AI is shaping the Future of Performance Marketing
Exploring the role of AI in revolutionizing Performance Marketing, from creative automation to predictive analytics, and finding the balance between automation and human expertise.
Performance marketing has long relied on data-driven strategies, human creativity, and time-consuming operational tasks such as campaign optimization, data analysis, and creative production. However, as AI continues to develop, traditional approaches in performance marketing are evolving rapidly, with automation and machine learning playing increasingly larger roles.
In this article, we explore the impact of AI on performance marketing, the shift toward generative AI & full automation, and how marketers can partner with AI.
Traditional approaches vs. the Future of AI-Driven Marketing
Current approaches in performance marketing involve time-consuming operational tasks such as campaign optimization, data analysis, reporting, testing, and creative production. These elements are essential for running campaigns that drive results and profitability.
However, we’re witnessing a significant shift toward increased automation through AI. While some platforms and services offer optimizers for campaign automation, their reliability can vary. Tools like MobileAction and Splitmetrics enable campaign automation by applying rules based on various metrics available in Apple Search Ads, including CPI, TTR, and ROAS. Similarly, platforms like Meta and TikTok have integrated their own automation tools, effectively delivering ads to the most relevant users and achieving impressive results.
In contrast, ironSource optimizers have shown limited success, as their learning processes still require substantial time and budget. However, a promising trend is emerging: the learning curve is getting shorter. Algorithms now require significantly fewer installs—sometimes just a dozen—to begin learning and optimizing effectively.
Content Generation and Personalization: the Next Frontier
One of the most exciting developments in AI-driven marketing is its potential to revolutionize creative production. AI is now capable of generating images, videos, music, and text for ads faster and more efficiently than ever before. This year has seen the emergence of several generative AI tools, from Meta’s announcement of its own AI-powered tools for creating images and videos with sound, to Google’s Veo for YouTube Shorts.
Since Apple introduced App Tracking Transparency (ATT), performance marketers have encountered significant challenges in tracking users and executing highly targeted campaigns. As privacy regulations tighten, the relevance of creative content has become more critical than ever. AI-generated creatives offer a solution by enabling rapid iteration and testing of ad variations.
AI tools are already delivering highly effective ads by optimizing various elements, from backgrounds to bidding strategies. For instance, platforms like Meta and Pinterest have integrated AI capabilities that enhance creatives, producing impressive results. The ultimate goal of these platforms is to automate the entire creative process, allowing marketers to focus solely on strategy.
Additionally, agencies like UGC Ninja are integrating AI into nearly all their processes, successfully delivering high-performing videos to promote their clients’ apps and games across platforms such as TikTok, Meta Ads, and YouTube. They even leverage AI avatars to create localized videos, ensuring their content resonates with audiences in different regions.
Balancing Automation with Human Expertise
Automation has been integrated into performance marketing for years, but its success varies across platforms. For example, AppLovin has excelled in optimizing campaigns through automation, while Unity has faced challenges due to limited access to data.
Historically, automation and optimization tools required substantial amounts of data to function efficiently, often needing hundreds of conversions before they could provide meaningful insights. However, we are now seeing advancements in AI models capable of optimizing campaigns with significantly fewer conversions—sometimes as few as 20 to 30—while still effectively reaching target audiences. This development marks a critical shift, allowing marketers to launch campaigns with less initial data and still achieve positive outcomes.
It’s important to note that AI-driven automation is not perfect from day one. Algorithms require time to learn and optimize, and marketers must remain cautious not to rely too heavily on automation tools.
The key is to view AI as a partner rather than a replacement. While AI can streamline processes, human oversight remains essential in ensuring that campaigns align with overall business goals. This sentiment is echoed by leaders in AI, emphasizing the need for unbiased AI systems and the importance of ethical standards in technology development.
Shifting away from Audience Segmentation
With the decline of audience segmentation due to privacy regulations, marketers must adapt their strategies. Third-party data is becoming less reliable, leading many companies to shift to first-party data. From neo-banks like Revolut to platforms like Netflix and Uber, businesses are creating their own ad networks by leveraging the data they collect from their users.
In addition, contextual advertising is making a comeback, powered by machine learning. Publishers can analyze website content in real-time, allowing them to display highly relevant ads that align with user interests. This approach not only improves ad relevance but also enhances conversion rates and engagement.
AI-driven ad placements are even extending to search tools like Google AI Search Overview and Perplexity, where ads are shown based on the user’s query and context. This opens up new opportunities for marketers to reach highly engaged users with precise messaging.
What’s Next for AI in Performance Marketing?
Generative AI for Creative Production
Automation of Campaign Optimization
Growth of Contextual Advertising
New Ad Placements Aligned with User Experience
Pros and cons
While AI offers many advantages, such as increased efficiency, relevancy, and reduced time spent on manual tasks, it also comes with risks. AI could optimize for performance while unintentionally promoting offensive or unethical content, potentially damaging brand reputation. Marketers must ensure that AI-generated content aligns with ethical standards and brand values.
Nick Bostrom’s famous “paperclip maximizer” thought experiment serves as a reminder: AI might focus on achieving its objective, no matter the consequences.
As marketers, it’s crucial to maintain human oversight to prevent AI from going too far in pursuit of optimization, ensuring that both performance and ethical standards are met.