deprecate third-party cookies in Chrome in 2025. And, of course, there’s the rapid development of generative AI, which is both presenting new challenges and introducing fresh possibilities for marketers.
To meet these challenges, advertisers are testing new technological solutions to help them adapt. But they’re also reaching back into their toolkits to rediscover legacy tactics and strategies like direct buying, contextual targeting, and brand lift studies. And, as it turns out, these legacy strategies haven’t just been sitting in the corner gathering dust: They’ve grown more sophisticated to meet the needs of today’s agencies and brands.
The advent of programmatic advertising brought advertisers a level of speed and scale that they couldn’t access via direct buying. In doing so, programmatic swiftly became the default digital buying method, accounting for a projected 91.3% of US digital display advertising in 2024 totaling $157.4 billion.
While programmatic isn’t going anywhere—online programmatic ad spend growth will slow this year, but is still expected to increase YoY—some advertisers are investing more of their budgets into direct buying methods as a way to prioritize consumer privacy and brand safety, and to protect themselves against fraud.
The factors driving signal loss and pushing the industry towards a privacy-first advertising model are rendering some of the main data sources that drive real-time bidding in the open exchange either unavailable or inadvisable due to privacy concerns—specifically, third-party cookies and mobile advertising IDs (MAIDs). As a result, in 2023, 53% of buy-side ad investment decision-makers said they plan to increase their focus on placing ads with publishers using first-party data.
Direct buying also boasts lower risks of fraud and fewer threats to brand safety than the open web—challenges that are growing more pressing as generative AI transforms the internet. In 2023, approximately 22% of all online ad spend was lost to ad fraud, and mitigating fraud ranked as one of the biggest concerns around media investment this year by US brands and agencies. AI-driven ad fraud is particularly problematic when it comes to ads served via the open exchange, due to the complexity between the purchase of a traditional programmatic ad and its delivery. At the same time, generative AI makes it easy to create low-quality websites, like made-for-advertising websites (MFAs)—where brands have squandered as much as 15% of their digital ad spend.
To protect their programmatic spend, advertisers can implement safeguards like allow lists and block lists (such as dynamic MFA block lists) and leverage third-party safety segments to exclude sensitive content and increase inventory quality. But adding direct buying gives marketers the ultimate control over their ad dollars—both in terms of audience and for minimizing risk.
“By contracting with publishers on their own inventory, media buyers know exactly what inventory they are running on and have the added benefit of leveraging the publisher’s first-party data—which is not only privacy-friendly, but empowers more accurate targeting and measurement,” says Lindsey Freed, SVP of Media Investment at Basis Technologies.
While direct cannot offer the same speed and scale of the open exchange, it has evolved considerably so that advertisers can benefit from the privacy-friendly and premium placements it offers in a more automated way. In addition to insertion orders (IOs), advertisers can turn to private marketplace deals (PMP)—ensuring exclusive, premium placements—or programmatic guaranteed, which combines the quality and assurance of direct with the efficiency and automation of programmatic, making it easier for media buyers to make data-driven decisions in real time. Advertisers can also leverage curated publisher lists for direct deals to mitigate some of the tactic’s scale-related drawbacks.
Programmatic advertising on the open exchange will, of course, remain a mainstay for marketers, especially as technologies advance to help them avoid AI-driven fraud and advertising on low-quality websites. However, making the most of all that direct buying offers will help marketing teams adapt to the cookieless world, and mitigate some of the brand safety and fraud risks that come with real-time bidding on the open exchange.
Signal loss is also driving a huge resurgence in contextual targeting. Contextual advertising spend is expected to double from 2023 to 2030, and as of late 2023, almost 94% of marketers were either already using the tactic or had plans to begin using it within the next 12 months.
And, like direct buying, contextual has advanced considerably in recent years.
“Advertisers can now tap into AI-powered contextual targeting, which analyzes and categorizes page content, allowing buyers to align creative messaging to the content their audience is consuming,” says Freed.
Contextual technology has also progressed to incorporate natural language processing, which ensures that ads are not only placed in environments relevant to their topics or keywords, but also where the overall sentiment and tone of the content match the ad being served.
