AI Makes learning cheap, launching the Always On Strategist.
At last week’s eMarketer conference, lead analyst Nate Elliott said that AI’s big impact will be on the less-sexy parts of advertising: strategy, research, planning, reporting. Speak for yourself. I (and I know he does too) find these parts of the industry deeply fascinating and compelling. But we fully agree. The introduction of AI to the ad agency operating model removes the steep economic barriers that kept strategy and analytics teams confined. Doing a test was expensive, so can’t do many. When testing is scarce, knowledge is rare. Strategy becomes episodic.
AI changes that economic equation and introduces the concept of the Always-On Strategist: a person/team who is constantly exploring and testing ideas, using results to consistently increase the performance of a campaign. Right now, we are beginning to see that person emerge, probably from product marketing or rev-ops roles.
But what has been missing (and what the product marketers and rev-operators have long had) is a platform to build their work on. Advertising is built around multi-channels and media mixes, making a single platform (like HubSpot, say) harder to come by. What we are seeing now (and what this week’s top stories focus on) are how various systems and approaches are converging to provide the various elements of what we will eventually call our Learning System for Always On Strategy (you know: the LSAOS).
Read below.
The IAB released the latest draft of its Agentic Advertising Management Protocols (AAMP) proposal for comment. The document outlines a proposed standard for buyer and seller agents to follow, enabling agent-to-agent discover and compare publisher offerings. A foundation not only for buying and managing but also negotiating and reporting.
This is a major offloading of manual work and paperwork shuffling of course. But for our discussion about the value of learning, this is one more step where the economics of learning is dramatically lowered. The cost of comparison, let alone of making shifts or splits or adjustments dramatically goes down. The ability to make clearer comparisons across multiple publishers becomes something not just doable, but (I imagine) required going forward. Clients need not only accept the agency’s single best pitch, but instead will want to understand what all of their options could look like.
Retail Media Networks (RMNs) are in a uniquely powerful position to take advantage of AI capabilities because of the depth and precision of their customer data. Sam’s Club’s RMN (Sam’s Club Connect) pushed something they call Predictive Precision Targeting” last week. P&G used it to predict who their next customers for Cascade would be, and got kick ass results.
For our Always On Strategist (AOS), this gives a powerful new dimension to the way they can think about and perform research. Usually, research is all about the rear-view mirror. But strategy and advertising is always about tomorrow. Using data to make predictions is really the job. Sam’s Club is now giving those predictions, creating the opportunity for the Always On Strategist to build new, forward-looking hypotheses that can be measured and tested.
I said that while the role of the Always On Strategist is growing, the platforms aren’t quite there yet. But that’s not to say there aren’t a few companies looking to fill that gap. Adform is one. This week at DMEXCO, they are showing of their MCP, promising to connect over 800 capabilities, covering everything from research to planning to activation and optimization.
This is really where the activity is happening: creating a platform where the Always On Strategist logs in every day and does their work.
Mover of the Week: learning Platforms.
The ability to generate a LOT of ads/variations of ads using AI is now well established. You can take the one asset from the creative team and drop it into any number of platforms and prompt it to generate a bunch of versions, tweaking visual cues as well as offers and headlines. Not a problem. And (assuming your buy is big enough), you can put them all into rotation with a few steps.
But that is hardly the way to manage a budget (especially a client’s budget). Most of those versions aren’t worth testing, and many are so close to each other, it would be redundant. And no one wants to see 100 versions of an ad.
What is missing is the piece between creation and placement. That’s the domain of the Always On Strategist. The problem though, is there is no single way to manage this work. That’s where we are starting to see increasing development and competition. Who will emerge as the new HubSpot for the AOS?
We will keep on watching.
Oh: the numbers. That is retired from this blog. We still use the system to manage the stories we track, but starting to realize that is more of an internal tool than a real piece of information. Build in public!
The Thursdai Index and report is the work of 61 Hawk founder Gary Stein with assistance from an OpenAI research agent. The agent sources, sorts and scores news stories. Everything you read was written by Gary, a real human being.
