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Purpose-driven marketing appears to have hit a crossroads. While the 2010s and early 2020s saw a rise in brands taking bold stands to advocate for social and environmental causes, many advertisers have grown more cautious in the past year—particularly in light of the Bud Light boycott and the Target Pride month merchandise backlash. These events seem to have advertisers wondering: Do brands have a place in social and environmental advocacy? And is there any way to get it right?
In the case of Bud Light, the brand partnered with influencer Dylan Mulvaney, a transgender woman, and was the subject of a boycott by conservative Americans who were unhappy that Bud Light was indicating support for transgender rights. In the case of Target, the brand drew backlash relating to its Pride-related merchandise—some of which was based on misinformation spread via social media—and consequently withdrew some of that merchandise from certain stores and from their website. Both brands then garnered further backlash from prominent LGBTQIA+ advocacy groups and consumers who felt the brands’ responses represented a failure to maintain support for their community.
Now, in the wake of those incidents and their ensuing fallout, many brands are reevaluating whether taking a social or environmental stand is worth the risk. In assessing whether purpose-driven marketing makes sense for their organizations—and, if so, how to execute it effectively—marketing leaders must determine whether they are running these campaigns for the right reasons and honestly assess whether the values they’re touting are truly fundamental to their brands or just a convenient marketing opportunity. To do so, they must not only look inward, but understand the history of social and environmental advocacy in advertising, how consumers feel about it today, and what successful purpose-driven marketing looks like.
Over the past decade, conscious consumerism has grown more mainstream as more and more consumers have embraced the idea of “voting with their dollars,” or purchasing from brands whose values align with their own. While the idea of dollar voting has been around for a while, the rise of the internet accelerated its adoption, as consumers grew fluent in using the web to research brands and circulate information about their activities.
And it’s not just consumers who have driven this shift—employees increasingly want to work for businesses who uphold social and environmental values. Over half of US workers say they would leave their current job for an employer who has a more positive impact on the world, including 71% of employees age 18-to-29 and 62% of those age 30-to-44.
These shifts are likely influenced by the fact that consumers are growing more diverse across multiple axes: Racial and ethnic diversity is increasing amongst the US population, and the percentage of US adults who self-identify as LGBTQ+ has more than doubled since 2012, including one in five Gen Z adults. As our society grows more diverse, there’s been rising demand for brands to not only represent that diversity in their marketing, but to authentically demonstrate their inclusion and support for different communities (particularly historically marginalized communities) as well.
Accordingly, more brands began to take social stands in their advertising. Nike made Colin Kaepernick, the former NFL quarterback who kneeled during the national anthem at his games to protest racial injustice and police brutality, the face of its “Just Do It” 30th anniversary campaign. Gillette’s “The Best Men Can Be” campaign took on toxic masculinity in conversation with the #MeToo movement. And numerous brands—including MAC Cosmetics, The North Face, and Levi’s—have run Pride campaigns supporting LGBTQIA+ causes during June each year.
But this kind of purpose-driven marketing can be a risky undertaking, as many social and environmental causes are politicized, and “taking a side” can mean running the risk of alienating a significant fraction of the public, especially in places like the US where political polarization is high. At the same time, consumers are quick to criticize brands who try to capitalize on the attention given to social and environmental issues in inauthentic ways.
As such, understanding when, why, and how to take a stand on social and environmental issues is a skill set that’s increasingly important for modern marketers. Today’s consumer base has high expectations around authenticity, and it takes work to build the trust, connection, and loyalty that brands covet—while, of course, avoiding the negative impacts of potential backlash. And although getting purpose-driven marketing right might seem like a daunting task, it’s simpler than advertisers might expect: Essentially, it comes down to knowing a brand’s values, and ensuring that brand is authentically living out these values before addressing a related social or environmental issue in your messaging.
Despite trends in conscious consumerism, employee preference for socially responsible employers, and growing demographic diversity, consumer sentiments around purpose-driven marketing aren’t unanimously positive. In a 2023 survey, 53% of respondents reported feeling that corporations should not engage with political or cultural issues. And, researchers have found that when brands promote controversial messages, their advertising outcomes suffer, even for brands who are well known for their activism. At the same time, social and environmental causes don’t trump other factors in consumers’ purchase decisions: Quality, price, and convenience are the most important factors influencing US consumers’ purchases. Considering these factors, some brands may reasonably decide that purpose-driven advertising isn’t right for them.