Contextual targeting offers a variety of benefits beyond its cookieless nature—in fact, that only ranks fourth on the list of what US agency and brand marketers find most beneficial about the tactic, behind “aligns with audience interests”, “improved ROI/ROAS”, and “increased ad engagement.” When used for display ads, contextual also serves to protect brand safety by ensuring that ads are placed in premium digital environments. Plus, notes Freed, “with the integration of curated contextual segments within advertising platforms, media buyers can search and select contextual segments across various advertising mediums more seamlessly.”
Of course, contextual can’t be the only privacy-friendly targeting tactic advertisers use to address signal loss in a cookieless world. And contextual does have its drawbacks, such as the fact that it can be difficult to retarget people who have seen contextual ads, which in turn makes it difficult to measure their performance. As a result, contextual is best used as one part of a multi-tactic cookieless targeting approach.
In a world driven by third-party cookies, advertisers were granted a lot of transparency into the performance of their campaigns. They could easily get an idea of their consumers' purchasing journeys and generate precise reports on view-through conversions and ad frequency.
Once third-party cookies are fully deprecated in Google’s Chrome browser, one of the preeminent challenges for advertisers will be to find new ways to measure campaign performance and attribution. In this context, advertisers are leaning into legacy measurement tactics, like brand lift studies, to gauge the success of awareness-driven campaigns.
“Brand lift measurement and brand health tracking is becoming more important, and we’ve seen an uptick in investment in these studies to understand the holistic impact of advertising efforts on a brand,” says Kelly Boyle, Group VP of Client Strategy and Insights at Basis Technologies. “Marketing mix modeling is also making a comeback, and these tools are evolving. Newer offerings are more robust, more precise, and some are even more affordable than traditional models were years ago.”
Zach Moore, SVP of Digital Media Operations at Basis Technologies, agrees that brand lift studies have grown easier and more streamlined, and that they’ll have an important place in the campaign measurement process in a cookieless world. “Brand studies have gotten a little smarter over the last decade or so,” says Moore, “with many being built into the various buying platforms directly, integrating actual sales or transaction data into their metrics, and having the ability to encompass multiple channels to provide a much wider view.”
Brand lift studies aren’t without their challenges, the biggest one being the size of the control and exposed groups used in these studies (which can be relevant if an advertiser needs the results to reach statistical significance).
“The issue with brand studies is you must have strict control and exposed groups, typically running lots of impressions,” says Moore. “The guideline is that 10-20% of impressions for all a brand’s media should be set aside for brand lift studies. However, since many brands request those impressions as ‘added value’ from partners, that can be a hard sell, since they’re essentially asking for free impressions.”
In situations where brand lift studies aren’t viable, Moore recommends prioritizing a model-based approach, such as a diminishing returns analysis.
As with contextual targeting, brand lift studies aren’t a comprehensive solution. Advertisers will need to tap into a variety of alternative measurement and attribution tools in a cookieless world, from brand lift studies to model-based approaches, to cookieless conversion attribution, and more. In light of this, advertising leaders should prepare for the measurement process to be much more time- and resource-intensive once third-party cookies are fully deprecated in Chrome.
All in all, embracing approaches that have served advertisers since the industry’s beginnings will be a critical way for advertising teams to find success—and security—amidst major paradigm shifts. Advertising leaders should also keep an eye out for technological innovations and advances related to these strategies, as it’s likely that these older tactics will continue to grow “newer” (i.e., more automated and sophisticated) as the industry—and technology—evolves.
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Want to learn more about how your peers are preparing (or not preparing) for one of the industry’s biggest paradigm shifts? Check out our report, Identity vs. Privacy: Digital Advertising in a Cookieless World, to get all the top findings from our survey on how advertising teams are preparing for the cookieless future.
Here we go again…
After months and months of promises, pinky promises, and stone-faced utterances of “No, we really really mean it this time!”, Google has officially announced what in recent weeks appeared increasingly inevitable: Third-party cookie deprecation in Chrome will be delayed. Again. This time, to an as-yet unannounced time beyond Q4 2024.
