In today’s digital advertising landscape, first-party data and clean rooms remain highly relevant. However, artificial intelligence (AI) has also been dominating much of the conversation lately.
And what’s becoming increasingly clear is that these technologies need to work in harmony for marketers to unlock their full potential.
Consider a scenario where a brand marketer wants to optimize its ad spend across multiple channels. By unifying first-party customer signals, third-party market insights and historical campaign results in a clean-room environment – and then layering in machine learning models – marketers can build campaigns that not only micro-segment audiences but also continuously optimize creative, reengage audiences, bidding and spend allocation in real time based on performance signals.
But how can marketers extract real value from this complex ecosystem and use these tools effectively together?
Bringing data and AI together for real impact
Generative AI derives its power from patterns learned from existing signals it processes over time. In today’s market, these signals are scattered across various entities: clean rooms, publishers, agencies and both first-party and third-party sources.
Ad agencies often serve as both tool providers and consumers of data for optimization. But to tackle significant business challenges through AI, they need to collaborate with other parties to access high-value signals. This is where advancements in clean room technology come into play, providing a secure way to facilitate collaborations on signals without requiring brands and partners to move their underlying data.
Building an effective data infrastructure is just the first step. Marketers still need to know how to build an AI algorithm correctly. Machine learning (ML), a specific approach within the field of AI, has recently gained more attention thanks to generative AI. While AI and ML models have been used for real-time bidding and dynamic creative optimization in the past, applying them to enable broader, upstream operational efficiency has been limited.
For example, traditional machine learning typically involves using large data sets to train algorithms to recognize patterns and make predictions. Another approach is the expert system, which simulates human decision-making by using a knowledge base and logical rules to derive conclusions, make decisions or generate predictions.
This application of machine learning helps advertisers create systems for optimization once campaign parameters are constructed. However, the upstream effort to organize audience segments, bidding strategy and planning insights on a full-funnel, campaign-by-campaign basis still requires manual setup.
With the evolution of AI tooling, marketers now have much more power at their fingertips to create iterative processes that learn and improve over time based on the signals they have or can obtain through clean-room collaborations.
Generative AI has the potential to improve operational tactics – from building creative and optimizing campaigns to developing channel strategies. Implementing AI functionality throughout the entire planning cycle, as opposed to just home-stretch optimization, is like evolving from a game of checkers to chess. Marketers now get to decide which pieces to move based on preferred outcomes tied directly to their most pressing business goals.
The future: Iterative, AI-first advertising
The future of advertising lies in the seamless integration of first-party signals with clean rooms and AI technologies. By using these tools in concert, marketers can create more efficient, effective and personalized advertising strategies that drive real business outcomes and iterate on them much faster.
At Amazon, we have built a team of experts and have worked with brands, agencies, independent software vendors and global system integrators to unify these tools, including the Amazon Marketing Cloud (AMC) on AWS Clean Rooms and AMC Custom Models, which helps bring an AI-driven approach to marketers who want to build their own algorithm for audience creation, bidding, measurement or any other use case.
Already, we are seeing growth in demand from technical consultancies, specialized vendors and measurement partners evolving their proprietary logic with these capabilities.
The game has changed, and those who adapt to the new AI-driven landscape will be the winners in the evolving world of digital advertising.