However, the consumer base of tomorrow may lean more towards favoring brands who take social stands. Millennials and Gen Z, who will soon claim the majority of buying power in the US, are more likely to be socially conscious purchasers and 27% more likely than older generations to buy from a brand that they believe cares about its social and environmental impact. They also understand their purchasing power more than older generations, which makes them more likely to engage in conscious consumerism. As a result, brands looking to engage younger audiences may want to consider purpose-driven marketing to show these consumers that they share their values.
Of course, meeting consumer sentiments and expectations isn’t the only reason brands adopt purpose-based marketing. Advertisers have the power to shape and define culture with the messages and representations they put out into the world, and I believe that power comes with great responsibility. When advertisements only feature actors and models who look a certain way, for example, that indirectly signals to consumers who look differently that they don’t belong. There’s a wealth of research highlighting how unrealistic beauty standards in advertising impact the mental health of girls and women: One study found that exposure to Instagram ads featuring thin or curvy women influence late-adolescent girls' views of their bodies, potentially increasing their willingness to take drastic actions to alter their appearance. I think more and more advertisers are understanding their impact and growing more thoughtful about their messaging as a result, and that’s another reason why brands are increasingly taking social and environmental stands with the goal of having a positive impact on our society.
With the rise in socially conscious consumers and socially conscious advertising, however, has also come a spike in brands who try “talk the talk” without also “walking the walk,” hoping to reap the benefits of purpose-based marketing without having to demonstrate a genuine commitment to those values. For example, a brand might run a Black History Month-themed campaign to capitalize on the attention given to the Black community during February, despite not having a racially diverse employee or vendor base and/or while their Black employees don’t feel supported.
This type of marketing has grown into such a problem that there are specific terms for brands trying to inauthentically capitalize on the attention being given to certain social causes, such as “greenwashing,” “rainbow-washing,” and “woke-washing.” And consumers, regulators, and organizations alike are increasingly cracking down on this behavior.
Brands get called out on social media every year for inauthentically tapping into social causes—for example, the Gender Pay Gap Bot on X highlights corporations’ International Women’s Day messages alongside their internal gender pay gaps to expose their inauthenticity. In Europe, regulators are introducing and enforcing legislation that makes greenwashing a legally punishable offense. And Pride in London, the nonprofit that organizes one of the UK’s biggest LGBTQ+ Pride festivals, is asking that advertisers who want to participate in this year’s festival engage with LGBTQIA+ causes throughout the year and promote LGBTQIA+ inclusion within their organizations.
All in all, inauthenticity when taking social stands can lead to consumer backlash and negative brand perception, and it is one of the main traps brands fall into when trying to embrace socially conscious advertising. In contrast, brands can find success in supporting social causes when they do so authentically and as part of a long-term strategy.
My biggest recommendation for brands looking to embrace social and environmental causes in their advertising is this: Make sure whatever cause you’re looking to advocate for is one that your organization actively and genuinely supports before you share that advocacy externally. For example, if a brand wants to run a campaign during Earth Month related to their commitment to sustainability, they need to start with an internal audit. What’s the environmental impact of how their products are made? Do they regularly donate to and partner with environmental causes and organizations? Do their employees feel that their brand authentically acts out a commitment to the environment? If so, that brand is well-positioned to leverage their strategists and creatives to create a sustainability-focused marketing campaign.
Brands also need to understand that taking a stand on a social issue may result in backlash, and they should have a plan for if and how they’ll respond if that happens. Target’s response to the backlash it received last year is, unfortunately, a good example of what not to do. In reducing its Pride-related merchandise in response to criticism, the brand received even more criticism from consumers who felt it had walked back its support of the LGBTQIA+ community. Panelists discussing LGBTQ brand advocacy at SXSW this year agreed that in the end, the situation only grew more toxic for Target when they engaged in that initial backlash by walking back their stance.
Due to the political polarization around many social issues, there’s a good chance that brands will receive backlash when they take social stands. This is why leaders need to plan for if and how they’ll respond to criticism. Ideally, social and environmental stands should be one part of a brand’s authentic, long-term engagement with a community or issue. When brands are deeply committed to their values, they accept the fact that not everyone will be aligned with those values. Organizations that understand this, and that are prepared to hold true to their values in the face of criticism and disagreement, will be best positioned to run a successful purpose-driven campaign without wavering under pressure and suffering any subsequent erosion of trust with its core audience for doing so.
Ultimately, the keys to taking a social stand effectively are to do so as part of a long-term strategy that includes both internal and external action, reflects a deep understanding of a brand’s values and of their audience’s values, and that doesn’t waver amidst criticism.
