Nova has been leading the market conversation around (re)generative AI and published their first thought leadership piece in September 2023.
Overview
The world of digital advertising is on the cusp of a seismic shift. A look at recent history shows the last ten years in the industry could be summarized as ‘the decade of data.’ Since 2014, when digital advertising was only about 25% of overall advertising spend (today it’s closer to two-thirds, representing $667B in annual global spend), the emergence of data has driven waves of innovation, changing how we identify audiences, target audiences, and apply measurement and attribution to determine campaign success.
Two companies were the clear winners in the ‘decade of data’: Google and Meta. Both companies built platforms that attracted a large user base. Just as importantly, both companies invested heavily in the data mechanisms and tools to target users with the right ads. Advertisers saw the performance lift and responded predictably. Buying search campaigns on Google and display campaigns on Facebook or Instagram became a de facto requirement for almost every campaign over the last 10 years, from small businesses to Fortune 500s.
But now the ‘decade of data’ is coming to a close. As usage of digital media continues to climb, more consumers are wary of sharing their data with these platforms. Infighting among major platforms, like Apple restricting Meta’s access to user data, has stepped on the air hose of how audience data flows between major players. And most notably, there are new regulatory pressures, both at the state and federal level levels in the US, and sweeping across Europe, that signal a clear message: the old ways of doing things won’t work any more.
Welcome to the ‘Decade of Creative’
As one door closes, another door opens. Three trends are now ushering in a new ‘decade of creative”:
Data pressures. With the increasing limitations on data collection and usage, companies are having to spend more on data targeting and applications to gain less visibility and poorer performance than they had previously. Ad suppliers like agencies, publishers, and ad tech platforms are under pressure to continue delivering more value at a lower cost. For the first time in recent history, the answer is not “more data.”
Research on the power of creative. Research continues to show that ad creative is still the largest contributor to ad effectiveness. Studies from major players like Google and Nielsen consistently have reinforced that as much as 70% of an ad’s performance is tied to the creative itself, not the audience targeting, brand association, or media where the ad is running. During the ‘decade of data,’ it was easy to ignore creative, which can be seen as messy to generate and expensive to produce for multiple channels. Companies now will have to find material ways to improve creative to capitalize on the breakthrough potential.
Focusing on audiences over channels. Over-reliance on channels like Google (search) and Meta (social) have caused ad suppliers to form their thinking, and mold their organizations, around these channels. Yet, consumers are increasingly omnichannel, fragmenting their time across multiple screens and platforms. As ad suppliers look to capitalize on the next area of value - the convergence of creative and AI - they will need to align with how today’s consumers experience media, and build campaigns that are not confined to a single channel.
The Limitations of Generative AI
The advertising world has taken notice since ChatGPT debuted 18 months ago and GenerativeAI became a household term. But many in the industry are wary of the risks GenerativeAI presents, and rightfully so. While the idea of going from a text prompt to a winning ad creative seems within reach, it’s worth spotlighting the pitfalls and concerns of adopting Generative AI:
Brand Management Risk. Generative AI struggles with subtleties, and carries the risk of not fully aligning with a brand’s value, voice, or identity.
Ethical and Legal Concerns. Generative AI is a black box (by design), with little visibility into source materials it’s pulling from, raising questions about copyright infringement, as well as misleading or deceptive content that can introduce legal challenges.
Biases and Inaccuracies. Since training data can vary in quality, and Generative AI is still prone to hallucinations, it’s still too easy for glaring biases or inaccuracies to come through in the finished product.
Quality Control. Ensuring the high quality and relevance of content and brand messaging from Generative AI can be challenging, requiring significant oversight and editing by human creators to meet brand standards.
However, the potential for creative AI to revolutionize digital advertising isn’t years away. To take advantage of AI and capitalize on the new ‘decade of creative’, it’s important to take a different lens on what is possible, and to adopt a system called “(Re)Generative AI.”
(Re)Generative AI: Defining a Better Path
(Re)Generative AI is still a lesser known term compared to Generative AI, but provides a key distinction. Understanding this distinction transforms AI from a nebulous concept that has application in the distant future to a clear set of steps that ad platforms, publishers, agencies and investors can capitalize on now.
Whereas Generative AI focuses on creating media from scratch (say, from a text prompt), (re)generative AI focuses on a paradigm shift in creative production, creating new assets from a range of previously approved materials. The advantages to this approach are:
Enhanced Creativity. (Re)generative AI can analyze existing creative assets and generate new, innovative versions, pushing the boundaries of original content and design.
Increased Efficiency. By automating and repurposing content across multiple platforms and formats, (re)generative AI significantly reduces the time and resources required for creative production.
Scalability Across Platforms. (Re)generative AI enables brands to quickly scale their creative efforts across platforms that may have different technical requirements or user preferences, including display ads, video, CTV, and out-of-home.
Enhanced User Engagement. With the ability to produce more relevant ads across more audience touchpoints, (re)generative AI aligns messaging and amplifies best-performing messages and creatives across every channel.
Agility and Adaptability. The brands that win the ‘decade of creative’ will be those that can quickly adapt their creative strategies to test messages, find winners, run them on several new channels, and to identify the point at which ads require a creative refresh in order to sustain their performance.
5 Ways to Apply (Re)Generative AI
With this in mind, we present five clear ideas for advertisers and ad suppliers to put (Re)generative AI into action today.
Start with Social. Brands have spent the last 10 years putting their best content on social media, and they know what works. With (re)generative AI, you can now lift any social post from the social platforms and run it seamlessly across all screens and channels, capitalizing on the creative performance you’re seeing on social media.
Look at TV as an Extension of Social. The rise of connected television isn’t just an infrastructure change; it’s a sea change in how viewers consume television, and what they expect from TV ads. With (re)generative AI, you can convert highly engaging social videos into TV Ads inexpensively, putting more money toward working media and driving user engagement through innovative formats.
Take an Omnichannel Approach By Default. Most advertisers get comfortable on one platform without realizing the missed potential of taking an omnichannel approach, mirroring how users actually consume content. With re(generative) AI, you can instantly create ads for other platforms, eliminating barriers of creative production, and avoiding the need to learn the technical nuances of each platform.
Keep Pace with the Rise of New Platforms. With (re)generative AI, the old questions about ‘channel mix’ go away. Media plans have historically focused on pre-set impression numbers bought on pre-set channels, partially as a way to manage creative production. Now, with a new creative production paradigm, it becomes easy to not only run on all channels, but test new channels as they arise.
Focus on Expanding Existing Success, Not Starting from Scratch. The magic and the power of Generative AI is that it gives the illusion you can create anything from scratch. While enticing, starting from scratch means starting from square one. With (re)generative AI, you can eliminate much of the guesswork, translate learnings and creative assets across channels, and get to your performance goals in significantly less time.