The deciding blow to this latest missed deadline came after a damning report by the UK’s Competition and Markets Authority (CMA) indicating that Google’s Privacy Sandbox would fall short of meeting the country’s regulatory standards.
Per Google’s announcement: “We recognize that there are ongoing challenges related to reconciling divergent feedback from the industry, regulators and developers, and will continue to engage closely with the entire ecosystem. It’s also critical that the CMA has sufficient time to review all evidence including results from industry tests, which the CMA has asked market participants to provide by the end of June. Given both of these significant considerations, we will not complete third-party cookie deprecation during the second half of Q4.”
The news was met with a mix of intrigue, side eye, and shrugs from an industry that has grown increasingly frustrated with Google’s approach to the issue and largely unsatisfied with the Privacy Sandbox’s inconsistent rollout.
“The entire ad industry can’t be ready for change if Google isn’t ready for it,” said Noor Naseer, VP of Media Innovations & Technology at Basis Technologies. “As things stand today, there’s a lot of ambiguity around the application of Privacy Sandbox tools—almost everything the average advertising professional knows about Privacy Sandbox is hearsay. Few have tested it, and they’re all waiting for more reviews on who else has done it, how they’ve done it, and to what degree of success. So this update is not a surprise, but a welcome sigh of relief, even if it’s just a temporary one.”
For now, it appears that Google is eyeing 2025 as its latest target for deprecating cookies from Chrome. But the delay is expected to be temporary, with the goal of giving the company, industry partners, and regulators enough time to work through their laundry list of concerns with the Privacy Sandbox APIs.
“While the industry is getting a bit more time—which certainly provides some relief—the way I see it, this isn’t a reason to take the foot off the accelerator,” said Ian Trider, VP of Product – DSP at Basis. “No matter what, the status quo will not persist forever, and there's basically zero chance that third-party cookie deprecation doesn’t happen at all. At this stage, it’s a matter of making sure that there are sensible technical solutions, and that Google is addressing any risk of anti-competitive behavior to the satisfaction of regulators.”
At Basis, the news of Google pushing back the third-party cookie phase-out to early 2025 is being seen as an opportunity for further refinement. “The delay isn't ideal, but it's an opportunity,” said Robert Kurtz, Group VP, Search Media Solutions at Basis. “We were prepared for the initial 2024 deadline, but with this extended runway, we can double-down on our cookieless targeting strategy, with more in-depth testing and optimization of privacy-focused solutions, first-party data initiatives, and additional partnerships to ensure a smooth transition.”
“By utilizing this delay strategically," said Kurtz, "the industry can emerge stronger in the cookieless future.”
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While third-party cookie deprecation in Chrome may be delayed, the advertising industry is already working to embrace a more privacy-friendly future. Learn more about how marketers are confronting signal loss in our report, Identity vs. Privacy: Digital Advertising in a Cookieless World.
Amidst all the networking, socializing, and poolside festivities at this year's Possible in Miami Beach, there was one topic that dominated all the rest: AI.
Throughout the event, artificial intelligence was on the top of attendees' minds and the tips of the speakers' tongues. How are you using it? Where can it provide the most (and least) benefits? What are the keys to harnessing AI’s power successfully? And how can business leaders fully exploit the potential of AI at their organizations while mitigating its safety risks?
The clearest takeaway from the industry's thought leaders? Marketing's AI revolution has officially commenced.
Here are some of the top insights to come out of the Fontainebleau in Miami Beach at this year's event.
Early usage indicates that AI could lead to a productivity boom, allowing marketers to get more done in less time. A Microsoft study found that users of its Copilot AI tool spend less time writing emails, summarizing long documents, and completing first drafts of documents. AI can “attend” meetings for us and summarize the key findings and takeaways. It can also provide new context around how those meetings are conducted, combing through transcripts to identify blind spots in the conversation and capture the tone and sentiment to help people improve the way they show up to work. When used all together, AI can help make us not just more productive, but better marketers and professionals.
In just the last few years, AI-powered capabilities grown at an extraordinary rate, and the idea of AI disrupting every aspect of our lives appears to be on the horizon—including marketing.