While purpose-driven marketing is by no means right for all brands, it’s something marketing leaders will want to consider as younger, socially conscious generations inherit the majority share of consumer buying power in the coming years. Agency leaders, in particular, will need to train their teams on how marketers can advocate for social and environmental causes in effective ways in their advertising, so that they’re prepared when their clients inevitably show interest in doing so.
At the end of the day, while the nuts and bolts of purpose-driven campaigns can be tricky to iron out, the recipe for success is fairly simple: Brands should take social stands only as part of authentic, longstanding commitments to those causes, which are reflected both inside and outside their organizations.
Derek Zolner is Basis Technologies’ General Counsel. Here, he offers insights into the draft APRA legislation and its potential impact on digital advertising.
Since its introduction in early April, the American Privacy Rights Act (APRA) has generated significant buzz due to its potential implications for the digital advertising industry in the United States. This proposed federal legislation aims to establish the first generally applicable national data privacy framework and represents Congress’ best chance yet to pass such a law, given that it appears to have both bicameral and bipartisan support.
It’s unclear, however, whether this legislation will escape the congressional purgatory where previous attempts at a federal data privacy framework have stalled. And the fact that it is moving through the legislative process during an election year could either help or hinder its progress, depending on how it aligns with key players’ legislative priorities.
Despite these uncertainties, the bill’s initial hearing in front of the House Energy and Commerce Committee garnered significant praise from witnesses and members of the Committee on both sides of the aisle. And the bill’s sponsors have described this draft legislation as “the best opportunity we’ve had in decades to establish a national data privacy and security standard.”
Given its potential to change both how digital advertising teams collect and utilize data and how audiences can control their personal data, it’s worth unpacking the APRA in its current form and examining its potential wider impacts on the digital advertising industry.
The APRA has the potential to truly change the landscape of data privacy in the US, as it would represent the first federal, comprehensive privacy act. Though there are currently sector-specific privacy laws, such as HIPAA for the healthcare industry and the Gramm-Leach-Bliley Act (GLBA) in financial services, the US has yet to enact a national data privacy framework that would be applicable to most businesses. Digital advertisers today are left navigating these sector-specific laws, alongside a varying patchwork of state-level data privacy laws.
The APRA would change that, establishing federal guidelines for how consumers’ personal online data can be collected and used, as well as what actions consumers can take when their data is misused. Much of the draft legislation mirrors what we currently see in state-level laws, such as the CCPA, which allow consumers to ask companies what personal information they have about them and how they are using it. These state laws also give consumers the right to correct that data, to delete it, or to opt out of its use in certain ways. The APRA includes similar consumer access and data rights provisions.
Unlike state laws such as the CCPA, which only permit private lawsuits in case of data breaches or provide no private right of action at all, the APRA would allow individuals to sue for any violation of the act. For instance, if a company fails to honor a California consumer’s personal data access request, that consumer cannot sue the company for its failure to do so. If the APRA was passed in its current form, however, that consumer would be able to pursue direct legal action against the company. As such, this inclusion of a private right of action for any violation of the APRA represents a significant and potentially costly shift, as it expands the range of circumstances in which individuals can pursue legal action against companies.
Beyond these consumer access and rights features, the APRA has an added focus on large social media companies and companies that process large volumes of personal data. Tech behemoths such as Meta and Google would fall into this category, as well as many adtech companies that have DSPs, SSPs, and DMPs, all of which handle substantial amounts of personal data for ad targeting and optimization.
This section of the APRA is a novel feature, not previously seen in state laws or even comprehensive laws in other jurisdictions, like the GDPR. It outlines very specific and expanded data security and reporting requirements that address these companies’ data handling practices. These requirements include designating a data privacy officer and chief data security officer as well as new annual reporting obligations.
Another notable feature of the APRA is its inclusion of a preemption provision, meaning that the APRA would preempt state laws that cover the same subject matter.
This is something that digital advertisers have long wanted: Instead of legal, data security, and privacy teams having 50 different state law compliance targets, the APRA would provide one comprehensive, standard law that would preempt any similar state laws. At the same time, its expanded scope would present a new compliance challenge in instances when the APRA would be stricter than the standards set by existing state-level laws. This would add an additional layer of complexity for digital advertisers who would have to adjust their practices to meet these new requirements.
What, then, could the APRA mean for digital advertisers?
