Applied AI and the New Marketing Revolution

Google and Meta didn’t win the last 15 years of marketing because their household data was better than AOL, or their creative options were better than Yahoo or their pockets were deeper than Microsoft. They won because of the AI tech they invented. 

Finding consumer insights not observable to humans to target and testing messaging at scale, deep learning neural networks and reinforcement learning/decisioning form an intelligent data-driven system where every the results of everything makes the system smarter.

As I discussed in Part 1 “Marketing AI & Customer Data: First Principles” , Marketing has always been about two questions: Who are we selling what to? What should we tell them about it? 

The platforms won by inventing and industrializing the AI to solve both ends of this equation and added the ever important performance economics for ‘how much is this worth.” Meta as the follower hiring from Google to hammer these auctionomics.

Neural Network/Deep Learning

Deep learning handles the “who/what” by finding structure in billions of messy signals: engagement, searches, time, events, behaviors, transactions, profiles, performance metrics and formulas. 

Not blunt rules like “customers who spend $500 on jeans.” 

Not the cardboard personas agencies still trot out like “Suzy Moonshine.” 

Segments derived from advanced computation across thousands of dimensions that discover affinities no human could observe. They look and perform like nothing our two-dimensional monkey brain can derive. 

That’s how a casual shopper in New Jersey and a heavy buyer in Texas both end up in a hidden “seasonal splurgers” segment, or how late-night weekday conversions reveal “at-risk impulse buyers for a brand.” Deep learning is segmentation reborn: from rules to exploration, from data minimization to data maximization.

Once the “who/what” is clear, reinforcement learning takes over the creative message and response/delivery optimization. 

Reinforcement learning/RL

Target a user.

Test an offer.

Adjust bids.

Swap creative.

Every impression you make can be a “test” with measurable results. If it works, exploit it; if it doesn’t, keep exploring and learning. That’s called optimization. RL is not especially new. Multi-arm bandits have been a backbone of Adtech ad servers for 15 years but RL has had a lot of focus in AI the past few years with agentic development that enables serverless RL, multi-agents, transfer learning and improved exploration.

And here’s the kicker…putting Deep Learning and Reinforcement Learning together creates closed loop optimization. 

It is a beautiful thing how these different applications of AI work together because it relates directly to ROI. The sharper the “who/what” the faster RL converges on the right message and offer. Knowing ahead of time this optimization is going to happen within thresholds related volume/performance is how the platforms generate profits. They control the efficiency. 

Performance Max and Advantage+ are deep learning and reinforcement learning running at scale, faster than any human team could ever iterate. It’s fueled every moment of relevance you’ve ever felt. Like getting a cashmere sweater from a brand you’d never heard of in your feed that you order seconds after being served the ad. NNs + RL power a combined 5 TRILLION in valuation for Google & Meta.  Imagine what these technologies will do for your little billion dollar brand!? 

The New Martech, Data + AI in Your Cloud

While Google and Meta were locking advertisers inside black boxes, they handed out ways to build your own boxes.  Google open-sourced TensorFlow. Meta gave away PyTorch. NVIDIA released CUDA and RAPIDS. These are pipes and processing that makes all of this run. Open sourcing wasn’t entirely an act of altruism –  it’s how the platforms ensure another 10 years of growth – but the building blocks of their dominance ended up on GitHub. 

We can bristle at Amazon, Meta and Google and debate if AI will make people irrelevant, but the takeaway shouldn’t be resentment. It should be recognition. And action. Their moats may be impenetrable, but new castles can now be built in the clouds.The data warehouse is already yours. But now for the first time the AI can be too. 

A decade ago Snowflake and Databricks did not exist. 5 years ago you didn’t have elastic GPUs. 2 years ago you could barely evaluate how deep learning models were chewing on data. Just a year ago it was impossibly hard to smooth the rough edges of workflow. Now marketers and data teams have it all available to them. And every day it is improving, getting bigger, faster and smarter. You can build or buy marketing AI products that use the same DNA but are tuned for your business, your data, your KPIs. Not Google’s or Meta’s. 

It’s already happening:

• A casino matched  “mid-tier” players with relevant offer strategies that drove more revenue than some VIPs.
• A streaming service cut churn by discovering then targeting segments of cross-device/cross-genre subscribers that were at-risk with relevant content.
• A QSR chain instantly boosted weekday lunch profits by targeting relevant coupons to only those who needed the nudge.

Optimizing Outcomes Everywhere

It’s no accident that part of this revolution is data sharing and seeing the platforms share data back to your Snowflake/Databricks/BigQuery Data Warehouse. This data can fuel NNs and RL applications that keep you spending (ideally because you are hitting your cost/volume goals & objective). Data sharing is the choke point the platforms keep for the price of inventing this shit. But every brand has an ROI number where their marketing spend will always be on – and that’s a very desirable metric for everyone.

As this new marketing revolution becomes clearer in 2026 to your BoD & CEO, your first-party data becomes the most valuable asset of your company. There is no doubt more companies than ever will achieve value and profit from their data. It’s almost computationally impossible not to improve with this intelligence. More companies will also start their own data businesses as “more data” becomes a competitive advantage in every market and can help make partners, vendors and services more revenues and profits as well.

Which leads to the last question about Marketing AI I want to talk about. If every brand can spin up its own close looped matching machine, how does that change the very shape of customer strategy, creativity, analytics and competition? That’s where we’ll go in Part 3.