Rex Briggs, Chief AI Officer at Claritas and author of the forthcoming book “The AI Conundrum,” noted that marketing is, in many ways, an “ideal use case” for AI.
With AI, marketers can now optimize campaigns in real time and with greater precision, leveraging AI’s proficiency at recognizing patterns and creating hyper-nuanced segments dynamically and on-the-fly to drive desired outcomes. The individual results of these optimizations can be small, but when added up, they can result in huge gains over baseline performance. What’s still to come (but appears to be just on the horizon) is enhancing the technology so that it can get better at explaining precisely what—and why—the AI is optimizing ads based on various criteria for more real-time transparency and clarity.
However, despite all the hubbub around technological innovation, we mere mortals still have a very valuable and unique role to play. Human marketers will be essential in making the final decisions around what to use—and what to change—throughout the campaign process. To skilled marketers, AI represents a powerful new tool to help them move faster, be more productive, and grow more effective.
Google re-iterated its commitment to both the Privacy Sandbox and to deprecating third-party cookies in Chrome by the end of 2024. But Amit Varia, Director of Google’s Privacy Sandbox, emphasized that Privacy Sandbox APIs are not intended to be a 1:1 “replacement” for third-party cookies, and that marketers are best suited to leveraging a range of identity solutions in unison as part of a larger privacy-friendly toolkit. And while initial users of these Privacy Sandbox APIs are seeing effective results, we are still effectively operating in a test environment, where just 1% of Chrome users are playing in the Privacy Sandbox sans third-party cookies, and even Privacy Sandbox evangelists noted there are still ample questions about how these solutions will perform at scale.
So, what value can AI provide marketers in a cookieless environment? MMA data showed that AI-powered personalization drove a 35-65% increase in ad performance within contextual environments. And AI can optimize first-party data to better target existing audience and to create new lookalike audiences.
Audio and display ads both play to generative AI’s current strengths, making them ideal channels for marketers looking to experiment with AI-generated assets and campaigns.
Progressive, for instance, has begun using AI to create more personalized audio ads. The insurance giant incorporated AI across every step of the campaign process, allowing them to go from brief to approval in just 6 weeks (vs. the 22 weeks it previously took them without AI). Leveraging AI-generated scripts and voice talent—after training the AI on Progressive’s brand and an extensive content archive—Progressive’s team was able to create 96 ads in a single week, then run and test them using dynamic creative optimization (DCO) to progressively adapt the ads as market conditions changed. In doing so, they were able to assess different ad parts and predict the right combination for the right audience.
“You can train AI on your own content, hit a button, and end up with different scripts, and then when you are happy with the scripts, you can hit another button to develop the audio, and then when you are happy with those, you can move on to approvals,” said Remi Kent, CMO at Progressive.
Though AI generated the scripts, the personas, and the background music ads, humans were involved and instrumental in every step of the process, in what Kent described as a “collaboration” between humans and generative AI. The result was a process that allowed Progressive to move faster and create more ads with more personalization—all at scale. By using AI-generated ads and leveraging AI-powered optimizations, Progressive was able to drive a 197% lift in quotes over baseline.
Effective utilization of AI for targeting, attribution, and optimization relies on high-quality (and high quantities) of data. The problem? Silos. So very many silos. Disconnected channels, siloed platforms, walled gardens, and a general lack of transparency and data centralization is all too often resulting in organizations having an incomplete picture of their data.
In a session on how marketers can “hack their adtech,” Richard Brandolino, Global Media Channels & Adtech Leader at IBM, recommended that agencies and brands keen on exploring (and exploiting) AI’s benefits should look to facilitate cohesion and interconnectivity across their tech stacks. Streamlining and unifying data flows can yield significant improvements in profitability, cost efficiency, and strategic agility.
Beyond even efficiency and improved campaign results, Possible speakers addressed one other aspect of AI in marketing: the balance between AI’s promise of innovation vs. its inherent risks.
Jaime Teevan, Chief Scientist at Microsoft, noted that the decisions we make with AI today will influence the future of jobs, our industry, and our world. Since introducing its AI-powered Copilot last year, Microsoft has strong feedback that the technology is making people more efficient and saving them time. What’s left to determine is what will people do with that time.