While advertisers have speculated about the possibility of the bill banning targeted advertising in the US, a more probable scenario is the implementation of an express or implicit requirement for user opt-in regarding cross-site tracking and targeting. Currently, when a user visits a website and encounters a cookie consent banner, opting out often requires several steps. The APRA could simplify this process by requiring a banner with clear options like “accept all” or “reject all.”
In fact, technological barriers—such as browsers deprecating third-party cookies—play an as big or bigger part in the future of targeted advertising. Even if users are allowed to opt-in under the APRA, if browsers are already blocking third-party cookies, then the “accept all” choice would be meaningless, because cookies aren’t supported.
So, while advertisers likely don’t have to worry about the APRA banning targeted advertising, the looming loss of cookies in Chrome remains. This means that advertising leaders should be exploring and embracing cookieless solutions, regardless of the outcome of this legislation.
Apart from the impact on cookies, adtech companies would also face increased data security and reporting requirements that would come into effect under the APRA, particularly for companies that would need to meet the obligations outlined for large data handlers. Beyond having to hire for new positions related to data security and privacy, these teams would also face a new compliance layer, including significant reporting requirements. And like with GDPR, the APRA would require a host of new contracts or amendments with customers, partners, and vendors.
As with all new regulation, this will be a boon for lawyers and other professional advisors at a significant cost to companies trying to comply. Companies would need a meaningful opt out mechanism and a solid process for allowing people to make data subject access requests. Teams would also have to prepare for more of these requests, since people in all 50 states—not just those with enacted privacy laws—would be able to make such requests.
In addition to representing what might be Congress’ most viable attempt yet at establishing a federal data privacy framework, the APRA’s development underscores the pressing need for industry-wide adaptation to an evolving regulatory landscape. While its fate remains uncertain, the potential implications of the APRA are undeniable and represent a pivotal opportunity for those in the digital advertising industry to embrace a privacy-centric approach to connecting with audiences.
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Want deeper insights into how your peers feel about privacy and user data, specifically within the context of signal loss? Learn how advertisers are navigating third-party cookie deprecation, data privacy, and more in our report, Identity vs. Privacy: Digital Advertising in a Cookieless World.
In today’s era of rapid technological innovation, it can be easy for marketing leaders to focus solely on building a top-notch adtech and martech stack as the ultimate way to empower their team. After all, tech is undeniably a critical and transformational piece of any marketing team’s effectiveness.
But a successful marketing team takes more than tech alone. To build and retain a team that reaches its full potential, marketing leaders must also prioritize the “soft skills” that empower effective collaboration and champion the kind of creative risk-taking that sets teams apart.
These human elements are especially relevant in the context of declining employee engagement, which after rising steadily throughout the 2010s has remained fairly stagnant since the early days of the pandemic. As of 2023, only 33% of US employees were engaged in their work and workplace, with unengaged workers representing $1.9 trillion in lost productivity on a national scale.
In this environment, finding and retaining talent is a top concern for C-suite executives. This is particularly true for marketing organizations, which lost 14% of their workforce between 2019 and 2022 and continue to grapple with an ongoing talent crunch. In fact, global marketers ranked talent management as one of their top five challenges in 2023.
As the CMO of an organization whose core ideology includes a dedication to the personal and professional growth of each employee, I’ve found that the way my team works together is one of the biggest predictors of our success. If our culture isn’t healthy, productive, and collaborative, the quality of what we produce suffers.
Similarly, as marketing leaders contend with major paradigm shifts in the industry, nurturing their team members’ social and emotional skills in service of the collaborative, innovative culture they foster will be a major differentiator for brands and agencies looking to retain top talent, advance innovation, and drive revenue.
Adopting a conscious leadership approach, which aims to build teams that work together as efficiently and as effectively as possible, is one of the most impactful things I’ve done in my tenure as a CMO (and I don’t just mean embodying conscious leadership principals myself, but making sure my team members are trained in embodying those principles as well). It can be easy to discount the so-called “soft skills” this kind of leadership focuses on—things like self-awareness, empathy, accountability, emotional intelligence, and curiosity—but these are the skills that give teams the cohesion and adaptability necessary to meet all the changes taking place in the advertising landscape. At the same time, building a team with these skills means creating a team culture that is positive, supportive, and fun: In other words, a culture that engages team members and makes them want to stay for the long haul.
