Thinking About the Future of an AI-Powered Media Ecosystem
posted by Michael Stoeckel on August 14, 2024
Much of the discourse around AI and advertising has focused on creative aspects, specifically in areas like versioning and optimization. However, more recently, media-related AI enhancements have surfaced to offer optimization in areas like Media Mix Modeling (MMM), auto-spending tools such as Google Performance Max (PMax), and a similar system that Apple is building for its App Store Ads. But can media ops teams and tools also be useful for leveraging the power of AI?
Where do media operations fit in the innovative world of AI?
To answer this question, consider some of the limitations of MMM and PMax-like offerings. MMM is only as good as the granularity and timing of the data fed into it, and most executions cannot get down to sub-campaign performance, such as order and “line item” level. A tool like PMax, despite its recently announced transparency and brand safety enhancements, is also not without limitations. can ingest very granular data, but—due to privacy concerns—it is typically constrained by performance details gathered from buying tools and related tech owned by the same company.
Either way, the bottom line there: AI tools will always be only as good as the data given.
This is where a media management platform like the MX Platform can play a part as a central enhancement to the media team’s AI-enhanced tech stack. The MX Platform has access to delivery information from many campaigns and related marketing efforts—all straight from optimizing advertisers. Thus, our clients can use us as a primary data source for media-analyzing AI models and ultimate return.
Should we apologize for being “just” a feeder into AI models? Heck, no. In fact, we see our role as essential.
A modern media management platform provides the foundation for the ad industry to evolve properly, keeping pace with advancing advertiser demands and ad tech capabilities.
The application of AI is just another example where we are an unmatched enabler.
Agree? Disagree? If you have thoughts, we'd love to hear them.
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Looking deeper at how we help media teams take full advantage of the latest capabilities AI has to offer, not only can we provide granular data to help optimize AI models, we also provide that data in a “normalized” fashion as the MX Platform is already managing comparative campaign performance across media types, buying tools, etc. This allows MMM and similar tools to utilize the MX Platform as a central source of delivery information, freeing them to adjust or build new integrations for new media types, buying tools, etc. being added mid-campaign.
It’s the “I”, or intelligence, that separates AI from predecessor optimization executions, so it truly does get smarter as you feed deeper and more granular data to the models. The data and control granularity available from the MX Platform is both unique and extremely relevant to the ad optimization goals of MMM and similar tools.
Bottom line: While few would say that ad operations and workflow tools are especially “sexy” aspects of media, they now have the opportunity to fill an integral role in this new AI-driven era: we have the power to “optimize the optimizers”—whether it be Performance Max, Omnicom’s Omni or PMG’s Alli—with better, more granular, and more intelligent data.
About the Author
Michael Stoeckel
Head of Platform Partnerships, Hudson MX
Michael is a digital advertising executive with diversified business and technology skills at the intersection of media, mathematics, and organizational leadership. With a strong reputation for navigating diverse stakeholder needs and mobilizing resources, Michael has over 3 decades of experience effectively partnering with technology, marketing, and sales teams to drive innovation in disruptive industries. Michael lives on the Jersey Shore with his wife and daughter. He enjoys sports activities like golf, softball, bicycle riding, body surfing, attending athletic and music events, and traveling with friends and family. Connect with Michael here.