At its best, AI has the potential to create a generational opportunity for innovation, but marketers’ experimentation should always be accompanied by careful consideration about not just the immediate impact of those decisions, but the third- (and fourth- and fifth-) degree effects of those decisions.
Teevan noted that, when first beginning to explore how Microsoft could incorporate OpenAI’s GPT 4 into its products, she began from a place of “How do we bring this technology to people, and do so in a responsible way?” Perhaps tellingly, Teevan’s implication throughout a session on AI seemed to be that the “responsible way” Microsoft has landed upon is to outsource much of this responsibility to its users, imploring them to do their own research, experimentation, and exploration with the technology and hoping they do so responsibly.
Curious about how leading marketers are using generative AI? Basis surveyed over 200 marketing and advertising professionals from top agencies and brands, brands, and publishers to see how marketers are feeling about AI today and gauge how they think it will shape the industry going forward.
In recent years, the digital advertising industry has come face to face with a barrage of new policies and regulations. With concerns mounting over data privacy, consumer protection, and AI, new laws have sprung up at a variety of levels—from state, to federal, to global. This regulatory frenzy underscores a complex balancing act between commercial interests and consumers’ needs.
Consumers accept ads’ presence in our digital ecosystem, with 95% saying they would prefer ads to paying higher costs for an ad-free online experience. A further 88% say they want ads that are personalized to their interests and needs—personalization that is largely dependent on the personal information that consumers do (or do not) share.
However, that being said, there is still a strong desire among consumers and regulators alike for increased transparency and consent around data collection and usage that’s built upon deep distrust of companies’ data practices, with 81% of Americans saying they are concerned about how companies use the data they collect about them and 67% admitting they have little to no understanding of what those companies are doing with that data once they collect it.
Advertisers, then, are faced with a daunting task: Prioritize consumer privacy and adapt to new and ever-evolving regulation, while simultaneously delivering personalized digital advertising experiences that resonate with audiences.
This challenge was at the forefront of conversations at the IAB’s recent Public Policy & Legal Summit, where industry leaders explored this juxtaposition and shared critical considerations for advertising teams. Whether in discussions around bias in AI, presentations on state-specific privacy legislation, or conversations on how to address kids’ safety online, navigating this complex landscape demands not only attention, but conscious action.
While federal regulation has remained largely in limbo, there’s been a flurry of enacted legislation at the state level. In 2023, new consumer data privacy acts took effect in California, Connecticut, Colorado, Utah and Virginia. By early 2026, the number of state-level laws will grow to 14 states.
In the absence of a federal framework, advertising teams are left with a patchwork of regulation—and one that varies significantly from state to state. These laws range from relatively baseline (for instance, VCPDA), to enhanced (like the Colorado Privacy Act), all the way to business-friendly (such as the Utah Consumer Privacy Act).
With such significant variation, a one-size-fits-all approach will not suffice. Many advertisers have attempted to find the strictest regulation—namely, California’s regulation, the CCPA and CPRA—and simply adhere to that in the hopes it will cover all their bases. However, regulation is evolving so rapidly—and there are many different types of consumer data—that trying to find and adopt the “strictest” laws will likely hinder teams and create self-imposed limits where they are not necessary. Instead, those organizations that prioritize flexibility, bolster their legal and technical teams, and take the time to truly understand these different policies and regulations will be most well-positioned for success.
Generative AI was, unsurprisingly, another major topic of conversation. The technology has garnered significant attention since its public debut in late 2022, generating considerable buzz within the digital advertising ecosystem. However, its oversight and regulation pose challenges, given the rapid pace at which this technology is evolving and becoming accessible.
Though the US has yet to enact widespread laws governing its use, President Biden signed an executive order in late 2023 aimed at addressing the “safe, secure, and trustworthy development and use of Artificial Intelligence.” Additionally, the House introduced a bill that would create a commission to spearhead AI regulation.