Research highlights the benefits of both leaders and team members having these skills: Leaders with strong emotional self-awareness are more likely to be perceived by their teams as creating environments that foster high performance, and the World Economic Forum has noted that “decades of research now point to emotional intelligence as the critical factor that sets star performers apart from the rest of the pack.” In kind, businesses are increasingly seeking leaders with social skills like empathy, self-awareness, effective listening and communication, and the ability to work with many different kinds of people. Similarly, the demand for social and emotional skills in the US workforce is forecast to increase by 26% between 2016 and 2030.
These skills are even more relevant in the context of marketing, where the ability to empathize with a target audience in service of understanding what messages might resonate most with them can make or break a campaign. As such, these social and emotional skills drive innovation and creativity on marketing teams, and boost revenue in kind.
To embody conscious leadership, leaders must develop skills like the ability to give feedback without blame or judgment and to receive feedback with openness and curiosity, and foster those skills in their team members as well. Those things don’t just happen naturally—we all tend to judge others harshly, to get defensive, and to take things personally when our ideas or performance are criticized. It takes a significant amount of personal development work to gain a tolerance for giving and receiving feedback in constructive ways. When an entire team gains that tolerance, it leads to a level of cohesion and trust that can be transformative for the team’s output.
Another conscious leadership skill is the ability to remain open to the opposite of your own perspective being true. As with giving and receiving feedback, this skill requires a lot of self-reflection. You have to ask yourself, “Am I holding onto this idea because I know it’s the best way forward, or because I’m attached to not being wrong?” This is an especially important skill for marketers, because the work we create is very visible, and thus very open to feedback and criticism.
The self-reflection necessary for conscious leadership is uncomfortable, difficult work, and it takes real time and effort to implement across a team. Leaders must lead by example, of course, but also offer their team professional development opportunities to grow these social and emotional skills, as well as make a practice of digging into situations where team members aren’t working well together. At Basis Technologies, our leadership team spends a significant amount of time meeting together to practice these skills in real-time. We know that if we’re not working those muscles, it’s easy to revert to our bad habits, like gossip and blame—and in doing so, we create a culture of gossip and blame that will be felt and adopted by our teams.
One of the goals of a conscious leadership approach is to create a culture of psychological safety in service of boosting creativity and innovation—one in which individuals feel secure enough to share ideas, take risks, and be vulnerable with their team members.
A psychologically safe environment has a host of benefits for businesses. Research indicates that psychological safety drives improved performance and employee retention, and is one of the most reliable indicators of team performance, productivity, quality, safety, creativity, and innovation. Psychological safety has also been called the “key to realizing the promise of diversity in teams”. All of these benefits are even more apparent in teams whose work is creative and collaborative, like marketing.
Alas, psychological safety is all too often underprioritized, with one survey of workers across industries finding that “only a handful” of business leaders create climates of psychological safety among their teams. As such, adopting conscious leadership in service of creating psychological safety and fostering engagement can give marketing leaders a significant competitive edge.
Even more, psychological safety creates environments in which team members can take risks. Risk-taking is critical for us as marketers, because it creates stand-out ideas—and Don Draper had a point when he said, “Success is related to standing out, not fitting in.” Marketers can’t come up with the kind of risky, stand-out ideas that really make a splash if they don’t feel safe sharing those ideas with their peers. At the same time, in my experience, when we take more risks, we have more fun. Risk-tasking creates a more exciting environment to work in, and those are the environments where marketers stay engaged and want to stay for the long-term.
I don’t have everything figured out, and I am still learning about the best ways to foster psychological safety amongst my team. But I’ve made it a priority because of the significant benefits it offers in terms of talent retention, innovation, and creativity.
All in all, the term “soft skills” drastically underrepresents the power social and emotional skills offer marketing leaders and their teams. In truth, things like empathy, emotional intelligence, and conscious communication form the foundation of a high performing team.
As advertisers grapple with a huge amount of industry turbulence, conscious leadership offers a reliable avenue for leaders to retain talent, harness innovation, and drive revenue. Even more, leaders who embody these skills—and who help their team members to develop them in kind—create cultures of psychological safety that lead to the kind of creative risk-taking that sets brands apart.
From an increasingly complex media landscape, to the Great Resignation and an ongoing talent crunch, to prolonged economic uncertainty, to signal loss and heightened regulatory action, advertising agency leaders have faced a myriad of challenges that have impacted their operations, strategic planning, and financial profitability over the past several years.
Beneath the surface of these more visible challenges, a quieter yet equally significant issue plagues agencies: inefficiency. In fact, in a recent survey of agency professionals, respondents identified inefficient processes as the biggest challenge facing their agency today. These processes show up in a variety of critical areas, spanning project management, communication methods, resource allocation, client management, technology utilization, workflow processes, and more, and add an additional layer of complexity to the challenges advertisers are already navigating.
