Embeddings in Practice — Lessons in GLP-1 User Journey Analytics

A single consumer purchase decision now spans multiple platforms, devices, and days. As deterministic IDs and traditional device graphs continue to erode, there is a fundamental shift in how touchpoints are stitched together. Embeddings, the geometric representations powering modern AI, have quietly become the universal interface for cross-platform behavior.

Using GLP-1 user journey signals as the case study, this presentation will demonstrate how to apply vector-based models to the data stack. Rather than a theoretical overview, this presentation will share the production capabilities developed using Measure Protocol’s work with Large Behavioral Models.

What you’ll walk away with:
• Understanding the "Intent Neighborhood" Effect: How the system autonomously clusters disparate GLP-1 signals, like smaller-size clothing, portion control, and specific skincare, into a singular intent neighborhood without a single hand-written rule.
• Operationalizing Vector Analysis: Production capabilities that enables cross-platform journey data.
• De-Risking the Stack: Four universal principles for evaluating embedding-driven systems, specifically focusing on the silent failure modes that don't trip standard engineering alerts but can quietly corrupt downstream insights.