Despite a lack of codified regulations, FTC leaders shared a few primary focus areas that should be top of mind for advertising leaders as they navigate AI. First, they encouraged teams to conduct AI-focused risk assessments and to ask their vendors to do the same, so that they can evaluate and mitigate any potential risks, such as privacy, security, or bias. They also flagged that advertisers need to be particularly attuned to the risk of bias in AI, since the data and content these models are trained by is generated by humans—and humans, inherently, have biases. Though these tools can prove useful across many aspects of digital advertising, it’s crucial that they be consistently and critically evaluated.
As the technology continues to develop, regulators are certain to prioritize its oversight to ensure that AI is employed in ways that safeguard human safety and prioritizes trust.
Regulation of the advertising industry appears to be focused on simultaneously protecting consumer privacy and ensuring their safety and security online. But regulators are also using these laws to address larger societal risks and issues that inevitably arise in an increasingly digital world. This convergence of privacy, trust, and safety was a major theme throughout the summit, and advertisers must recognize the significance of this overlap as they navigate today’s complex regulatory landscape.
The balance of power around user data in marketing appears to be swinging away from corporations and toward consumers, and companies will be well-served to take notice and act accordingly. Take, for example, the FTC’s recent action against Amazon: The agency’s complaint relates specifically to consumer data, but it goes beyond standard privacy protections and accuses the company of using data to manipulate people into unwittingly spending more than they intend, alleging that Amazon’s “manipulative, coercive, or deceptive user-interface designs known as "dark patterns” essentially “trick” customers into auto-renewing their Prime subscriptions.” The move indicates a new regulatory outlook where protecting consumers’ data isn’t enough, and companies must also ensure that data isn’t being used in a way that harms consumers or goes against their best interests.
This shift in power between consumers and corporations is also evident in regulators’ approach to children’s data—a particularly pressing issue, given that one in three internet users globally is a child under 18 years old. Marketing leaders must remember that kids’ data is, inherently, sensitive data, and that there are strict regulations aimed at safeguarding both this data and the kids themselves. This has been especially evident in social media, where regulators are concerned not only with the collection of children’s user data, but also the digital environment that data is subsequently used to create. Social media algorithms, in particular, have come under fire for their role in increasing mental health concerns, with some states passing legislation aimed specifically at restricting algorithms that target young users.
The FTC has also proposed changes to the Children’s Online Privacy Protection Rule (COPPA) that would shift the onus from parents to providers for ensuring that all digital spaces are safe and secure for children. Though only recommended changes at present, the rules can serve as useful guidelines as advertising teams consider how they’re collecting, storing, and using kids’ data to create experiences for these users online.
Navigating ever-evolving regulation can be challenging, though there are certain strategies that can help.
First, advertising teams should constantly assess their relationships with vendors and third-party partners and review the processes they have in place to ensure they’re meeting all necessary laws and regulations. The IAB recently launched a new tool called the IAB Diligence Platform, which aims to guide these assessments by sharing a set of standardized privacy diligence questions for professionals across the digital advertising industry.
It also helps to have a strong legal team that can stay abreast of new regulatory developments and craft internal guidance and best practices. When regulations inevitably change, these legal teams can assess the implications for your organization, compare them with existing protocols to develop updated guidance, and share these changes in a way that is clear, consistent, and accessible.
Additionally, marketing and agency leaders should invest in internal training and education to ensure teams' compliance with any new rules or regulations. Leaders should ensure they establish clear communication channels to relay regulatory changes affecting their teams, conduct thorough briefings on updated legal guidance and clearly outline the implications of these changes for day-to-day operations.
Advertisers should also be deliberate when selecting vendors and partners to ensure they share similar values and priorities around data privacy and regulatory adherence, but organizations cannot simply rely on their vendors and assume that they’re checking all the boxes when it comes to adhering to regulations—all of this is a shared responsibility, and teams that acknowledge and embrace that can meet today’s regulatory demands proactively and effectively.
In today’s complex digital ecosystem, prioritizing consumer privacy and safety isn’t simply a best practice—it’s a necessity. Balancing consumer data privacy and online safety with the delivery of personalized advertising experiences poses a unique challenge. However, by staying informed on the latest regulation and legislation, maintaining flexibility, and committing to making decisions centered around consumer needs, advertising teams can cultivate trust while still delivering tailored messaging that resonates with their target audiences.