Not only do inefficient processes drain valuable time and resources, but they also hinder agencies’ ability to deliver high-quality campaigns promptly. When teams are burdened with inefficiency, it can create a ripple effect that damages client relationships, leads to employee burnout and turnover, and ultimately impacts an agency’s bottom line. As such, assessing and improving process efficiency is crucial for agency leaders who want to remain competitive, adapt to rapid industry changes, and ensure sustainable growth.
High turnover rates have long been a challenge for advertising agencies, with recent years seeing an outsized impact on junior-level employees. Though many factors impact employee turnover, inefficient processes can be a significant driver—particularly at a time when agency professionals already feel as though their jobs are harder than they were in the past.
Amidst these pressures, inefficiency can severely affect wellbeing and job satisfaction. Inefficient, duplicative workflows exacerbate stress, leading to frustration as employees spend excessive time on repetitive, low-value tasks. This also leaves less time for mentorship and meaningful collaboration, both of which are critical for engaging and retaining younger generations of talent. Frustration from inefficiency often culminates in burnout and/or disengagement, as workers feel overburdened by obstacles that impede their productivity and hinder their ability to deliver high-quality work.
For agencies that are already navigating talent retention woes, inefficiency can further exacerbate them: As skilled professionals become disillusioned with the lack of progress and innovation within their team, they might become more likely to seek opportunities elsewhere. And, the resulting high turnover rates not only disrupt team dynamics but also incur significant costs in terms of recruitment, training, and lost expertise. Research has found that employee disengagement and attrition could cost a median-size S&P 500 company $228 million per year—or more.
Addressing inefficiency is therefore crucial for building a positive workforce culture and preventing turnover and burnout—particularly as many agencies strive to accomplish more with fewer resources. By carefully evaluating processes and looking for ways to streamline operations, agencies can significantly improve both productivity and job satisfaction.
Specifically, leaders might consider using employee surveys and/or an internal efficiency audit to gauge how existing processes are working—or not working—within their agencies. For instance, if team members are overwhelmed by repetitive tasks, AI tools could help optimize workflows and free up employees to focus on more strategic, creative, and high-value projects. Such tools not only enhance collaboration but also allow for a more dynamic and responsive work environment, in which agency professionals feel empowered and fulfilled by their work. Or, if employees are making errors due to using too many different communication channels, leaders might consider unifying those efforts into a single platform to minimize errors and allow their teams to focus their energy on more creative, fulfilling tasks. By leveraging employee insights to identify ineffective processes, leaders can ensure they’re prioritizing the efficiency improvements that will have the greatest impact on their teams’ work and wellbeing.
Beyond impacting agency workforces, efficiency—or lack thereof—shapes client relationships. Nearly half of agency professionals say client relationships are more strained today than they were two years ago, and that sentiment is even more pronounced among those who feel that digital advertising has grown more difficult over that same time period.
When agencies struggle with inefficient processes, project timelines can become unpredictable, leading to missed deadlines and delayed campaign launches. Clients rely on timely delivery to meet their marketing goals, and any delay can disrupt their strategic plans, resulting in frustration and dissatisfaction. Additionally, inefficiency can lead to inconsistent communication and coordination, eroding trust and weakening the client-agency relationship over time.
Inefficiency can also compromise the quality of the work produced. When teams are bogged down by redundant tasks, they have less time to focus on creativity and innovation. The resulting campaigns may lack the strategic insight and originality that clients expect, ultimately affecting their brand’s performance in the market. As agency leaders look to minimize inefficiency among their teams to improve client relationships, tools like advertising automation software can prove particularly useful: By breaking down siloes and integrating all advertising activities in one place, leaders can ensure better coordination, communication, and execution across their teams.
Inefficiency can also significantly impact agencies’ bottom lines by draining resources and reducing overall profitability. For instance, consider an advertising agency tackling multiple high priority client campaigns during a particularly busy period. Due to outdated project management processes, employees spend excessive time completing manual data entry, communicating with clients over a variety of disparate channels, and navigating repetitive approval processes. Instead of using a unified platform, the team relies on multiple spreadsheets and email chains to track project progress, leading to confusion and errors. This disorganization requires employees to work overtime to meet deadlines, resulting in higher labor costs. And when working overtime to manage inefficiency becomes the norm, high rates of burnout and turnover are sure to follow.
