Adaptive Personalization with AI

How AI can personalize information by using the same feedback loop that scales across an entire customer base.

Illustrative dataset of major posts. See limitations below.

Click a post and the globe remembers your selection, then opens facing it the next time you load the page. While this display uses a click counter rather than a trained model, it shows the same click, store, rank, and adapt loop that a production system could apply across a business's entire customer base instead of within one browser.

Click a post

→

Store the interaction locally

→

Update the preference ranking

→

Change the default view on the next visit

View

Behavior window

Drag to rotate, then click a post to select it.

Why am I seeing this?

No clicks recorded yet in this browser, so this is the same as the plain default view.

Local event history

No clicks recorded in this browser yet.

This data stays in your browser's local storage. It's never sent to a server and isn't shared across visitors or devices.

What this shows

A single click can become a signal that changes what someone sees next, and the same feedback loop can support personalization across a much larger customer base. Replace the click counter with a trained model and the browser with a customer data platform, and the system can adapt the experience across many customers and interactions.

How it works

Every post you click is logged locally in your browser with a timestamp. The globe reads that history, identifies your most-clicked post using either an all-time or recent-window view, and opens facing that location the next time you load the page. It doesn't require an account or server, and it doesn't track activity across visitors.

Design decisions and guardrails

This project recreates the feedback loop behind AI personalization, which is the same loop covered in the Flywheel section of my framework. Use creates feedback, then that feedback changes what the system shows next. Here, the loop runs through a simple click counter, but a production version could replace it with a trained model operating inside a customer data platform. Keeping the demonstration small makes the mechanism easier to see before scaling it across a larger system.

Limitations

This version personalizes the experience within one browser. A production system would connect interactions across an account or customer profile, which would allow the same loop to personalize information across devices and over a longer customer relationship. It would also need safeguards against noisy or manipulated signals.

Production next steps

A production version would need the infrastructure this demonstration intentionally leaves out, including a backend that aggregates signals across visitors, a customer data platform that stores those signals, and a trained model that ranks preferences instead of relying on a simple click count. Together, those additions would allow the same feedback loop to support personalization across a full customer base.

Technology used

React state and the browser's localStorage API stand in for the recommendation model and customer data platform that a production version would use. The underlying mechanism remains the same, but the scale changes.