The CDP is the AI | Jacques Corby-Tuech

Contents

The frontier

What brands can actually buy: support versus decisioning

Most brands aren't using any of it

Why the gap exists and is widening

Consequences

Cross-industry parallels

Open questions

What this means for the lifecycle role, and where it ends up

I was at a Bloomreach breakfast recently, a room full of fintech marketers from brands large and small, all there to talk about "AI". We didn't, really. Or rather, we talked about it the way you talk about a holiday you can't afford yet. The recurring theme, from everyone in the room regardless of company size, was getting their data into a usable shape in the first place. Nobody was stuck on which model to use or which clever feature to switch on. They were stuck on the fact that their data is a mess, spread across half a dozen systems that don't talk to each other, and until that's fixed none of the clever stuff means anything.

That's the whole piece, really, and I could stop there. But the gap that room kept bumping into is bigger and more structural than "we need to sort our data out", and it's widening.

At one end of the market there's a small number of consumer platforms running production machine learning for messaging that is several capability generations ahead of anything a brand can buy. At the other end there's the long tail of mid-market and SMB brands whose access to ML in their lifecycle stack runs from "a few predictive features in our ESP" to "nothing we've actually turned on". In between, a newer category of product is trying to sell brands something much closer to the frontier. The catch, and it's the catch the breakfast table kept circling, is that all of it depends on having your data in order, and most brands don't.

The thing the trade press calls "channel maturity" is more accurately described as sorting by AI sophistication. Some senders can afford to make a channel work. Most can't. And increasingly, the deciding factor isn't budget or headcount. It's whether your data is in a state that lets you do anything at all.

The frontier

Pinterest's notification system

A handful of consumer technology companies publish peer-reviewed work on production notification and messaging systems. These are not white papers or vendor case studies. These are KDD, RecSys, WSDM and CIKM submissions, written by PhD-staffed teams who have to put their methodology in front of academic reviewers and answer for it. The papers aren't exhaustive descriptions of what the companies do internally (they never are), but they're a useful floor on capability. The internal systems are at least as good as what gets published, usually better, and the act of publishing signals that the team has the organisational backing to do this work seriously.

A quick tour of the visible frontier:

Pinterest set a weekly notification budget per user, optimising against long-term site engagement rather than click-through, on the finding that the incremental value of a notification is highest for casual users; the heavy openers have high click-through because they engage with everything, not because the notification moved them.1

Duolingo used a bandit algorithm to pick which reminder template to send each user, and reported a 0.5% lift in daily active users and a 2% lift in new-user retention over a strong baseline.2

Twitter used model-based reinforcement learning to decide whether to send a push at all, modelling the effect over a multi-day horizon. The published trade-off is the interesting part: the settings that cut volume hardest pushed open rate up by as much as 14%, but those same settings reduced daily active users; only the most conservative setting, an open-rate gain of about 8%, improved daily actives at all, and then by 0.2%. Maximising the headline number and serving the real objective pointed in opposite directions.3

LinkedIn framed notification decisioning as offline reinforcement learning, a Double Deep Q-Network with a conservatism penalty, trained on logged data and deployed: sessions up a quarter of a percent, click-through up a couple of points, notification volume down, all at once.4 By 2026 the same lineage had reached email: BanditLP pairs neural Thompson Sampling with a linear program large enough for billions of variables to choose, under business constraints, what each member is sent.5

Zillow governs email and push volume with a boosted-tree classifier deciding send-or-don't per user, tuned to keep 98% of the clicks while shedding the surplus sends and the unsubscribes they cause. No reinforcement learning required, which is its own lesson: the cheapest method on this list still wins by sending less.6

Meta treated Instagram's notification slots as an auction: the 550-plus internal teams that want to message you bid against each other (with the platform able to subsidise bids) so no single user is flooded by competing product surfaces. In test it sent slightly fewer notifications, lifted...