Even more, despite those additional hours worked, the quality of the output does not improve—that extra time is spent on managing chaos rather than enhancing creativity or delving more deeply into strategic planning. The campaigns delivered likely lack the innovative edge expected by clients and fail to capture audience attention in today’s competitive digital environment. This can lead to the agency’s profitability suffering as they pay more wages without seeing an improvement in deliverables, suffer from strained client relationships, and face the potential of lost business as a result of this strain.
To address these inefficiencies, this agency might opt to centralize their task management, workflow, communication, and collaboration into one unified platform, reducing their reliance on spreadsheets and email chains. Additionally, they could use AI tools to help with the low-level tasks that currently monopolize their employees’ time, freeing them up to focus on more fulfilling, creative work.
Addressing inefficiency, then, not only helps agency leaders to build a strong workforce and maintain good relationships with their clients, but also increases their profitability and ensures their long-term sustainability. By streamlining processes, adopting automation and AI tools, and fostering a culture of efficiency, agencies can reduce labor costs, enhance the quality of their work, deliver more impactful campaigns, and set themselves up for long-term growth and profitability.
Though advertising agency leaders face a variety of challenges, inefficiency is one that cannot be ignored. With its potential to strain workforces, worsen talent retention woes, burden client relationships, and hurt agencies’ bottom lines, ignoring inefficiency comes at a steep price. In prioritizing efficiency, agency leaders can not only enhance productivity and morale but also position their businesses for long-term success in an increasingly competitive industry landscape.
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Want more insights on how agency professionals feel about the challenges and opportunities impacting their jobs, agencies, and industry as a whole? We surveyed advertising professionals across the US to understand how they feel about the state of advertising agencies in 2024. Check out our 2024 Advertising Agency Report for all the top takeaways.
After several tumultuous years, agencies entered 2024 with optimism. Yes, there were some lingering concerns about the overall state of the economy, but forecasters were projecting an uptick in media spend. Sure, there were some high-profile layoffs, but overall, staffing in the industry was at an all-time high.
And fragmentation was still a problem, it’s true, but streamers were introducing new inventory on new platforms to showcase new content, with Hollywood productions back on track after two
prolonged strikes.
Overall, the advertising industry was looking strong. But what about the people working in it?
To find out, we surveyed advertising industry professionals from agencies across the United States, exploring how they feel about their jobs, their agencies, their industry, and the challenges and opportunities that are shaping their futures.
Among the findings:
To get more insights into the state of advertising agencies today, fill out the form to download the full report.
If you ever find yourself questioning the power of a well-defined brand identity, just look at companies like Coca-Cola and Nike. These giants are known for not only their products, but the emotions and stories associated with their unique brand identities: Coca-Cola is not simply a beverage, but a symbol of joy and unity; Nike’s ubiquitous swoosh logo and and “Just Do It” slogan are a celebration of determination and resilience. Such brands highlight the lasting influence of brand marketing, demonstrating its ability to shape consumer behavior and brand perceptions over extended periods of time.
Despite brand marketing’s time-proven effectiveness, performance marketing has captured the spotlight in recent years. With its focus on driving specific actions or results, as well as its ability to provide near-immediate measurement and attribution insights, many marketing teams have shifted their focus from brand building efforts to performance tactics. And given the mounting pressure on CMOs and advertising leaders to show the financial impact of their marketing efforts, the clear and tangible metrics offered by performance marketing hold significant appeal.
But such a narrow focus on performance tactics can lead brands and advertising teams to get stuck in a loop at the bottom of the funnel—one in which marketers may be driving short-term sales, but are doing little to build long-term brand identity and awareness. And emerging research is providing insight into the true value that brand building efforts can yield, demonstrating the need for marketing and advertising leaders to reconsider the connection between brand building and performance marketing, as well as how they communicate the value of these combined advertising efforts to key stakeholders.
In today’s digital age, performance marketing has become a dominating force. As advertising leaders face increasing pressure to demonstrate tangible returns on marketing investments—particularly after the economic turbulence of the past several years—performance marketing offers clear, measurable, and swift results, allowing companies to justify their marketing spend and optimize for maximum return on investment.
Brand building, with its focus on fostering long-term brand equity and emotional connections with consumers, often requires delayed gratification in terms of both seeing and measuring its impact. For smaller brands and clients, in particular, it can be difficult to justify these longer-term investments, as stakeholders want to see performance and assess its impact right away. Unfortunately, this has led many leaders to take an “either/or” approach to brand and performance marketing, rather than seeing them as complementary strategies. However, such a disparate approach to these marketing strategies can have negative impacts for brands and advertisers—particularly in the long-term.
