Databricks’ Jake LaDuke: Marketing is Shifting From an Attention Economy to a Prediction Economy
The way marketers prove value is changing. Attention metrics that have anchored advertising since its inception are giving way to something more demanding. It starts with the ability to predict business outcomes before a campaign even launches. “CMOs need to be able to have a predictive layer of performance before they even green light something. This is shifting from a lagging indicator of performance into a leading indicator of performance,” Jake LaDuke, Global GTM Lead for Media, Entertainment and Advertising
So we’ve been speaking a lot about advertising and really marketing more generally, going through this shift from an attention currency that it sort of been since its inception into a prediction economy. And what that means is some of the standard ways and means of assessing whether something’s successful or not just don’t hold water anymore. Uh part of what we’re seeing is CMOs needing to be able to have a predictive layer of performance before they even green light something. Uh that comes to life through some of the audience uh definitions and planning work all the way through to measurement. Uh this is shifting from sort of a lagging indicator of performance into a leading indicator of performance.
Jake, there’s a lot of talk about agentic marketing, the agentic era that we’re in right now. What does it actually look like in practice and what has to be true for agentic to actually meaningfully work?
Yeah. So I’ll give a quick customer story. So there is a very large retailer specialized in all things pets. Uh part of what this customer was able to do on data bricks is to be able to start to orchestrate all of the oceans of data that they have and to be able to uh shift away from how they’ve done business as usual. So they shifted away from sort of a product calendar, a services calendar into being able to harness all of their loyalty data and be able to unleash agents to be able to send out personalized messaging. And what this has done is this has driven performance exceptionally well. Uh they’re seeing 22% incremental uh performance on some of their components and they had a 12% lift on uh transactions as well.
How should CMOs be thinking about proving the value of AI generally and those predictive capabilities to their CFO?
Yeah, that’s one thing that uh is been so interesting to see sort of the evolution of. So I come from the agency background and I’ve always thought of my job while I was at the agency as making the CMO look great. Uh and now what we’re seeing is it’s not just about making the CMO uh look great. It’s about how well that CMO looks for his or her CFO as well. Going back to this notion of the predictive economy, part of what a CFO wants to understand is to be able to look across to their CMO and say, hey, if this is the budget, what can I expect as far as business results? So this frequently comes to life through the needs around measurement orchestration so that you’re able to harness all of the various sources of truth of what performance looks like across your media as well as your advertising and to be able to orchestrate those into something that is uh a singular metric on what it did to the business.
Jake, what’s the biggest barrier to the prediction economy becoming reality across the industry and not just at the leading edge?
Yeah, I think historically, some of the barriers had been siloed data and data estates. Uh really what we’re seeing at Databricks is first principles for any type of AI transformation, really need to center around what we call the four Cs. So it’s about how are you unleashing context uh for your agents to your broader data estate. How well are you able to have a level of control so that you are controlling not just who has access to that from a people perspective, but also from your agent’s perspective, uh how well are you able to optimize what the costs of this is. Part of what we’re seeing between CMOs and CFOs is this shift from a world of token maxing into a world of value maxing. And then the last piece and it’s somewhat related is choice. Being able to have the right model for the right job is uh one of the those are all four first principles that we see behind any successful AI transformation.
Jake, we’ve run through a lot of great points about the prediction properties of marketing to the agentic era and what that means. What’s one thing you’d want every viewer to take away from today’s discussion?
Yeah, I think two things are true with good AI transformations. It’s thinking big about how AI can be that really that growth lever for your business and then aiming small when it comes to the use cases you need to unlock in order for that to be successful. Uh whether it is audience planning, whether that is activation or whether that is measurement orchestration, using those four Cs as first principles are really what we’re seeing uh allow marketers and advertisers go further faster. So again, it’s context, it’s control, it’s cost, and it’s choice.