Research has shown that the long-term effects of marketing account for 60% of total ad spend ROI, where short-term effects make up only 40%. If advertising teams focus all their investments on short-term performance tactics, they’re going to miss out on those potential long-term gains.
As such, taking a unified approach to brand and performance marketing is a must. As leaders reconsider their approach to this divide, it can be helpful to rethink the language used to describe these marketing approaches: All marketing—regardless of what it is labeled—is aimed at driving performance. Branding efforts are still performance, it just takes more time to measure and quantify their impacts.
Even more, taking a performance approach to brand marketing can be a helpful way to bridge the disconnect between brand vs. performance tactics. By calling it performance branding, and then having a measurement plan to back it up, leaders can more clearly demonstrate the value of brand advertising efforts.
And just what, precisely, is that value? According to the MMA’s Brand as Performance research series, brands that invested in brand building and increasing favorability with consumers saw a four times increase in sales lift and a five times increase in penetration lift. Brands with higher awareness also drive growth more efficiently than those with lower awareness—in other words, the stronger the brand awareness, the further each dollar of lower-funnel ad spending will go. And additional research has demonstrated that the long-term ROI of advertising is double the short-term ROI, further showing the value of investing in brand building alongside more lower-funnel efforts.
By recognizing the complementary nature of brand building and performance marketing and remembering that all advertising is intended to drive performance, leaders can increase their ROI and drive results in both the short and long term. They can also avoid the consequences of neglecting brand building in pursuit of immediate returns.
To effectively harness the power of brand marketing and maximize its full potential, leaders first need to be open to investing in brand building efforts.
For those on the fence or hesitant to move budgets away from more short-term tactics, it can be helpful to find small opportunities for testing. For example, marketers might choose to increase brand efforts in just a few markets via awareness-building video, then measure and assess the impacts over a few months and use the results to justify further brand-focused investments moving forward.
Once leaders are in a place where they are prepared to strike a balance between more traditional “performance” and brand building media investments, the question becomes: How much spending should go towards those shorter-term tactics vs. long-term brand marketing efforts? The answer will vary significantly by brand and client, and requires research based on a given brand, what’s happening in that brand’s category, what its competitors are doing, how consumers are behaving, and more. It also requires an openness to testing and learning to determine what mix is most beneficial for driving desired results.
For instance, let’s say you’re a B2B company. Research shows that, at any given time, 95% of potential buyers in the B2B space are not actively looking to make a purchase. But just because these customers are “out-market” now doesn’t mean they shouldn’t be advertised to. When it comes time for these consumers to make a purchase decision, they are probably going to gravitate towards the companies and brands they are familiar with—in other words, those advertisers who have invested in brand building efforts. Rather than only trying to connect with these audiences during the short and infrequent windows when they’re actually “in-market,” B2B brands should invest in long-term tactics to build awareness and familiarity so that, when it’s time for these audiences to make a purchase decision, their brand is top-of-mind. As such, for B2B companies, having a media mix that skews more heavily towards brand building often makes sense.
For a B2C company, on the other hand, it might drive better ROI to strike a greater balance between brand building and performance marketing tactics. These companies want to ensure they’re both generating sales in the short-term and building brand awareness and identity in the long-term to drive repeat purchases and brand loyalty, and they often aren’t dealing with the longer purchase cycles that B2B brands face.
Though the balance between performance and brand building media may vary, there are certain elements that are critical for all brand marketing efforts. Perhaps most fundamental is the development of compelling and consistent creative that resonates with target audiences. The primary goal of brand marketing is to establish and enhance the perception, awareness, and identity of a brand among target audiences, so taking the time to craft strong creative, A/B test it, and optimize it is crucial. Consistency is another key element of effective brand efforts, as it ensures that consumers receive a coherent and unified brand experience across various touchpoints, reinforcing brand identity and fostering trust and recognition. By maintaining consistency in messaging, visuals, and tone, brands can build familiarity and loyalty among their target audience, ultimately driving brand equity and long-term success.
Though performance marketing has garnered significant attention and investment by advertising leaders in recent years, neglecting brand marketing can have negative long-term impacts—particularly since brand efforts can amplify the results of performance marketing tactics. Brand and agency leaders seeking both short- and long-term results should take a more unified approach and consider how all their media drives performance. In doing so, marketers can maximize ROI on all their advertising investments.
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.